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Aims To present and validate a fully automated, deep learning (DL)-based branch-wise coronary artery calcium (CAC) scoring algorithm on a multi-centre dataset. Methods and results We retrospectively included 1171 patients referred for a CAC computed tomography examination. Total CAC scores for each case were manually evaluated by a human reader. Next, each dataset was fully automatically evaluated by the DL-based software solution with output of the total CAC score and sub-scores per coronary artery (CA) branch [right coronary artery (RCA), left main (LM), left anterior descending (LAD), and circumflex (CX)]. Three readers independently manually scored the CAC for all CA branches for 300 cases from a single centre and formed the consensus using a majority vote rule, serving as the reference standard. Established CAC cut-offs for the total Agatston score were used for risk group assignments. The performance of the algorithm was evaluated using metrics for risk class assignment based on total Agatston score, and unweighted Cohen’s Kappa for branch label assignment. The DL-based software solution yielded a class accuracy of 93% (1085/1171) with a sensitivity, specificity, and accuracy of detecting non-zero coronary calcium being 97%, 93%, and 95%. The overall accuracy of the algorithm for branch label classification was 94% (LM: 89%, LAD: 91%, CX: 93%, RCA: 100%) with a Cohen's kappa of k = 0.91. Conclusion Our results demonstrate that fully automated total and vessel-specific CAC scoring is feasible using a DL-based algorithm. There was a high agreement with the manually assessed total CAC from a multi-centre dataset and the vessel-specific scoring demonstrated consistent and reproducible results.

To evaluate the performance of an AI-powered algorithm for the automatic detection of pulmonary embolism (PE) on chest computed tomography pulmonary angiograms (CTPAs) on a large dataset. We retrospectively identified all CTPAs conducted at our institution in 2017 (n = 1499). Exams with clinical questions other than PE were excluded from the analysis (n = 34). The remaining exams were classified into positive (n = 232) and negative (n = 1233) for PE based on the final written reports, which defined the reference standard. The fully anonymized 1-mm series in soft tissue reconstruction served as input for the PE detection prototype algorithm that was based on a deep convolutional neural network comprising a Resnet architecture. It was trained and validated on 28,000 CTPAs acquired at other institutions. The result series were reviewed using a web-based feedback platform. Measures of diagnostic performance were calculated on a per patient and a per finding level. The algorithm correctly identified 215 of 232 exams positive for pulmonary embolism (sensitivity 92.7%; 95% confidence interval [CI] 88.3–95.5%) and 1178 of 1233 exams negative for pulmonary embolism (specificity 95.5%; 95% CI 94.2–96.6%). On a per finding level, 1174 of 1352 findings marked as embolus by the algorithm were true emboli. Most of the false positive findings were due to contrast agent–related flow artifacts, pulmonary veins, and lymph nodes. The AI prototype algorithm we tested has a high degree of diagnostic accuracy for the detection of PE on CTPAs. Sensitivity and specificity are balanced, which is a prerequisite for its clinical usefulness. • An AI-based prototype algorithm showed a high degree of diagnostic accuracy for the detection of pulmonary embolism on CTPAs. • It can therefore help clinicians to automatically prioritize exams with a high suspection of pulmonary embolism and serve as secondary reading tool. • By complementing traditional ways of worklist prioritization in radiology departments, this can speed up the diagnostic and therapeutic workup of patients with pulmonary embolism and help to avoid false negative calls.

To assess if adding perfusion information from dynamic contrast-enhanced (DCE MRI) acquisition schemes with high spatiotemporal resolution to T2w/DWI sequences as input features for a gradient boosting machine (GBM) machine learning (ML) classifier could better classify prostate cancer (PCa) risk groups than T2w/DWI sequences alone. One hundred ninety patients (68 ± 9 years) were retrospectively evaluated at 3T MRI for clinical suspicion of PCa. Included were 201 peripheral zone (PZ) PCa lesions. Histopathological confirmation on fusion biopsy was matched with normal prostate parenchyma contralaterally. Biopsy results were grouped into benign tissue and low-, intermediate-, and high-risk groups (Gleason sum score 6, 7, and > 7, respectively). DCE MRI was performed using golden-angle radial sparse MRI. Perfusion maps (Ktrans, Kep, Ve), apparent diffusion coefficient (ADC), and absolute T2w signal intensity were determined and used as input features for building two ML models: GBM with/without perfusion maps. Areas under the receiver operating characteristic curve (AUC) values for correlated models were compared. For the classification of benign vs. malignant and intermediate- vs. high-grade PCa, perfusion information added relevant information (AUC values 1 vs. 0.953 and 0.909 vs. 0.700, p

