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Objective: This study aimed to evaluate the image quality and lesion conspicuity of the deep learning image reconstruction (DLIR) algorithm compared with standard image reconstruction algorithms on abdominal enhanced computed tomography (CT) scanning with a wide range of body mass indexes (BMIs). Methods: A total of 112 participants who underwent contrast-enhanced abdominal CT scans were divided into three groups according to BMIs: the 80-kVp group (BMI ≤ 23.9 kg/m2), 100-kVp group (BMI 24–28.9 kg/m2), and 120-kVp group (BMI ≥ 29 kg/m2). All images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction-V of 50% level (IR), and DLIR at low, medium, and high levels (DL, DM, and DH, respectively). Subjective noise, artifact, overall image quality, and low- and high-contrast hepatic lesion conspicuity were all graded on a 5-point scale. The CT attenuation value (in HU), image noise, and contrast-to-noise ratio (CNR) were quantified and compared. Results: DM and DH improved the qualitative and quantitative parameters compared with FBP and IR for all three BMI groups. DH had the lowest image noise and highest CNR value, while DM had the highest subjective overall image quality and low- and high-contrast lesion conspicuity scores for the three BMI groups. Based on the FBP, the improvement in image quality and lesion conspicuity of DM and DH images was greater in the 80-kVp group than in the 100-kVp and 120-kVp groups. Conclusion: For all BMIs, DLIR improves both image quality and hepatic lesion conspicuity, of which DM would be the best choice to balance both. Clinical relevance statement: The study suggests that utilizing DLIR, particularly at the medium level, can significantly enhance image quality and lesion visibility on abdominal CT scans across a wide range of BMIs. Key Points: • DLIR improved the image quality and lesion conspicuity across a wide range of BMIs. • DLIR at medium level had the highest subjective parameters and lesion conspicuity scores among all reconstruction levels. • On the basis of the FBP, the 80-kVp group had improved image quality and lesion conspicuity more than the 100-kVp and 120-kVp groups. [ABSTRACT FROM AUTHOR]

Objectives: To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR) compared to standard-dose single-energy CT (SECT) with DLIR or iterative reconstruction (IR). Methods: In this prospective study, two groups of 40 participants each underwent abdominal contrast-enhanced scans with full-dose SECT (120-kVp images, DLIR and IR algorithms) or reduced-dose DECT (40- to 60-keV virtual monochromatic images [VMIs], DLIR algorithm), with 122 and 106 metastases, respectively. Groups were matched by age, sex ratio, body mass index, and cross-sectional area. Noise power spectrum of liver images and task-based transfer function of metastases were calculated to assess the noise texture and low-contrast resolution. The image noise, signal-to-noise ratios (SNR) of liver and portal vein, liver-to-lesion contrast-to-noise ratio (LLR), lesion conspicuity, lesion detection rate, and the subjective image quality metrics were compared between groups on 1.25-mm reconstructed images. Results: Compared to 120-kVp images with IR, 40- and 50-keV VMIs with DLIR showed similar noise texture and LLR, similar or higher image noise and low-contrast resolution, improved SNR and lesion conspicuity, and similar or better perceptual image quality. When compared to 120-kVp images with DLIR, 50-keV VMIs with DLIR had similar low-contrast resolution, SNR, LLR, lesion conspicuity, and perceptual image quality but lower frequency noise texture and higher image noise. For the detection of hepatic metastases, reduced-dose DECT by 34% maintained observer lesion detection rates. Conclusion: DECT assisted with DLIR enables a 34% dose reduction for detecting hepatic metastases while maintaining comparable perceptual image quality to full-dose SECT. Clinical relevance statement: Reduced-dose dual-energy CT with deep learning image reconstruction is as accurate as standard-dose single-energy CT for the detection of liver metastases and saves more than 30% of the radiation dose. Key Points: • The 40- and 50-keV virtual monochromatic images (VMIs) with deep learning image reconstruction (DLIR) improved lesion conspicuity compared with 120-kVp images with iterative reconstruction while providing similar or better perceptual image quality. • The 50-keV VMIs with DLIR provided comparable perceptual image quality and lesion conspicuity to 120-kVp images with DLIR. • The reduction of radiation by 34% by DLIR in low-keV VMIs is clinically sufficient for detecting low-contrast hepatic metastases. [ABSTRACT FROM AUTHOR]

Objectives: To present software for automated adipose tissue quantification of abdominal magnetic resonance imaging (MRI) data using fully convolutional networks (FCN) and to evaluate its overall performance—accuracy, reliability, processing effort, and time—in comparison with an interactive reference method. Materials and methods: Single-center data of patients with obesity were analyzed retrospectively with institutional review board approval. Ground truth for subcutaneous (SAT) and visceral adipose tissue (VAT) segmentation was provided by semiautomated region-of-interest (ROI) histogram thresholding of 331 full abdominal image series. Automated analyses were implemented using UNet-based FCN architectures and data augmentation techniques. Cross-validation was performed on hold-out data using standard similarity and error measures. Results: The FCN models reached Dice coefficients of up to 0.954 for SAT and 0.889 for VAT segmentation during cross-validation. Volumetric SAT (VAT) assessment resulted in a Pearson correlation coefficient of 0.999 (0.997), relative bias of 0.7% (0.8%), and standard deviation of 1.2% (3.1%). Intraclass correlation (coefficient of variation) within the same cohort was 0.999 (1.4%) for SAT and 0.996 (3.1%) for VAT. Conclusion: The presented methods for automated adipose-tissue quantification showed substantial improvements over common semiautomated approaches (no reader dependence, less effort) and thus provide a promising option for adipose tissue quantification. Clinical relevance statement: Deep learning techniques will likely enable image-based body composition analyses on a routine basis. The presented fully convolutional network models are well suited for full abdominopelvic adipose tissue quantification in patients with obesity. Key Points: • This work compared the performance of different deep-learning approaches for adipose tissue quantification in patients with obesity. • Supervised deep learning–based methods using fully convolutional networks were suited best. • Measures of accuracy were equal to or better than the operator-driven approach. [ABSTRACT FROM AUTHOR]

Objective: To investigate performance of 1-mm, sharp kernel, low-dose chest computed tomography (LDCT) for coronary artery calcium scoring (CACS) using deep learning (DL)–based denoising technique. Methods: This retrospective, intra-individual comparative study consisted of four image datasets of 131 participants who underwent LDCT and calcium CT on the same day between January and February 2020; 1-mm LDCT with DL, 1-mm LDCT with iterative reconstruction (IR), 3-mm LDCT, and calcium CT. CACS from calcium CT were considered as reference and CACS were categorized as 0, 1–10, 11–100, 101–400, and > 400. We compared CACS from LDCTs with that from calcium CT. Results: Mean CACS was 104.8 ± 249.1 and proportion of positive CACS was 45% (59/131). CACS from LDCT images tended to be underestimated than those from calcium CT: 1-mm LDCT with DL (93.5 ± 249.6, p = 0.002), 1-mm LDCT with IR (94.7 ± 249.9, p < 0.001), and 3-mm LDCT (90.3 ± 245.3, p = 0.004). All LDCT datasets showed excellent agreement with calcium CT: intraclass correlation coefficient (ICC) = 0.961 (95% confidence interval (CI), 0.945–0.972) for DL, 0.969 (95% CI, 0.956–0.978) for IR, and 0.952 (95% CI, 0.932–0.966) for 3-mm LDCT; weighted kappa for CACS classification, 0.930 (95% CI, 0.893–0.966) for 1-mm LDCT with DL, 0.908 (95% CI, 0.866–0.950) for 1-mm LDCT with IR, and 0.846 (95% CI, 0.780–0.912) for 3-mm LDCT. The accuracy of CACS classification of 1-mm LDCT with DL (90%) tended to be better than 1-mm LDCT with IR (87%) and 3-mm LDCT (84.7%) (p = 0.10). Conclusion: DL-based noise reduction algorithm can offer reliable calcium scores in 1-mm LDCT reconstructed with sharp kernel. Key Points: • Deep learning (DL)–based noise reduction enables calcium scoring at 1-mm, sharp kernel reconstructed low-dose chest CT (LDCT). • Both iterative reconstruction and DL-based noise reduction underestimated calcium score, but agreement were excellent with those from calcium CT. • Accuracy of categorical classification of calcium scoring tended to be highest in 1-mm LDCT with DL compared to 1-mm LDCT with IR and 3-mm LDCT (90%, 87%, and 84.7%, p = 0.10). [ABSTRACT FROM AUTHOR]

Objectives: Evaluation and follow-up of idiopathic pulmonary fibrosis (IPF) mainly rely on high-resolution computed tomography (HRCT) and pulmonary function tests (PFTs). The elastic registration technique can quantitatively assess lung shrinkage. We aimed to investigate the correlation between lung shrinkage and morphological and functional deterioration in IPF. Methods: Patients with IPF who underwent at least two HRCT scans and PFTs were retrospectively included. Elastic registration was performed on the baseline and follow-up HRCTs to obtain deformation maps of the whole lung. Jacobian determinants were calculated from the deformation fields and after logarithm transformation, log_jac values were represented on color maps to describe morphological deterioration, and to assess the correlation between log_jac values and PFTs. Results: A total of 69 patients with IPF (male 66) were included. Jacobian maps demonstrated constriction of the lung parenchyma marked at the lung base in patients who were deteriorated on visual and PFT assessment. The log_jac values were significantly reduced in the deteriorated patients compared to the stable patients. Mean log_jac values showed positive correlation with baseline percentage of predicted vital capacity (VC%) (r = 0.394, p < 0.05) and percentage of predicted forced vital capacity (FVC%) (r = 0.395, p < 0.05). Additionally, the mean log_jac values were positively correlated with pulmonary vascular volume (r = 0.438, p < 0.01) and the number of pulmonary vascular branches (r = 0.326, p < 0.01). Conclusions: Elastic registration between baseline and follow-up HRCT was helpful to quantitatively assess the morphological deterioration of lung shrinkage in IPF, and the quantitative indicator log_jac values were significantly correlated with PFTs. Key Points: • The elastic registration on HRCT was helpful to quantitatively assess the deterioration of IPF. • Jacobian logarithm was significantly reduced in deteriorated patients and mean log_jac values were correlated with PFTs. • The mean log_jac values were related to the changes of pulmonary vascular volume and the number of vascular branches. [ABSTRACT FROM AUTHOR]

Objectives: To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR). Methods: A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR. Results: The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: −0.112; 95% confidence interval [CI]: −0.178 to 0.047) and full-dose IR (difference: −0.123; 95% CI: −0.182 to 0.053) (p < 0.001). Conclusion: DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR. Key Points: • Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm). [ABSTRACT FROM AUTHOR]

Objectives: To develop a visual ensemble selection of deep convolutional neural networks (CNN) for 3D segmentation of breast tumors using T1-weighted dynamic contrast-enhanced (T1-DCE) MRI. Methods: Multi-center 3D T1-DCE MRI (n = 141) were acquired for a cohort of patients diagnosed with locally advanced or aggressive breast cancer. Tumor lesions of 111 scans were equally divided between two radiologists and segmented for training. The additional 30 scans were segmented independently by both radiologists for testing. Three 3D U-Net models were trained using either post-contrast images or a combination of post-contrast and subtraction images fused at either the image or the feature level. Segmentation accuracy was evaluated quantitatively using the Dice similarity coefficient (DSC) and the Hausdorff distance (HD95) and scored qualitatively by a radiologist as excellent, useful, helpful, or unacceptable. Based on this score, a visual ensemble approach selecting the best segmentation among these three models was proposed. Results: The mean and standard deviation of DSC and HD95 between the two radiologists were equal to 77.8 ± 10.0% and 5.2 ± 5.9 mm. Using the visual ensemble selection, a DSC and HD95 equal to 78.1 ± 16.2% and 14.1 ± 40.8 mm was reached. The qualitative assessment was excellent (resp. excellent or useful) in 50% (resp. 77%). Conclusion: Using subtraction images in addition to post-contrast images provided complementary information for 3D segmentation of breast lesions by CNN. A visual ensemble selection allowing the radiologist to select the most optimal segmentation obtained by the three 3D U-Net models achieved comparable results to inter-radiologist agreement, yielding 77% segmented volumes considered excellent or useful. Key Points: • Deep convolutional neural networks were developed using T1-weighted post-contrast and subtraction MRI to perform automated 3D segmentation of breast tumors. • A visual ensemble selection allowing the radiologist to choose the best segmentation among the three 3D U-Net models outperformed each of the three models. • The visual ensemble selection provided clinically useful segmentations in 77% of cases, potentially allowing for a valuable reduction of the manual 3D segmentation workload for the radiologist and greatly facilitating quantitative studies on non-invasive biomarker in breast MRI. [ABSTRACT FROM AUTHOR]

