ISMRM25 - AI Prediction via Multiparametric MRI

ISMRM & ISMRT Annual Meeting & Exhibition • 10-15 May 2025 • Honolulu, Hawai'i

AI Prediction via Multiparametric MRI

Digital Poster

AI & Machine Learning

Wednesday, 14 May 2025

Exhibition Hall

09:15 - 10:15

Session Number: D-45

No CME/CE Credit

		Computer Number: 49 3390. Utilizing combined quantitative multiparametric MRI as potential biomarkers for improved early-stage Parkinson's disease diagnosis Y. Yang, C. Li, C. Wang, H. Zhao, J. Zhang, Z. Xu The First People’s Hospital of Foshan, Foshan, China Impact: This study demonstrates the potential of combining QSM and DKI features to improve early-stage Parkinson's disease diagnosis, offering clinicians a non-invasive tool for detection. It paves the way for future research into MRI-based biomarkers for disease progression monitoring.
		Computer Number: 50 3391. Integrating Whole-Brain Tumor Burden MRI Features with Inflammatory Mediators to Predict High Risk of Neurocognitive Decline in Glioma Patients S. Zhang, H. Sun, Q. Gong, Q. Yue west china hospital of sichuan university, Chengdu, China Impact: This study underscores the critical role of whole-brain tumor burden MRI features, pathological and inflammatory markers to predict ND. It encourages new research into inflammation-cognition links, promotes personalized care in glioma treatment, and could revolutionize approaches in neuro-oncology patient management.
		Computer Number: 51 3392. Mathematical Modelling of Malignant Transformation in Low Grade Gliomas and survival prediction with XGBoost. T. Lily, J. Ruffle, S. Brandner, P. Nachev, H. Hyare UCL, London, United Kingdom Impact: Prediction of malignant transformation of low grade gliomas will allow precision treatment decisions and personalized medicine.
		Computer Number: 3393. WITHDRAWN
		Computer Number: 52 3394. Predicting the Molecular Subtypes of 2021 WHO Grade 4 Glioma by a Multiparametric MRI-Based Machine Learning Model View Presentation Video W. Xu, Y. Tan Shanxi Medical University, Taiyuan, China Impact: l The multiparametric MRI machine learining model can accurately predict molecular subtypes of 2021 WHO grade 4 glioma, offers substantial prognostic value and provides a new perspective for clinical decision-making.
		Computer Number: 53 3395. Prediction of meningioma brain invasion based on preoperative MRI noninvasive deep transfer learning radiomics model D. Yuan, J. Zhang, T. Ma, S. Diao, X. Zhang, T. Han Tianjin Huanhu Hospital, Tianjin, China Impact: Our model, which combines clinical and deep-transfer learning radiomics features, demonstrates high efficacy in predicting brain invasion in meningiomas and may contribute to improved prognoses for patients.
		Computer Number: 54 3396. ReMiDi: Reconstruction of Microstructure from Diffusion MRI Signal View Presentation Video P. P. Khole, Z. Petiwala, S. P. Magesh, E. Mirafzali, U. Gupta, J-R Li, A. Ianus, R. Marinescu University of California Santa Cruz, Santa Cruz, United States Impact: Our work achieves a next-generation dMRI reconstruction, opening the ability to reconstruct brain microstructure with arbitrary meshes. This will enable in-vivo mesoscopic mapping of the human brain, and lead to improved biomarkers for Multiple Sclerosis and Traumatic Brain Injury.
		Computer Number: 55 3397. Transmit radiofrequency field maps can be predicted from standard brain images using machine learning. View Presentation Video M. Zakershobeiri, C. Beaulieu, P. Seres, A. Wilman University of Alberta, Edmonton, Canada Impact: Prediction of B1⁺ maps from standard clinical brain images will enable more widespread use of quantitative MRI in clinical settings where B1⁺ maps are not typically acquired.
