High spatiotemporal resolution dynamic contrast-enhanced MRI improves the image-based discrimination of histopathology risk groups of peripheral zone prostate cancer: a supervised machine learning approach.

OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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 < 0.001 and p = 0.038), while no statistically significant effect was found for low- vs. intermediate- and high-grade PCa. CONCLUSION: Perfusion information from DCE MRI acquisition schemes with high spatiotemporal resolution to ML classifiers enables a superior risk stratification between benign and malignant and intermediate- and high-risk PCa in the PZ compared with classifiers based on T2w/DWI information alone. KEY POINTS: • In the recent guidelines, the role of DCE MRI has changed from a mandatory to recommended sequence. • DCE MRI acquisition schemes with high spatiotemporal resolution (e.g., GRASP) have been shown to improve the diagnostic performance compared with conventional DCE MRI sequences. • Using perfusion information acquired with GRASP in combination with ML classifiers significantly improved the prediction of benign vs. malignant and intermediate- vs. high-grade peripheral zone prostate cancer compared with non-contrast sequences.

Gamification of Electronic Learning in Radiology Education to Improve Diagnostic Confidence and Reduce Error Rates.

OBJECTIVE. The purpose of this study is to validate an electronic learning, or e-learning, concept featuring gamification elements, rapid case reading, and instant feedback. SUBJECTS AND METHODS. An e-learning concept was devised that offered game levels for the purpose of providing training in the detection of pneumothorax in 195 cases, with questions read in rapid succession and instant feedback provided for each case. The user's task was to locate the pneumothorax on chest radiographs and indicate its presence by clicking a mouse. The game level design included an entry test consisting of 15 cases, training levels with increasing difficulty that involved 150 cases, and a final test that including 30 cases (the 15 cases from the entry test plus 15 new cases). A total of 126 candidates were invited via e-mail to participate and were asked to complete a survey before and after playing the game, which is known as RapRad. The level of diagnostic confidence and the error rate before and after playing the game were compared using a Wilcoxon signed rank test. RESULTS. Fifty-nine of 126 participants (47%) responded to the first survey and finished the game. Of these 59 participants, 29 (49%) responded to the second survey after completing the game. Diagnostic confidence in pneumothorax detection improved significantly, from a mean (± SD) score of 4.3 ± 2.1 on the entry test to a final score of 7.3 ± 2.1 (p < 0.01) after playing RapRad, with the score measured on a 10-point scale, with 10 denoting the highest possible score. Of the participants, 93% indicated that they would use the game for learning purposes again, and 87% indicated that they had fun using RapRad (7% had a neutral response and 6% had a negative response). The error rate (i.e., the number of failed attempts to answer a question correctly) significantly decreased from 39% for the entry test to 22% for the final test (p < 0.01). CONCLUSION. Our e-learning concept is capable of improving diagnostic confidence, reducing error rates in training pneumothorax detection, and offering fun in interaction with the platform.

Gadoxetate Disodium versus Gadoterate Meglumine: Quantitative Respiratory and Hemodynamic Metrics by Using Compressed-Sensing MRI.

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.

Compressed Sensing Radial Sampling MRI of Prostate Perfusion: Utility for Detection of Prostate Cancer.

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 [Ktrans] and rate constant [Kep]) 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 Ktrans or Kep 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 Ktrans and Kep). 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. © RSNA, 2018.

Regional Mapping of Aortic Wall Stress by Using Deformable, Motion-coherent Modeling based on Electrocardiography-gated Multidetector CT Angiography: Feasibility Study.