Objectives: To assess the impact of a new artificial intelligence deep-learning reconstruction (Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction (IR) algorithm in abdominal CT for different clinical indications. Methods: Acquisitions on phantoms were performed at 5 dose levels (CTDIvol: 13/11/9/6/1.8 mGy). Raw data were reconstructed using level 4 of iDose4 (i4) and 3 levels of AI-DLR (Smoother/Smooth/Standard). Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d′) were computed: d′ modelled detection of a liver metastasis (LM) and hepatocellular carcinoma at portal (HCCp) and arterial (HCCa) phases. Image quality was subjectively assessed on an anthropomorphic phantom by 2 radiologists. Results: From Standard to Smoother levels, noise magnitude and average NPS spatial frequency decreased and the detectability (d′) of all simulated lesions increased. For both inserts, TTF values were similar for all three AI-DLR levels from 13 to 6 mGy but decreased from Standard to Smoother levels at 1.8 mGy. Compared to the i4 used in clinical practice, d′ values were higher using the Smoother and Smooth levels and close for the Standard level. For all dose levels, except at 1.8 mGy, radiologists considered images satisfactory for clinical use for the 3 levels of AI-DLR, but rated images too smooth using the Smoother level. Conclusion: Use of the Smooth and Smoother levels of AI-DLR reduces the image noise and improves the detectability of lesions and spatial resolution for standard and low-dose levels. Using the Smooth level is apparently the best compromise between the lowest dose level and adequate image quality. Key Points: • Evaluation of the impact of a new artificial intelligence deep-learning reconstruction (AI-DLR) on image quality and dose compared to a hybrid iterative reconstruction (IR) algorithm. • The Smooth and Smoother levels of AI-DLR reduced the image noise and improved the detectability of lesions and spatial resolution for standard and low-dose levels. • The Smooth level seems the best compromise between the lowest dose level and adequate image quality. [ABSTRACT FROM AUTHOR]

To prospectively compare image quality and apparent diffusion coefficient (ADC) quantification for reduced field-of-view (rFOV)- and multi-shot echo-planar imaging (msEPI)-based diffusion weighted imaging (DWI), using single-shot echo-planar-imaging (ssEPI) DWI as the reference. Under IRB approval and after informed consent, msEPI, rFOV, and ssEPI DWI acquisitions were prospectively added to clinical prostate MRI exams at 3.0 T. Image distortion was quantitatively evaluated by root-mean-squared displacement (d r.m.s.). Histogram-based quantitative ADC parameters were compared in a sub-set of patients for proven sites of prostate cancer and matched non-cancerous prostate. Three radiologists also independently evaluated the DWI sequences for subjective image quality and distortion/artifact on a 5-point Likert scale. Twenty-five patients were included (15 with proven sites of cancer). Average d r.m.s. demonstrated a small but statistically significant reduction in distortion for both rFOV and msEPI relative to ssEPI. Quantitative ADC parameters for prostate tumors demonstrated no significant difference across the 3 DWI acquisitions and each acquisition demonstrated a statistically significant decrease in mean ADC for tumor compared to normal prostate. Qualitative reader assessment demonstrated favorable image quality for rFOV and msEPI, more notable for msEPI. rFOV and msEPI DWI techniques achieved reduction in image distortion, improvement in image quality, and maintained reproducible ADC quantification compared to the standard ssEPI. • rFOV and msEPI DWI techniques reduce image distortion • ADC quantification is not significantly altered • Even slight distortion reductions may be clinically significant given the importance of DWI for prostate cancer assessment [ABSTRACT FROM AUTHOR]

Objectives: To evaluate a novel deep learning image reconstruction (DLIR) technique for dual-energy CT (DECT) derived virtual monoenergetic (VM) images compared to adaptive statistical iterative reconstruction (ASIR-V) in low kiloelectron volt (keV) images.Methods: We analyzed 30 venous phase acute abdominal DECT (80/140 kVp) scans. Data were reconstructed to ASIR-V and DLIR-High at four different keV levels (40, 50, 74, and 100) with 1- and 3-mm slice thickness. Quantitative Hounsfield unit (HU) and noise assessment were measured within the liver, aorta, fat, and muscle. Subjective assessment of image noise, sharpness, texture, and overall quality was performed by two board-certified radiologists.Results: DLIR reduced image noise by 19.9-35.5% (p < 0.001) compared to ASIR-V in all reconstructions at identical keV levels. Contrast-to-noise ratio (CNR) increased by 49.2-53.2% (p < 0.001) in DLIR 40-keV images compared to ASIR-V 50 keV, while no significant difference in noise was identified except for 1 and 3 mm in aorta and for 1-mm liver measurements, where ASIR-V 50 keV showed 5.5-6.8% (p < 0.002) lower noise levels. Qualitative assessment demonstrated significant improvement particularly in 1-mm reconstructions (p < 0.001). Lastly, DLIR 40 keV demonstrated comparable or improved image quality ratings when compared to ASIR-V 50 keV (p < 0.001 to 0.22).Conclusion: DLIR significantly reduced image noise compared to ASIR-V. Qualitative assessment showed that DLIR significantly improved image quality particularly in thin sliced images. DLIR may facilitate 40 keV as a new standard for routine low-keV VM reconstruction in contrast-enhanced abdominal DECT.Key Points: • DLIR enables 40 keV as the routine low-keV VM reconstruction. • DLIR significantly reduced image noise compared to ASIR-V, across a wide range of keV levels in VM DECT images. • In low-keV VM reconstructions, improvements in image quality using DLIR were most evident and consistent in 1-mm sliced images. [ABSTRACT FROM AUTHOR]

Objectives: To develop an MRI-based multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis (RRMS) and its mimicker neuropsychiatric systemic lupus erythematosus (NPSLE). Methods: A total of 112 patients with RRMS (n = 63) or NPSLE (n = 49) were assigned to training and test sets with a ratio of 3:1. All lesions across the whole brain were manually segmented on T2-weighted fluid-attenuated inversion recovery images. For each single lesion, 371 radiomics features were extracted and trained using machine learning algorithms, producing Radiomics Index for Lesion (RIL) for each lesion and a single-lesion radiomics model. Then, for each subject, single lesions were assigned to one of two disease courts based on their distance to decision threshold, and a Radiomics Index for Subject (RIS) was calculated as the mean RIL value of lesions on the higher-weighted court. Accordingly, a subject-level discrimination model was constructed and compared with performances of two radiologists. Results: The subject-based discrimination model satisfactorily differentiated RRMS and NPSLE in both training (AUC = 0.967, accuracy = 0.892, sensitivity = 0.917, and specificity = 0.872) and test sets (AUC = 0.962, accuracy = 0.931, sensitivity = 1.000, and specificity = 0.875), significantly better than the single-lesion radiomics method (training: p < 0.001; test: p = 0.001) Besides, the discrimination model significantly outperformed the senior radiologist in the training set (training: p = 0.018; test: p = 0.077) and the junior radiologist in both the training and test sets (training: p = 0.008; test: p = 0.023). Conclusions: The multi-lesion radiomics model could effectively discriminate between RRMS and NPSLE, providing a supplementary tool for accurate differential diagnosis of the two diseases. Key Points: • Radiomic features of brain lesions in RRMS and NPSLE were different. • The multi-lesion radiomics model constructed using a merging strategy was comprehensively superior to the single-lesion-based model for discrimination of RRMS and NPSLE. • The RRMS-NPSLE discrimination model showed a significantly better performance or a trend toward significance than the radiologists. [ABSTRACT FROM AUTHOR]

Objectives: To compare the accuracy of lesion detection of trauma-related injuries using combined "all-in-one" fused (AIO) and conventionally reconstructed images (CR) in acute trauma CT.Methods: In this retrospective study, trauma CT of 66 patients (median age 47 years, range 18-96 years; 20 female (30.3%)) were read using AIO and CR. Images were independently reviewed by 4 blinded radiologists (two residents and two consultants) for trauma-related injuries in 22 regions. Sub-analyses were performed to analyze the influence of experience (residents vs. consultants) and body region (chest, abdomen, skeletal structures) on lesion detection. Paired t-test was used to compare the accuracy of lesion detection. The effect size was calculated (Cohen's d). Linear mixed-effects model with patients as the fixed effect and random forest models were used to investigate the effect of experience, reconstruction/image processing, and body region on lesion detection.Results: Reading time of residents was significantly faster using AIO (AIO: 266 ± 72 s, CR: 318 ± 113 s; p < 0.001; d = 0.46) while no significant difference was observed in the accuracy of lesion detection (AIO: 93.5 ± 6.0%, CR: 94.6 ± 6.0% p = 0.092; d =  - 0.21). Reading time of consultants showed no significant difference (AIO: 283 ± 82 s, CR: 274 ± 95 s; p = 0.067; d = 0.16). Accuracy was significantly higher using CR; however, the difference and effect size were very small (AIO 95.1 ± 4.9%, CR: 97.3 ± 3.7%, p = 0.002; d =  - 0.39). The linear mixed-effects model showed only minor effect of image processing/reconstruction for lesion detection.Conclusions: Residents at the emergency department might benefit from faster reading time without sacrificing lesion detection rate using AIO for trauma CT.Key Points: • Image fusion techniques decrease the reading time of acute trauma CT without sacrificing diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Objective: To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR). Methods: PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image reconstruction techniques FBP, hybrid IR, and DLR. Mean differences in CT values between reconstruction techniques were analyzed. A comparison between signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of FBP, hybrid IR, and DLR was made. A comparison of diagnostic confidence between hybrid IR and DLR was made. Results: Sixteen articles were included, six being suitable for meta-analysis. In the liver, the mean difference between hybrid IR and DLR was − 0.633 HU (p = 0.483, SD ± 0.902 HU). In the spleen, the mean difference between hybrid IR and DLR was − 0.099 HU (p = 0.925, SD ± 1.061 HU). In the pancreas, the mean difference between hybrid IR and DLR was − 1.372 HU (p = 0.353, SD ± 1.476 HU). In 14 articles, CNR was described. In all cases, DLR showed a significantly higher CNR. In 9 articles, SNR was described. In all cases but one, DLR showed a significantly higher SNR. In all cases, DLR showed a significantly higher diagnostic confidence. Conclusions: There were no significant differences in CT values reconstructed by FBP, hybrid IR, and DLR in abdominal organs. This shows that these reconstruction techniques are consistent in reconstructing CT values. DLR images showed a significantly higher SNR and CNR, compared to FBP and hybrid IR. Key Points: CT values of abdominal CT images are similar between deep learning reconstruction (DLR), filtered back-projection (FBP), and hybrid iterative reconstruction (IR). DLR results in improved image quality in terms of SNR and CNR compared to FBP and hybrid IR images. DLR can thus be safely implemented in the clinical setting resulting in improved image quality without affecting CT values. [ABSTRACT FROM AUTHOR]

Objectives: We evaluated the influence of image reconstruction kernels on the diagnostic accuracy of CT-derived fractional flow reserve (FFRCT) compared to invasive FFR in patients with coronary artery disease. Methods: Sixty-nine patients, in whom coronary CT angiography was performed and who were further referred for invasive coronary angiography with FFR measurement via pressure wire, were retrospectively included. CT data sets were acquired using a third-generation dual-source CT system and rendered with medium smooth (Bv40) and sharp (Bv49) reconstruction kernels. FFRCT was calculated on-site using prototype software. Coronary stenoses with invasive FFR ≤ 0.80 were classified as significant. Agreement between FFRCT and invasive FFR was determined for both reconstruction kernels. Results: One hundred analyzed vessels in 69 patients were included. Twenty-five vessels were significantly stenosed according to invasive FFR. Using a sharp reconstruction kernel for FFRCT resulted in a significantly higher correlation with invasive FFR (r = 0.74, p < 0.01 vs. r = 0.58, p < 0.01; p = 0.04) and a higher AUC in ROC curve analysis to correctly identify/exclude significant stenosis (AUC = 0.92 vs. AUC = 0.82 for sharp vs. medium smooth kernel, respectively, p = 0.02). A FFRCT value of ≤ 0.8 using a sharp reconstruction kernel showed a sensitivity of 88% and a specificity of 92% for detecting ischemia-causing lesions, resulting in a diagnostic accuracy of 91%. The medium smooth reconstruction kernel performed worse (sensitivity 60%, specificity 89%, accuracy 82%). Conclusion: Compared to invasively measured FFR, FFRCT using a sharp image reconstruction kernel shows higher diagnostic accuracy for detecting lesions causing ischemia, potentially altering decision-making in a clinical setting. Key Points: • Image reconstruction parameters influence the diagnostic accuracy of simulated fractional flow reserve derived from coronary computed tomography angiography. • Using a sharp kernel image reconstruction algorithm delivers higher diagnostic accuracy compared to medium smooth kernel image reconstruction (gold standard invasive fractional flow reserve). [ABSTRACT FROM AUTHOR]

