Faculty Bibliography

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.

MRI is widely used for the diagnosis and management of various abdominal diseases involving organs such as the liver, pancreas, and kidneys. However, one major limitation of MRI is its relatively slow imaging speed compared to other modalities. In addition, respiratory motion poses a significant challenge in abdominal MRI, often requiring patients to hold their breath multiple times during an exam. This requirement can be particularly challenging for sick, elderly, and pediatric patients, who may have reduced breath-holding capacity. As a result, rapid imaging plays an important role in routine clinical abdominal MRI exams. Accelerated data acquisition not only reduces overall exam time but also shortens breath-hold durations, thereby improving patient comfort and compliance. Over the past decade, significant advancements in rapid MRI have led to the development of various accelerated imaging techniques for routine clinical use. These methods improve abdominal MRI by enhancing imaging speed, motion compensation, and overall image quality. Integrating these techniques into clinical practice also enables new applications that were previously challenging. This paper provides a concise yet comprehensive overview of rapid imaging techniques applicable to abdominal MRI and discusses their advantages, limitations, and potential clinical applications. By the end of this review, readers are expected to learn the latest advances in accelerated abdominal MRI and explore new frontiers in this evolving field. Evidence Level: N/A Technical Efficacy: Stage 5.

RATIONALE AND OBJECTIVE/OBJECTIVE:A single-shot T2-weighted deep-learning-based image reconstruction (DL-HASTE) has been recently developed allowing for shorter acquisition time than conventional half-Fourier acquisition single-shot turbo-spin echo (HASTE). The purpose of this study was to compare image quality of conventional 6 mm HASTE with DL-HASTE at 4 mm and 6 mm slice thickness. MATERIALS AND METHODS/METHODS:91 patients (51 female; mean±SD age: 44±10years) who underwent 3T MR enterography from 5/15/2023-7/15/2023 including pelvic conventional HASTE and DL-HASTE were included. Patients either had 4 mm-DL-HASTE or 6 mm-DL-HASTE. Four abdominal radiologists, blinded to sequence type, independently evaluated overall image quality, artifacts over bowel, bowel wall sharpness, and confidence for the presence/absence of bowel abnormalities on 5-point Likert scales. Readers recorded the presence/absence of ileal wall thickening, ileal inflammation, stricture, and penetrating disease on each sequence. Wilcoxon signed-rank test with continuity correction was used for paired comparisons and Wilcoxon rank sum test was used for unpaired ordinal comparisons. A p < .05 indicated statistical significance. RESULTS:Acquisition times for 6 mm HASTE, 4 mm-DL-HASTE, and 6 mm-DL-HASTE were 64 s, 51 s, and 49 s, respectively. Overall image quality and bowel sharpness were significantly improved for 4 mm-DL-HASTE versus HASTE for 3/4 readers (all p < .05) and similar for the 4th reader (p > .05). Diagnostic confidence was similar for all readers (p > .05). 6 mm-DL-HASTE was similar to HASTE for bowel sharpness, image quality, and confidence for 3/4 readers (all p > .05). The presence of ileal thickening, ileal inflammation, stricture, and penetrating disease were similar for all readers for HASTE, 4 mm-DL-HASTE, and 6 mm-DL-HASTE (all p > .05). CONCLUSION/CONCLUSIONS:4 mm-DL-HASTE had superior image quality than conventional HASTE at shorter acquisition time.

