ISMRM25 - Software Tools

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

Software Tools

Power Pitch

Analysis Methods

Wednesday, 14 May 2025

Power Pitch Theatre 1

13:30 - 15:30

Moderators: Ari Borthakur & Jamal Derakhshan

Session Number: PP-06

No CME/CE Credit

13:30		Screen Number: 1 0937. NeuroAnalyst: Developing a Standardized, Containerized Framework for Reproducible Neuroimaging Analysis View Presentation Video C. Mokashi, S. Bajaj, C. Quarles University of Texas MD Anderson Cancer Center, Houston, United States Impact: NeuroAnalyst aims to significantly reduce technical barriers in neuroimaging research and enhance reproducibility. This standardized platform will enable more efficient collaboration, potentially leading to faster advancements in understanding brain function and neurological disorders.
13:32		Screen Number: 2 0938. GACELLE: GPU-AcCELerated toolbox for high-throughput multidimensionaL quantitative parameter Estimation View Presentation Video K-S Chan, Y. Ma, H. Lee, S. Huang, J. Marques, H-H Lee Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States Impact: GACELLE removes the obstacle of time-consuming data processing associated with quantitative MRI multi-parametric non-linear problems, promoting wider adoption of quantitative MRI for the scientific community.
13:34		Screen Number: 3 0939. ArteryX: Enhancing Sensitivity in Brain Artery Feature Extraction Using Arterial Graph Network View Presentation Video A. Faiyaz, N. Hoang, G. Schifitto, M. N. Uddin University of Rochester, Rochester, United States Impact: Demonstrated increase in sensitivity and usability of artery features, hold promise of our approach's reliable application in other cerebrovascular studies. Features generated from this approach will be used for training and validation of automated artery extraction technique.
13:36		Screen Number: 4 0940. The Cancer Imaging Data Commons: Paving the Way for Open Science PDF Poster View Presentation Video R. Kikinis, D. Krishnaswamy, S. Pieper, A. Fedorov Harvard Medical School, Boston, United States Impact: IDC accelerates cancer research by promoting data sharing and reproducibility, empowering researchers to collaborate, develop innovative algorithms, and improve patient care.
13:38		Screen Number: 5 0941. Process & Analysis of MRI data: 3D Slicer: A Versatile Toolkit for Personalized MRI Analysis View Presentation Video Z. Kikinis, R. Kikinis Brigham and Women's Hospital, Boston, United States Impact: 3D Slicer’s versatility supports research innovation in MRI analysis, facilitating advances in imaging-based biomarkers and new approaches to surgical planning and diagnostics.
13:40		Screen Number: 6 0942. Vessel segmentation toolbox for susceptibility imaging: Region-growing algorithm and deep learning View Presentation Video T. Kim, H. Park, S. Ji, J. Lee Seoul National University, Seoul, Korea, Republic of Impact: The vessel segmentation toolbox generates a high-quality vessel mask through a user-friendly GUI, supporting the reliability of analysis of χ-separation results by effectively excluding vessel artifacts for improved quantification of iron and myelin.
13:42		Screen Number: 7 0943. A Modular End-to-End Open-Source Software Pipeline to Simulate the Entire MRI Experiment View Presentation Video E. Montin, J. E. Cruz Serrallés, I. Giannakopoulos, A. Artiges, C. Castillo-Passi, R. Lattanzi New York University Grossman School of Medicine, New York, United States Impact: The comprehensive cloud-compatible simulation pipeline will make MR research freely accessible via internet. By enabling virtual experiments, this tool will allow users to generate synthetic data to train neural networks and will constitute a valuable tool for education and training.
13:44		Screen Number: 8 0944. WITHDRAWN
13:46		Screen Number: 9 0945. Cardiometry: Open-Source, Reliable and Efficient Computation of Quantitative Parameters in Cardiovascular Magnetic Resonance View Presentation Video C. Ammann, T. Hadler, P. Reisdorf, R. Hickstein, H. Noyan, J. Gröschel, J. Gavrysh, J. Schulz-Menger Working Group on CMR, Experimental and Clinical Research Center, a joint cooperation between the Charité – Universitätsmedizin Berlin and the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany Impact: Cardiometry produces equivalent clinical results to proprietary medical software. As an open-source library, it enables efficient and automated quantification of cardiac function, T1 and T2 parametric mapping and late gadolinium enhancement for research applications in cardiovascular magnetic resonance.
13:48		Screen Number: 10 0946. EZ-QC: A User-Friendly Tool for Multi-site, Multimodal Neuroimaging Data Triage and Visual Quality Control View Presentation Video J. DeBrosse, T. Goodwin-Allcock, R. Tripathi, K. L. Phan, L. Wang, T. Huerta, H. Zhang, N. Kraguljac The Ohio State University, Columbus, United States Impact: EZ-QC improves QC efficiency by prioritizing data that are most likely to indicate systemic errors and standardizing rater training. This approach supports consistent and efficient QC, helping to maximize usable data and ensure reliable data quality in large, multisite studies.
13:50		Screen Number: 11 0947. Anatomic-Based Metabolic Phantom Suite: Open-Source, Digital Hyperpolarized 13C MR Imaging View Presentation Video A. Bennett Haller, S. Sahin, D. Yeh, J. Gordon, Y. Kim, A. Sinha, J. Hu, T. Nickles, D. Vigneron, P. Larson University of California, San Francisco, San Francisco, United States Impact: Metabolic digital phantoms create ground truth data providing opportunities to accurately characterize the performance of new sampling and acquisition strategies, validate advanced data analysis techniques and create faithful synthetic data to supplement training datasets for machine learning applications.