Objectives : To investigate sources of variation in a multicenter rectal cancer MRI dataset focusing on hardware and image acquisition, segmentation methodology, and radiomics feature extraction software. Methods: T2W and DWI/ADC MRIs from 649 rectal cancer patients were retrospectively acquired in 9 centers. Fifty-two imaging features (14 first-order/6 shape/32 higher-order) were extracted from each scan using whole-volume (expert/non-expert) and single-slice segmentations using two different software packages (PyRadiomics/CapTk). Influence of hardware, acquisition, and patient-intrinsic factors (age/gender/cTN-stage) on ADC was assessed using linear regression. Feature reproducibility was assessed between segmentation methods and software packages using the intraclass correlation coefficient. Results: Image features differed significantly (p < 0.001) between centers with more substantial variations in ADC compared to T2W-MRI. In total, 64.3% of the variation in mean ADC was explained by differences in hardware and acquisition, compared to 0.4% by patient-intrinsic factors. Feature reproducibility between expert and non-expert segmentations was good to excellent (median ICC 0.89–0.90). Reproducibility for single-slice versus whole-volume segmentations was substantially poorer (median ICC 0.40–0.58). Between software packages, reproducibility was good to excellent (median ICC 0.99) for most features (first-order/shape/GLCM/GLRLM) but poor for higher-order (GLSZM/NGTDM) features (median ICC 0.00–0.41). Conclusions: Significant variations are present in multicenter MRI data, particularly related to differences in hardware and acquisition, which will likely negatively influence subsequent analysis if not corrected for. Segmentation variations had a minor impact when using whole volume segmentations. Between software packages, higher-order features were less reproducible and caution is warranted when implementing these in prediction models. Key Points: • Features derived from T2W-MRI and in particular ADC differ significantly between centers when performing multicenter data analysis. • Variations in ADC are mainly (> 60%) caused by hardware and image acquisition differences and less so (< 1%) by patient- or tumor-intrinsic variations. • Features derived using different image segmentations (expert/non-expert) were reproducible, provided that whole-volume segmentations were used. When using different feature extraction software packages with similar settings, higher-order features were less reproducible. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the robustness of radiomic features between three dual-energy CT (DECT) systems. Methods: An anthropomorphic body phantom was scanned on three different DECT scanners, a dual-source (dsDECT), a rapid kV-switching (rsDECT), and a dual-layer detector DECT (dlDECT). Twenty-four patients who underwent abdominal DECT examinations on each of the scanner types during clinical follow-up were retrospectively included (n = 72 examinations). Radiomic features were extracted after standardized image processing, following ROI placement in phantom tissues and healthy appearing hepatic, splenic and muscular tissue of patients using virtual monoenergetic images at 65 keV (VMI65keV) and virtual unenhanced images (VUE). In total, 774 radiomic features were extracted including 86 original features and 8 wavelet transformations hereof. Concordance correlation coefficients (CCC) and analysis of variances (ANOVA) were calculated to determine inter-scanner robustness of radiomic features with a CCC of ≥ 0.9 deeming a feature robust. Results: None of the phantom-derived features attained the threshold for high feature robustness for any inter-scanner comparison. The proportion of robust features obtained from patients scanned on all three scanners was low both in VMI65keV (dsDECT vs. rsDECT:16.1% (125/774), dlDECT vs. rsDECT:2.5% (19/774), dsDECT vs. dlDECT:2.6% (20/774)) and VUE (dsDECT vs. rsDECT:11.1% (86/774), dlDECT vs. rsDECT:2.8% (22/774), dsDECT vs. dlDECT:2.7% (21/774)). The proportion of features without significant differences as per ANOVA was higher both in patients (51.4–71.1%) and in the phantom (60.6–73.4%). Conclusions: The robustness of radiomic features across different DECT scanners in patients was low and the few robust patient-derived features were not reflected in the phantom experiment. Future efforts should aim to improve the cross-platform generalizability of DECT-derived radiomics. Key Points: • Inter-scanner robustness of dual-energy CT-derived radiomic features was on a low level in patients who underwent clinical examinations on three DECT platforms. • The few robust patient-derived features were not confirmed in our phantom experiment. • Limited inter-scanner robustness of dual-energy CT derived radiomic features may impact the generalizability of models built with features from one particular dual-energy CT scanner type [ABSTRACT FROM AUTHOR]

Objective: We studied the repeatability and the relative intra-scan variability across acquisition protocols in CT using phantom and unenhanced abdominal series. Methods: We used 17 CT scans from the Credence Cartridge Radiomics Phantom database and 20 unenhanced multi-site non-pathologic abdominal patient series for which we measured spleen and liver tissues. We performed multiple measurements in extracting 9 radiomics features. We defined a "tandem" as the measurement of a given tissue (or material) by a given radiomics. For each tandem, we assessed the proportion of the variability attributable to repetitions, acquisition protocols, material, or patient. We analyzed the distribution of the intra-scan correlation between pairs of tandems and checked the impact of correlation coefficient greater than 0.90 in comparing paired and unpaired differences. Results: The repeatability of radiomics features depends on the measured material; 56% of tandems were highly repeatable. Histogram-derived radiomics were generally less repeatable. Nearly 60% of relative radiomics measurements had a correlation coefficient higher than 0.90 allowing paired measurements to improve reliability in detecting the difference between two materials. The analysis of liver and spleen tissues showed that measurement variability was negligible with respect to other variabilities. As for phantom data, we found that gray level zone length matrix (GLZLM)–derived radiomics and gray level co-occurrence matrix (GLCM)–derived radiomics were the most correlating features. For these features, relative intra-scan measurements improved the detection of different materials or tissues. Conclusions: We identified radiomics features for which the intra-scan measurements between tissues are linearly correlated. This property represents an opportunity to improve tissue characterization and inter-site harmonization. Key Points: • The repeatability of radiomics features on CT depends on the measured material or tissue. • Some tandems of radiomics features/tissues are linearly affected by the variability of acquisition protocols on CT. • Relative intra-scan measurements are an opportunity for improving quantitative imaging on CT. [ABSTRACT FROM AUTHOR]

Objectives: To develop a deep learning–based pulmonary vessel segmentation algorithm (DLVS) from noncontrast chest CT and to investigate its clinical implications in assessing vascular remodeling of chronic obstructive lung disease (COPD) patients. Methods: For development, 104 pulmonary CT angiography scans (49,054 slices) using a dual-source CT were collected, and spatiotemporally matched virtual noncontrast and 50-keV images were generated. Vessel maps were extracted from the 50-keV images. The 3-dimensional U-Net-based DLVS was trained to segment pulmonary vessels (with a vessel map as the output) from virtual noncontrast images (as the input). For external validation, vendor-independent noncontrast CT images (n = 14) and the VESSEL 12 challenge open dataset (n = 3) were used. For each case, 200 points were selected including 20 intra-lesional points, and the probability value for each point was extracted. For clinical validation, we included 281 COPD patients with low-dose noncontrast CTs. The DLVS-calculated volume of vessels with a cross-sectional area < 5 mm2 (PVV5) and the PVV5 divided by total vessel volume (%PVV5) were measured. Results: DLVS correctly segmented 99.1% of the intravascular points (1,387/1,400) and 93.1% of the extravascular points (1,309/1,400). The areas-under-the receiver-operating characteristic curve (AUROCs) were 0.977 and 0.969 for the two external validation datasets. For the COPD patients, both PPV5 and %PPV5 successfully differentiated severe patients whose FEV1 < 50 (AUROCs; 0.715 and 0.804) and were significantly correlated with the emphysema index (Ps <.05). Conclusions: DLVS successfully segmented pulmonary vessels on noncontrast chest CT by utilizing spatiotemporally matched 50-keV images from a dual-source CT scanner and showed promising clinical applicability in COPD. Key Points: • We developed a deep learning pulmonary vessel segmentation algorithm using virtual noncontrast images and 50-keV enhanced images produced by a dual-source CT scanner. • Our algorithm successfully segmented vessels on diseased lungs. • Our algorithm showed promising results in assessing the loss of small vessel density in COPD patients. [ABSTRACT FROM AUTHOR]

Objectives: To determine the diagnostic accuracy of dual-energy CT (DECT) virtual noncalcium (VNCa) reconstructions for assessing thoracic disk herniation compared to standard grayscale CT. Methods: In this retrospective study, 87 patients (1131 intervertebral disks; mean age, 66 years; 47 women) who underwent third-generation dual-source DECT and 3.0-T MRI within 3 weeks between November 2016 and April 2020 were included. Five blinded radiologists analyzed standard DECT and color-coded VNCa images after a time interval of 8 weeks for the presence and degree of thoracic disk herniation and spinal nerve root impingement. Consensus reading of independently evaluated MRI series served as the reference standard, assessed by two separate experienced readers. Additionally, image ratings were carried out by using 5-point Likert scales. Results: MRI revealed a total of 133 herniated thoracic disks. Color-coded VNCa images yielded higher overall sensitivity (624/665 [94%; 95% CI, 0.89–0.96] vs 485/665 [73%; 95% CI, 0.67–0.80]), specificity (4775/4990 [96%; 95% CI, 0.90–0.98] vs 4066/4990 [82%; 95% CI, 0.79–0.84]), and accuracy (5399/5655 [96%; 95% CI, 0.93–0.98] vs 4551/5655 [81%; 95% CI, 0.74–0.86]) for the assessment of thoracic disk herniation compared to standard CT (all p <.001). Interrater agreement was excellent for VNCa and fair for standard CT (ϰ = 0.82 vs 0.37; p <.001). In addition, VNCa imaging achieved higher scores regarding diagnostic confidence, image quality, and noise compared to standard CT (all p <.001). Conclusions: Color-coded VNCa imaging yielded substantially higher diagnostic accuracy and confidence for assessing thoracic disk herniation compared to standard CT. Key Points: • Color-coded VNCa reconstructions derived from third-generation dual-source dual-energy CT yielded significantly higher diagnostic accuracy for the assessment of thoracic disk herniation and spinal nerve root impingement compared to standard grayscale CT. • VNCa imaging provided higher diagnostic confidence and image quality at lower noise levels compared to standard grayscale CT. • Color-coded VNCa images may potentially serve as a viable imaging alternative to MRI under circumstances where MRI is unavailable or contraindicated. [ABSTRACT FROM AUTHOR]

Objectives: Current prognostic systems for intrahepatic cholangiocarcinoma (IHCC) rely on surgical pathology data and are not applicable to a preoperative setting. We aimed to develop and validate preoperative models to predict postsurgical outcomes in mass-forming IHCC patients based on clinical, radiologic, and radiomics features. Methods: This multicenter retrospective cohort study included patients who underwent curative-intent resection for mass-forming IHCC. In the development cohort (single institution data), three preoperative multivariable Cox models for predicting recurrence-free survival (RFS) were constructed, including the clinical-radiologic, radiomics, and clinical-radiologic-radiomics (CRR) models based on clinical and CT findings, CT-radiomics features, and a combination of both, respectively. Model performance was evaluated in the test cohort (data from five institutions) using Harrell's C-index and compared with postoperative prognostic systems. Results: A total of 345 patients (233, development cohort; 112, test cohort) were evaluated. The clinical-radiologic model included five independent CT predictors (infiltrative contour, multiplicity, periductal infiltration, extrahepatic organ invasion, and suspicious metastatic lymph node) and showed similar performance in predicting RFS to the radiomics model (C-index, 0.65 vs. 0.68; p = 0.43 in the test cohort). The CRR model showed significantly improved performance (C-index, 0.71; p = 0.01) than the clinical-radiologic model and demonstrated similar performance to the postoperative prognostic systems in predicting RFS (C-index, 0.71–0.73 vs. 0.70–0.73; p ≥ 0.40) and overall survival (C-index, 0.68–0.71 vs. 0.64–0.74; p ≥ 0.27) in the test cohort. Conclusions: A model integrating clinical, CT, and radiomics information may be useful for the preoperative assessment of postsurgical outcomes in patients with mass-forming IHCC. Key Points: • The radiomics analysis had incremental value in predicting recurrence-free survival of patients with intrahepatic mass-forming cholangiocarcinoma. • The clinical-radiologic-radiomics model demonstrated similar performance to the postoperatively available prognostic systems (including 8th AJCC system) in predicting recurrence-free survival and overall survival. • The clinical-radiologic-radiomics model may be useful for the preoperative assessment of postsurgical outcomes in patients with mass-forming intrahepatic cholangiocarcinoma. [ABSTRACT FROM AUTHOR]

