Faculty Bibliography

PURPOSE/OBJECTIVE:Pancreatic cystic lesions (PCL) commonly undergo surveillance using MRI with MR cholangiopancreatography (MRCP). Our objective is to compare the performance of a single-shot fat-saturated T2-weighted technique with deep-learning reconstruction (DL HASTE-FS) to a conventional T2-weighted Half fourier Single-shot Turbo spin-Echo (HASTE) sequence and to MRCP for the purpose of PCL detection, characterization, and surveillance. METHODS:In this retrospective study, 91 consecutive patients underwent 3T abdominal MRI with MRCP protocol including DL HASTE-FS and conventional HASTE between 8/2/2023 and 10/3/2023. Three abdominal radiologists rated overall and lesion-specific image quality on a 5-point Likert scale, including pancreatic margin and duct sharpness, and PCL conspicuity. A subset of 70 preselected index PCLs were evaluated for cyst features, confidence of diagnosing side-branch IPMN, and suitability of DL HASTE-FS in replacing MRCP for PCL surveillance. RESULTS:DL HASTE-FS received higher scores for pancreatic duct border sharpness (4.1 vs. 3.9; p = .004), pancreatic duct visibility compared to MRCP (2.0 vs. 1.9; p = .04), cyst conspicuity (4.4 vs. 3.9; p < .001), and sharpness of cyst wall and internal septations (4.3 vs. 3.7; p < .001) compared to conventional HASTE. In contrast, conventional HASTE received higher scores for pancreatic margin sharpness (4.2 vs. 3.8; p < .001) and peripancreatic vessel clarity (4.2 vs. 3.4; p < .001). For the 70 preselected index PCLs, readers visualized more PCLs and had higher confidence in diagnosing SB-IPMN on DL HASTE-FS than on conventional HASTE (3.6 vs. 3.4; p < .001). Finally, DL HASTE-FS was deemed a suitable replacement to MRCP for more cases than conventional HASTE (83% vs. 48%; p < .001). CONCLUSION/CONCLUSIONS:DL HASTE-FS outperforms conventional HASTE for PCL detection and characterization, and is a suitable alternative to 3D MRCP in the context of PCL surveillance, potentially reducing exam time and cost.

PURPOSE/OBJECTIVE: METHODS:Rep2Rep learning extends the Noise2Noise framework by training a neural network on two repeated MRI acquisitions, using one repetition as input and another as target, without requiring ground-truth data. It incorporates noise-adaptive training, enabling denoising generalization across varying noise-levels and flexible inference with any number of repetitions. Performance was evaluated on both synthetic noisy Brain MRI and 0.55T Prostate MRI data, and compared against supervised learning and Monte Carlo Stein's Unbiased Risk Estimator (MC-SURE). RESULTS:Rep2Rep learning outperforms MC-SURE on both synthetic and 0.55T MRI datasets. On synthetic Brain data, it achieved denoising quality comparable to supervised learning and surpassed MC-SURE, particularly in preserving structural details and reducing residual noise. On the 0.55T Prostate MRI data a reader study showed that Rep2Rep-denoised 2-average images outperformed 8-average noisy images. Rep2Rep demonstrated robustness to noise-level discrepancies between training and inference, supporting its practical implementation. CONCLUSION/CONCLUSIONS:Rep2Rep learning offers an effective self-supervised denoising for low-field MRI by leveraging routinely acquired multi-repetition data. Its noise-adaptivity enables generalization to different SNR regimes without clean reference images. This makes Rep2Rep learning a promising tool for improving image quality and scan efficiency in low-field MRI.

