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This study aimed to assess the effectiveness of practical preventive strategies (i.e., venous vulnerability assessment and prevention scan protocol rules) taken by our radiology team (radiology nurses, radiology technicians, radiologists) on reducing extravasation of contrast media (ECM) during CT.A total of 73,931 patients who underwent contrast-enhanced CT scans between January 2013 and December 2019 were retrospectively included. Venous vulnerability assessment by the radiology team began in 2015, and prevention scan protocol rules for the prevention of ECM were added in 2017. We defined each period as follows: 2013-2014, no prevention (Period A); 2015-2016, early prevention (Period B, venous vulnerability assessment only); and 2017-2019: late prevention (Period C, venous vulnerability assessment with prevention scan protocol rules). The incident reports, radiology reports, and medical records of patients in whom ECM occurred were reviewed. We compared the frequency of ECM during each period.ECM occurred in 0.39% (292/73,931) of the patients. The frequencies of ECM for Periods A, B, and C were 0.62% (121/19,505), 0.43% (89/20,847), and 0.24% (82/33,579), respectively. There were significant differences in the frequencies of ECM among the three periods (Chi-squared test, p0.01).Implementation of venous vulnerability assessment and prevention scan protocol rules by a radiology team can be a practical and simple solution to reduce the risk of ECM during CT.

Daratumumab, an anti-human CD38 monoclonal antibody, has become the standard of care in patients with systemic light-chain (AL) amyloidosis and multiple myeloma (MM). Herein, we report two cases of AL cardiac amyloidosis with MM who were treated with daratumumab, lenalidomide, and dexamethasone (DRd). Serial evaluation of cardiac biomarkers, echocardiography, and cardiac magnetic resonance imaging (CMR) were performed during 12 months of DRd treatment. A complete hematologic response was achieved three months after treatment initiation and sustained during the observation period. Twelve months after DRd treatment, we found improvements in levels (values for case 1 and case 2, respectively) of B-type natriuretic peptide (593.2 → 312.2 pg/mL and 202.4 → 104.3 pg/mL), N-terminal pro-brain natriuretic peptide (4005 → 1800 pg/mL and 2576 → 1170 pg/mL), high-sensitivity cardiac troponin T (0.156 → 0.072 ng/mL and 0.0678 → 0.0467 ng/mL), and global longitudinal strain (−6.8 → −10.4 % and −11.8 → −14.8 %). CMR revealed no noticeable changes in native T1 value or extracellular volume fraction. However, one case showed decreased native T2 value (61 → 55 ms). In conclusion, DRd treatment improved heart failure and cardiac function, relieved myocardial damage, and prevented amyloid deposition progression in the patients with AL cardiac amyloidosis. Cardiac biomarkers and imaging findings may be useful for monitoring the therapeutic effects of daratumumab-containing regimens. LEARNING OBJECTIVE: Daratumumab-containing regimens led to a rapid complete hematologic response, improvements in heart failure symptoms, cardiac function, and regression of myocardial damage in light-chain cardiac amyloidosis. This treatment prevents additional amyloid deposition and suppresses the direct cardiotoxic effects of amyloidogenic immunoglobulin light-chains. Serial assessments of cardiac biomarkers and imaging findings are useful for evaluating the therapeutic effect of daratumumab-containing regimens.

PURPOSE: To evaluate the diagnostic performance of myocardium-to-lumen R1 (1/T1) ratio on postcontrast T1 maps for the detection of cardiac amyloidosis in a large patient sample. MATERIALS AND METHODS: This retrospective study included consecutive patients who underwent MRI-derived extracellular volume fraction (MRI ECV) analysis between March 2017 and July 2021 because of known or suspected heart failure or cardiomyopathy. Pre- and postcontrast T1 maps were generated using the modified Look-Locker inversion recovery sequence. Diagnostic performances of MRI ECV and myocardium-to-lumen R1 ratio on postcontrast T1 maps (a simplified index not requiring a native T1 map and hematocrit level data) for detecting cardiac amyloidosis were evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: Of 352 patients (mean age, 63 years ± 16 [SD]; 235 men), 136 had cardiac amyloidosis. MRI ECV showed 89.0% (121 of 136; 95% CI: 82%, 94%) sensitivity and 98.6% (213 of 216; 95% CI: 96%, 100%) specificity for helping detect cardiac amyloidosis (cutoff value of 40% [AUC, 0.99 {95% CI: 0.97, 1.00}; P < .001]). Postcontrast myocardium-to-lumen R1 ratio showed 92.6% (126 of 136; 95% CI: 89%, 96%) sensitivity and 93.1% (201 of 216; 95% CI: 89%, 96%) specificity (cutoff value of 0.84 [AUC, 0.98 {95% CI: 0.95, 0.99}; P < .001]). There was no evidence of a difference in AUCs for each parameter (P = .10). CONCLUSION: Postcontrast myocardium-to-lumen R1 ratio showed excellent diagnostic performance comparable to that of MRI ECV in the detection of cardiac amyloidosis. Keywords: MR Imaging, Cardiac, Heart, Cardiomyopathies Supplemental material is available for this article. © RSNA, 2023