BACKGROUND: To investigate if supervised machine learning (ML) classifiers would be able to predict clinically significant cancer (sPC) from a set of quantitative image-features and to compare these results with established PI-RADS v2 assessment scores. METHODS: We retrospectively included 201, histopathologically-proven, peripheral zone (PZ) prostate cancer lesions. Gleason scores ≤3+3 were considered as clinically insignificant (inPC) and Gleason scores ≥3+4 as sPC and were encoded in a binary fashion, serving as ground-truth. MRI was performed at 3T with high spatiotemporal resolution DCE using Golden-angle RAdial SParse (GRASP) MRI. Perfusion maps (Ktrans, Kep, Ve), apparent diffusion coefficient (ADC), and absolute T2-signal intensities (SI) were determined in all lesions and served as input parameters for four supervised ML models: Gradient Boosting Machines (GBM), Neural Networks (NNet), Random Forest (RF) and Support Vector Machines (SVM). ML results and PI-RADS scores were compared with the ground-truth. Next ROC-curves and AUC values were calculated. RESULTS: All ML models outperformed PI-RADS v2 assessment scores in the prediction of sPC (RF, GBM, NNet and SVM vs. PI-RADS: AUC 0.899, 0.864, 0.884 and 0.874 vs. 0.595, all P

Background Gadoxetate disodium has been associated with various respiratory irregularities at arterial imaging MRI. Purpose To measure the relationship between gadolinium-based contrast agent administration and irregularities by comparing gadoxetate disodium and gadoterate meglumine at free breathing. Materials and Methods This prospective observational cohort study (January 2015 to May 2017) included consecutive abdominal MRI performed with either gadoxetate disodium or gadoterate meglumine enhancement. Participants underwent dynamic imaging by using the golden-angle radial sparse parallel sequence at free breathing. The quantitative assessment evaluated the aortic contrast enhancement, the respiratory hepatic translation, and the k-space-derived respiratory pattern. Analyses of variance compared hemodynamic metrics, respiratory-induced hepatic motion, and respiratory parameters before and after respiratory gating. Results A total of 497 abdominal MRI examinations were included. Of these, 338 participants were administered gadoxetate disodium (mean age, 59 years ± 15; 153 women) and 159 participants were administered gadoterate meglumine (mean age, 59 years ± 17; 85 women). The arterial bolus of gadoxetate disodium arrived later than gadoterate meglumine (19.7 vs 16.3 seconds, respectively; P < .001). Evaluation of the hepatic respiratory translation showed respiratory motion occurring in 70.7% (239 of 338) of participants who underwent gadoxetate-enhanced examinations and in 28.9% (46 of 159) of participants who underwent gadoterate-enhanced examinations (P < .001). The duration of motion irregularities was longer for gadoxetate than for gadoterate (19.2 seconds vs 17.2 seconds, respectively) and the motion irregularities were more severe (P < .001). Both the respiratory frequency and amplitude were shorter for participants administered gadoxetate from the prebolus phase to the late arterial phase compared with gadoterate (P < .001). Conclusion The administration of two different gadolinium-based contrast agents, gadoxetate and gadoterate, at free-breathing conditions potentially leads to respiratory irregularities with differing intensity and onset. © RSNA, 2019 Online supplemental material is available for this article.