BACKGROUND:Liver Imaging Reporting and Data System M (LR-M) lesions may appear targetoid or nontargetoid, but their clinicopathologic and prognostic differences remain unclear. PURPOSE/OBJECTIVE:To compare clinical, pathological, and prognostic features of targetoid and nontargetoid LR-M lesions on dynamic contrast enhanced-MRI (DCE-MRI). STUDY TYPE/METHODS:Retrospective. SUBJECTS/METHODS:119 consecutive patients (82 male, mean age = 62.9 ± 10.3 years) with 119 LR-M observations (75 targetoid, 44 nontargetoid) and at least 2 years of follow-up. FIELD STRENGTH/SEQUENCE/UNASSIGNED:1.5T and 3.0T; T2-weighted fast spin echo sequence, diffusion-weighted image, and dynamic T1-weighted-gradient-echo sequence using an extracellular contrast agent. ASSESSMENT/RESULTS:Three radiologists categorized lesions as targetoid or nontargetoid. Clinical, laboratory, imaging, and histopathologic data were collected. STATISTICAL TESTS/METHODS:Group differences were evaluated using t-tests and chi-square/Fisher's exact tests. Survival outcomes were assessed using Kaplan-Meier method with log-rank test and Cox proportional hazards regression. Inverse probability of treatment weighting (IPTW) was applied before survival analysis. A p-value < 0.05 was considered significant. RESULTS:The nontargetoid group had significantly higher serum AFP (6684.7 ± 15,988 vs. 194.9 ± 898.4 ng/mL), larger lesion size (9.10 ± 5.55 cm vs. 3.55 ± 2.96 cm), cirrhosis (95% vs. 76%), extrahepatic disease (50% vs. 19%), and malignancy (95% vs. 82%). Nontargetoid group showed significantly higher mortality (75% vs. 41%), progression (77% vs. 45%), shorter overall survival (477 ± 629 vs. 1226 ± 1147 days), and time-to-progression (333 vs. 1003 days). On multivariable analysis with Cox proportional hazards regression, targetoid morphology was significantly associated with improved overall survival (HR = 0.28) and progression-free survival (HR = 0.36), whereas histology was not significant (HCC vs. non-HCC). DATA CONCLUSION/CONCLUSIONS:Targetoid morphology is significantly associated with improved survival and delayed progression, supporting its role as a prognostic imaging biomarker. EVIDENCE LEVEL/METHODS:3. TECHNICAL EFFICACY/UNASSIGNED:Stage 5.

OBJECTIVES/OBJECTIVE:This study aims to evaluate the diagnostic accuracy of an open-source deep learning (DL) model for detecting clinically significant prostate cancer (csPCa) in biparametric MRI (bpMRI). It also aims to outline the necessary components of the model that facilitate effective sharing and external evaluation of PCa detection models. MATERIALS AND METHODS/METHODS:This retrospective diagnostic accuracy study evaluated a publicly available DL model trained to detect PCa on bpMRI. External validation was performed on bpMRI exams from 151 biologically male patients (mean age, 65 ± 8 years). The model's performance was evaluated using patient-level classification of PCa with both radiologist interpretation and histopathology serving as the ground truth. The model processed bpMRI inputs to generate lesion probability maps. Performance was assessed using the area under the receiver operating characteristic curve (AUC) for PI-RADS ≥ 3, PI-RADS ≥ 4, and csPCa (defined as Gleason ≥ 7) at an exam level. RESULTS:The model achieved AUCs of 0.86 (95% CI: 0.80-0.92) and 0.91 (95% CI: 0.85-0.96) for predicting PI-RADS ≥ 3 and ≥ 4 exams, respectively, and 0.78 (95% CI: 0.71-0.86) for csPCa. Sensitivity and specificity for csPCa were 0.87 and 0.53, respectively. Fleiss' kappa for inter-reader agreement was 0.51. CONCLUSION/CONCLUSIONS:The open-source DL model offers high sensitivity to clinically significant prostate cancer. The study underscores the importance of sharing model code and weights to enable effective external validation and further research. KEY POINTS/CONCLUSIONS:Question Inter-reader variability hinders the consistent and accurate detection of clinically significant prostate cancer in MRI. Findings An open-source deep learning model demonstrated reproducible diagnostic accuracy, achieving AUCs of 0.86 for PI-RADS ≥ 3 and 0.78 for CsPCa lesions. Clinical relevance The model's high sensitivity for MRI-positive lesions (PI-RADS ≥ 3) may provide support for radiologists. Its open-source deployment facilitates further development and evaluation across diverse clinical settings, maximizing its potential utility.