Objectives: The individual course of disease in idiopathic pulmonary fibrosis (IPF) is highly variable. Assessment of disease activity and prospective estimation of disease progression might have the potential to improve therapy management and indicate the onset of treatment at an earlier stage. The aim of this study was to evaluate whether regional ventilation, lung perfusion, and late enhancement can serve as early imaging markers for disease progression in patients with IPF. Methods: In this retrospective study, contrast-enhanced dual-energy CT scans of 32 patients in inspiration and delayed expiration were performed at two time points with a mean interval of 15.4 months. The pulmonary blood volume (PBV) images obtained in the arterial and delayed perfusion phase served as a surrogate for arterial lung perfusion and parenchymal late enhancement. The virtual non-contrast (VNC) images in inspiration and expiration were non-linearly registered to provide regional ventilation images. Image-derived parameters were correlated with longitudinal changes of lung function (FVC%, DLCO%), mean lung density in CT, and CT-derived lung volume. Results: Regional ventilation and late enhancement at baseline preceded future change in lung volume (R - 0.474, p 0.006/R - 0.422, p 0.016, respectively) and mean lung density (R - 0.469, p 0.007/R - 0.402, p 0.022, respectively). Regional ventilation also correlated with a future change in FVC% (R - 0.398, p 0.024). Conclusion: CT-derived functional parameters of regional ventilation and parenchymal late enhancement are potential early imaging markers for idiopathic pulmonary fibrosis progression. Key Points: • Functional CT parameters at baseline (regional ventilation and late enhancement) correlate with future structural changes of the lung as measured with loss of lung volume and increase in lung density in serial CT scans of patients with idiopathic pulmonary fibrosis. • Functional CT parameter measurements in high-attenuation areas (- 600 to - 250 HU) are significantly different from normal-attenuation areas (- 950 to - 600 HU) of the lung. • Mean regional ventilation in functional CT correlates with a future change in forced vital capacity (FVC) in pulmonary function tests. [ABSTRACT FROM AUTHOR]

Objectives: To reduce the dose of intravenous iodine-based contrast media (ICM) in CT through virtual contrast-enhanced images using generative adversarial networks. Methods: Dual-energy CTs in the arterial phase of 85 patients were randomly split into an 80/20 train/test collective. Four different generative adversarial networks (GANs) based on image pairs, which comprised one image with virtually reduced ICM and the original full ICM CT slice, were trained, testing two input formats (2D and 2.5D) and two reduced ICM dose levels (−50% and −80%). The amount of intravenous ICM was reduced by creating virtual non-contrast series using dual-energy and adding the corresponding percentage of the iodine map. The evaluation was based on different scores (L1 loss, SSIM, PSNR, FID), which evaluate the image quality and similarity. Additionally, a visual Turing test (VTT) with three radiologists was used to assess the similarity and pathological consistency. Results: The −80% models reach an SSIM of > 98%, PSNR of > 48, L1 of between 7.5 and 8, and an FID of between 1.6 and 1.7. In comparison, the −50% models reach a SSIM of > 99%, PSNR of > 51, L1 of between 6.0 and 6.1, and an FID between 0.8 and 0.95. For the crucial question of pathological consistency, only the 50% ICM reduction networks achieved 100% consistency, which is required for clinical use. Conclusions: The required amount of ICM for CT can be reduced by 50% while maintaining image quality and diagnostic accuracy using GANs. Further phantom studies and animal experiments are required to confirm these initial results. Key Points: • The amount of contrast media required for CT can be reduced by 50% using generative adversarial networks. • Not only the image quality but especially the pathological consistency must be evaluated to assess safety. • A too pronounced contrast media reduction could influence the pathological consistency in our collective at 80%. [ABSTRACT FROM AUTHOR]

Objectives: To compare image quality and radiation dose between dual-energy subtraction (DES)–based bone suppression images (D-BSIs) and software-based bone suppression images (S-BSIs). Methods: Chest radiographs (CXRs) of forty adult patients were obtained with the two X-ray devices, one with DES and one with bone suppression software. Three image quality metrics (relative mean absolute error (RMAE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM)) between original CXR and BSI for each of D-BSI and S-SBI groups were calculated for each bone and soft tissue areas. Two readers rated the visual image quality for original CXR and BSI for each of D-BSI and S-SBI groups. The dose area product (DAP) values were recorded. Paired t test was used to compare the image quality and DAP values between D-BSI and S-BSI groups. Results: In bone areas, S-BSIs had better SSIM values than D-BSI (94.57 vs. 87.77) but worse RMAE and PSNR values (0.50 vs. 0.20; 20.93 vs. 34.37) (all p < 0.001). In soft tissue areas, S-BSIs had better SSIM values than D-BSI (97.56 vs. 91.42) but similar RMAE and PSNR values (0.29 vs. 0.27; 31.35 vs. 29.87) (all p < 0.001). Both readers gave S-BSIs significantly higher image quality scores than D-BSI (p < 0.001). The mean DAP in software-related images (0.98 dGy·cm2) was significantly lower than that in the DES-related images (1.48 dGy·cm2) (p < 0.001). Conclusion: Bone suppression software significantly improved the image quality of bone suppression images with a relatively lower radiation dose, compared with dual-energy subtraction technique. Key Points: • Bone suppression software preserves structure similarity of soft tissues better than dual-energy subtraction technique in bone suppression images. • Bone suppression software achieves superior image quality for lung lesions than dual-energy subtraction technique in bone suppression images. • Bone suppression software can decrease the radiation dose over the hardware-based image processing technique. [ABSTRACT FROM AUTHOR]

Objective: To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT. Methods: Vendor-agnostic deep learning post-processing model (DLM), vendor-specific deep learning image reconstruction (DLIR, high level), and adaptive statistical iterative reconstruction (ASiR, 70%) algorithms were employed. One hundred consecutive ultralow-dose noncontrast CT scans (CTDIvol; mean, 0.33 ± 0.056 mGy) were reconstructed with five algorithms: DLM-stnd (standard kernel), DLM-shrp (sharp kernel), DLIR, ASiR-stnd, and ASiR-shrp. Three thoracic radiologists blinded to the reconstruction algorithms reviewed five sets of 100 images and assessed subjective noise, spatial resolution, distortion artifact, and overall image quality. They selected the most preferred algorithm among five image sets for each case. Image noise and signal-to-noise ratio were measured. Edge-rise-distance was measured at a pulmonary vessel, i.e., the distance between two points where attenuation was 10% and 90% of maximal intravascular intensity. The skewness of attenuation was calculated in homogeneous areas. Results: DLM-stnd, followed by DLIR, showed the best subjective noise on both lung and mediastinal windows, while DLIR yielded the least measured noise (ps <.0001). Compared to DLM-stnd, DLIR showed inferior subjective spatial resolution on lung window and higher edge-rise-distance (ps <.0001). Additionally, DLIR showed the most frequent distortion artifacts and deviated skewness (ps <.0001). DLM-stnd scored the best overall image quality, followed by DLM-shrp and DLIR (mean score 3.89 ± 0.19, 3.68 ± 0.24, and 3.53 ± 0.33; ps <.001). Two among three readers preferred DLM-stnd on both windows. Conclusion: Although DLIR provided the best quantitative noise profile, DLM-stnd showed the best overall image quality with fewer artifacts and was preferred by two among three readers. Key Points: • A vendor-agnostic deep learning post-processing algorithm applied to ultralow-dose chest CT exhibited the best image quality compared to vendor-specific deep learning algorithm and ASiR techniques. • Two out of three readers preferred a vendor-agnostic deep learning post-processing algorithm in comparison to vendor-specific deep learning algorithm and ASiR techniques. • A vendor-specific deep learning reconstruction algorithm yielded the least image noise, but showed significantly more frequent specific distortion artifacts and increased skewness of attenuation compared to a vendor-agnostic algorithm. [ABSTRACT FROM AUTHOR]

Objective: To identify textural features on dual-energy CT (DECT)–based bone marrow images in myeloma which correlate with serum markers of myeloma activity and the degree of medullary involvement. Methods: A total of 110 patients (63.0 ± 11.0 years, 51 female) who underwent unenhanced whole-body DECT between September 2015 and February 2019 were retrospectively included, which was approved by our institutional ethics committee with a waiver of the informed consent requirement. All patients had current hematologic laboratory tests. Using DECT post-processing, non-calcium bone marrow images were reconstructed. The vertebral bodies T10–L5 were segmented for quantification of textural features, which were compared with serologic parameters and myeloma stages by the Mann-Whitney U test. In a subgroup of 56/110 patients with current bone marrow biopsies, textural features were correlated with the degree of bone marrow infiltration. Results: First-order features were higher in patients with advanced stage of myeloma (p <.02), whereas the 2nd-order "gray-level co-occurrence matrix (GLCM) cluster prominence" was lower (p <.04). In patients with elevated serum-free light chains (SFLC) or kappa/lambda SFLC ratio above 1.56, the "entropy" and 2nd-order GLCM features were lower (p <.03). The degree of bone marrow infiltration correlated with 1st-order features (e.g., "uniformity"; rP = 0.49; p <.0001), whereas "entropy" and 2nd-order GLCM features were negatively correlated (e.g., "difference entropy"; rP = − 0.54; p <.0001). Conclusions: CT textural features applied on non-calcium bone marrow images correlate well with myeloma-related serologic parameters and histology showing a more uniform tissue structure and higher attenuation with increasing medullary infiltration and could therefore be used as additional imaging biomarkers for non-invasive assessment of medullary involvement. Key Points: • Texture analysis applied on dual-energy reconstructed non-calcium bone marrow images provides information about marrow structure and attenuation. • Myeloma-related serologic parameters and the degree of myeloma cell infiltration correlate with 1st- and 2nd-order features which could be useful as additional imaging biomarkers for non-invasive assessment of medullary involvement. [ABSTRACT FROM AUTHOR]

Objectives: To investigate potential radiation dose reduction for multi-detector computed tomography (MDCT) exams of the spine by using sparse sampling and virtually lowered tube currents combined with statistical iterative reconstruction (SIR). Methods: MDCT data of 26 patients (68.9 ± 11.7 years, 42.3% males) were retrospectively simulated as if the scans were acquired at 50%, 10%, 5%, and 3% of the original X-ray tube current or number of projections, using SIR for image reconstructions. Two readers performed qualitative image evaluation considering overall image quality, artifacts, and contrast and determined the number and type of degenerative changes. Scoring was compared between readers and virtual low-dose and sparse-sampled MDCT, respectively. Results: Image quality and contrast decreased with virtual lowering of tube current and sparse sampling, but all degenerative changes were correctly detected in MDCT with 50% of tube current as well as MDCT with 50% of projections. Sparse-sampled MDCT with only 10% of initial projections still enabled correct identification of all degenerative changes, in contrast to MDCT with virtual tube current reduction by 90% where non-calcified disc herniations were frequently missed (R1: 23.1%, R2: 21.2% non-diagnosed herniations). The average volumetric CT dose index (CTDIvol) was 1.4 mGy for MDCT with 10% of initial projections, compared with 13.8 mGy for standard-dose imaging. Conclusions: MDCT with 50% of original tube current or projections using SIR still allowed for accurate diagnosis of degenerative changes. Sparse sampling may be more promising for further radiation dose reductions since no degenerative changes were missed with 10% of initial projections. Key Points: • Most common degenerative changes of the spine can be diagnosed in multi-detector CT with 50% of tube current or number of projections. • Sparse-sampled multi-detector CT with only 10% of initial projections still enables correct identification of degenerative changes, in contrast to imaging with 10% of original tube current. • Sparse sampling may be a promising option for distinct lowering of radiation dose, reducing the CTDIvolfrom 13.8 to 1.4 mGy in the study cohort. [ABSTRACT FROM AUTHOR]