The aim of this study was to investigate whether structured reports (SRs) of prostate MRI results are more suitable than non-structured reports (NSRs) for promoting the more accurate assessment of the location of a single prostate cancer lesion by novices in MRI-targeted biopsy. 50 NSRs and 50 SRs describing a single prostatic lesion were presented to 5 novices in MRI-targeted biopsy. The participants were asked to plot the tumor location in a two-dimensional prostate diagram and to answer a questionnaire on the quality of the reports. The accuracy of the plotted tumor position was evaluated with a validated 30-point scoring system that distinguished between “major” and “minor” mistakes. The overall mean score for the accuracy of the tumor plotting was significantly higher for SRs than for NSRs (26.4 vs. 20.7, p

OBJECTIVE The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans. MATERIALS AND METHODS We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1-5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss κ), and average reading time without DL-CAD assistance were evaluated. RESULTS The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS ≥4) was 0.84 (95% confidence interval [CI], 0.79-0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83-0.94) with an improvement of 4.4% (95% CI, 1.1%-7.7%; P = 0.010). Interreader concordance (in terms of Fleiss κ) increased from 0.22 to 0.36 (P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS ≥3 was improved by 2.9% (P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds (P < 0.001). CONCLUSIONS Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time.

Background: Opportunistic prostate cancer (PCa) screening is a controversial topic. Magnetic resonance imaging (MRI) has proven to detect prostate cancer with a high sensitivity and specificity, leading to the idea to perform an image-guided prostate cancer (PCa) screening; Methods: We evaluated a prospectively enrolled cohort of 49 healthy men participating in a dedicated image-guided PCa screening trial employing a biparametric MRI (bpMRI) protocol consisting of T2-weighted (T2w) and diffusion weighted imaging (DWI) sequences. Datasets were analyzed both by human readers and by a fully automated artificial intelligence (AI) software using deep learning (DL). Agreement between the algorithm and the reports—serving as the ground truth—was compared on a per-case and per-lesion level using metrics of diagnostic accuracy and k statistics; Results: The DL method yielded an 87% sensitivity (33/38) and 50% specificity (5/10) with a k of 0.42. 12/28 (43%) Prostate Imaging Reporting and Data System (PI-RADS) 3, 16/22 (73%) PI-RADS 4, and 5/5 (100%) PI-RADS 5 lesions were detected compared to the ground truth. Targeted biopsy revealed PCa in six participants, all correctly diagnosed by both the human readers and AI. Conclusions: The results of our study show that in our AI-assisted, image-guided prostate cancer screening the software solution was able to identify highly suspicious lesions and has the potential to effectively guide the targeted-biopsy workflow.

PURPOSE: To investigate the diagnostic performance of a dual-parameter approach by combining either volumetric interpolated breath-hold examination (VIBE)- or golden-angle radial sparse parallel (GRASP)–derived dynamic contrast agent–enhanced (DCE) MRI with established diffusion-weighted imaging (DWI) compared with traditional single-parameter evaluations on the basis of DWI alone. MATERIALS AND METHODS: Ninety-four male participants (66 years ± 7 [standard deviation]) were prospectively evaluated at 3.0-T MRI for clinical suspicion of prostate cancer. Included were 101 peripheral zone prostate cancer lesions. Histopathologic confirmation at MRI transrectal US fusion biopsy was matched with normal contralateral prostate parenchyma. MRI was performed with diffusion weighting and DCE by using GRASP (temporal resolution, 2.5 seconds) or VIBE (temporal resolution, 10 seconds). Perfusion (influx forward volume transfer constant [K(trans)] and rate constant [K(ep)]) and apparent diffusion coefficient (ADC) parameters were determined by tumor volume analysis. Areas under the receiver operating characteristic curve were compared for both sequences. RESULTS: Evaluated were 101 prostate cancer lesions (GRASP, 61 lesions; VIBE, 40 lesions). In a combined analysis, diffusion and perfusion parameters ADC with K(trans) or K(ep) acquired with GRASP had higher diagnostic performance compared with diffusion characteristics alone (area under the curve, 0.97 ± 0.02 [standard error] vs 0.93 ± 0.03; P < .006 and .021, respectively), whereas ADC with perfusion parameters acquired with VIBE had no additional benefit (area under the curve, 0.94 ± 0.03 vs 0.93 ± 0.04; P = .18and .50, respectively, for combination of ADC with K(trans) and K(ep)). CONCLUSION: If used in a dual-parameter model, incorporating diffusion and perfusion characteristics, the golden-angle radial sparse parallel acquisition technique improves the diagnostic performance of multiparametric MRI examinations of the prostate. This effect could not be observed combining diffusing with perfusion parameters acquired with volumetric interpolated breath-hold examination.

