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PURPOSE: The aim of this study was to investigate the diagnostic value of descriptive prostate perfusion parameters derived from signal enhancement curves acquired using golden-angle radial sparse parallel dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with high spatiotemporal resolution in advanced, quantitative evaluation of prostate cancer compared with the usage of apparent diffusion coefficient (ADC) values. METHODS: A retrospective study (from January 2016 to July 2019) including 75 subjects (mean, 65 years; 46–80 years) with 2.5-second temporal resolution DCE-MRI and PIRADS 4 or 5 lesions was performed. Fifty-four subjects had biopsy-proven prostate cancer (Gleason 6, 15; Gleason 7, 20; Gleason 8, 13; Gleason 9, 6), whereas 21 subjects had negative MRI/ultrasound fusion-guided biopsies. Voxel-wise analysis of contrast signal enhancement was performed for all time points using custom-developed software, including automatic arterial input function detection. Seven descriptive parameter maps were calculated: normalized maximum signal intensity, time to start, time to maximum, time-to-maximum slope, and maximum slope with normalization on maximum signal and the arterial input function (SMN1, SMN2). The parameters were compared with ADC using multiparametric machine-learning models to determine classification accuracy. A Wilcoxon test was used for the hypothesis test and the Spearman coefficient for correlation. RESULTS: There were significant differences (P < 0.05) for all 7 DCE-derived parameters between the normal peripheral zone versus PIRADS 4 or 5 lesions and the biopsy-positive versus biopsy-negative lesions. Multiparametric analysis showed better performance when combining ADC + DCE as input (accuracy/sensitivity/specificity, 97%/93%/100%) relative to ADC alone (accuracy/sensitivity/specificity, 94%/95%/95%) and to DCE alone (accuracy/sensitivity/specificity, 78%/79%/77%) in differentiating the normal peripheral zone from PIRADS lesions, biopsy-positive versus biopsy-negative lesions (accuracy/sensitivity/specificity, 68%/33%/81%), and Gleason 6 versus ≥7 prostate cancer (accuracy/sensitivity/specificity, 69%/60%/72%). CONCLUSIONS: Descriptive perfusion characteristics derived from high-resolution DCE-MRI using model-free computations show significant differences between normal and cancerous tissue but do not reach the accuracy achieved with solely ADC-based classification. Combining ADC with DCE-based input features improved classification accuracy for PIRADS lesions, discrimination of biopsy-positive versus biopsy-negative lesions, and differentiation between Gleason 6 versus Gleason ≥7 lesions.

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.

The aim of this study was to assess the accuracy of iodine quantification based on spectral dual-energy computed tomography (DECT) extraction with additional noise reduction using iterative reconstruction in simulated optimal and obese patient environments.Two custom-designed DECT phantoms were containing 10 vials with iodine concentrations representing arterial/parenchymal enhancement ranging from water isodensity to -150 Hounsfield units and, in addition, 40 vials simulating enhancement seen in nondiluted thoracic inlet vasculature and urinary bladder/renal collecting systems of up to -2000 Hounsfield units.Dual-energy computed tomography acquisition was performed using a dual-source scanner at 140 kVp/90 mAs and 80 kVp/495 mAs. Backprojection-based soft tissue kernels and corresponding iteratively reconstructed kernels generated dual-energy series used for iodine extraction.Fractional variations between known and spectrally determined iodine concentration were calculated for each concentration step; paired t tests evaluated variations between backprojected and iteratively reconstructed data sets for small and obese phantoms. Bland-Altman plots with regression analyses assessed concentration differences observed in backprojected and iteratively reconstructed data.For backprojected data, mean concentration variations of 8.7% ± 8.4 and 12.2% ± 6.3 were detected in small and large phantoms, respectively, compared with significantly less variation observed in iteratively reconstructed data with 6.1% ± 6.2 and 11.0% ± 6.5, respectively. Dual-energy quantification systematically overestimated concentrations in lower concentration ranges and underestimated concentrations in higher concentration ranges. Regression analyses showed cubic distribution of concentration differences for backprojected (R = 0.697) and linear distribution for iteratively reconstructed data (R = 0.701).Spectral DECT-based iodine quantification is able to accurately quantify iodine in phantoms simulating optimal and large patients; iterative reconstruction improves the accuracy of iodine detection. Systematic deviations of the spectrally determined iodine concentrations could potentially be corrected with weighting curves.

To report our initial experience with dual-energy computed-tomography (CT) cholangiography in potential donors for living-related liver transplantation.Seventeen potential donors for living-related liver transplantation (6 women and 11 men; average age 37.8 +/- 10.4 years) underwent contrast-enhanced dual-energy CT cholangiography. A dual-energy CT scan of the liver was carried out with acquisition of 2 raw datasets at tube currents of 140 and 80 kV, respectively. A third weighted average dataset were obtained (weighting ratio: 70% 140 kV, 30% 80 kV). Pure iodine images (fourth dataset) and contrast-optimized images (fifth dataset) were reconstructed. Analysis of all datasets comprised determination of bile duct visualization scores (on a scale of 1 to 4: 1, not visualized; 2, faintly seen; 3, identified but the origin or portions of the duct are not visualized; and 4, excellent visualization from origin to branches), maximum bile duct diameters, bile duct attenuation, and liver parenchyma attenuation as well as image noise, signal-to-noise ratio, and contrast-to-noise ratio.Highest maximum bile duct diameters were detected for optimized-contrast images and the 80 kV dataset, intermediate for pure iodine images and the weighted average dataset and lowest for the 140 kV dataset with significant differences. Highest bile duct attenuation was detected for optimized-contrast images (535.7 +/- 148.3 HU) and the 80 kV dataset (533.7 +/- 145.9 HU) with significant differences compared with pure iodine images (344.9 +/- 106.5 HU) and the weighted average dataset (355.5 +/- 87.6 HU) (P0.001). Highest image noise was detected for the 80 kV dataset (39.3 +/- 5.4 HU) with significant differences compared with the optimized-contrast images (31.5 +/- 4.0) (P0.001). Highest signal-to-noise ratio and contrast-to-noise ratio were detected for pure iodine images (18.3 +/- 7.1 and 17.6 +/- 7.0) and optimized-contrast images (17.3 +/- 5.8 and 14.8 +/- 5.5) with significant differences compared with the 80 kV dataset (14.0 +/- 5.2 and 11.8 +/- 4.8) and the weighted average dataset (15.1 +/- 4.4 and 12.1 +/- 4.1) (P0.001 and P0.01).Dual-energy CT cholangiography in potential donors for living-related liver transplantation is remarkable. Pure iodine images and contrast-optimized images allow precise analysis of the biliary system with increased image quality compared with conventional images. Contrast-optimized images should be used for detection and localization of the bile ducts and pure iodine images for quantitative description of the anatomic dimensions of the biliary segments.