To compare the effects of tube voltage and iodinated concentration on increasing the iodinated radiation dose in computed tomography (CT).Water and iodinated materials were inserted in an anthropomorphic thorax phantom. Helical CT scans were performed with tube voltages of 80 and 120 kV. Monte Carlo simulation of radiation doses in CT images was used to study the radiation dose profiles. The radiation doses at different iodine to water ratios in images were calculated from cumulative radiation doses for iodine and water at each iodine concentration, tube voltage, and peripheral/central location. A radiation dose ratio from 80 to 120 kV was calculated from cumulative radiation doses at the same iodine concentration.The iodinated radiation doses with small and large phantoms were 1.56-2.04 and 1.61-1.82 times higher at 80 kV and 1.55-2.23 and 1.22-1.79 times higher at 120 kV than those in water. In the central portion, the iodinated radiation dose ratio decreased by 1.14-0.75 and 1.20-0.93 times at 80 kV and 1.29-1.23 and 1.17-1.23 times at 120 kV with increasing iodinated concentrations. In the peripheral portion, the iodinated radiation dose was slightly higher for 80 kV than for 120 kV. In the central portion, the ratio decreased with increasing iodinated concentration.The increase in iodinated radiation dose caused by photoelectric absorption was greater with increased iodine load than with lower tube voltage.

Purpose This study aimed to evaluate whether quantification of myocardial susceptibility by cardiac magnetic resonance imaging (CMR) can be an imaging biomarker for cardiac amyloidosis (CA). Materials and methods Twenty-six patients with CA underwent CMR, including magnetic phase imaging with a 3.0-T magnetic resonance imaging scanner. Myocardial susceptibility was quantified as a phase shift slope value by magnetic phase analysis. Those values from patients with CA were compared with corresponding values from 18 controls and 15 healthy volunteers. A univariate logistic regression analysis was conducted to identify significant parameters related to CA. Results The phase shift slope, a quantitative parameter of myocardial susceptibility, was significantly lower in the CA group compared with the control group and compared with healthy volunteers (p p ≤ 0.05) during the univariate analysis. The phase shift slope yielded the best AUC of 0.89 (95% CI = 0.79–0.98) for the prediction of CA (p r = − 0.39, p r = − 0.35, p = 0.02). Conclusion Myocardial susceptibility analysis by CMR helps in the diagnosis of patients with CA and can be a new quantitative imaging biomarker for CA.

Optimizing the CT acquisition parameters to obtain diagnostic image quality at the lowest possible radiation dose is crucial in the radiosensitive pediatric population. The image quality of low-dose CT can be severely degraded by increased image noise with filtered back projection (FBP) reconstruction. Iterative reconstruction (IR) techniques partially resolve the trade-off relationship between noise and radiation dose but still suffer from degraded noise texture and low-contrast detectability at considerably low-dose settings. Furthermore, sophisticated model-based IR usually requires a long reconstruction time, which restricts its clinical usability. With recent advances in artificial intelligence technology, deep learning-based reconstruction (DLR) has been introduced to overcome the limitations of the FBP and IR approaches and is currently available clinically. DLR incorporates convolutional neural networks-which comprise multiple layers of mathematical equations-into the image reconstruction process to reduce image noise, improve spatial resolution, and preserve preferable noise texture in the CT images. For DLR development, numerous network parameters are iteratively optimized through an extensive learning process to discriminate true attenuation from noise by using low-dose training and high-dose teaching image data. After rigorous validations of network generalizability, the DLR engine can be used to generate high-quality images from low-dose projection data in a short reconstruction time in a clinical environment. Application of the DLR technique allows substantial dose reduction in pediatric CT performed for various clinical indications while preserving the diagnostic image quality. The authors present an overview of the basic concept, technical principles, and image characteristics of DLR and its clinical feasibility for low-dose pediatric CT. ©RSNA, 2021.