Respiratory motion-induced image blurring and artifacts can compromise image quality in dynamic contrast-enhanced MRI (DCE-MRI) of the liver. Despite remarkable advances in respiratory motion detection and compensation in past years, these techniques have not yet seen widespread clinical adoption. The accuracy of image-based motion detection can be especially compromised in the presence of contrast enhancement and/or in situations involving deep and/or irregular breathing patterns. This work proposes a framework that combines GRASP-Pro (Golden-angle RAdial Sparse Parallel MRI with imProved performance) MRI with a new radial sampling scheme called navi-stack-of-stars for free-breathing DCE-MRI of the liver without the need for explicit respiratory motion compensation. A prototype 3D golden-angle radial sequence with a navi-stack-of-stars sampling scheme that intermittently acquires a 2D navigator was implemented. Free-breathing DCE-MRI of the liver was conducted in 24 subjects at 3T including 17 volunteers and 7 patients. GRASP-Pro reconstruction was performed with a temporal resolution of 0.34-0.45 s per volume, whereas standard GRASP reconstruction was performed with a temporal resolution of 15 s per volume. Motion compensation was not performed in all image reconstruction tasks. Liver images in different contrast phases from both GRASP and GRASP-Pro reconstructions were visually scored by two experienced abdominal radiologists for comparison. The nonparametric paired two-tailed Wilcoxon signed-rank test was used to compare image quality scores, and the Cohen's kappa coefficient was calculated to evaluate the inter-reader agreement. GRASP-Pro MRI with sub-second temporal resolution consistently received significantly higher image quality scores (P < 0.05) than standard GRASP MRI throughout all contrast enhancement phases and across all assessment categories. There was a substantial inter-reader agreement for all assessment categories (ranging from 0.67 to 0.89). The proposed technique using GRASP-Pro reconstruction with navi-stack-of-stars sampling holds great promise for free-breathing DCE-MRI of the liver without respiratory motion compensation.

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.

This work proposes MP-Grasp4D (magnetization-prepared golden-angle radial sparse parallel 4D) MRI, a free-breathing, inversion recovery (IR)-prepared, time-resolved 4D MRI technique with improved T1-weighted contrast. MP-Grasp4D MRI acquisition incorporates IR preparation into a radial gradient echo sequence. MP-Grasp4D employs a golden-angle navi-stack-of-stars sampling scheme, where imaging data of rotating radial stacks and navigator stacks (acquired at a consistent rotation angle) are alternately acquired. The navigator stacks are used to estimate a temporal basis for low-rank subspace-constrained reconstruction. This allows for the simultaneous capture of both IR-induced contrast changes and respiratory motion. One temporal frame of the imaging volume in MP-Grasp4D MRI is reconstructed from a single stack and an adjacent navigator stack on average, resulting in a nominal temporal resolution of 0.16 seconds per volume. Images corresponding to the optimal inversion time (TI) can be retrospectively selected for providing the best image contrast. Reader studies were conducted to assess the performance of MP-Grasp4D MRI in liver imaging across 30 subjects in comparison with standard Grasp4D MRI without IR preparation. MP-Grasp4D MRI received significantly higher scores (P < 0.05) than Grasp4D in all assessment categories. There was a moderate to almost perfect agreement (kappa coefficient from 0.42 to 0.9) between the two readers for image quality assessment. When the scan time is reduced, MP-Grasp4D MRI preserves image contrast and quality, demonstrating additional acceleration capability. MP-Grasp4D MRI improves T1-weighted contrast for free-breathing time-resolved 4D MRI and eliminates the need for explicit motion compensation. This method is expected to be valuable in different MRI applications such as MR-guided radiotherapy.

OBJECTIVE:Diffusion-weighted MRI is a technique that can infer microstructural and microcirculatory features from biological tissue, with particular application to renal tissue. There is extensive literature on diffusion tensor imaging (DTI) of anisotropy in the renal medulla, intravoxel incoherent motion (IVIM) measurements separating microstructural from microcirculation effects, and combinations of the two. However, interpretation of these features and adaptation of more specific models remains an ongoing challenge. One input to this process is a whole organ distillation of corticomedullary contrast of diffusion metrics, as has been explored for other renal biomarkers. MATERIALS AND METHODS/METHODS:In this work, we probe the spatial dependence of diffusion MRI metrics with concentrically layered segmentation in 11 healthy kidneys at 3 T. The metrics include those from DTI, IVIM, a combined approach titled "REnal Flow and Microstructure AnisotroPy (REFMAP)", and a multiply encoded model titled "FC-IVIM" providing estimates of fluid velocity and branching length. RESULTS:Fractional anisotropy decreased from the inner kidney to the outer kidney with the strongest layer correlation in both parenchyma (including cortex and medulla) and medulla with Spearman correlation coefficients and p-values (r, p) of (0.42, <0.001) and (0.37, <0.001), respectively. Also, dynamic parameters derived from the three models significantly decreased with a high correlation from the inner to the outer parenchyma or medulla with (r, p) ranges of (0.46-0.55, <0.001). CONCLUSIONS:These spatial trends might find implications for indirect assessments of kidney physiology and microstructure using diffusion MRI.