The aim of this study was to test the diagnostic performance of a deep learning-based triage system for the detection of acute findings in abdominal computed tomography (CT) examinations.Using a RIS/PACS (Radiology Information System/Picture Archiving and Communication System) search engine, we obtained 100 consecutive abdominal CTs with at least one of the following findings: free-gas, free-fluid, or fat-stranding and 100 control cases with absence of these findings. The CT data were analyzed using a convolutional neural network algorithm previously trained for detection of these findings on an independent sample. The validation of the results was performed on a Web-based feedback system by a radiologist with 1 year of experience in abdominal imaging without prior knowledge of image findings through both visual confirmation and comparison with the clinically approved, written report as the standard of reference. All cases were included in the final analysis, except those in which the whole dataset could not be processed by the detection software. Measures of diagnostic accuracy were then calculated.A total of 194 cases were included in the analysis, 6 excluded because of technical problems during the extraction of the DICOM datasets from the local PACS. Overall, the algorithm achieved a 93% sensitivity (91/98, 7 false-negative) and 97% specificity (93/96, 3 false-positive) in the detection of acute abdominal findings. Intra-abdominal free gas was detected with a 92% sensitivity (54/59) and 93% specificity (39/42), free fluid with a 85% sensitivity (68/80) and 95% specificity (20/21), and fat stranding with a 81% sensitivity (42/50) and 98% specificity (48/49). False-positive results were due to streak artifacts, partial volume effects, and a misidentification of a diverticulum (each n = 1).The algorithm's autonomous detection of acute pathological abdominal findings demonstrated a high diagnostic performance, enabling guidance of the radiology workflow toward prioritization of abdominal CT examinations with acute conditions.

To explore the feasibility of a fully automated workflow for whole-body volumetric analyses based on deep reinforcement learning (DRL) and to investigate the influence of contrast-phase (CP) and slice thickness (ST) on the calculated organ volume. This retrospective study included 431 multiphasic CT datasets—including three CP and two ST reconstructions for abdominal organs—totaling 10,508 organ volumes (10,344 abdominal organ volumes: liver, spleen, and kidneys, 164 lung volumes). Whole-body organ volumes were determined using multi-scale DRL for 3D anatomical landmark detection and 3D organ segmentation. Total processing time for all volumes and mean calculation time per case were recorded. Repeated measures analyses of variance (ANOVA) were conducted to test for robustness considering CP and ST. The algorithm calculated organ volumes for the liver, spleen, and right and left kidney (mean volumes in milliliter (interquartile range), portal venous CP, 5 mm ST: 1868.6 (1426.9, 2157.8), 350.19 (45.46, 395.26), 186.30 (147.05, 214.99) and 181.91 (143.22, 210.35), respectively), and for the right and left lung (2363.1 (1746.3, 2851.3) and 1950.9 (1335.2, 2414.2)). We found no statistically significant effects of the variable contrast phase or the variable slice thickness on the organ volumes. Mean computational time per case was 10 seconds. The evaluated approach, using state-of-the art DRL, enables a fast processing of substantial amounts irrespective of CP and ST, allowing building up organ-specific volumetric databases. The thus derived volumes may serve as reference for quantitative imaging follow-up.

Prostate cancer (PCa) is the most prevalent and one of the leading causes of cancer death among men. Multi-parametric MRI (mp-MRI) is a prominent diagnostic scan, which could help in avoiding unnecessary biopsies for men screened for PCa. Artificial intelligence (AI) systems could help radiologists to be more accurate and consistent in diagnosing clinically significant cancer from mp-MRI scans. Lack of specificity has been identified recently as one of weak points of such assistance systems. In this paper, we propose a novel false positive reduction network to be added to the overall detection system to further analyze lesion candidates. The new network utilizes multiscale 2D image stacks of these candidates to discriminate between true and false positive detections. We trained and validated our network on a dataset with 2170 cases from seven different institutions and tested it on a separate independent dataset with 243 cases. With the proposed model, we achieved area under curve (AUC) of 0.876 on discriminating between true and false positive detected lesions and improved the AUC from 0.825 to 0.867 on overall identification of clinically significant cases.