Background The development of an accurate, practical, noninvasive, and widely available diagnostic approach to characterize lipid-poor adrenal lesions (greater than 10 HU at unenhanced CT) remains an ongoing demand. Purpose To investigate whether combined assessment of unenhanced and portal venous phase CT allows for the differentiation of lipid-poor adrenal adenomas from nonadenomas. Materials and Methods Patients with lipid-poor adrenal lesions who underwent unenhanced and portal venous phase CT with a single-energy scanner between January 2016 and March 2020 were identified retrospectively. For each lesion, the unenhanced and contrast-enhanced attenuation were measured; the absolute enhancement (contrast-enhanced minus unenhanced attenuation [HU]) and relative enhancement ratio ([absolute enhancement divided by unenhanced attenuation] × 100%) were calculated. The sensitivity achieved at 95% specificity to distinguish adenomas from nonadenomas was determined with receiver operating characteristic curve analysis and compared among parameters with use of the McNemar test. Results A total of 220 patients (mean age ± standard deviation, 66 years ± 12; 134 men) with 131 lipid-poor adenomas and 89 nonadenomas were analyzed. The sensitivity (achieved at 95% specificity) of the relative enhancement ratio (86% [113 of 131 adenomas; 95% CI: 79, 92] at a threshold of >210%) was higher than that of unenhanced attenuation (50% [66 of 131 adenomas; 95% CI: 42, 59] at a threshold of ≤21 HU), contrast-enhanced attenuation (3% [four of 131 adenomas; 95% CI: 1, 8] at a threshold of >120 HU), and absolute enhancement (24% [32 of 131 adenomas; 95% CI: 17, 33] at a threshold of >74 HU; all P < .001). The sensitivities of the relative enhancement ratio were 100% (58 of 58 adenomas; 95% CI: 94, 100), 83% (52 of 63 adenomas; 95% CI: 71, 91), and 30% (three of 10 adenomas; 95% CI: 7, 65) for adenomas measuring unenhanced attenuation of more than 10 HU up to 20 HU, 21-30 HU, and more than 30 HU, respectively. Conclusion A relative enhancement ratio threshold of greater than 210%, measured at unenhanced and portal venous phase CT, accurately differentiated lipid-poor adenomas from nonadenomas, particularly for lesions with unenhanced attenuation of 10-30 HU. © RSNA, 2021 Online supplemental material is available for this article.

Background Large studies on the diagnostic performance of CT-derived myocardial extracellular volume fraction (ECV) for detecting cardiac amyloidosis are lacking. A simple and practical index as a surrogate for CT ECV would be clinically useful. Purpose To compare the diagnostic performances between CT-derived myocardial ECV and myocardium-to-lumen signal ratio for the detection of cardiac amyloidosis in a large patient sample. Materials and Methods This retrospective study included patients who underwent CT ECV analysis because of suspected heart failure or cardiomyopathy between January 2018 and July 2021. CT ECV was quantified using routine pre-transcatheter aortic valve replacement planning cardiac CT, pre-atrial fibrillation ablation planning cardiac CT, or coronary CT angiography with the addition of unenhanced and delayed phase cardiac CT scans. The diagnostic performances of CT ECV and myocardium-to-lumen signal ratio in delayed phase cardiac CT (a simplified index not requiring unenhanced CT and hematocrit) for detecting cardiac amyloidosis were evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results Of 552 patients (mean age, 69 years ± 14 [SD]; 295 men), 41 had cardiac amyloidosis. The sensitivity of CT ECV for amyloidosis was 90% (37 of 41 patients [95% CI: 77, 97]), with a specificity of 92% (472 of 511 patients [95% CI: 90, 95]) and optimal ECV cutoff value of 37% (AUC, 0.97 [95% CI: 0.96, 0.99]). The sensitivity of myocardium-to-lumen signal ratio was 88% (36 of 41 patients [95% CI: 74, 96]), with a specificity of 92% (469 of 511 patients [95% CI: 89, 94]) and optimal myocardium-to-lumen signal ratio cutoff value of 0.87 (AUC, 0.96 [95% CI: 0.94, 0.97]

To assess the image quality of diffusion-weighted imaging (DWI) using multiband (MB) imaging with variable-rate selective excitation (VERSE) and compare it to conventional DWI.We retrospectively evaluated hepatic DWI images of patients (n = 76) according to either the conventional method (SENSE, acceleration factor = 2) (n = 38) or fast scanning method (MB imaging with VERSE, acceleration factor = 2 × 2) (n = 38). We also conducted a volunteer study (n = 15) for those scanning methods. During quantitative analysis, the signal-to-noise ratio (SNR), apparent diffusion coefficient values, and contrast in the liver, spleen, and spinal cord were compared between the 2 groups. During qualitative analysis, all images were independently and blindly evaluated by 2 board-certified radiologists. The image contrast, noise, artifacts, and sharpness were assessed, and the performance of classification was measured using receiver operating characteristic curve analysis.In the retrospective study, the SNRs of the hepatic parenchyma and spinal cord between the 2 protocols were significantly different (liver, 8.9 [interquartile range {IQR}, 7.6-12.2] vs 13.0 [IQR, 10.0-16.7]; P0.001 and spinal cord, 6.0 [IQR, 4.7-9.4] vs 4.3 [IQR, 3.8-6.8]; P0.02). No significant differences between the 2 protocols in the other retrospective analyses were noted. In the receiver operating characteristic curve analysis, area under the curve was 0.49 (95% confidence intervals, 0.40-0.58).Multiband VERSE reduced scan time and SNR of hepatic DWI; however, subjective image quality parameters were not significantly impacted.