Purpose To investigate the feasibility of deformable, motion-coherent modeling based on electrocardiography-gated multidetector computed tomographic (CT) angiography of the thoracic aorta and to evaluate whether quantifiable information on aortic wall stress as a function of patient-specific cardiovascular parameters can be gained. Materials and Methods For this institutional review board-approved, HIPAA-compliant study, thoracic electrocardiography-gated dual-source multidetector CT angiographic images were used from 250 prospectively enrolled patients (150 men, 100 women; mean age, 79 years). On reconstructed 50-phase CT angiographic images, aortic strain and deformation were determined at seven cardiac and aortic locations. One-way analysis of variance was used by assessing the magnitude for longitudinal and axial strain and axial deformation, as well as time-resolved peak and maxima count for longitudinal strain and axial deformation. Interdependencies between aortic strain and deformation with extracted hemodynamic parameters were evaluated. Results With increasing heart rates, there was a significant decrease in longitudinal strain (P = .009, R(2) = 0.95) and a decrease in the number of longitudinal strain peaks (P < .001, R(2) = 0.79); however, a significant increase in axial deformation (P < .001, R(2) = 0.31) and axial strain (P = .009, R(2) = 0.61) was observed. Increasing aortic blood velocity led to increased longitudinal strain (P = .018, R(2) = 0.42) and longitudinal strain peak counts (P = .011, R(2) = 0.48). Pronounced motion in the longitudinal direction limited motion in the axial plane (P < .019, R(2) = 0.29-0.31). Conclusion The results of this study render a clinical basis and provide proof of principle for the use of deformable, motion-coherent modeling to provide quantitative information on physiological motion of the aorta under various hemodynamic circumstances. (©) RSNA, 2016 Online supplemental material is available for this article.

Bone marrow enhancement during time-resolved magnetic resonance angiography of the pelvis.

OBJECTIVE: This study aimed to determine whether visible bone marrow enhancement (BME) at pelvic magnetic resonance angiography (MRA) correlates with anemia. METHODS: This is an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study. Seventy-five female patients with a hemoglobin (Hb) test within 10 days of an MRA examination of the pelvis are included, mean age of 46 years (range, 18-81 years). The BME was graded using a 4-point scale. Fifteen subjects had sufficient imaging data for calculation of quantitative perfusion parameters. Receiver operating characteristic and analysis of covariance were performed for 2 levels of anemia. RESULTS: Enhancement was visually present in 44% (33/75) of cases; 66% (19/29) of anemic subjects (Hb < 12 g/dL), and 30% (14/46) of nonanemic subjects (P = 0.002). Enhancement had a sensitivity of 87.5% and a specificity of 92.5% for the diagnosis of severe anemia (Hb < 10 g/dL); 88% (7/8) of these subjects had moderate to marked enhancement. A high extraction coefficient (Kep) in the iliac crest was associated with anemia, with an area under the curve of the receiver operating characteristic of 0.85 for mild anemia and 0.92 for severe anemia. CONCLUSIONS: Visible BME during time-resolved MRA is common and may be related to anemia.

Estimation of differential renal function on routine abdominal imaging employing compressed-sensed contrast-enhanced MR: a feasibility study referenced against dynamic renal scintigraphy in patients with deteriorating renal retention parameters.

PURPOSE: To assess whether high temporal/spatial resolution GRASP MRI acquired during routine clinical imaging can identify several degrees of renal function impairment referenced against renal dynamic scintigraphy. METHODS: This retrospective study consists of method development and method verification parts. During method development, patients subject to renal imaging using gadoterate meglumine and GRASP post-contrast MRI technique (TR/TE 3.3/1.6 ms; FoV320 × 320 mm; FA12°; Voxel1.1 × 1.1x2.5 mm) were matched into four equally-sized renal function groups (no-mild-moderate-severe impairment) according to their laboratory-determined estimated glomerular filtration rates (eGFR); 60|120 patients|kidneys were included. Regions-of-interest (ROIs) were placed on cortices, medullary pyramids and collecting systems of bilateral kidneys. Cortical perfusion, tubular concentration and collecting system excretion were determined as TimeCortex=Pyramid(sec), SlopeTubuli (sec-1), and TimeCollecting System (sec), respectively, and were measured by a combination of extraction of time intensity curves and respective quantitative parameters. For method verification, patients subject to GRASP MRI and renal dynamic scintigraphy (99mTc-MAG3, 100 MBq/patient) were matched into three renal function groups (no-mild/moderate-severe impairment). Split renal function parameters post 1.5-2.5 min as well as MAG3 TER were correlated with time intensity parameters retrieved using GRASP technique; 15|30 patients|kidneys were included. RESULTS: Method development showed differing values for TimeCortex=Pyramid(71|75|93|122 s), SlopeTubuli(2.6|2.1|1.3|0.5 s-1) and TimeCollecting System(90|111|129|139 s) for the four renal function groups with partial significant tendencies (several p-values < 0.001). In method verification, 29/30 kidneys (96.7%) were assigned to the correct renal function group. CONCLUSION: High temporal and spatial resolution GRASP MR imaging allows to identify several degrees of renal function impairment using routine clinical imaging with a high degree of accuracy.

