Faculty Bibliography

Deep learning has demonstrated strong potential for MRI reconstruction. However, conventional supervised learning requires high-quality, high-SNR reference data for network training, which are often difficult or impossible to obtain, particularly in low-field MRI. Self-supervised learning eliminates the need for reference training data but may suffer from degraded performance under low-SNR conditions. To address these limitations, we propose hybrid learning, a new training framework that integrates self-supervised and supervised learning for joint MRI reconstruction and denoising when only low-SNR training data are available.

Methods: Hybrid learning is implemented in two sequential stages. In the first stage, self-supervised learning is applied to fully sampled low-SNR data to generate higher-quality pseudo-references. In the second stage, these pseudo-references are then used as targets for supervised learning to reconstruct and denoise undersampled noisy data. The proposed method was evaluated in four experiments using simulated and real noisy MRI data of the breast, lung and brain across different field strengths (0.3T to 3T), sampling trajectories (Cartesian, spiral, and radial), noise levels, and undersampling ratios. 

Results: Hybrid learning consistently improved reconstruction quality relative to both supervised and self-supervised baselines under different acceleration rates, noise levels, and sampling patterns in all experiments. Compared with standard supervised learning using noisy references, it achieved up to 167.70% higher structural similarity index (SSIM), 95.41% lower normalized mean square error (NMSE), and 90.70% lower high-frequency error norm (HFEN). Compared with standard self-supervised learning, it achieved up to 23.88% higher SSIM, 60.85% lower NMSE, and 49.13% lower HFEN. 

Conclusion: Hybrid learning enables improved MRI reconstruction under low-SNR imaging conditions by jointly addressing noise and undersampling. It provides a practical solution for robust deep learning-based reconstruction and is particularly well suited for applications such as low-field MRI, where image quality is limited by reduced SNR.

INTRODUCTION/UNASSIGNED:Tc-DTPA) tracer clearance is the gold standard for bilateral kidney function, involving extended clearance times and radioactivity. Imaging-derived total kidney volumes are functional proxies but do not probe tissue quality. METHODS/UNASSIGNED:tests. RESULTS/UNASSIGNED:= 0.880 and 0.700, respectively). In addition, MR metrics differentiated proteinuria status. DISCUSSION/UNASSIGNED:Advanced DW MRI metrics may provide surrogates of mGFR and proteinuria. Parameters from bipolar encoding in diastole (emphasizing tubular flow) and flow compensation in systole (emphasizing vascular flow) were often informative.

Respiratory motion poses a major challenge in abdominopelvic MRI and often leads to strong artifacts. Self-navigation techniques have been proposed for motion correction, allowing free-breathing examination. However, self-navigation methods typically fail to track respiratory motion for dynamic contrast-enhanced (DCE) scans due to concurrent signal changes caused by the contrast injection. In this study, we investigated the use of a pilot-tone (PT) transmitter integrated into the receive coil as an alternative approach for obtaining respiratory curves. The amplitude of the received PT signal gets modulated by respiratory motion due to changes in the coil load. A pipeline for extracting respiratory curves from the PT signal was developed and applied for motion-corrected free-breathing abdominal DCE-MRI. Validation experiments included real-time cine imaging and comparison with self-navigation for five late-phase liver scans. Dynamic experiments were performed for a liver DCE scan of one healthy volunteer and kidney DCE scans of 24 patients. A motion-weighted GRASP algorithm was used for reconstructing dynamic images. Quantitative metrics, including gradient entropy for individual images and the L2 norm of the temporal differences for each dynamic frame set, were calculated. Motion-weighted DCE GRASP reconstructions using PT-derived respiratory curves showed improved image sharpness, especially in regions such as the diaphragm, kidney boundaries, and vessels, as well as better temporal consistency compared to conventional GRASP reconstructions. These improvements were observed both qualitatively and quantitatively. This work demonstrates that the PT technique can accurately detect respiration during contrast injections, reducing motion blurring in free-breathing dynamic DCE-MRI. The proposed technique can be easily implemented in the clinical workflow by using a coil with an integrated PT transmitter and requires no external belts or sensors.