Background The low-tube-voltage scan generally needs a higher tube current than the conventional 120 kVp to maintain the image noise. In addition, the low-tube-voltage scan increases the photoelectric effect, which increases the radiation absorption in organs. Purpose To compare the organ radiation dose caused by iodine contrast medium between low tube voltage with low contrast medium and that of conventional 120-kVp protocol with standard contrast medium. Material and Methods After the propensity-matching analysis, 66 patients were enrolled including 33 patients with 120 kVp and 600 mgI/kg and 33 patients with 80 kVp and 300 mgI/kg (50% iodine reduction). The pre- and post-contrast phases were assessed in all patients. The Monte Carlo simulation tool was used to simulate the radiation dose. The computed tomography (CT) numbers for 10 organs and the organ doses were measured. The organ doses were normalized by the volume CT dose index, and the 120-kVp protocol was compared with the 80-kVp protocol. Results On contrast-enhanced CT, there were no significant differences in the mean CT numbers of the organs between 80-kVp and 120-kVp protocols except for the pancreas, kidneys, and small intestine. The normalized organ doses at 80 kVp were significantly lower than those of 120 kVp in all organs (e.g. liver, 1.6 vs. 1.9; pancreas, 1.5 vs. 1.8; spleen, 1.7 vs. 2.0) on contrast-enhanced CT. Conclusion The low tube voltage with low-contrast-medium protocol significantly reduces organ doses at the same volume CT dose index setting compared with conventional 120-kVp protocol with standard contrast medium on contrast-enhanced CT.

The purpose of this study was to investigate the feasibility of non-contrast renal MRA using multi-shot gradient echo planar imaging (MSG-EPI) with a 3-T MRI system. Seventeen healthy volunteers underwent non-contrast renal MRA using MSG-EPI and balanced steady-state free precession (b-SSFP) sequences on a 3-T MRI system. Two radiologists independently recorded the images’ contrast, noise, sharpness, artifacts, and overall quality on 4-point scales. The signal-to-noise ratio (SNR) for the renal artery, the contrast ratio (CR) between the renal artery and erector spinae, and acquisition time were compared between the two sequences. The SNR and CR were significantly higher with MSG-EPI than with the b-SSFP sequence (17.80 ± 3.67 vs. 10.84 ± 2.86 and 0.77 ± 0.05 and 0.66 ± 0.09, respectively; p < 0.05), and the acquisition time was significantly lower (164.5 ± 34.0 vs. 261.5 ± 39.3 s, respectively; p < 0.05). There were significant differences in image contrast, noise, sharpness, artifacts, and overall image quality between the two sequences (p < 0.01). The MSG-EPI sequence is a promising technique that can shorten the scan time and improve the image quality of non-contrast renal MRA with a 3-T MRI system. • The multi-shot gradient echo planar imaging with an inversion pulse is a brand-new fast scan technique for an unenhanced renal MRA. • The image quality of multi-shot gradient echo planar imaging is better than that of b-SSFP for an unenhanced renal MRA.

Background The image quality directly affects the accuracy of computed tomography (CT) extracellular volume (ECV) quantification. Purpose To investigate the effects of image quality and acquisition protocol on the accuracy of ECV quantification. Material and Methods One-volume scans were performed on a 320-row multidetector CT volume scanner using a multi-energy CT phantom. To simulate the blood pool and myocardium, solid rods representing blood and soft tissue were used in precontrast CT. Moreover, the solid rods including different iodine concentrations were used in postcontrast CT. The tube voltage was set at 120 kVp, and the tube current was changed from 750 mA (100% dose) to 190 mA (25% dose). All images underwent full- and half-scan reconstructions based on model-based iterative reconstruction. The ECV was calculated from the CT numbers between pre- and postcontrast. Results The mean ECV with full- and half-scan reconstructions at the central portion was 0.275 at 100% scan dose to 0.271 at 25% scan dose and 0.276 at 100% scan dose to 0.269 at 25% scan dose. Compared with that in the 100% scan dose, the variation in each ECV increased with decreasing radiation dose. The ECV at the center of the image along the z-axis had lower variation than that at outer portion of the images. On the reconstruction algorithm, there was no statistical difference in ECVs with full- and half-scan reconstructions. Conclusion For stable ECV quantifications, excessive radiation dose reduction may be inappropriate, and it is better to consider the variations in ECV values depending on the slice location.

Purpose: To evaluate the effects of deep learning reconstruction (DLR) in qualitative and quantitative image quality of non-contrast magnetic resonance coronary angiography (MRCA). Methods: Ten healthy volunteers underwent conventional MRCA (C-MRCA) and high-resolution (HR) MRCA on a 3T magnetic resonance imaging with a voxel size of 1.8 × 1.1 × 1.7 mm3 and 1.8 × 0.6 × 1.0 mm3, respectively, for C-MRCA and HR-MRCA. High-resolution magnetic resonance coronary angiography was also reconstructed with the DLR technique (DLR-HR-MRCA). We compared the contrast-to-noise ratio (CNR) and visual evaluation scores for vessel sharpness and traceability of proximal and distal coronary vessels on a 4-point scale among 3 image series. Results: The vascular CNR value on the C-MRCA and the DLR-HR-MRCA was significantly higher than that on the HR-MRCA in the proximal and distal coronary arteries (13.9 ± 6.4, 11.3 ± 4.4, and 7.8 ± 2.6 for C-MRCA, DLR-HR-MRCA, and HR-MRCA, P < .05, respectively). Mean visual evaluation scores for the vessel sharpness and traceability of proximal and distal coronary vessels were significantly higher on the HR-DLR-MRCA than the C-MRCA ( P < .05, respectively). Conclusion: Deep learning reconstruction significantly improved the CNR of coronary arteries on HR-MRCA, resulting in both higher visual image quality and better vessel traceability compared with C-MRCA.