Objective: To develop a discrimination pipeline concerning both radiomics and spatial distribution features of brain lesions for discrimination of multiple sclerosis (MS), aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder (NMOSD), and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder (MOGAD).Hyperintensity T2 lesions were delineated in 212 brain MRI scans of MS (n = 63), NMOSD (n = 87), and MOGAD (n = 45) patients. To avoid the effect of fixed training/test dataset sampling when developing machine learning models, patients were allocated into 4 sub-groups for cross-validation. For each scan, 351 radiomics and 27 spatial distribution features were extracted. Three models, i.e., multi-lesion radiomics, spatial distribution, and joint models, were constructed using random forest and logistic regression algorithms for differentiating: MS from the others (MS models) and MOGAD from NMOSD (MOG-NMO models), respectively. Then, the joint models were combined with demographic characteristics (i.e., age and sex) to create MS and MOG-NMO discriminators, respectively, based on which a three-disease discrimination pipeline was generated and compared with radiologists.For classification of both MS-others and MOG-NMO, the joint models performed better than radiomics or spatial distribution model solely. The MS discriminator achieved AUC = 0.909 ± 0.027 and bias-corrected C-index = 0.909 ± 0.027, and the MOG-NMO discriminator achieved AUC = 0.880 ± 0.064 and bias-corrected C-index = 0.883 ± 0.068. The three-disease discrimination pipeline differentiated MS, NMOSD, and MOGAD patients with 75.0% accuracy, prominently outperforming the three radiologists (47.6%, 56.6%, and 66.0%).The proposed pipeline integrating multi-lesion radiomics and spatial distribution features could effectively differentiate MS, NMOSD, and MOGAD.The discrimination pipeline merging both radiomics and spatial distribution features of brain lesions may facilitate the differential diagnoses of multiple sclerosis, neuromyelitis optica spectrum disorder, and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder.• Our study introduces an approach by combining radiomics and spatial distribution models.• The joint model exhibited superior performance in distinguishing multiple sclerosis from aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder as well as discriminating the latter two diseases.• The three-disease discrimination pipeline showcased remarkable accuracy, surpassing the performance of experienced radiologists, highlighting its potential as a valuable diagnostic tool.Methods: To develop a discrimination pipeline concerning both radiomics and spatial distribution features of brain lesions for discrimination of multiple sclerosis (MS), aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder (NMOSD), and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder (MOGAD).Hyperintensity T2 lesions were delineated in 212 brain MRI scans of MS (n = 63), NMOSD (n = 87), and MOGAD (n = 45) patients. To avoid the effect of fixed training/test dataset sampling when developing machine learning models, patients were allocated into 4 sub-groups for cross-validation. For each scan, 351 radiomics and 27 spatial distribution features were extracted. Three models, i.e., multi-lesion radiomics, spatial distribution, and joint models, were constructed using random forest and logistic regression algorithms for differentiating: MS from the others (MS models) and MOGAD from NMOSD (MOG-NMO models), respectively. Then, the joint models were combined with demographic characteristics (i.e., age and sex) to create MS and MOG-NMO discriminators, respectively, based on which a three-disease discrimination pipeline was generated and compared with radiologists.For classification of both MS-others and MOG-NMO, the joint models performed better than radiomics or spatial distribution model solely. The MS discriminator achieved AUC = 0.909 ± 0.027 and bias-corrected C-index = 0.909 ± 0.027, and the MOG-NMO discriminator achieved AUC = 0.880 ± 0.064 and bias-corrected C-index = 0.883 ± 0.068. The three-disease discrimination pipeline differentiated MS, NMOSD, and MOGAD patients with 75.0% accuracy, prominently outperforming the three radiologists (47.6%, 56.6%, and 66.0%).The proposed pipeline integrating multi-lesion radiomics and spatial distribution features could effectively differentiate MS, NMOSD, and MOGAD.The discrimination pipeline merging both radiomics and spatial distribution features of brain lesions may facilitate the differential diagnoses of multiple sclerosis, neuromyelitis optica spectrum disorder, and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder.• Our study introduces an approach by combining radiomics and spatial distribution models.• The joint model exhibited superior performance in distinguishing multiple sclerosis from aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder as well as discriminating the latter two diseases.• The three-disease discrimination pipeline showcased remarkable accuracy, surpassing the performance of experienced radiologists, highlighting its potential as a valuable diagnostic tool.Results: To develop a discrimination pipeline concerning both radiomics and spatial distribution features of brain lesions for discrimination of multiple sclerosis (MS), aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder (NMOSD), and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder (MOGAD).Hyperintensity T2 lesions were delineated in 212 brain MRI scans of MS (n = 63), NMOSD (n = 87), and MOGAD (n = 45) patients. To avoid the effect of fixed training/test dataset sampling when developing machine learning models, patients were allocated into 4 sub-groups for cross-validation. For each scan, 351 radiomics and 27 spatial distribution features were extracted. Three models, i.e., multi-lesion radiomics, spatial distribution, and joint models, were constructed using random forest and logistic regression algorithms for differentiating: MS from the others (MS models) and MOGAD from NMOSD (MOG-NMO models), respectively. Then, the joint models were combined with demographic characteristics (i.e., age and sex) to create MS and MOG-NMO discriminators, respectively, based on which a three-disease discrimination pipeline was generated and compared with radiologists.For classification of both MS-others and MOG-NMO, the joint models performed better than radiomics or spatial distribution model solely. The MS discriminator achieved AUC = 0.909 ± 0.027 and bias-corrected C-index = 0.909 ± 0.027, and the MOG-NMO discriminator achieved AUC = 0.880 ± 0.064 and bias-corrected C-index = 0.883 ± 0.068. The three-disease discrimination pipeline differentiated MS, NMOSD, and MOGAD patients with 75.0% accuracy, prominently outperforming the three radiologists (47.6%, 56.6%, and 66.0%).The proposed pipeline integrating multi-lesion radiomics and spatial distribution features could effectively differentiate MS, NMOSD, and MOGAD.The discrimination pipeline merging both radiomics and spatial distribution features of brain lesions may facilitate the differential diagnoses of multiple sclerosis, neuromyelitis optica spectrum disorder, and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder.• Our study introduces an approach by combining radiomics and spatial distribution models.• The joint model exhibited superior performance in distinguishing multiple sclerosis from aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder as well as discriminating the latter two diseases.• The three-disease discrimination pipeline showcased remarkable accuracy, surpassing the performance of experienced radiologists, highlighting its potential as a valuable diagnostic tool.Conclusions: To develop a discrimination pipeline concerning both radiomics and spatial distribution features of brain lesions for discrimination of multiple sclerosis (MS), aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder (NMOSD), and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder (MOGAD).Hyperintensity T2 lesions were delineated in 212 brain MRI scans of MS (n = 63), NMOSD (n = 87), and MOGAD (n = 45) patients. To avoid the effect of fixed training/test dataset sampling when developing machine learning models, patients were allocated into 4 sub-groups for cross-validation. For each scan, 351 radiomics and 27 spatial distribution features were extracted. Three models, i.e., multi-lesion radiomics, spatial distribution, and joint models, were constructed using random forest and logistic regression algorithms for differentiating: MS from the others (MS models) and MOGAD from NMOSD (MOG-NMO models), respectively. Then, the joint models were combined with demographic characteristics (i.e., age and sex) to create MS and MOG-NMO discriminators, respectively, based on which a three-disease discrimination pipeline was generated and compared with radiologists.For classification of both MS-others and MOG-NMO, the joint models performed better than radiomics or spatial distribution model solely. The MS discriminator achieved AUC = 0.909 ± 0.027 and bias-corrected C-index = 0.909 ± 0.027, and the MOG-NMO discriminator achieved AUC = 0.880 ± 0.064 and bias-corrected C-index = 0.883 ± 0.068. The three-disease discrimination pipeline differentiated MS, NMOSD, and MOGAD patients with 75.0% accuracy, prominently outperforming the three radiologists (47.6%, 56.6%, and 66.0%).The proposed pipeline integrating multi-lesion radiomics and spatial distribution features could effectively differentiate MS, NMOSD, and MOGAD.The discrimination pipeline merging both radiomics and spatial distribution features of brain lesions may facilitate the differential diagnoses of multiple sclerosis, neuromyelitis optica spectrum disorder, and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder.• Our study introduces an approach by combining radiomics and spatial distribution models.• The joint model exhibited superior performance in distinguishing multiple sclerosis from aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder as well as discriminating the latter two diseases.• The three-disease discrimination pipeline showcased remarkable accuracy, surpassing the performance of experienced radiologists, highlighting its potential as a valuable diagnostic tool.Clinical relevance statement: To develop a discrimination pipeline concerning both radiomics and spatial distribution features of brain lesions for discrimination of multiple sclerosis (MS), aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder (NMOSD), and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder (MOGAD).Hyperintensity T2 lesions were delineated in 212 brain MRI scans of MS (n = 63), NMOSD (n = 87), and MOGAD (n = 45) patients. To avoid the effect of fixed training/test dataset sampling when developing machine learning models, patients were allocated into 4 sub-groups for cross-validation. For each scan, 351 radiomics and 27 spatial distribution features were extracted. Three models, i.e., multi-lesion radiomics, spatial distribution, and joint models, were constructed using random forest and logistic regression algorithms for differentiating: MS from the others (MS models) and MOGAD from NMOSD (MOG-NMO models), respectively. Then, the joint models were combined with demographic characteristics (i.e., age and sex) to create MS and MOG-NMO discriminators, respectively, based on which a three-disease discrimination pipeline was generated and compared with radiologists.For classification of both MS-others and MOG-NMO, the joint models performed better than radiomics or spatial distribution model solely. The MS discriminator achieved AUC = 0.909 ± 0.027 and bias-corrected C-index = 0.909 ± 0.027, and the MOG-NMO discriminator achieved AUC = 0.880 ± 0.064 and bias-corrected C-index = 0.883 ± 0.068. The three-disease discrimination pipeline differentiated MS, NMOSD, and MOGAD patients with 75.0% accuracy, prominently outperforming the three radiologists (47.6%, 56.6%, and 66.0%).The proposed pipeline integrating multi-lesion radiomics and spatial distribution features could effectively differentiate MS, NMOSD, and MOGAD.The discrimination pipeline merging both radiomics and spatial distribution features of brain lesions may facilitate the differential diagnoses of multiple sclerosis, neuromyelitis optica spectrum disorder, and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder.• Our study introduces an approach by combining radiomics and spatial distribution models.• The joint model exhibited superior performance in distinguishing multiple sclerosis from aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder as well as discriminating the latter two diseases.• The three-disease discrimination pipeline showcased remarkable accuracy, surpassing the performance of experienced radiologists, highlighting its potential as a valuable diagnostic tool.Key Points: To develop a discrimination pipeline concerning both radiomics and spatial distribution features of brain lesions for discrimination of multiple sclerosis (MS), aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder (NMOSD), and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder (MOGAD).Hyperintensity T2 lesions were delineated in 212 brain MRI scans of MS (n = 63), NMOSD (n = 87), and MOGAD (n = 45) patients. To avoid the effect of fixed training/test dataset sampling when developing machine learning models, patients were allocated into 4 sub-groups for cross-validation. For each scan, 351 radiomics and 27 spatial distribution features were extracted. Three models, i.e., multi-lesion radiomics, spatial distribution, and joint models, were constructed using random forest and logistic regression algorithms for differentiating: MS from the others (MS models) and MOGAD from NMOSD (MOG-NMO models), respectively. Then, the joint models were combined with demographic characteristics (i.e., age and sex) to create MS and MOG-NMO discriminators, respectively, based on which a three-disease discrimination pipeline was generated and compared with radiologists.For classification of both MS-others and MOG-NMO, the joint models performed better than radiomics or spatial distribution model solely. The MS discriminator achieved AUC = 0.909 ± 0.027 and bias-corrected C-index = 0.909 ± 0.027, and the MOG-NMO discriminator achieved AUC = 0.880 ± 0.064 and bias-corrected C-index = 0.883 ± 0.068. The three-disease discrimination pipeline differentiated MS, NMOSD, and MOGAD patients with 75.0% accuracy, prominently outperforming the three radiologists (47.6%, 56.6%, and 66.0%).The proposed pipeline integrating multi-lesion radiomics and spatial distribution features could effectively differentiate MS, NMOSD, and MOGAD.The discrimination pipeline merging both radiomics and spatial distribution features of brain lesions may facilitate the differential diagnoses of multiple sclerosis, neuromyelitis optica spectrum disorder, and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder.• Our study introduces an approach by combining radiomics and spatial distribution models.• The joint model exhibited superior performance in distinguishing multiple sclerosis from aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder as well as discriminating the latter two diseases.• The three-disease discrimination pipeline showcased remarkable accuracy, surpassing the performance of experienced radiologists, highlighting its potential as a valuable diagnostic tool. [ABSTRACT FROM AUTHOR]