BACKGROUND:MRI plays a critical role in prostate cancer (PCa) detection and management. Bi-parametric MRI (bpMRI) offers a faster, contrast-free alternative to multi-parametric MRI (mpMRI). Routine use of mpMRI for all patients may not be necessary, and a tailored imaging approach (bpMRI or mpMRI) based on individual risk might optimize resource utilization. PURPOSE/OBJECTIVE:To develop and evaluate a deep learning (DL) model for classifying clinically significant PCa (csPCa) using bpMRI and to assess its potential for optimizing MRI protocol selection by recommending the additional sequences of mpMRI only when beneficial. STUDY TYPE/METHODS:Retrospective and prospective. POPULATION/METHODS:The DL model was trained and validated on 26,129 prostate MRI studies. A retrospective cohort of 151 patients (mean age 65 ± 8) with ground-truth verification from biopsy, prostatectomy, or long-term follow-up, alongside a prospective cohort of 142 treatment-naïve patients (mean age 65 ± 9) undergoing bpMRI, was evaluated. FIELD STRENGTH/SEQUENCE/UNASSIGNED:3 T, Turbo-spin echo T2-weighted imaging (T2WI) and single shot EPI diffusion-weighted imaging (DWI). ASSESSMENT/RESULTS:The DL model, based on a 3D ResNet-50 architecture, classified csPCa using PI-RADS ≥ 3 and Gleason ≥ 7 as outcome measures. The model was evaluated on a prospective cohort labeled by consensus of three radiologists and a retrospective cohort with ground truth verification based on biopsy or long-term follow-up. Real-time inference was tested on an automated MRI workflow, providing classification results directly at the scanner. STATISTICAL TESTS/METHODS:AUROC with 95% confidence intervals (CI) was used to evaluate model performance. RESULTS:In the prospective cohort, the model achieved an AUC of 0.83 (95% CI: 0.77-0.89) for PI-RADS ≥ 3 classification, with 93% sensitivity and 54% specificity. In the retrospective cohort, the model achieved an AUC of 0.86 (95% CI: 0.80-0.91) for Gleason ≥ 7 classification, with 93% sensitivity and 62% specificity. Real-time implementation demonstrated a processing latency of 14-16 s for protocol recommendations. DATA CONCLUSION/CONCLUSIONS:The proposed DL model identifies csPCa using bpMRI and integrates it into clinical workflows. EVIDENCE LEVEL/METHODS:1. TECHNICAL EFFICACY/UNASSIGNED:Stage 2.

PURPOSE/OBJECTIVE:As the pancreas is a low contrast visibility organ, pancreatic ductal adenocarcinoma detection is challenging due to subtle attenuation differences between tumor and pancreatic parenchyma. Photon counting CT (PCCT) has superior iodine contrast-to-noise ratio than conventional CT and also affords the creation of low keV virtual monoenergetic images, both of which increase adenocarcinoma conspicuity. The purpose therefore was to identify the optimal virtual monoenergy for visualizing PDAC during the pancreatic parenchymal phase of enhancement at PCCT using both quantitative and qualitative analyses. METHODS:Consecutive patients with pancreatic parenchymal phase PCCT source data were retrospectively identified by PACS search. For the quantitative analysis, region of interest (ROI) measurements were drawn in the pancreatic head, body, tail, pancreatic adenocarcinoma (if present), and psoas muscles on 40-120 keV virtual monoenergetic images in 10 keV increments. Based on the quantitative analysis results and vendor recommendations, four virtual monoenergies(40 keV, 55 keV, 70 keV, and 85 keV) were selected for additional qualitative analysis. Three radiologists blinded to four virtual monoenergies assessed overall image quality, image noise, pancreatic enhancement, and pancreatic mass conspicuity on 5-point Likert scales. RESULTS:54 patients (28/54 male, mean[SD] age: 62 [13] years) were included. Quantitatively, 40 keV had the highest pancreatic parenchymal CNR and attenuation difference between the adenocarcinoma and parenchyma, but also the highest noise (HUsd). Qualitatively, 70 keV had the best overall image quality (Mean [SE]: 3.7[0.1]) and lower noise than 40 and 55 keV (3.6[0.08] vs. 1.8[0.07] and 2.7[0.05], respectively, p < .001). 40 keV had the greatest pancreatic enhancement (mean[SE] 4.6[0.11]). Adenocarcinoma conspicuity ratings were greatest at 40 keV and 55 keV, and not significantly different from each other (mean[SE] 4.4[0.13] and 4.3[0.14], respectively, Tukey adj-p =.20). 55 keV had greater overall image quality and lower noise than 40 keV (mean[SE] 3.4[0.08] vs. 2.5[0.08], Tukey adj-p < .001 and 2.7[0.05] vs. 1.8[0.07], Tukey adj-p < .001 respectively). CONCLUSION/CONCLUSIONS:55 keV pancreatic parenchymal phase virtual monoenergetic images afford optimal pancreatic assessment at PCCT for the visualization of pancreatic adenocarcinoma. Routinely viewing 55 keV virtual monoenergetic images at PCCT may improve PDAC detection.