The purpose of this study was to determine whether computed tomography (CT) angiography with machine learning (ML) can be used to predict the rapid growth of abdominal aortic aneurysm (AAA).This retrospective study was approved by our institutional review board. Fifty consecutive patients (45 men, 5 women, 73.5 years) with small AAA (38.5 ± 6.2 mm) had undergone CT angiography. To be included, patients required at least 2 CT scans a minimum of 6 months apart. Abdominal aortic aneurysm growth, estimated by change per year, was compared between patients with baseline infrarenal aortic minor axis. For each axial image, major axis of AAA, minor axis of AAA, major axis of lumen without intraluminal thrombi (ILT), minor axis of lumen without ILT, AAA area, lumen area without ILT, ILT area, maximum ILT area, and maximum ILT thickness were measured. We developed a prediction model using an ML method (to predict expansion4 mm/y) and calculated the area under the receiver operating characteristic curve of this model via 10-fold cross-validation.The median aneurysm expansion was 3.0 mm/y. Major axis of AAA and AAA area correlated significantly with future AAA expansion (r = 0.472, 0.416 all P0.01). Machine learning and major axis of AAA were a strong predictor of significant AAA expansion (4 mm/y) (area under the receiver operating characteristic curve were 0.86 and 0.78).Machine learning is an effective method for the prediction of expansion risk of AAA. Abdominal aortic aneurysm area and major axis of AAA are the important factors to reflect AAA expansion.

To evaluate the image properties of lung-specialized deep-learning-based reconstruction (DLR) and its applicability in ultralow-dose CT (ULDCT) relative to hybrid- (HIR) and model-based iterative-reconstructions (MBIR).An anthropomorphic chest phantom was scanned on a 320-row scanner at 50-mA (low-dose-CT 1 [LDCT-1]), 25-mA (LDCT-2), and 10-mA (ULDCT). LDCT were reconstructed with HIR; ULDCT images were reconstructed with HIR (ULDCT-HIR), MBIR (ULDCT-MBIR), and DLR (ULDCT-DLR). Image noise and contrast-to-noise ratio (CNR) were quantified. With the LDCT images as reference standards, ULDCT image qualities were subjectively scored on a 5-point scale (1 = substantially inferior to LDCT-2, 3 = comparable to LDCT-2, 5 = comparable to LDCT-1). For task-based image quality analyses, a physical evaluation phantom was scanned at seven doses to achieve the noise levels equivalent to chest phantom; noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated. Clinical ULDCT (10-mA) images obtained in 14 nonobese patients were reconstructed with HIR, MBIR, and DLR; the subjective acceptability was ranked.Image noise was lower and CNR was higher in ULDCT-DLR and ULDCT-MBIR than in LDCT-1, LDCT-2, and ULDCT-HIR (p0.01). The overall quality of ULDCT-DLR was higher than of ULDCT-HIR and ULDCT-MBIR (p0.01), and almost comparable with that of LDCT-2 (mean score: 3.4 ± 0.5). DLR yielded the highest NPS peak frequency and TTFDLR optimized for lung CT improves image quality and provides possible greater dose optimization opportunity than HIR and MBIR.

This clinical and phantom study aimed to evaluate the impact of deep learning-based reconstruction (DLR) on image quality and its radiation dose optimization capability for multiphase hepatic CT relative to hybrid iterative reconstruction (HIR).Task-based image quality was assessed with a physical evaluation phantom; the high- and low-contrast detectability of HIR and DLR images were computed from the noise power spectrum and task-based transfer function at five different size-specific dose estimate (SSDE) values in the range 5.3 to 18.0-mGy. For the clinical study, images of 73 patients who had undergone multiphase hepatic CT under both standard-dose (STD) and lower-dose (LD) examination protocols within a time interval of about four-months on average, were retrospectively examined. STD images were reconstructed with HIR, while LD with HIR (LD-HIR) and DLR (LD-DLR). SSDE, quantitative image noise, and contrast-to-noise ratio (CNR) were compared between protocols. The noise magnitude, noise texture, streak artifact, image sharpness, interface smoothness, and overall image quality were subjectively rated by two independent radiologists.In phantom study, the high- and low-contrast detectability of DLR images obtained at 5.3-mGy and 7.3-mGy, respectively, were slightly higher than those obtained with HIR at the STD protocol dose (18.0-mGy). In clinical study, LD-DLR yielded lower image noise, higher CNR, and higher subjective scores for all evaluation criteria than STD (all, p ≤ 0.05), despite having 52.8% lower SSDE (8.0 ± 2.5 vs. 16.8 ± 3.4-mGy).DLR improved the subjective and objective image quality of multiphase hepatic CT compared with HIR techniques, even at approximately half the radiation dose.