Purpose The aim was to investigate the variation of the arterial input function (AIF) within and between various DCE MRI sequences. Material and methods A dynamic flow-phantom and steady signal reference were scanned on a 3T MRI using fast low angle shot (FLASH) 2d, FLASH3d (parallel imaging factor (P) = P0, P2, P4), volumetric interpolated breath-hold examination (VIBE) (P = P0, P3, P2 × 2, P2 × 3, P3 × 2), golden-angle radial sparse parallel imaging (GRASP), and time-resolved imaging with stochastic trajectories (TWIST). Signal over time curves were normalized and quantitatively analyzed by full width half maximum (FWHM) measurements to assess variation within and between sequences. Results The coefficient of variation (CV) for the steady signal reference ranged from 0.07-0.8%. The non-accelerated gradient echo FLASH2d, FLASH3d, and VIBE sequences showed low within sequence variation with 2.1%, 1.0%, and 1.6%. The maximum FWHM CV was 3.2% for parallel imaging acceleration (VIBE P2 × 3), 2.7% for GRASP and 9.1% for TWIST. The FWHM CV between sequences ranged from 8.5–14.4% for most non-accelerated/accelerated gradient echo sequences except 6.2% for FLASH3d P0 and 0.3% for FLASH3d P2; GRASP FWHM CV was 9.9% versus 28% for TWIST. Conclusion MRI acceleration techniques vary in reproducibility and quantification of the AIF. Incomplete coverage of the k-space with TWIST as a representative of view-sharing techniques showed the highest variation within sequences and might be less suited for reproducible quantification of the AIF

Multi-parametric MRI (mp-MRI) has recently been established in major guidelines as a first-line diagnostic test for men suspected of having prostate cancer (PCa) primarily to detect and classify clinically significant lesions. However, widespread utilization is still challenged by 1) the difficulty of interpretation specifically for radiologists less experienced in reading mp-MRI scans, and 2) decreased productivity associated with increased time spent per case for reading these complex scans. Deep learning based lesion detection and segmentation methods have been proposed for radiologists to perform their tasks more accurately and efficiently. In this work, we present a novel panoptic lesion detection and segmentation method with both semantic and instance branches as well as an attention module to optimally incorporate both local and global image features. In a free-response receiver operating characteristics (FROC) analysis for lesion sensitivity on an independent dataset with 243 patients, our method has achieved 89% sensitivity and 85% with 0.94 and 0.62 false positives per patient, respectively. Using the proposed method, we have achieved an unprecedented area under ROC curve (AUC) of 0.897 in identifying clinically significant cases.

To evaluate potential confounding factors in the quantitative assessment of liver fibrosis and cirrhosis using T1 relaxation times.The study population is based on a radiology-information-system database search for abdominal MRI performed from July 2018 to April 2019 at our institution. After applying exclusion criteria 200 (59 ± 16 yrs) remaining patients were retrospectively included. 93 patients were defined as liver-healthy, 40 patients without known fibrosis or cirrhosis, and 67 subjects had a clinically or biopsy-proven liver fibrosis or cirrhosis. T1 mapping was performed using a slice based look-locker approach. A ROI based analysis of the left and the right liver was performed. Fat fraction, R2*, liver volume, laboratory parameters, sex, and age were evaluated as potential confounding factors.T1 values were significantly lower in healthy subjects without known fibrotic changes (1.5 T MRI: 575 ± 56 ms; 3 T MRI: 857 ± 128 ms) compared to patients with acute liver disease (1.5 T MRI: 657 ± 73 ms, p 0.0001; 3 T MRI: 952 ± 37 ms, p = 0.028) or known fibrosis or cirrhosis (1.5 T MRI: 644 ± 83 ms, p 0.0001; 3 T MRI: 995 ± 150 ms, p = 0.018). T1 values correlated moderately with the Child-Pugh stage at 1.5 T (p = 0.01, ρ = 0.35).T1 mapping is a capable predictor for detection of liver fibrosis and cirrhosis. Especially age is not a confounding factor and, hence, age-independent thresholds can be defined. Acute liver diseases are confounding factors and should be ruled out before employing T1-relaxometry based thresholds to screen for patients with liver fibrosis or cirrhosis.