Deep learning reconstruction (DLR) provides an elegant solution for MR acceleration while preserving image quality. This advancement is crucial for body imaging, which is frequently marred by the increased likelihood of motion-related artifacts. Multiple vendor-specific models focusing on T2, T1, and diffusion-weighted imaging have been developed for the abdomen, pelvis, and chest, with the liver and prostate being the most well-studied organ systems. Variational networks with supervised DL models, including data consistency layers and regularizers, are the most common DLR methods. The common theme for all single-center studies on this subject has been noninferior or superior image quality metrics and lesion conspicuity to conventional sequences despite significant acquisition time reduction. DLR also provides a potential for denoising, artifact reduction, increased resolution, and increased signal-noise ratio (SNR) and contrast-to-noise ratio (CNR) that can be balanced with acceleration benefits depending on the imaged organ system. Some specific challenges faced by DLR include slightly reduced lesion detection, cardiac motion-related signal loss, regional SNR variations, and variabilities in ADC measurements as reported in different organ systems. Continued investigations with large-scale multicenter prospective clinical validation of DLR to document generalizability and demonstrate noninferior diagnostic accuracy with histopathologic correlation are the need of the hour. The creation of vendor-neutral solutions, open data sharing, and diversifying training data sets are also critical to strengthening model robustness.

OBJECTIVE:To perform image quality comparison between deep learning-based multiband diffusion-weighted sequence (DL-mb-DWI), accelerated multiband diffusion-weighted sequence (accelerated mb-DWI), and conventional multiband diffusion-weighted sequence (conventional mb-DWI) in patients undergoing clinical liver magnetic resonance imaging (MRI). METHODS:Fifty consecutive patients who underwent clinical MRI of the liver at a 1.5-T scanner, between September 1, 2021, and January 31, 2022, were included in this study. Three radiologists independently reviewed images using a 5-point Likert scale for artifacts and image quality factors, in addition to assessing the presence of liver lesions and lesion conspicuity. RESULTS:DL-mb-DWI acquisition time was 65.0 ± 2.4 seconds, significantly (P < 0.001) shorter than conventional mb-DWI (147.5 ± 19.2 seconds) and accelerated mb-DWI (94.3 ± 1.8 seconds). DL-mb-DWI received significantly higher scores than conventional mb-DWI for conspicuity of the left lobe (P < 0.001), sharpness of intrahepatic vessel margin (P < 0.001), sharpness of the pancreatic contour (P < 0.001), in-plane motion artifact (P = 0.002), and overall image quality (P = 0.005) by reader 2. DL-mb-DWI received significantly higher scores for conspicuity of the left lobe (P = 0.006), sharpness of the pancreatic contour (P = 0.020), and in-plane motion artifact (P = 0.042) by reader 3. DL-mb-DWI received significantly higher scores for strength of fat suppression (P = 0.004) and sharpness of the pancreatic contour (P = 0.038) by reader 1. The remaining quality parameters did not reach statistical significance for reader 1. CONCLUSIONS:Novel diffusion-weighted MRI sequence with deep learning-based image reconstruction demonstrated significantly decreased acquisition times compared with conventional and accelerated mb-DWI sequences, while maintaining or improving image quality for routine abdominal MRI. DL-mb-DWI offers a potential alternative to conventional mb-DWI in routine clinical liver MRI.