To evaluate diffusion-weighted imaging (DWI) using echo planar imaging (EPI) with compressed SENSE (EPICS) of the head and neck magnetic resonance imaging (MRI).We retrospectively observed 32 patients who underwent head and neck DWI according to either the conventional method (SENSE, reduction factor = 2), fast scanning method (SENSE, reduction factor = 4), or fast scanning method with EPICS (EPICS, reduction factor = 4). For quantitative analysis, contrast-to-noise-ratio (CNR), apparent diffusion coefficient (ADC) values, geometric distortion, and coefficient of variations (CV) were measured and compared. For qualitative analysis, all images were independently and blindly evaluated by two board-certified radiologists.EPICS revealed the higher CNR between all location compared to those of SENSE with reduction factor = 4. Distortion in the anterior-posterior direction was significantly lower on EPICS than on the conventional scan (p = 0.02). A comparison between the ADC values of the EPICS and conventional scan revealed no significant differences. The CV was significantly lower for EPICS than the conventional scan [DWI: 0.22 (IQR: 0.15-0.30) vs 0.32 (IQR: 0.24-0.40), p = 0.02].Compressed SENSE combined with the high acceleration factor can improve image quality, homogeneity, and distortion in the head and neck DWI maintaining ADC values and the scan time duration.

To evaluate the feasibility of spectral imaging with dual-layer spectral detector computed tomography (CT) for the diagnosis of acute coronary syndrome. We identified 30 consecutive patients who underwent cardiac CT using dual-layer spectral detector CT and were diagnosed with acute ischemic syndrome by an invasive coronary angiography. We reconstructed 120 kVp images and generated virtual monochromatic images (VMIs; 40-200 keV in 10 keV increments), iodine concentration maps, and effective atomic number (Z) maps. We calculated the contrast and contrast-to-noise ratio (CNR) between myocardial normal and hypo-perfusion and chose the VMIs with the best CNR for quantitative analysis. We compared the image noise, contrast, and CNR of 120 kVp images and the best VMIs, CT value, iodine concentration, and effective Z between myocardial normal and hypo-perfusion with the paired t test. As the X-ray energy decreased, venous attenuation, contrast, and CNR gradually increased. The 40 keV image yielded the best CNR. The contrast and CNR between myocardial normal and hypo-perfusion were significantly higher in 40 keV images than those in 120 kVp images. The iodine concentration and the effective Z were significantly higher in normal myocardium than those in hypo-perfused myocardium. Spectral imaging with dual-layer spectral detector CT is a feasible technique to detect the hypo-perfused area of acute ischemic syndrome.

To evaluate the performance of a machine learning method to differentiate malignant from benign soft tissue tumors based on textural features on multiparametric magnetic resonance imaging (mpMRI).We enrolled 163 patients with soft tissue tumors whose diagnosis was pathologically proven (71 malignant, 92 benign). All patients underwent mpMRI. Twelve histographic and textural parameters were assessed on T1-weighted imaging (T1WI), T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced T1WI imaging. We compared mean signals of all sequences from the malignant and benign tumors using Welch's t-test. Prediction models were developed via a machine learning technique (support vector machine) using textural features of each sequence, clinical information (sex + age + tumor size), and the combined model incorporating all features. Areas under the receiver operating characteristic curves (AUCs) of these models were calculated using fivefold cross validation.The diagnostic ability of clinical information model (AUC 0.85) was not inferior to the model with textural features of each sequence (AUC 0.79-0.84). The combined model showed the highest diagnostic ability (AUC 0.89). The AUC of the combined model (0.89) was comparable to those of two board-certified radiologists (0.89 and 0.87).Machine learning methods based on textural features on mpMRI and clinical information offer adequate diagnostic performance to differentiate between malignant and benign soft tissue tumors.

In patients with atrial fibrillation (AF), diffuse left ventricular (LV) fibrosis may occur as a result of AF-induced or -mediated cardiomyopathy (AF-CM). It is believed that AF-CM is triggered in part due to tachycardia, irregular rhythm, dyssynchrony, lack of atrial contraction, and functional

BACKGROUND AND PURPOSE: Deep brain stimulation electrodes induce massive artifacts on CT images, deteriorating the diagnostic value of examinations. We aimed to investigate the usefulness and potential limitations of a single-energy metal artifact reduction algorithm in head CT performed in patients with implanted deep brain stimulation devices. MATERIALS AND METHODS: Thirty-four patients with deep brain stimulation (bilateral, n = 28) who underwent head CT on a 320–detector row scanner and whose images were reconstructed with and without single-energy metal artifact reduction at the examinations were retrospectively included. The severity of artifacts around electrodes was assessed objectively using SDs and an artifact index. Two radiologists subjectively evaluated the severity of artifacts from electrodes, the visibility of electrode localization and surrounding structures, and overall diagnostic confidence on 4-point scales. Background image quality (GM-WM contrast and image noise) was subjectively and objectively assessed. The presence and location of artifacts newly produced by single-energy metal artifact reduction were analyzed. RESULTS: Single-energy metal artifact reduction provided lower objective and subjective metal artifacts and improved visualization of electrode localization and surrounding structures and diagnostic confidence compared with non-single-energy metal artifact reduction images, with statistical significance (all, P