		Computer Number: 56 3398. Alzheimer’s Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model View Presentation Video J. Salazar Cavazos, S. Peltier University of Michigan, Ann Arbor, United States Impact: The 4D CNN model improves Alzheimer’s disease diagnosis for rs-fMRI data, enabling earlier detection and better interventions. Future research could explore task-based fMRI applications and regression tasks, enhancing understanding of cognitive performance and disease progression.
		Computer Number: 57 3399. Fast and Reliable Myelin Water Fraction Estimation Using Neural Network Informed Non-Linear Least Squares Fitting View Presentation Video J. Baek, D. Kim Yonsei University, Seoul, Korea, Republic of Impact: By combining ANN and NLLS, the proposed method enhances speed and accuracy in MWF estimation, enabling clinicians to reliably assess more patients with neuroinflammatory diseases in the same amount of time, potentially improving diagnosis and treatment outcomes.
		Computer Number: 58 3400. A Deep Learning-based Assessment Tool for the Objective Evaluation of Intracranial MRI Quality in Stereotactic Radiosurgery View Presentation Video M. Kim, S. Oh, K. Renick, A. Heermann, F. Cherop, D. Kim, J. Chun, M. Schmidt, T. Kim Washington University in St. Louis, St. Louis, United States Impact: Due to the precise nature of Gamma Knife Stereotactic Radiosurgery, accurate target delineation is crucial. The development of an objective intracranial MRI evaluation tool will contribute to ensuring consistent image quality in treatment planning, thereby improving clinical outcomes.
		Computer Number: 59 3401. Virtual Cell Type Atlas of Mouse Brain from MRI Signatures using Attention Res-UNet View Presentation Video Y. Shen, Y. Shen, H. Xu, T. Zheng, Y. Huang, S. Li, Q. Zhu, Z. Cao, Z. Zhao, D. Wu Zhejiang University, Hangzhou, China Impact: We demonstrated MRI-based deep learning could predict three-dimensional representations of different cell types at whole-brain level, which were consistent with typical cell distribution patterns and regional characters. Our findings highlight the potential of MRI for predicting three-dimensional cell atlas.
		Computer Number: 60 3402. Predicting Quantitative Myelin Images from Clinical Scans and DTI using Linear and Neural Network Models View Presentation Video K. Bohlke, F. Bagnato, A. Stokes, R. Dortch Barrow Neurological Institute, Phoenix, United States Impact: Leveraging typical clinical scans to predict PSR maps using a simple linear model can improve diagnostic capabilities for multiple.
		Computer Number: 61 3403. Location patterns of WMH in a multi-cohort study — generalisability and evaluation View Presentation Video X. Zhao, I. Malone, D. Cash, A. Wong, N. Chaturvedi, A. Hughes, J. Schott, J. Barnes, C. Sudre University College London, London, United Kingdom Impact: This framework provides a reproducible solution for identifying WMH patterns in over 31,000 participants across 4 cohorts. By characterising WMH distribution and progression, it informs future research into ageing and neurological disease pathways, and enhances the foundation for personalised care.
		Computer Number: 62 3404. Knowledge-based Labeled Data Selection in Semi-Supervised Learning View Presentation Video A. Saxena, V. Singhal, C. Bhushan, D. Shanbhag GE Healthcare, Bangalore, India Impact: We present the importance of data diversity representation in the labelled data of semi-supervised model learning.
		Computer Number: 63 3405. Safety-Optimized SAR Prediction for MRI Using Deep Learning Method at 5.0 T View Presentation Video S. Hayat, S. Che, J. Liu, Z. Cui, S. Ding, C. Wang, T. Meersman, X. Zhang, Y. Li Lauterbur Imaging Research Center, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China Impact: This study offers more accurate SAR predictions that support safe, efficient MRI operations without excessive safety margins using machine learning methods. The model's precision increases patient safety and reduces scan times in high-field MRI procedures.

Similar Session(s)