Purpose: We prospectively investigate the feasibility of a patient specific automated tube voltage selection (ATVS)-based coronary artery calcium scoring (CACS) protocol, using a kV-independent reconstruction algorithm, to achieve significant dose reductions while maintaining the overall cardiac risk classification. Methods: Forty-three patients (mean age, 61.8 ± 9.0 years; 40% male) underwent a clinically indicated CACS scan at 120kVp, as well as an additional CACS acquisition using an individualized tube voltage between 70 and 130kVp based on the ATVS selection (CARE-kV). Datasets of the additional CACS scans were reconstructed using a kV-independent algorithm that allows for calcium scoring without changing the weighting threshold of 130HU, regardless of the tube voltage chosen for image acquisition. Agatston scores and radiation dose derived from the different ATVS-based CACS studies were compared to the standard acquisition at 120kVp. Results: Thirteen patients displayed a score of 0 and were correctly identified with the ATVS protocol. Agatston scores derived from the standard 120kVp (median, 33.4; IQR, 0–289.7) and the patient-tailored kV-independent protocol (median, 47.5; IQR, 0–287.5) showed no significant differences (p = 0.094). The intra-class correlation for Agatston scores derived from the two different protocols was excellent (ICC = 0.99). The mean dose-length-product was 29.8 ± 11.9 mGy × cm using the ATVS protocol and 31.7 ± 11.4 mGy × cm using the standard 120kVp protocol (p < 0.001). Additionally, 95% of patients were classified into the same risk category (0, 1–10, 11–100, 101–400, or > 400) using the patient-tailored protocol. Conclusions: ATVS-based CACS, using a kV-independent algorithm, allows for high accuracy compared to the standard 120kVp scanning, while significantly reducing radiation dose parameters. Key Points: • ATVS allows for CT scanning with reduced radiation dose values. • KV-independent CACS is feasible at any tube voltage between 70 and 130 kVp. • ATVS applied to kV-independent CACS can significantly reduce the radiation dose. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the effect of the combined use of virtual monochromatic imaging (VMI) and projection-based metal artifact reduction (PB-MAR) methods on further artifact reduction and image quality in total knee arthroplasty (TKA) patients. Methods: Fifty-seven knee joints from 36 patients who had a previous history of TKA for bilateral or unilateral knee joints were included in this study. Four sets of images were compared between non-MAR, PB-MAR, VMI, and VMI + PB-MAR. For quantitative analysis, the area, mean attenuation, artifact index (AI), and contrast-to-noise ratio (CNR) were calculated for each protocol. Regarding qualitative analysis, overall artifact and depiction of soft tissue and bony structure were compared using relative visual grading analysis. Results: In the femoral region, the VMI + PB-MAR protocol showed the best MAR performance in quantitative measures including area, mean attenuation, and AI (p <.001). However, MAR protocols with VMI showed significantly lower CNRs than did the protocols without VMI in the tibial region (p <.001). On qualitative analysis, VMI + PB-MAR also showed fewer overall artifacts than did the other two MAR protocols. Soft tissue was best depicted in VMI + PB-MAR protocol, and PB-MAR showed the best performance for the depiction of bony structure (p <.001). Conclusions: The combined use of VMI and PB-MAR showed better MAR performance than did PB-MAR or VMI alone. In areas with severe metal artifacts, the VMI + PB-MAR and PB-MAR protocols were useful for the evaluation of soft tissue and bone structure, respectively. Key Points: • The combination of artifact reduction methods is effective in reducing metal artifacts in CT. • Soft tissue was best depicted in the combined protocol of projection- and dual-energy-based methods, and projection-based protocol showed the best performance for the depiction of bone structure in case of severe metal artifacts. [ABSTRACT FROM AUTHOR]

Objectives: To explore the prognostic value of positron emission tomography (PET) radiomic features in the field of diffuse large B cell lymphoma (DLBCL) treated with a first-line immunochemotherapy.Methods: One-hundred thirty-two patients newly diagnosed with DLBCL were retrospectively included. PET studies were reconstructed using an ordered subset expectation maximisation algorithm with point spread function modelling. The total metabolic tumour volume (MTV) was recorded for each patient, and the volume of interest structure of the largest target lesion was used to compute 18F-FDG textural parameters. Data was randomly split into training and validation datasets. Optimal cutoff values were determined by means of 2-year event-free survival (EFS) ROC analyses. Two-year EFS analyses were performed using Kaplan-Meier survival analyses and univariable and multivariable Cox regression models.Results: The median follow-up was 27 months, and the 2-year event-free survival (2y-EFS) was 77.3% in the entire population. ROC analyses for the 2y-EFS reached statistical significance for total MTV as well as four second-order metrics (homogeneity, contrast, correlation, dissimilarity) and five third-order metrics (LZE (Long-Zone Emphasis), LZLGE (Long-Zone Low-Grey Level Emphasis), LZHGE (Long-Zone High-Grey Level Emphasis), GLNU (Grey-Level Non-Uniformity) and ZP (Zone Percentage)). LZHGE displayed the highest ROC analysis accuracy (acc. = 0.76) and the best discriminant value on univariable Kaplan-Meier analysis (p < 0.0001, HR = 4.54). On multivariable analysis, including IPIaa, total MTV and LZHGE, LZHGE was the only independent predictor of 2y-EFS. These results were confirmed on the validation dataset.Conclusions: Baseline 18F-FDG PET heterogeneity of the largest lymphoma lesion is a promising predictor of 2y-EFS in newly diagnosed DLBCL treated with immunochemotherapy.Key Points: •18F-FDG metabolic heterogeneity emerges as a new tool for survival prognostication of patients and has been explored in many solid tumours with promising results. • Baseline18F-FDG PET heterogeneity of the largest lymphoma lesion is an independent predictor of 2y-EFS in newly diagnosed DLBCL treated with immunochemotherapy. • DLBCL patients presenting with a heterogeneous tumour displayed a worse prognosis. [ABSTRACT FROM AUTHOR]

Ethanol solubilizes cell membranes, making it useful for various ablation applications. We examined the effect of time and alcohol type on the extent of ablation, quantified as Euclidean distances between color coordinates. We obtained biopsy punch samples (diameter, 6 mm) of left atrial appendage, atrial, ventricular, and septal tissue from porcine hearts and placed them in transwell plates filled with ethanol or methanol for 10, 20, 30, 40, 50, or 60 min. Control samples were taken for each time point. At each time point, samples were collected, cut transversely, and photographed. With use of a custom MATLAB program, all images were analyzed in the CIELAB color space, which is more perceptually uniform than the red-green-blue color space. Euclidean distances were calculated from CIELAB coordinates. The mean and standard error of these distances were analyzed. Two-way analysis of variance was used to test for differences among time points, and 2-tailed t tests, for differences between the alcohol datasets at each time point. Generally, Euclidean distances differed significantly between all time points, except for those immediately adjacent, and methanol produced larger Euclidean distances than ethanol did. Some tissue showed a plateauing effect, potentially indicating transmurality. Mean Euclidean distances effectively indexed alcohol ablation in cardiac tissue. Furthermore, we found that methanol ablated tissue more effectively than ethanol did. With ethanol, the extent of ablation for atrial tissue was largest at 60 min. We conclude that to achieve full transmurality in clinical applications, ethanol must remain in contact with atrial tissue for at least one hour. [ABSTRACT FROM AUTHOR]

Objectives: Peritoneal carcinomatosis (PC) is a prognostically relevant metastatic disease which may be difficult to depict in postoperative patients, particularly in early stages. This study aimed to determine whether PC could be diagnosed more accurately when using a combination of spectral detector CT (SDCT)-derived conventional images (CI) and iodine overlay images (IO) compared with CI only.Methods: Thirty patients with PC and 30 patients with benign peritoneal alterations (BPA) who underwent portal-venous abdominal SDCT were included. Four radiologists determined the presence/absence of PC for each patient and assessed lesion conspicuity, diagnostic certainty, and image quality using 5-point Likert scales. Subjective assessment was conducted in two sessions comprising solely CI and CI/IO between which a latency of 6 weeks was set. Iodine uptake and HU attenuation were determined ROI-based to analyze quantitative differentiation of PC/BPA.Results: Specificity for PC was significantly higher when using CI/IO compared with using CI only (0.86 vs. 0.78, p ≤ 0.05), while sensitivity was comparable (0.79 vs. 0.81, p = 1). In postoperative patients, the increase in specificity was the highest (0.93 vs. 0.80, p ≤ 0.05). Lesion conspicuity was rated higher in CI/IO (4 (3-5)) compared with that in CI only (3 (3-4); p ≤ 0.05). Diagnostic certainty was comparable (both 4 (3-5); p = 0.5). CI/IO received the highest rating for overall image quality and assessability (CI/IO 5 (4-5) vs. CI 4 (4-4) vs. IO 4 (3-4); p ≤ 0.05). Area under the receiver operating characteristics curve (AUC) for quantitative differentiation between PC and BPA was higher for iodine (AUCIodine = 0.95, AUCHU = 0.90).Conclusions: Compared with CI, combination of CI/IO improves specificity in the assessment of peritoneal carcinomatosis at comparable sensitivity, particularly in postoperative patients.Key Points: • Combination of iodine overlays and conventional images improves specificity when assessing patients with peritoneal carcinomatosis at comparable sensitivity. • Particularly in postsurgical patients, iodine overlays could help to avoid false-positive diagnosis of peritoneal disease. • Iodine overlays alone provided inferior image quality and assessability than conventional images, while the combination of both received the highest ratings. Iodine overlays should therefore be used in addition to and not as a substitute for conventional images. [ABSTRACT FROM AUTHOR]

The identification and counting of red blood cells (RBCs) in microscopic blood images can provide useful information concerning the health of patients. Software-based cell counting has the advantages of objectivity, speed, and convenience over the manual method. Most of the automated RBC counting techniques in literature employed the grayscale or the green component of the red-green-blue (RGB) color images of blood smears. This work focuses on comparing the effect of using different color layers on the performance of software-based RBC counting. Ten color layers were extracted from different color models of blood smear images along with the grayscale conversion. Two comparisons were made: a comparison of contrast and a comparison of RBC counting performance using 52 blood smear images. The RBC contrast in the magenta layer of the cyanmagenta-yellow-key (CMYK) color model was at least 230% higher than that in the other layers. Additionally, our results indicated that using the magenta layer can provide better RBC counting performance when compared to the green, grayscale, and key layers with the p-values of p=0.0283, p<0.0001, and p<0.0001, respectively. [ABSTRACT FROM AUTHOR]

Objective: To clarify the relationship between entrance surface dose (ESD) and physical image quality of original and bone-suppressed chest radiographs acquired using high and low tube voltages.Methods: An anthropomorphic chest phantom and a 12-mm diameter spherical simulated nodule with a CT value of approximately + 100 HU were used. The lung field in the chest radiograph was divided into seven areas, and the nodule was set in a total of 66 positions. A total of 264 chest radiographs were acquired using four ESD conditions: approximately 0.3 mGy at 140 and 70 kVp and approximately 0.2 and 0.1 mGy at 70 kVp. The radiographs were processed to produce bone-suppressed images. Differences in contrast and contrast-to-noise ratio (CNR) values of the nodule between each condition and between the original and bone-suppressed images were analyzed by a two-sided Wilcoxon signed-rank test.Results: In the areas not overlapping with the ribs, both contrast and CNR values were significantly increased with the bone-suppression technique (p < 0.01). In the bone-suppressed images, these values of the three conditions at 70 kVp were equal to or significantly higher than those of the condition at 140 kVp. There was no apparent decrease in these values between the ESD of approximately 0.3 and 0.1 mGy at 70 kVp.Conclusion: By using the shortest exposure time and the lowest tube voltage possible not to increase in blurring artifact and image noise, it is possible to improve the image quality of bone-suppressed images and reduce the patient dose.Key Points: • The effectiveness of bone-suppression techniques differs in areas of lung field. • Image quality of bone-suppressed chest radiographs is improved by lower tube voltage. • Applying lower tube voltage to bone-suppressed chest radiographs leads to dose reduction. [ABSTRACT FROM AUTHOR]