Rationale and Objectives Delayed-phase acquisition of the computed tomography (CT) angiography is important for the evaluation of type II endoleaks after endovascular aortic aneurysm repair because the endoleak cavity area is associated with aneurysm sac expansion. Contrast enhancement boost (CE-boost) is a postprocessing technique for increasing the degree of contrast enhancement on contrast-enhanced CT. We aimed to investigate the usefulness of the CE-boost technique for the visualization of type II endoleaks. Materials and Methods This retrospective study included 28 patients with type II endoleaks after endovascular aortic aneurysm repair who underwent triphasic contrast-enhanced CT. Objective (CT number, signal-to-noise ratio, and contrast-to-noise ratio) and subjective quality analyses using a four-point scale (1, poor; 4, excellent) were performed for the conventional early- and delayed-phase images as well as CE-boost delayed-phase images. Results The CE-boost delayed-phase images yielded a significantly higher CT number (134.5 ± 41.7 HU), signal-to-noise ratio (23.4 ± 10.5), and contrast-to-noise ratio (15.3 ± 8.4) and showed a significantly larger endoleak area (145.0 ± 134.8 mm2) than did the conventional early-phase (95.6 ± 53.2 HU, 7.3 ± 4.7, 4.0 ± 4.2, and 56.2 ± 99.3 mm2, respectively) and delayed-phase (110.5 ± 33.3 HU, 8.2 ± 2.7, 4.9 ± 2.0, and 124.8 ± 131.9 mm2, respectively) images (p Conclusion The CE-boost technique facilitates clear visualization of type II endoleak cavities.

Uterine leiomyomas with high signal intensity on T2-weighted imaging (T2WI) can be difficult to distinguish from sarcomas. This study assessed the feasibility of using machine learning to differentiate uterine sarcomas from leiomyomas with high signal intensity on T2WI on multiparametric magnetic resonance imaging.This retrospective study included 80 patients (50 with benign leiomyoma and 30 with uterine sarcoma) who underwent pelvic 3 T magnetic resonance imaging examination for the evaluation of uterine myometrial smooth muscle masses with high signal intensity on T2WI. We used six machine learning techniques to develop prediction models based on 12 texture parameters on T1WI and T2WI, apparent diffusion coefficient maps, and contrast-enhanced T1WI, as well as tumor size and age. We calculated the areas under the curve (AUCs) using receiver-operating characteristic analysis for each model by 10-fold cross-validation and compared these to those for two board-certified radiologists.The eXtreme Gradient Boosting model gave the highest AUC (0.93), followed by the random forest, support vector machine, multilayer perceptron, k-nearest neighbors, and logistic regression models. Age was the most important factor for differentiation (leiomyoma 44.9 ± 11.1 years; sarcoma 58.9 ± 14.7 years; p0.001). The AUC for the eXtreme Gradient Boosting was significantly higher than those for both radiologists (0.93 vs 0.80 and 0.68, p = 0.03 and p0.001, respectively).Machine learning outperformed experienced radiologists in the differentiation of uterine sarcomas from leiomyomas with high signal intensity on T2WI.

Purpose This study aimed to investigate whether a machine learning-based computed tomography (CT) texture analysis could predict the mutation status of V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) in colorectal cancer. Method This retrospective study comprised 40 patients with pathologically confirmed colorectal cancer who underwent KRAS mutation testing, contrast-enhancement CT, and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) before treatment. Of the 40 patients, 20 had mutated KRAS genes, whereas 20 had wild-type KRAS genes. Fourteen CT texture parameters were extracted from portal venous phase CT images of primary tumors, and the maximum standard uptake values (SUVmax) on 18F-FDG PET images were recorded. Univariate logistic regression was used to develop predictive models for each CT texture parameter and SUVmax, and a machine learning method (multivariate support vector machine) was used to develop a comprehensive set of CT texture parameters. The area under the receiver operating characteristic (ROC) curve (AUC) of each model was calculated using five-fold cross validation. In addition, the performance of the machine learning method with the CT texture parameters was compared with that of SUVmax. Results In the univariate analyses, the AUC of each CT texture parameter ranged from 0.4 to 0.7, while the AUC of the SUVmax was 0.58. Comparatively, the multivariate support vector machine with comprehensive CT texture parameters yielded an AUC of 0.82, indicating a superior prediction performance when compared to the SUVmax. Conclusions A machine learning-based CT texture analysis was superior to the SUVmax for predicting the KRAS mutation status of a colorectal cancer.