BACKGROUND:Pancreatic adenocarcinoma (PC) is a highly lethal malignancy with a survival rate of only 12%. Surveillance is recommended for high-risk individuals (HRIs), but it is not widely adopted. To address this unmet clinical need and drive early diagnosis research, we established the Pancreatic Cancer Early Detection (PRECEDE) Consortium. METHODS:PRECEDE is a multi-institutional international collaboration that has undertaken an observational prospective cohort study. Individuals (aged 18-90 years) are enrolled into 1 of 7 cohorts based on family history and pathogenic germline variant (PGV) status. From April 1, 2020, to November 21, 2022, a total of 3,402 participants were enrolled in 1 of 7 study cohorts, with 1,759 (51.7%) meeting criteria for the highest-risk cohort (Cohort 1). Cohort 1 HRIs underwent germline testing and pancreas imaging by MRI/MR-cholangiopancreatography or endoscopic ultrasound. RESULTS:A total of 1,400 participants in Cohort 1 (79.6%) had completed baseline imaging and were subclassified into 3 groups based on familial PC (FPC; n=670), a PGV and FPC (PGV+/FPC+; n=115), and a PGV with a pedigree that does not meet FPC criteria (PGV+/FPC-; n=615). One HRI was diagnosed with stage IIB PC on study entry, and 35.1% of HRIs harbored pancreatic cysts. Increasing age (odds ratio, 1.05; P<.001) and FPC group assignment (odds ratio, 1.57; P<.001; relative to PGV+/FPC-) were independent predictors of harboring a pancreatic cyst. CONCLUSIONS:PRECEDE provides infrastructure support to increase access to clinical surveillance for HRIs worldwide, while aiming to drive early PC detection advancements through longitudinal standardized clinical data, imaging, and biospecimen captures. Increased cyst prevalence in HRIs with FPC suggests that FPC may infer distinct biological processes. To enable the development of PC surveillance approaches better tailored to risk category, we recommend adoption of subclassification of HRIs into FPC, PGV+/FPC+, and PGV+/FPC- risk groups by surveillance protocols.

Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.

OBJECTIVES/OBJECTIVE:To evaluate the clinical significance of low-field MRI lung opacity severity. METHODS:Retrospective cross-sectional analysis of post-acute Covid-19 patients imaged with low-field MRI from 9/2020 through 9/2022, and within 1 month of pulmonary function tests (PFTs), 6-min walk test (6mWT), and symptom inventory (SI), and/or within 3 months of St. George Respiratory Questionnaire (SGRQ) was performed. Univariate and correlative analyses were performed with Wilcoxon, Chi-square, and Spearman tests. The association between disease and demographic factors and MR opacity severity, PFTs, 6mWT, SI, and SGRQ, and association between MR opacity severity with functional and patient-reported outcomes (PROs), was evaluated with mixed model analysis of variance, covariance and generalized estimating equations. Two-sided 5 % significance level was used, with Bonferroni multiple comparison correction. RESULTS:81 MRI exams in 62 post-acute Covid-19 patients (median age 57, IQR 41-64; 25 women) were included. Exams were a median of 8 months from initial illness. Univariate analysis showed lung opacity severity was associated with decreased %DLCO (ρ = -0.55, P = .0125), and lung opacity severity quartile was associated with decreased %DLCO, predicted TLC, FVC, and increased FEV1/FVC. Multivariable analysis adjusting for sex, initial disease severity, and interval from Covid-19 diagnosis showed MR lung opacity severity was associated with decreased %DLCO (P < .001). Lung opacity severity was not associated with PROs. CONCLUSION/CONCLUSIONS:Low-field MRI lung opacity severity correlated with decreased %DLCO in post-acute Covid-19 patients, but was not associated with PROs.