Objectives: The aim was to evaluate the image quality and sensitivity to artifacts of compressed sensing (CS) acceleration technique, applied to 3D or breath-hold sequences in different clinical applications from brain to knee. Methods: CS with an acceleration from 30 to 60% and conventional MRI sequences were performed in 10 different applications in 107 patients, leading to 120 comparisons. Readers were blinded to the technique for quantitative (contrast-to-noise ratio or functional measurements for cardiac cine) and qualitative (image quality, artifacts, diagnostic findings, and preference) image analyses. Results: No statistically significant difference in image quality or artifacts was found for each sequence except for the cardiac cine CS for one of both readers and for the wrist 3D proton density (PD)–weighted CS sequence which showed less motion artifacts due to the reduced acquisition time. The contrast-to-noise ratio was lower for the elbow CS sequence but not statistically different in all other applications. Diagnostic findings were similar between conventional and CS sequence for all the comparisons except for four cases where motion artifacts corrupted either the conventional or the CS sequence. Conclusions: The evaluated CS sequences are ready to be used in clinical daily practice except for the elbow application which requires a lower acceleration. The CS factor should be tuned for each organ and sequence to obtain good image quality. It leads to 30% to 60% acceleration in the applications evaluated in this study which has a significant impact on clinical workflow. Key Points: • Clinical implementation of compressed sensing (CS) reduced scan times of at least 30% with only minor penalty in image quality and no change in diagnostic findings. • The CS acceleration factor has to be tuned separately for each organ and sequence to guarantee similar image quality than conventional acquisition. • At least 30% and up to 60% acceleration is feasible in specific sequences in clinical routine. [ABSTRACT FROM AUTHOR]

Computed tomography with multiple detectors and the advancement of processors improved rendered images and three-dimensional reconstructions in clinical practice. Traditional axial slices form non-intuitive images because they are seen in only one plane. The three-dimensional reconstructions can show structures details and diseases with complex anatomy in different perspectives. Cinematic rendering is a newly three-dimensional reconstruction technique, already approved for clinical use, which can produce realistic images from traditional computed tomography data. The algorithm used is based on light trajectory methods and the global lighting model, which simulate thousands of images from all possible directions. Thus, the technique shapes the physical propagation of light and generates a realistic three-dimensional image with depth, shadows and more anatomic details. It is a multidimensional rendering acquired through complex lighting effects. The aim of this article was to show the advance of three-dimensional technology with the cinematic rendering in images exams of the thoracic wall. [ABSTRACT FROM AUTHOR]

Objective: To develop a computational algorithm applied to magnetic resonance imaging for automatic segmentation of brain tumors. Methods: A total of 130 magnetic resonance images were used in the T1c, T2 and FSPRG T1C sequences and in the axial, sagittal and coronal planes of patients with brain cancer. The algorithms employed contrast correction, histogram normalization and binarization techniques to disconnect adjacent structures from the brain and enhance the region of interest. Automatic segmentation was performed through detection by coordinates and arithmetic mean of the area. Morphological operators were used to eliminate undesirable elements and reconstruct the shape and texture of the tumor. The results were compared with manual segmentations by two radiologists to determine the efficacy of the algorithms implemented. Results: The correlated correspondence between the segmentation obtained and the gold standard was 89.23%. Conclusion: It is possible to locate and define the tumor region automatically with no the need for user interaction, based on two innovative methods to detect brain extreme sites and exclude non-tumor tissues on magnetic resonance images. [ABSTRACT FROM AUTHOR]

Objectives: In multiple sclerosis (MS), the heterogeneous and numerous appearances of lesions may impair diagnostic accuracy. This study investigates if a combined automated co-registration and lesion color-coding method (AC) improves assessment of MS follow-up MRI compared with conventional reading (CR).Methods: We retrospectively assessed 70 follow-up MRI of 53 patients. Heterogeneous datasets of diverse scanners and institutions were used. Two readers determined presence of (a) progression, (b) regression, (c) mixed change, or (d) stable disease between the two examinations using corresponding FLAIR sequences in CR and AC-assisted reading. Consensus reference reading was provided by two blinded radiologists. Kappa statistics tested interrater agreement, McNemar's test dichotomous variables, and Wilcoxon's test continuous variables (statistical significance p ≤ 0.05).Results: The cohort comprised 41 female and 12 male patients with a mean age of 40 (± 14) years. Average rating time was reduced from 78 (± 36) to 44 (±22) s with the AC approach (p < 0.001). The time needed to start and match datasets with AC was 14 (± 1) s. Compared with CR, AC improved interrater agreement, both between raters (0.52 vs. 0.67) and between raters and consensus reference reading (0.47/0.5 vs. 0.83/0.78). Compared with CR, the diagnostic accuracy increased from 67 to 90% (reader 1, p < 0.01) and from 70 to 87% (reader 2, p < 0.05) in the AC-assisted reading.Conclusions: Compared with CR, automated co-registration and lesion color-coding of MS-associated FLAIR-lesions in follow-up MRI increased diagnostic accuracy and reduced the time required for follow-up evaluation significantly. The AC algorithm therefore appears to be helpful to improve MS follow-up assessments in clinical routine.Key Points: • Automated co-registration and lesion color-coding increases diagnostic accuracy in the assessment of MRI follow-up examinations in patients with multiple sclerosis. • Automated co-registration and lesion color-coding reduces reading time of MRI follow-up examinations in patients with multiple sclerosis. • Automated co-registration and lesion color-coding improved interrater agreement in the assessment of MRI follow-up examinations in patients with multiple sclerosis. [ABSTRACT FROM AUTHOR]

Objective: To evaluate feasibility and diagnostic performance of multi-level calcium suppression in spectral detector computed tomography (SDCT) for assessment of bone metastasis.Materials and Methods: Retrospective IRB-approved study on 21 patients who underwent SDCT (120 kV, reference mAs 116) and MRI. Thoracic and lumbar vertebrae (n = 357) were included and categorized as normal (n = 133) or metastatic (n = 203) based on MRI (STIR, T1w, ±contrast). The multi-level virtual non-calcium (VNCa) algorithm computes dynamic soft tissue/calcium pairs allowing for computation of different suppression index levels to address inter-individual variance of prevalent calcium composition weights. We computed images with low, medium, and high calcium suppression indices and compared them with conventional images (VNCa_low/med/high and conventional images (CI)). For quantitative image analysis, regions of interest were placed in normal and metastatic bone. Two readers reviewed the datasets independently in multiple sessions. They determined the presence of vertebral metastases on a per vertebra basis using a binary scale. Statistic assessment was performed using ANOVA with Tukey HSD, Student's T test, and ROC analysis.Results: Attenuation of both normal and metastatic bone was lower in VNCa images than that in conventional images (e.g., CI/VNCa_low, - 46.3 to 238.8 HU/343.3-60.2 HU; p ≤ 0.05). VNCa_low+med improved separation of normal and metastatic bone in ROC analysis (AUC, CI/VNCa_low/VNCa_med = 0.74/0.95/0.98; p ≤ 0.05). In subjective analysis, both sensitivity and specificity were clearly improved in VNCa_low as compared with CI (0.85/0.84 versus 0.78/0.82). Readers showed a good inter-rater reliability (kappa = 0.65).Conclusions: Multi-level VNCa reconstructed from SDCT improve quantitative separation of normal and metastatic bone and subjective determination of bone metastases when using low to intermediate calcium suppression indices.Key Points: • Spectral detector CT allows for multi-level calcium suppression in CT images and low and medium calcium suppression indices improved separation of normal and metastatic bone. • Thus, multi-level calcium suppression allows to optimize image contrast in regard to dedicated pathologies. • Low-level virtual non-calcium images (index 25-50) improved diagnostic performance regarding detection of metastasis. [ABSTRACT FROM AUTHOR]

Objectives: To compare observer performance of detecting bone metastases between bone scintigraphy, including planar scan and single-photon emission computed tomography, and computed tomography (CT) temporal subtraction (TS).Methods: Data on 60 patients with cancer who had undergone CT (previous and current) and bone scintigraphy were collected. Previous CT images were registered to the current ones by large deformation diffeomorphic metric mapping; the registered previous images were subtracted from the current ones to produce TS. Definitive diagnosis of bone metastases was determined by consensus between two radiologists. Twelve readers independently interpreted the following pairs of examinations: NM-pair, previous and current CTs and bone scintigraphy, and TS-pair, previous and current CTs and TS. The readers assigned likelihood levels to suspected bone metastases for diagnosis. Sensitivity, number of false positives per patient (FPP), and reading time for each pair of examinations were analysed for evaluating observer performance by performing the Wilcoxon signed-rank test. Figure-of-merit (FOM) was calculated using jackknife alternative free-response receiver operating characteristic analysis.Results: The sensitivity of TS was significantly higher than that of bone scintigraphy (54.3% vs. 41.3%, p = 0.006). FPP with TS was significantly higher than that with bone scintigraphy (0.189 vs. 0.0722, p = 0.003). FOM of TS tended to be better than that of bone scintigraphy (0.742 vs. 0.691, p = 0.070).Conclusion: Sensitivity of TS in detecting bone metastasis was significantly higher than that of bone scintigraphy, but still limited to 54%. TS might be superior to bone scintigraphy for early detection of bone metastasis.Key Points: • Computed tomography temporal subtraction was helpful in early detection of bone metastases. • Sensitivity for bone metastasis was higher for computed tomography temporal subtraction than for bone scintigraphy. • Figure-of-merit of computed tomography temporal subtraction was better than that of bone scintigraphy. [ABSTRACT FROM AUTHOR]

Objective: To investigate the image quality and radiation dose of dual-energy computed tomography (DECT) with automatic spectral imaging protocol selection (ASIS) compared with those of low-kVp CT in abdominal multiphase CT.Methods: Four groups of 60 patients each underwent abdominal scans with low-kVp CT (A, 80 kVp/300 mg I/kg, body mass index [BMI] ≤ 23.9 kg/m2; C, 100 kVp/400 mg I/kg, BMI ranging from 24 to 28.9 kg/m2) or DECT with ASIS, and the 40- to 60-keV virtual monochromatic images (VMIs) generated (B and D) were matched by age, gender, BMI, cross-sectional area, and contrast agent dose; 9 patients were excluded due to technical failures. The CT number, image noise, contrast-to-noise ratio, and subjective image quality were compared between the matched protocols (A and B or C and D) on 1.25-mm reconstructed images.Results: VMIs at approximately 55 keV and 62 keV had CT numbers and contrast similar to those of 80-kVp and 100-kVp CT images, respectively. Compared to matched low-kVp images, VMIs at 50 keV provided a higher CT number and image noise and a similar or higher contrast and overall image quality. The radiation dose for DECT was higher than that of 80-kVp CT (increased by 10%), but was similar to that of 100-kVp CT.Conclusion: Compared to matched low-kVp CT, VMIs at 50 keV in DECT with ASIS provided similar or higher overall image quality, with no or minimal dose penalty in small- and medium-sized patients.Key Points: • Virtual monochromatic images at approximately 55 keV and 62 keV have CT numbers and contrast similar to those of 80-kVp and 100-kVp CT images, respectively, with a given noise index. • The radiation dose in dual-energy CT with automatic spectral imaging protocol selection was slightly higher than that of 80-kVp CT (increased by 10%) but was similar to that of 100-kVp CT. • Dual-energy CT may be able to replace l00-kVp CT for routine clinical abdominal contrast-enhanced CT scans. [ABSTRACT FROM AUTHOR]

