Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition

Body Diffusion MRI Methods

Digital Poster

Diffusion

Monday, 11 May 2026

Digital Posters Row F

13:50 - 14:45

Session Number: 365-03

No CME/CE Credit

Similar Sessions

This session highlights innovative methods in improving data acquisition and reconstruction for diffusion-based microstructure measurements in a range of body applications such as tumours, muscle, kidney, heart, liver and more.

		365-03-001. Perfusion fraction mapping in the prostate with state-of-the-art intravoxel incoherent motion (IVIM) MRI Malwina Molendowska, Ivan Rashid, Kieran Foley, Lars Mueller, Lars Olsson, Patrik Brynolfsson, Filip Szczepankiewicz Lund University, Lund, Sweden Impact: Our state-of-the-art velocity-compensated IVIM exposes that conventional prostate IVIM yields biases in diffusivity and perfusion fraction. This warrants the consideration of velocity-compensation for reliable IVIM experiments, pending further validation.
		365-03-002. A Deep learning-based Spectral Analysis of Multi-Exponential Intravoxel Incoherent Motion (D-SAME-IVIM) MRI of the Kidney Julia Stabinska, Thomas Thiel, Alexandra Ljimani, Hans-Joerg Wittsack, Helge Zöllner F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States of America Impact: A robust and fast method for spectral diffusion analysis capable of detecting pathophysiological changes in the kidney from multi-b-value DWI data.
		365-03-003. Deep learning reconstruction for accelerated and quality-preserving liver IVIM protocols Anna Casellato, Alessandra Bertoldo, Marco Castellaro, Giovanni Morana, Laura Alabiso, Angiola Saccomanno, Robert Grimm, Omar Darwish, Giovanna Nordio, Davide Piccini University of Padova, Padova, Italy Impact: The use of deep learning-based image reconstruction on IVIM-DWI liver imaging allows accelerating the acquisition while simultaneously preserving image quality and improving the reliability of IVIM parameter estimation, potentially offering a faster framework for non-contrast liver imaging.
		365-03-004. Image Quality in Breast DWI Using DL readout-segmented EPI: A Comparison with Standard readout-segmented EPI at 3T Zoia Laraib, Marialena Tsarouchi, Carla Sitges, Antonio Portaluri, Gregor Thoermer, Marnix Maas, Elisabeth Weiland, Wei Liu, Ritse Mann Radboud University Medical Center, Nijmegen, Netherlands Impact: DL rs-EPI improves diffusion image quality and lesion visibility without affecting maintaining similar ADC values. Its shorter acquisition time and comparable performance supports clinical implementation for faster, more robust breast MRI in the clinical practice.
		365-03-005. Optimization of Respiratory Synchronization for Abdominal Intravoxel Incoherent Motion MRI in the Renal Medulla Chinatsu Yamakawa, Shingo Nito, atsushi kondo, Saki Tsuchihashi, Keita Nagawa, Iichiro Osawa, Masami Yoneyama, Kaiji Inoue, Eito Kozawa Dept.of Central Radiological Technology, Saitama Medical University Hospital, Saitama, Japan Impact: This study provides practical guidance for optimizing abdominal intravoxel incoherent motion acquisition. Trigger and track synchronization and a 3-beat repetition time can improve the accuracy and reproducibility of perfusion and diffusion parameters in the renal medulla, enhancing diagnostic reliability.
		365-03-006. Hybrid Diffusion of Cervical-Thoracic Pediatric Spinal Cord: Normative Analysis of NODDI, DKI, and DTI Metrics Zahra Sadeghi Adl, Devon Middleton, Laura Krisa, Sara Naghizadehkashani, Mahdi Alizadeh, Slimane Tounekti, Scott Faro, Adam Flanders, Julien Cohen-Adad, Feroze Mohamed Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, Pennsylvania, United States of America Impact: First pediatric, tissue-specific diffusion reference curves (DTI, DKI, NODDI) across C1-T12 enable z-score outlier detection and age-adjusted interpretation. They provide effect-size anchors for normal development and a baseline for clinical and research uses, including pediatric SCI, transverse myelitis, and myelopathy.
		365-03-007. Evaluation of generalist, on-the-scanner, deep learning recon for M2SE cardiac DTI Daniel Atkinson, Peter Gatehouse, Vanessa Ferreira, Elizabeth Tunnicliffe, Rebecca Mills, Betty Raman, Patricia Lan, Haonan Wang, Xinzeng Wang, Stephen Jermy, James Grist, Damian Tyler, Margarita Gorodezky, Stefan Piechnik Oxford Centre for Clinical MR Research (OCMR), University of Oxford, Oxford, United Kingdom Impact: Generalist, on-the-scanner, deep learning recon appears to achieve comparable to non-deep learning cardiac diffusion tensor imaging (DTI) results with a reduced scan time. This may improve feasibility of cardiac DTI on clinical scanners where time is short.