Evaluation of liver fibrosis and cirrhosis on the basis of quantitative T1 mapping: Are acute inflammation, age and liver volume confounding factors?

PURPOSE: To evaluate potential confounding factors in the quantitative assessment of liver fibrosis and cirrhosis using T1 relaxation times. METHODS: 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 ±â€¯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. RESULTS: T1 values were significantly lower in healthy subjects without known fibrotic changes (1.5 T MRI: 575 ±â€¯56 ms; 3 T MRI: 857 ±â€¯128 ms) compared to patients with acute liver disease (1.5 T MRI: 657 ±â€¯73 ms, p < 0.0001; 3 T MRI: 952 ±â€¯37 ms, p = 0.028) or known fibrosis or cirrhosis (1.5 T MRI: 644 ±â€¯83 ms, p < 0.0001; 3 T MRI: 995 ±â€¯150 ms, p = 0.018). T1 values correlated moderately with the Child-Pugh stage at 1.5 T (p = 0.01, ρ = 0.35). CONCLUSION: 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.

Revisiting DCE-MRI: Classification of Prostate Tissue Using Descriptive Signal Enhancement Features Derived From DCE-MRI Acquisition With High Spatiotemporal Resolution.

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.

Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation.

PURPOSE: To evaluate the performance of an artificial intelligence (AI) based software solution tested on liver volumetric analyses and to compare the results to the manual contour segmentation. MATERIALS AND METHODS: We retrospectively obtained 462 multiphasic CT datasets with six series for each patient: three different contrast phases and two slice thickness reconstructions (1.5/5 mm), totaling 2772 series. AI-based liver volumes were determined using multi-scale deep-reinforcement learning for 3D body markers detection and 3D structure segmentation. The algorithm was trained for liver volumetry on approximately 5000 datasets. We computed the absolute error of each automatically- and manually-derived volume relative to the mean manual volume. The mean processing time/dataset and method was recorded. Variations of liver volumes were compared using univariate generalized linear model analyses. A subgroup of 60 datasets was manually segmented by three radiologists, with a further subgroup of 20 segmented three times by each, to compare the automatically-derived results with the ground-truth. RESULTS: The mean absolute error of the automatically-derived measurement was 44.3 mL (representing 2.37 % of the averaged liver volumes). The liver volume was neither dependent on the contrast phase (p = 0.697), nor on the slice thickness (p = 0.446). The mean processing time/dataset with the algorithm was 9.94 s (sec) compared to manual segmentation with 219.34 s. We found an excellent agreement between both approaches with an ICC value of 0.996. CONCLUSION: The results of our study demonstrate that AI-powered fully automated liver volumetric analyses can be done with excellent accuracy, reproducibility, robustness, speed and agreement with the manual segmentation.

Iodine quantification using dual-energy multidetector computed tomography imaging: phantom study assessing the impact of iterative reconstruction schemes and patient habitus on accuracy.

PURPOSE: 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. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: 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.