Objective: To explore the value of texture analysis based on diffusion-weighted imaging (DWI), blood oxygen level-dependent MRI (BOLD), and susceptibility-weighted imaging (SWI) in evaluating renal dysfunction.Methods: Seventy-two patients (mean age 53.72 ± 13.46 years) underwent MRI consisting of DWI, BOLD, and SWI. According to their estimated glomerular filtration rate (eGFR), the patients were classified into either severe renal function impairment (sRI, eGFR < 30 mL/min/1.73 m2), non-severe renal function impairment (non-sRI, eGFR ≥ 30 mL/min/1.73 m2, and < 80 mL/min/1.73 m2), or control (CG, eGFR ≥ 80 mL/min/1.73 m2) groups. Thirteen texture features were extracted and then were analyzed to select the most valuable for discerning the three groups with each imaging method. A ROC curve was performed to compare the capacities of the features to differentiate non-sRI from sRI or CG.Results: Six features proved to be the most valuable for assessing renal dysfunction: 0.25QuantileDWI, 0.5QuantileDWI, HomogeneityDWI, EntropyBOLD, SkewnessSWI, and CorrelationSWI. Three features derived from DWI (0.25QuantileDWI, 0.5QuantileDWI, and HomogeneityDWI) were smaller in sRI than in non-sRI; EntropyBOLD and CorrelationSWI were smaller in non-sRI than in CG (p < 0.05). 0.25QuantileDWI, 0.5QuantileDWI, and HomogeneityDWI showed similar capacities for differentiating sRI from non-sRI. Similarly, EntropyBOLD and CorrelationSWI showed equal capacities for differentiating non-sRI from CG.Conclusion: Texture analysis based on DWI, BOLD, and SWI can assist in assessing renal dysfunction, and texture features based on BOLD and SWI may be suitable for assessing renal dysfunction during early stages.Key Points: • Texture analysis based on MRI techniques allowed for assessing renal dysfunction. • Texture features based on BOLD and SWI, but not DWI, may be suitable for assessing renal function impairment during early stages. • SWI exhibited a similar capacity to BOLD for assessing renal dysfunction. [ABSTRACT FROM AUTHOR]

Purpose: The goal of this study was to develop and evaluate a volumetric three-dimensional (3D) approach to improve the accuracy of ablation margin assessment following thermal ablation of hepatic tumors.Methods: The 3D margin assessment technique was developed to generate the new 3D assessment metrics: volumes of insufficient coverage (VICs) measuring volume of tissue at risk post-ablation. VICs were computed for the tumor and tumor plus theoretical 5- and 10-mm margins. The diagnostic accuracy of the 3D assessment to predict 2-year local tumor progression (LTP) was compared to that of manual 2D assessment using retrospective analysis of a patient cohort that has previously been reported as a part of an outcome-centered study. Eighty-six consecutive patients with 108 colorectal cancer liver metastases treated with radiofrequency ablation (2002-2012) were used for evaluation. The 2-year LTP discrimination power was assessed using receiver operating characteristic area under the curve (AUC) analysis.Results: A 3D assessment of margins was successfully completed for 93 out of 108 tumors. The minimum margin size measured using the 3D method had higher discrimination power compared with the 2D method, with an AUC value of 0.893 vs. 0.790 (p = 0.01). The new 5-mm VIC metric had the highest 2-year LTP discrimination power with an AUC value of 0.923 (p = 0.004).Conclusions: Volumetric semi-automated 3D assessment of the ablation zone in the liver is feasible and can improve accuracy of 2-year LTP prediction following thermal ablation of hepatic tumors.Key Points: • More accurate prediction of local tumor progression risk using volumetric 3D ablation zone assessment can help improve the efficacy of image-guided percutaneous thermal ablation of hepatic tumors. • The accuracy of evaluation of ablation zone margins after thermal ablation of colorectal liver metastases can be improved using a volumetric 3D semi-automated assessment approach and the volume of insufficient coverage assessment metric. • The new 5-mm volume-of-insufficient-coverage metric, indicating the volume of tumor plus 5-mm margin that remained untreated, had the highest 2-year local tumor progression discrimination power. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the growth pattern of temporal bone pneumatization (TBP) in consecutive age groups from birth to 18 years old using 3D air-density reconstruction of high-resolution CT (HRCT).Methods: A total of 570 patients under 18 years old who performed temporal bone HRCT and with no structural abnormalities on both sides were included. Nineteen groups were created to represent consecutive ages, with 15 males and 15 females in each group. Volume rendering of air density was performed using a segmentation threshold of − 200 HU to obtain TBP volume of each side. The differences in TBP between the sides, sexes, and ages were statistically analyzed.Results: The mean volume of TBP was measured as 1.17 ± 0.44 ml in patients less than 1 year old and 6.81 ± 1.93 ml as adult size. High consistencies of TBP volume between different sides or sexes were found in most of age groups. Significant differences were shown between sides only in 9- and 11-year-old female groups (p9 = 0.031, p11 = 0.016) while between sexes only in 6- and 16-year-old groups (p6 = 0.001, p16 = 0.043), although the volume was larger in all the male groups older than 6 years. The curve drawn by the mean volume in consecutive age groups showed that TBP continued to grow linearly in both males and females until they reached 14 years old. The corresponding linear regression equations were as follows: ym = 1.553 + 0.370x, (x ≤ 14 years, R2 = 0.596); yf = 1.561 + 0.304x, (x ≤ 14 years, R2 = 0.565).Conclusion: The TBP volume shows a linear growth pattern from birth until reaching adult size after 14 years old. [ABSTRACT FROM AUTHOR]

Objectives: To assess whether contralateral parenchymal enhancement reproduces as an independent biomarker for patient survival in an independent patient cohort from a different cancer institution.Methods: This is a HIPAA-compliant IRB approved retrospective study. Patients with ER-positive/HER2-negative operable invasive ductal carcinoma and preoperative dynamic contrast-enhanced MRI were consecutively included between 2005 and 2009. The parenchyma of the breast contralateral to known cancer was segmented automatically on MRI and contralateral parenchymal enhancement (CPE) was calculated. CPE was split into tertiles and tested for association with invasive disease-free survival (IDFS) and overall survival (OS). Propensity score analysis with inverse probability weighting (IPW) was used to adjust CPE for patient and tumour characteristics as well as systemic therapy.Results: Three hundred and two patients were included. The median age at diagnosis was 48 years (interquartile range, 42-57). Median follow-up was 88 months (interquartile range, 76-102); 15/302 (5%) patients died and 37/302 (13%) had a recurrence or died. In context of multivariable analysis, IPW-adjusted CPE was associated with IDFS [hazard ratio (HR) = 0.27, 95% confidence interval (CI) = 0.05-0.68, p = 0.004] and OS (HR = 0.22, 95% CI = 0.00-0.83, p = 0.032).Conclusions: Contralateral parenchymal enhancement on pre-treatment dynamic contrast-enhanced MRI as an independent biomarker of survival in patients with ER-positive/HER2-negative breast cancer has been upheld in this study. These findings are a promising next step towards a practical and inexpensive test for risk stratification of ER-positive/HER2-negative breast cancer.Key Points: • High parenchymal-enhancement in the disease-free contralateral breast reproduces as biomarker for survival. • This is in patients with ER-positive/HER2-negative breast cancer from an independent cancer centre. • This is independent of patient and pathology parameters and systemic therapy. [ABSTRACT FROM AUTHOR]

Objective: To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach.Methods: Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one.Results: In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180).Conclusion: Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels.Key Points: • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features. [ABSTRACT FROM AUTHOR]

Objectives: To determine whether use of a stylus user input device (UID) would be superior to a mouse for CMR segmentation.Methods: Twenty-five consecutive clinical cardiac magnetic resonance (CMR) examinations were selected. Image analysis was independently performed by four observers. Manual tracing of left (LV) and right (RV) ventricular endocardial contours was performed twice in 10 randomly assigned sessions, each session using only one UID. Segmentation time and the ventricular function variables were recorded. The mean segmentation time and time reduction were calculated for each method. Intraclass correlation coefficients (ICC) and Bland-Altman plots of function variables were used to assess intra- and interobserver variability and agreement between methods. Observers completed a Likert-type questionnaire.Results: The mean segmentation time (in seconds) was significantly less with the stylus compared to the mouse, averaging 206±108 versus 308±125 (p<0.001) and 225±140 versus 353±162 (p<0.001) for LV and RV segmentation, respectively. The intra- and interobserver agreement rates were excellent (ICC≥0.75) regardless of the UID. There was an excellent agreement between measurements derived from manual segmentation using different UIDs (ICC≥0.75), with few exceptions. Observers preferred the stylus.Conclusion: The study shows a significant reduction in segmentation time using the stylus, a subjective preference, and excellent agreement between the methods.Key Points: • Using a stylus for MRI ventricular segmentation is faster compared to mouse • A stylus is easier to use and results in less fatigue • There is excellent agreement between stylus and mouse UIDs. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the effects of the single-energy metal artifact reduction (SEMAR) algorithm on image quality of cerebral CT and CT angiography (CTA) for patients who underwent intracranial aneurysm coiling.Methods: Twenty patients underwent cerebral CT and CTA using a 320-detector row CT after intracranial aneurysm coiling. Images with and without application of the SEMAR algorithm (SEMAR CT and standard CT images, respectively) were reconstructed for each patient. The images were qualitatively assessed by two independent radiologists in a blinded manner for the depiction of anatomical structures around the coil, delineation of the arteries around the coil, and the depiction of the status of coiled aneurysms. Artifact strength was quantitatively assessed by measuring the standard deviation of attenuation values around the coil.Results: The strength of artifacts measured in SEMAR CT images was significantly lower than that in standard CT images (25.7 ± 10.2 H.U. vs. 80.4 ± 67.2 H.U., p < 0.01, Student’s paired t test). SEMAR CT images were significantly improved compared with standard CT images in the depiction of anatomical structures around the coil (p < 0.01, the sign test), delineation of the arteries around the coil (p < 0.01), and the depiction of the status of coiled aneurysms (p < 0.01).Conclusion: The SEMAR algorithm significantly reduces metal artifacts from intracranial aneurysm coiling and improves visualization of anatomical structures and arteries around the coil, and depiction of the status of coiled aneurysms on post-interventional cerebral CT. [ABSTRACT FROM AUTHOR]

Coronary computed tomographic angiography (CCTA) with prospective electrocardiographic gating reduces radiation exposure, but its prognostic power for predicting cardiovascular risk in patients with suspected CAD has not been fully validated. To determine whether prospective gating performs as well as retrospective gating in this population, we compared these scan modes in patients undergoing 64-slice CCTA. From January 2009 through September 2011, 1,407 patients underwent CCTA; of these, 915 (mean age, 57.8 ± 13.5 yr; 54% male) had suspected coronary artery disease at the time of CCTA and were included in the study. Prospective gating was used in 195 (21%) and retrospective gating in 720 (79%). The mean follow-up duration was 2.4 ± 0.9 years. Overall, 390 patients (42.6%) had normal results on CCTA, 382 (41.7%) had nonobstructive coronary artery disease, and 143 (15.6%) had obstructive disease. Major adverse cardiac events occurred in 32 patients (3.5%): 11 cardiac deaths, 15 late revascularizations, and 6 nonfatal myocardial infarctions. Total event occurrences were similar in both groups (retrospective, 3.8%; prospective, 2.6%; P=0.42), as were the occurrences of each type of event. On adjusted multivariate analysis, nonobstructive (P=0.015) and obstructive (P <0.001) coronary artery disease were independently associated with major adverse cardiac events. Scan mode was not a predictor of outcome. The mean effective radiation dose was 4 ± 2 mSv for prospective compared with 12 ± 4 mSv for retrospective gating (P <0.01). The prognostic value of CCTA with prospective electrocardiographic gating compares favorably with that of retrospective gating, and it involves significantly less radiation exposure. [ABSTRACT FROM AUTHOR]

Objectives: Differences in noise and density values in MDCT images obtained using ultra-low doses with FBP, ASIR, and MBIR may possibly affect implant site density analysis. The aim of this study was to compare density and noise measurements recorded from dental implant sites using ultra-low doses combined with FBP, ASIR, and MBIR.Methods: Cadavers were scanned using a standard protocol and four low-dose protocols. Scans were reconstructed using FBP, ASIR-50, ASIR-100, and MBIR, and either a bone or standard reconstruction kernel. Density (mean Hounsfield units [HUs]) of alveolar bone and noise levels (mean standard deviation of HUs) was recorded from all datasets and measurements were compared by paired t tests and two-way ANOVA with repeated measures.Results: Significant differences in density and noise were found between the reference dose/FBP protocol and almost all test combinations. Maximum mean differences in HU were 178.35 (bone kernel) and 273.74 (standard kernel), and in noise, were 243.73 (bone kernel) and 153.88 (standard kernel).Conclusions: Decreasing radiation dose increased density and noise regardless of reconstruction technique and kernel. The effect of reconstruction technique on density and noise depends on the reconstruction kernel used.Key Points: • Ultra-low-dose MDCT protocols allowed more than 90 % reductions in dose. • Decreasing the dose generally increased density and noise. • Effect of IRT on density and noise varies with reconstruction kernel. • Accuracy of low-dose protocols for interpretation of bony anatomy not known. • Effect of low doses on accuracy of computer-aided design models unknown. [ABSTRACT FROM AUTHOR]