		365-03-008. Two-Step Fitting Enables Improved Precision of Simultaneous IVIM and Compartmental R2 Quantification Gregory Simchick, Diego Hernando University of Wisconsin - Madison, Madison, United States of America Impact: Two- versus one-step fitting improved the noise performance and test-retest repeatability of the perfusion signal fraction and R₂ of blood when performing simultaneous IVIM and compartmental R₂ quantification. The improved repeatability may enhance clinical diagnostic value and enable treatment monitoring.
		365-03-009. Comparison of Conventional and Deep Neural Network IVIM Fitting Strategies for Soft-Tissue Tumor Characterization Julia Augusta de Souza Buratti, Huseyin Ekin Ergi, Ahmet Peker, Guangyu Dan, Albert Yen, Yusuf Oner, Xiaohong Joe Zhou, Muge Karaman University of Illinois Chicago, Chicago, United States of America Impact: This study demonstrates that DNN–based IVIM fitting achieves reproducible diffusion parameters and comparable diagnostic performance to IVIM segmented nonlinear least squares fitting, while reducing parameter variability. These findings may promote clinical translation of IVIM for reliable soft-tissue tumor characterization.
		365-03-010. Decoding Tumor and Muscle Microstructure in HNC via Simplified IVIM MRI Kai-Lun Cheng, Hsueh-Ju Lu, Chao-Yu Shen, Hui-Yu Wang, Ying-Hsiang Chou, Yeu-Sheng Tyan, Ping-Huei Tsai Chung Shan Medical University Hospital, Taichung, Taiwan Impact: This study demonstrates the potential of simplified IVIM MRI to evaluate both macrostructural and microstructural changes in the masseter muscle of HNC patients, providing valuable insights into altered tumor microenvironments and cancer-related muscle inflammation for improved prognosis prediction.
		365-03-011. Enhanced Intravoxel Incoherent Motion three-compartmental modeling using fusion neural network Nhat Hoang, Abrar Faiyaz, Emmanuel Mensah, Md Nasir Uddin, Jianhui Zhong, Giovanni Schifitto University of Rochester, Rochester, United States of America Impact: We propose a fusion network for Intravoxel Incoherent Motion (IVIM) modeling that leverages T1-weighted and FLAIR anatomical priors to improve estimation of tissue diffusion and perfusion characteristics.
		365-03-012. Highly Efficient Cardiac Diffusion Tensor Imaging Using Interleaved Simultaneous Multi-Slice Motion-Compensated Spin-Echo Ke Wen, Eun Ji Lim, Yaqing Luo, Pedro Ferreira, Alberto di Biase, Dudley Pennell, Andrew Scott, Sonia Nielles-Vallespin Imperial College London, London, United Kingdom Impact: The proposed interleaved simultaneous multi-slice flip-back motion-compensated spin-echo significantly improves the efficiency of cDTI acquisitions, enabling whole-heart coverage within 20-30 minutes while maintaining image quality, paving the way for whole-heart cDTI as an add-on to clinical exams.
		365-03-013. AI-THiNR: A Modular AI Toolbox for Quantitative Multicontrast MRI of Head and Neck Tumors Muhammad Awais, Ramesh Paudyal, Akash Shah, Vaios Hatzoglou, Nora Katabi, Eve LoCastro, Richard Wong, Ashok Shaha, R. M. Tuttle, Nadeem Riaz, Nancy Lee, Lawrence Schwartz, Amita Shukla-Dave Memorial Sloan Kettering Cancer Center, New York, United States of America Impact: AI-THiNR is a comprehensive AI toolbox for multicontrast MRI analysis. It integrates convolutional neural network (CNN)-based deep feature extraction and physics-based diffusion modeling, enabling tumor characterization, improved diagnostic precision, and scalable clinical translation in head-and-neck oncology.
		365-03-014. Gender and Age Effects on Lumbar Paraspinal Muscles: A Multimodal MRI Study Correlating DTI, PDFF and Muscle Strength bo hu, Dandan Zheng, ling wang, xiaoguang cheng beijing jishuitan hospital, capital medical university, Beijing, China Impact: This study provides novel insights into paraspinal muscle degeneration, demonstrating multimodal MRI's capability to detect microstructural changes predictive of functional decline, potentially enabling early intervention for age-related spinal disorders.
		365-03-015. Definition of a liver function index using intra-voxel incoherent motion modelling for function-guided liver cancer radiation Fidel Navarro Salazar, Yu Sun, Tim Wang, Sheryl Foster, Naeim Sanaei, Jonathan Sykes, Annette Haworth, Sirisha Tadimalla The University of Sydney, Sydney, Australia Impact: This study introduces a liver functional index to generate voxel-wise liver function maps, enabling functional-avoidance radiation therapy for patients with HCC and impaired liver function.