Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition - Innovations in Brain Tumor Imaging: Quantitative MRI, Radiogenomics, and Deep-Learning Approaches

Innovations in Brain Tumor Imaging: Quantitative MRI, Radiogenomics, and Deep-Learning Approaches

Digital Poster

Neuro B

Monday, 11 May 2026

Digital Posters Row E

09:15 - 10:10

Session Number: 364-02

No CME/CE Credit

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This session showcases cutting-edge advances in quantitative MRI and artificial intelligence for precision brain tumor diagnosis, characterization, and treatment monitoring. Research presentations span ultra-high-field imaging (7T and 9.4T), advanced diffusion techniques (DKI, CEST, PETRA), radiomics, and deep learning applications that enable molecular classification, prognostic assessment, and treatment response evaluation in gliomas, meningiomas, and CNS lymphomas. Key clinical applications include differentiating tumor types, detecting pseudoprogression versus recurrence, grading gliomas, predicting genetic mutations, and assessing radiation-induced changes—all driven by quantitative biomarkers that move beyond binary classification toward continuous, clinically actionable insights.

Skill Level: Intermediate

		364-02-001. Differentiating glioma recurrence from Pseudoprogression Using Multi-Model Parameters Derived from DKI Sequences Yujie Chen, Peiquan liu, xiaoxiao zhang, Jiaxuan Zhang, Wenzhen Zhu Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1095, Wuhan, China Impact: Diffusion models and their parameters based on Diffusional Kurtosis Imaging (DKI) sequences perform well in distinguishing glioma recurrence from pseudoprogression; Volume fraction of the isotropic compartment (FISO) is expected to become a sensitive imaging biomarker.
		364-02-002. Conductivity Tensor Imaging for Molecular Classification and Grading of Adult-Type Diffuse Gliomas: A Pilot Study Siyuan He, Tong Sun, Nengjin Zhu, Liwei Mazu, Zuqi Xia, Hao dong Qin, Hongjiang Wei, Jianping Chu The First Affiliated Hospital, Sun Yat-sen University, China Impact: CTI can provide tissue electrophysiological information that aids the molecular classification and grading of adult-type diffuse gliomas, with performance comparable to ADC.
		364-02-003. Characterization of radiation-induced changes in healthy mice brain using CEST MRI at 9.4 T Florian Kroh, Anika Simon, Markus Alber, Sebastian Regnery, Jürgen Debus, Armin Lühr, Mark Ladd, Andreas Korzowski, Daniel Paech, Julia Bauer, Philip Boyd German Cancer Research Center (DKFZ), Heidelberg, Germany Impact: In this work, we demonstrate that CEST MRI can identify radiation-induced molecular changes in healthy mouse brain tissue after helium-ion therapy, enhancing understanding of dose-dependent effects and aiding the interpretation of CEST contrasts in post-treatment glioma imaging and radiotherapy monitoring.
		364-02-004. An exploratory study on meningeal imaging using the Pointwise Encoding Time reduction with Radial Acquisition（PETRA）sequence Han Dou The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
		364-02-005. Poor Baseline Brain Reserve Increases Mortality Risk in Glioblastoma Sree Gongala, Robin Ghotra, George Wang, Jose Garcia, Rubo Xing, Parisa Arjmand, Pamela LaMontagne, Josep Puig, Danial Marcus, Yilun Sun, Haley Perlow, Tiffany Hodges, Christos Davatzikos, Chaitra Badve Case Western Reserve University, Cleveland, United States of America Impact: This study demonstrates the independent influence of baseline brain reserve on glioblastoma outcomes underscoring its potential as a clinical biomarker for patient prognosis.
