Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition - Prostate MRI: Methodological Developments

Prostate MRI: Methodological Developments

Oral

Body

Thursday, 14 May 2026

Hall 1B

13:40 - 15:30

Moderators: Yannik Ott

Session Number: 602-02

No CME/CE Credit

Similar Sessions

This session includes presentations covering the use of MRI in prostate cancer and prostatic diseases.

Skill Level: Intermediate

13:40		602-02-001. Short TE From a Nonlinear Gradient Coil Improves Image Quality and Lesion Conspicuity of Prostate DWI Horace Zhang, Nahla Elsaid, Terence Nixon, Andrew Dewdney, Dana Peters, Jeffrey Weinreb, Preston Sprenkle, R. Todd Constable, Gigi Galiana Yale University, New Haven, United States of America Impact: The proposed nonlinear gradient coil provides an accessible and cost-effective alternative to whole-body strong-gradient systems, significantly boosting prostate DWI quality and cancer lesion conspicuity on conventional MRI scanners.
13:51		602-02-002. Diffusion-Relaxation Correlation Spectroscopic MRI for Characterizing Prostate Cancer versus Whole-Mount Histopathology Elif Aygun, Zhaohuan Zhang, Steven Raman, Robert Reiter, Anthony Sisk, KyungHyun Sung, Holden Wu David Geffen School of Medicine, University of California Los Angeles, Los Angeles, United States of America Impact: Prostate microstructure parameters obtained from diffusion-relaxation correlation spectroscopic MRI (DR-CSI) demonstrated differences between indolent and clinically significant prostate cancer. DR-CSI has the potential to improve the characterization of prostate cancer.
14:02		602-02-003. Water-exchange DCE-MRI for Prostate Lesions: Complementary Value of Kio and Ktrans in Stratifying Lesions and Guiding Targete Da Li, chao wang, ruiliang bai, Lei Zhang, xiaoqian xu, lei tian, Sicong Wang, Fenghai Liu Cangzhou Central Hospital Affiliated to Hebei Medical University, Cangzhou, China Impact: Water-exchange DCE-MRI yields complementary biomarkers for prostate lesions: K^trans separates lesion categories, while Kio correlates with PSA density and consistently highlights clinically significant cores, supporting precision MRI-targeted biopsy and decision-making.
14:13		602-02-004. MRI-Guided Transgluteal Prostate Biopsy at 0.55T: Clinical Experience Vikas Gulani, Yun Jiang, Tejinder Kaur, John Wei, Shane Wells, Elaine Caoili University of Michigan, Ann Arbor, United States of America Impact: We share experience with MRI-guided percutaneous transgluteal biopsy technique using a wide bore, 0.55T scanner, showing this is a clinically feasible and highly accurate approach with high clinical impact, for sampling prostatic cancer suspicious lesions in patients.
14:24		602-02-005. Decoding the Determinants of Prostate MRI Quality: Multicenter Analysis and an AI Model for Early Prediction of Image Failure jeff brender, Mitsuki Ota, Nathan Nguyen, Joshua Ford, Shun Kishimoto, Murali Krishna, Peter Choyke, Baris Turkbey Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, United States of America Impact: The deep learning approach enables early detection of potential quality issues during prostate MRI, allowing corrective actions before completing the full protocol. The model's strong generalization across sites suggests broad clinical applicability, potentially reducing repeat scans and improving diagnostic accuracy.
14:35		602-02-006. Deep Learning-based Automatic Oblique Scan Angulation for Axial Prostate MRI Robert Grimm, Robin Hoepp, Tristan Rauhut, Cornelius Jacob, Guillaume Chabin, Heinrich von Busch, Florian Knoll, Sohrab Mirak, Leonardo BIttencourt Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany Impact: An optimal scan orientation for axial prostate MRI scans, perpendicular to the anterior rectal, is automatically computed from sagittal localizer images. This helps streamlining the examination and can reduce inter-operator variability.
14:46		602-02-007. Deep Learning-Based Detection of Clinically Significant Prostate Cancer from Degraded MRI Patricia Johnson, Tarun Dutt, Angela Tong, Luke Ginocchio, Lavanya Umapathy, Sumit Chopra, Daniel Sodickson, Hersh Chandarana New York University Grossman School of Medicine, New York, United States of America Impact: AI maintained prostate cancer detection accuracy despite degraded MRI quality, demonstrating that diagnostic performance is not constrained by traditional image standards. These findings could enable faster, lower-cost MRI protocols and open new avenues for AI-first imaging and screening.
14:57		602-02-008. Deep Learning-Based Detection of Extraprostatic Extension of Prostate Cancer on bpMRI compared to Readers on bpMRI and mpMRI Angela Tong, Alexander Daniels, Luke Ginocchio, Tarun Dutt, Lavanya Umapathy, Sumit Chopra, Daniel Sodickson, Hersh Chandarana, Patricia Johnson NYU Grossman School of Medicine, New York, United States of America Impact: Deep learning algorithms can identify extraprostatic extension on bpMRI with high sensitivity and accuracy, which may be applied to optimizing management or serving as a decision maker at the scanner level to tailor protocols to patients’ needs.
15:08		602-02-009. Aggressive Tumor Fraction as a Biomarker for Prostate Cancer Progression: Risk Assessment of Active Surveillance Patients Ahmad Algohary, Veronica Wallaengen, Adrian Breto, Noah Lowry, Arpita Dutta, Nicolas Stoll, Sandra Gaston, Alan Pollack, Sanoj Punnen, Radka Stoyanova University of Miami, Miami, United States of America Impact: The Habitat Risk Score–derived Aggressive Tumor Fraction (ATF) quantifies intratumoral heterogeneity and predicts rapid prostate cancer progression. Incorporating ATF into a composite risk model improves active-surveillance patient stratification, enabling earlier intervention for high-risk individuals while reducing overtreatment of indolent disease.