To compare the effects of hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR) that incorporates a beam-hardening model for myocardial extracellular volume (ECV) quantification by cardiac CT using MRI as a reference standard. In this retrospective study, a total of 34 patients were evaluated using cardiac CT and MRI. Paired CT image sets were created using HIR and MBIR with a beam-hardening model. We calculated mean absolute differences and correlations between the global mid-ventricular ECV derived from CT and MRI via Pearson correlation analysis. In addition, we performed qualitative analysis of image noise and beam-hardening artifacts on postcontrast images using a four-point scale: 1 = extensive, 2 = strong, 3 = mild, and 4 = minimal. The mean absolute difference between the ECV derived from CT and MRI for MBIR was significantly smaller than that for HIR (MBIR 3.74 ± 3.59%; HIR 4.95 ± 3.48%, p = 0.034). MBIR improved the correlation between the ECV derived from CT and MRI when compared with HIR (MBIR, r = 0.60, p

Myocardial extracellular volume (ECV) measured by cardiac magnetic resonance imaging (MRI) has been suggested as a marker of disease severity in pulmonary hypertension (PH). However, consistency between ECVs quantified by computed tomography (CT) and MRI has not been sufficiently investigated in (PH). We investigated the utility of CT-ECV in PH, using MRI-ECV as a reference standard.We evaluated 20 patients with known or suspected PH who underwent dual-energy CT, cardiac MRI, and right heart catheterization. We used Pearson correlation analysis to investigate correlations between CT-ECV and MRI-ECV. We also assessed correlations between ECV and mean pulmonary artery pressure (mPAP).CT-ECV showed a very strong correlation with MRI-ECV at the anterior (r = 0.83) and posterior right ventricular insertion points (RVIPs) (r = 0.84). CT-ECV and MRI-ECV were strongly correlated in the septum and left ventricular free wall (r = 0.79-0.73) but weakly correlated in the right ventricular free wall (r = 0.26). CT-ECV showed a strong correlation with mPAP in the anterior RVIP (r = 0.64) and a moderate correlation in the posterior RVIP and septum (r = 0.50-0.42). Compared with CT-ECV, MRI-ECV had a higher correlation with mPAP; however, the difference was not significant (anterior RVIP, r = 0.72 [MRI-ECV] vs. 0.64 [CT-ECV], p = 0.663; posterior RVIP, r = 0.67 vs. 0.50, p = 0.446).Dual-energy CT can quantify myocardial ECV and yield results comparable to those obtained using cardiac MRI. CT-ECV in the anterior RVIP could be a noninvasive surrogate marker of disease severity in PH.

Cardiotoxicity in the late phase after anthracycline drugs administration remains to be defined. Of the 44 patients who received anthracycline treatment, 7 were found to have cancer therapeutics-related cardiac dysfunction (CTRCD). The global longitudinal strain determined by echocardiography and myocardial extracellular volume fraction (ECV) determined by cardiac computed tomography (CCT) of the CTRCD(+) group were significantly higher than those of the control group and CTRCD(-) group, whereas there were no significant differences between the control and CTRCD(-) groups. Our findings indicated that CCT may be a tool comparable to echocardiography, indicating the effective evaluation of CTRCD by CCT.

Purpose: To evaluate the feasibility of spectral imaging with dual-layer spectral detector computed tomography (CT) for the diagnosis of acute coronary syndrome.Methods: We identified 33 consecutive patients who underwent cardiac CT using dual-layer spectral detector CT and were diagnosed with acute ischemic syndrome by an invasive coronary angiography. We reconstructed 120 kVp images and generated virtual monochromatic images (VMIs; 40–200 keV in 10 keV increments), iodine concentration maps, and effective atomic number (Z) maps. We calculated the contrast and contrast-to-noise noise ratio (CNR) between myocardial normal and hypo-perfusion and chose the VMIs with the best CNR for quantitative analysis. We compared the image noise, contrast, and CNR of 120 kVp images and the best VMIs, CT value, iodine concentration, and effective Z between myocardial normal and hypo-perfusion with the paired t-test.Results: As the X-ray energy decreased, venous attenuation, contrast, and CNR gradually increased. The 40 keV image yielded the best CNR. There was no significant difference in image noise between the 120 kVp and 40 keV images. The contrast and CNR between myocardial normal and hypo-perfusion were significantly higher in 40 keV images than those in 120 kVp images. The iodine concentration and the effective Z were significantly higher in normal myocardium than those in hypo-perfused myocardium.Conclusion: Spectral imaging with dual-layer spectral detector CT is a feasible technique to detect the hypo-perfused area of acute ischemic syndrome.

Purpose To assess the diagnostic performance of unenhanced electrocardiogram (ECG)-gated cardiac CT for myocardial edema, using T2 mapping as the reference standard. Methods This study included 34 patients (23 men; age 64.7 ± 14.6 years) who underwent cardiac CT and MRI. On the unenhanced ECG-gated cardiac CT images, regions of interest (ROIs) were drawn on the septal myocardium. Using T2 mapping as the reference standard, the diagnostic performance of unenhanced cardiac CT for myocardial edema was evaluated by using the area under the receiver operating characteristic curve (AUC) with sensitivity and specificity. Results Mean CT values moderately correlated with mean T2 values (Rho = -0.41; P