		364-02-006. Assessment of CT-Based Synthetic T1CE MRI Using a Deep Learning Model for Meningioma Screening: A Multicenter Study Jin Cui, Pu-Yeh Wu, Nan Mei, Ji Xiong, Dongdong Wang, Yue Hu, Kaiyi Liang, Qiufeng Zhao, Lei Fang, Yao Chen, Zhisong Zheng, Wenxue Feng, Kaiyue Zhang, Mengping Hong, Jie Chen, Yiping Lu, Bo Yin Huashan Hospital, Fudan University, China Impact: This study demonstrates that CT-based synthetic T1CE MRI can approximate contrast-enhanced MRI for meningioma screening. The approach may reduce dependence on gadolinium-based contrast agents, offering a rapid, cost-effective alternative for early detection in resource-limited or contrast-contraindicated settings.
		364-02-007. Quantitative MRI dynamics after Tumor Treating Fields in glioblastoma and the association with recurrence Yujue Zhong, Xiaoxiao Ma, Junfeng Zhang, xiaojun yu, Xin Lou The First Medical Center, Chinese PLA General Hospital, Beijing, China Impact: This study reveals dynamic MRI parameter changes in glioblastomas following TTFields therapy, offering new insights for recurrence monitoring and imaging biomarker exploration.
		364-02-008. Simulation-Based Deep Learning Framework for Mapping Cell Size Distribution from GESFIDE Susceptibility-Contrast MRI Natenael Semmineh, Indranil Guha, Jerrold Boxerman, C. Chad Quarles The University of Texas MD Anderson Cancer Center, Houston, United States of America Impact: Non-invasive cell-size distribution imaging enables quantitative characterization of tissue microstructure and heterogeneity without biopsy. Once matured, it could enhance diagnostic accuracy, guide personalized therapy, and serve as a biomarker of treatment response across diverse organs and disease contexts.
		364-02-009. The combination of ASL and quantitative synthetic MRI for predicting IDH1 gene mutation in glioma patients Dan Luo, Jiankun Dai, Zexiang Deng, Xinru Deng, Xinlan Xiao The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China Impact: Our study suggested the combination of ASL and syMRI was superior for non-invasively identifying IDH1 gene status than using them separately. Simultaneous application of ASL and syMRI would be beneficial for guiding targeted treatment selection for glioma patients.
		364-02-010. Radiogenomic profiling reveals molecular heterogeneity in H3 K27-altered diffuse midline glioma Qianqian Zheng, Xiaorui Su, Yujiao Deng, Xibiao Yang, Shuang Tang, Qiang Yue West China Hospital of Sichuan University, Chendu, China Impact: Radiogenomic profiling of H3 K27-altered DMGs reveals MRI-derived features that can noninvasively distinguish histopathologic subgroups and reflect underlying molecular heterogeneity, offering potential biomarkers for tumor grading, prognostication, and precision management.
		364-02-011. Biomechanical Assessment in Glioblastoma Using 2D Cine DENSE MRI Sile Wang, Huiming Dong, Shu-Fu Shih, Yuxiao Wu, Ayla Pirodan, Steve Braunstein, Ke Sheng, Wensha Yang, Xiaodong Zhong University of California Los Angeles, Los Angeles, United States of America Impact: This DENSE-based motion mapping framework may provide a new tool to assess tumor-brain mechanical interactions for radiotherapy planning and treatment response monitoring.
		364-02-012. Radiation-Induced Brain Changes in Glioma Patients: A Longitudinal MRI and PCA Study Erika Ludena Maza, Juliana Pavoni, Renata Leoni University of Sao Paulo, Ribeirao Preto, Brazil Impact: This study identifies key factors influencing radiation-induced brain alterations to guide future longitudinal analyses and improve radiotherapy planning strategies to preserve healthy tissue, reduce neurocognitive decline, and enhance long-term outcomes for glioma patients.
		364-02-013. Self-Supervised Deep Learning for Label-Free Brain Metastasis Detection in Clinical MR Imaging Anne Rückert, Oscar van der Heide, Mark Savenije, Jelmer van Lune, Niels C.P.J Raaijmakers, Marielle Philippens, Enrica Seravalli, Mischa de Ridder, Cornelis van den Berg UMC Utrecht, Utrecht, Netherlands Impact: This self-supervised approach effectively localizes brain metastases without manual labels, demonstrating robust generalization from standardized open-source research data to heterogeneous in-house clinical scans, and offering potential to support physicians and reduce diagnostic workload.