OBJECTIVE: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. MATERIALS AND METHODS: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. RESULTS: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88). CONCLUSION: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

BackgroundEffective interdisciplinary communication of imaging findings is vital for patient care, as referring physicians depend on the contained information for the decision-making and subsequent treatment. Traditional radiology reports contain non-structured free text and potentially tangled information in narrative language, which can hamper the information transfer and diminish the clarity of the report. Therefore, this study investigates whether newly developed structured reports (SRs) of prostate magnetic resonance imaging (MRI) can improve interdisciplinary communication, as compared to non-structured reports (NSRs).Methods50 NSRs and 50 SRs describing a single prostatic lesion were presented to four urologists with expert level experience in prostate cancer surgery or targeted MRI TRUS fusion biopsy. They were subsequently asked to plot the tumor location in a 2-dimensional prostate diagram and to answer a questionnaire focusing on information on clinically relevant key features as well as the perceived structure of the report. A validated scoring system that distinguishes between "major" and "minor" mistakes was used to evaluate the accuracy of the plotting of the tumor position in the prostate diagram.ResultsThe mean total score for accuracy for SRs was significantly higher than for NSRs (28.46 [range 13.33-30.0] vs. 21.75 [range 0.0-30.0], p < 0.01). The overall rates of major mistakes (54% vs. 10%) and minor mistakes (74% vs. 22%) were significantly higher (p < 0.01) for NSRs than for SRs. The rate of radiologist re-consultations was significantly lower (p < 0.01) for SRs than for NSRs (19% vs. 85%). Furthermore, SRs were rated as significantly superior to NSRs in regard to determining the clinical tumor stage (p < 0.01), the quality of the summary (4.4 vs. 2.5; p < 0.01), and overall satisfaction with the report (4.5 vs. 2.3; p < 0.01), and as more valuable for further clinical decision-making and surgical planning (p < 0.01).ConclusionsStructured reporting of prostate MRI has the potential to improve interdisciplinary communication. Through SRs, expert urologists were able to more accurately assess the exact location of single prostate cancer lesions, which can facilitate surgical planning. Furthermore, structured reporting of prostate MRI leads to a higher satisfaction level of the referring physician.

To compare image quality and radiation dose of abdominal split-filter dual-energy CT (SF-DECT) combined with monoenergetic imaging to single-energy CT (SECT) with automatic tube voltage selection (ATVS). Two-hundred single-source abdominal CT scans were performed as SECT with ATVS (n = 100) and SF-DECT (n = 100). SF-DECT scans were reconstructed and subdivided into composed images (SF-CI) and monoenergetic images at 55 keV (SF-MI). Objective and subjective image quality were compared among single-energy images (SEI), SF-CI and SF-MI. CNR and FOM were separately calculated for the liver (e.g. CNRliv) and the portal vein (CNRpv). Radiation dose was compared using size-specific dose estimate (SSDE). Results of the three groups were compared using non-parametric tests. Image noise of SF-CI was 18% lower compared to SEI and 48% lower compared to SF-MI (p 0.628). Subjective sharpness was equal between single-energy and monoenergetic images and diagnostic confidence was equal between single-energy and composed images. FOMliv was highest for SF-CI. FOMpv was equal for SEI and SF-MI (p = 0.78). SSDE was significant lower for SF-DECT compared to SECT (p < 0.022). The combined use of split-filter dual-energy CT images provides comparable objective and subjective image quality at lower radiation dose compared to single-energy CT with ATVS. • Split-filter dual-energy results in 18% lower noise compared to single-energy with ATVS. • Split-filter dual-energy results in 11% lower SSDE compared to single-energy with ATVS. • Spectral shaping of split-filter dual-energy leads to an increased dose-efficiency.