PURPOSE/OBJECTIVE:To improve 0.55T T2-weighted PROPELLER lung MRI by developing a self-supervised framework for joint reconstruction and denoising. METHODS:T2-weighted 0.55T lung MRI datasets from 44 patients with prior COVID-19 infection were used. Each PROPELLER blade was split along the readout direction into two disjoint subsets: one subset for training an unrolled network, and the other for loss calculation. Following the Noise2Noise paradigm, this framework split k-space into two subsets with independent, matched noise but identical underlying signal, enabling joint reconstruction and denoising without external training references. For comparison, coil-wise Marchenko-Pastur Principal Component Analysis (MPPCA) denoising followed by parallel imaging reconstruction was performed. The reconstructed images were evaluated by two experienced chest radiologists. RESULTS:The self-supervised model generated lung images with improved clarity, better delineation of parenchymal and airway structures, and maintained high fidelity in cases with available CT references. In addition, the proposed framework also enabled further reduction of scan time by reconstructing images with adequate diagnostic quality from only half the number of blades. The reader study confirmed that the proposed method outperformed MPPCA across all categories (Wilcoxon signed-rank test, p < 0.001), with moderate inter-reader agreement (weighted Cohen's kappa = 0.55; percentage of exact and within ±1 point agreement = 91%). CONCLUSION/CONCLUSIONS:By leveraging the intrinsic data redundancy in PROPELLER sampling and extending the Noise2Noise concept, the proposed self-supervised framework enabled simultaneous reconstruction and denoising of lung images at 0.55T to address the low-SNR challenge at low-field. It holds great potential for broad use in other low-field MRI applications.

Prostate MRI has transformed lesion detection and risk stratification in prostate cancer, but its impact is constrained by the high cost of the exam, variability in interpretation, and limited scalability. False negatives, false positives, and moderate inter-reader agreement undermine reliability, while long acquisition times restrict throughput. Artificial intelligence (AI) offers potential solutions to address many of the limitations of prostate MRI in the clinical management pathway. Machine learning-based triage can refine patient selection to optimize resources. Deep learning reconstruction enables accelerated acquisition while preserving diagnostic quality, with multiple FDA-cleared products now in clinical use. Ongoing development of automated quality assessment and artifact correction aims to improve reliability by reducing nondiagnostic exams. In image interpretation, AI models for lesion detection and clinically significant prostate cancer prediction achieve performance comparable to radiologists, and the PI-CAI international reader study has provided the strongest evidence to date of non-inferiority at scale. More recent work extends MRI-derived features into prognostic modeling of recurrence, metastasis, and functional outcomes. This review synthesizes progress across five domains-triage, accelerated acquisition and reconstruction, image quality assurance, diagnosis, and prognosis-highlighting the level of evidence, validation status, and barriers to adoption. While acquisition and reconstruction are furthest along, with FDA-cleared tools and prospective evaluations, triage, quality control, and prognosis remain earlier in development. Ensuring equitable performance across populations, incorporating uncertainty estimation, and conducting prospective workflow trials will be essential to move from promising prototypes to routine practice. Ultimately, AI could accelerate the adoption of prostate MRI toward a scalable platform for earlier detection and population-level prostate cancer management. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: 3.

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:Access to prostate MRI remains limited due to resource constraints and the need for expert interpretation. PURPOSE/OBJECTIVE:To develop machine learning (ML) models that enable risk-based triage for prostate MRI (ProMT-ML) in the evaluation of prostate cancer. STUDY TYPE/METHODS:Retrospective and prospective. POPULATION/METHODS:A total of 11,879 retrospective MRI scans for suspected prostate cancer from a multi-hospital health system, divided into training (N = 9504) and test (N = 2375) sets. A total of 4551 records for prospective validation. FIELD STRENGTH/SEQUENCE/UNASSIGNED:1.5T and 3T/Turbo-spin echo T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE). ASSESSMENT/RESULTS:Prostate Imaging Reporting and Data System (PI-RADS) scores were retrieved from MRI reports. The Boruta algorithm was used to select final input features from candidate features. Two models were developed using supervised ML to estimate the likelihood of an abnormal MRI, defined as PI-RADS ≥ 3: Model A (with prostate volume) and Model B (without prostate volume). Models were compared to PSA. Prostate biopsy pathology was assessed to evaluate potential clinical impact. STATISTICAL TESTS/METHODS:Area under the receiver operating characteristic curve (AUC) was the primary performance metric. RESULTS:A total of 5580 (46.9%) subjects had a PI-RADS score ≥ 3. After feature selection, Model A included age, PSA, body mass index, and prostate volume, while Model B included age, PSA, body mass index, and systolic blood pressure. Both models A (AUC 0.711) and B (AUC 0.616) significantly outperformed PSA (AUC 0.593). Compared to PSA threshold > 4 ng/mL, Model A demonstrated significantly improved specificity (28.3% vs. 21.9%) and no significant difference in sensitivity (89.0% vs. 86.7%). Among false negatives (Model A: 8.0% (62/776); Model B: 16.8% (130/776)), most (Model A: 87%; Model B: 69%) had benign or clinically insignificant disease on biopsy. On prospective validation, both versions of ProMT-ML significantly outperformed PSA. DATA CONCLUSION/CONCLUSIONS:ProMT-ML provides personalized risk estimates of abnormal prostate MRI and can support triage of this test. TECHNICAL EFFICACY/UNASSIGNED:Stage 4.