13:52		Screen Number: 12 0948. Open-source analysis of magnetic resonance spectroscopic imaging (MRSI) data in Osprey View Presentation Video H. Zöllner, D. Senapati, İ. Özdemir, D. Lin, G. Oeltzschner, P. Barker The Johns Hopkins University School of Medicine, Baltimore, United States Impact: The implemented MRSI analysis workflow offers state-of-the-art methods with minimal user interaction available for non-expert users and easily visualized in FSLeyes. The modular workflow allows rapid adoption of new model approaches to be developed in Osprey.
13:54		Screen Number: 13 0949. The value of whole - tumor ADC histogram parameters combined with imaging biomarkers in predicting PNI/LVI in rectal adenocarcinoma H. Wang, H. Liu, Z. Wang, R. Gao, K. Zhu, J. Liu, P. Luo, Y. Sun, Y. Li The Second Hospital of Lanzhou University, LAN ZHOU, China Impact: Combining whole-tumor ADC histogram parameters with mrEMVI can significantly improve the accuracy of preoperative PNI/LVI prediction in rectal adenocarcinoma. This approach has the potential to become an important tool in clinical diagnosis, advancing the application of imaging in tumor detection.
13:56		Screen Number: 14 0950. The Brainstem Navigator Toolkit v1.0: An Atlas of Brainstem Nuclei and Coregistration Tutorial in Younger Adults View Presentation Video F. F. Hannanu, S. Cauzzo, G. Garcia-Gomar, K. Singh, N. Toschi, M. Bianciardi Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States Impact: The Brainstem Navigator toolkit v1.0 includes a probabilistic atlas of 31 brainstem nuclei and comprehensive coregistration routines for 3Tesla/7Tesla MRI. This toolkit facilitates precise structural, diffusion, and functional MRI coregistration, significantly advancing research into brainstem-related disorders and neuroscientific inquiries.
13:58		Screen Number: 15 0951. Hazen: an open-source MRI QA analysis software library View Presentation Video M. Buckley, Z. Ratkai, T. Roberts, R. Satnarine, J. Ansell, L. Gabriel, Y. Azma, R. Thornley, L. Jenkins, B. Laureano, M. Lowe, S. Shah, R. Franklin, R. Johnstone, S. Ahmad, M. Liljeroth, L. Jackson, L. Cester, D. Vilic, S. Culley, J. Tracey, H. Richardson, B. Johnston, A. Drysdale, P. Wilson, N. Heraghty, D. Price, H. Shuaib, G. Charles-Edwards Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom Impact: Hazen enables clinicians and scientists to perform rapid, standardised MRI QA across multiple vendors, reducing analysis time by ~75%. This facilitates faster troubleshooting, improves imaging consistency, overcomes licensing barriers, and fosters collaboration - ultimately enhancing diagnostic quality and patient care.
14:00		Screen Number: 16 0952. Building a standalone automated High-Throughput LCModel prototype application for 1H-MRS data processing: HT-LCModel View Presentation Video R. Burman, S. Guthrie, Y. Pang, A. Bag, K. Krull, W. Reddick, P. Bagga St. Jude Children's Research Hospital, Memphis, United States Impact: HT-LCModel provides an intuitive, user-friendly, high-throughput pipeline for MRS processing for processing 1H-MRS data with LCModel, the most widely used MRS-processing application. By offering a built-in viewer accessible on any computer, HT-LCModel enhances research consistency and accessibility in MRS studies.
14:02		Screen Number: 17 0953. A data-agnostic and automatic tool for quality protocol adherence Ó. Peña-Nogales, E. Neylon, T. Boshkovski, M. Ramos, V. Ferrer-Gallardo, P. Rodrigues, V. Prchkovska, K. Trivodaliev QMENTA, Barcelona, Spain Impact: The protocol adherence automation tool agnostically and automatically assesses the fidelity of the acquired data to a predefined set of rules. Consequently, it allows real-time identification of protocol deviations across manufacturers and sites minimizing potential human error and reducing costs.
14:04		Screen Number: 18 0954. Smart MRI Departments: Intelligent Patient Scheduling Using Real-Time Appointment Duration Metrics and an FNN to Predict Lateness View Presentation Video O. Lally, A. Schneider, M. Buckley, S. Shah Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom Impact: By increasing patient throughput, our work will be a crucial step towards a smart, sustainable MRI department. More patients will be scanned with less scan idle-time, improving patient outcomes in the long-term, whilst contributing to a greener and cost-effective service.
14:06		Screen Number: 19 0955. Prediction of Graves’ ophthalmopathy activity via histogram analysis of T2WI Z. Yingcong, B. Qiu, G. Xiarong, W. Yunzhu College of Medicine,Kunming University of Science and Technology，Kunming，China；, Kunming, China Impact: T2WI histogram analysis is reliable for predicting GO activity, enhancing clinical decision-making, and providing treatment personalization.
14:08		Screen Number: 20 0956. VesselDigitizer: A deep learning-powered 3D Slicer extension for digitizing cerebral vasculature from 3D medical images View Presentation Video Z. Chen, L. Liu, X. Chao, J. Wang, Y. Liao, X. Li, H. Wang Fudan University, Shanghai, China Impact: VesselDigitizer makes large-scale analysis of cerebral vasculatures easier and more accurate.
14:10		Screen Number: 21 0957. Digital MR brain phantom with vascular territory information: Application to MR imaging simulation for cerebrovascular dependent contrast View Presentation Video H. Kabasawa, Y. Kato International University of Health and Welfare, Narita, Japan Impact: This study demonstrated the proposed method could generate blood flow depended MR contrast as synthetic images. The proposed system can be used to predict MR contrast change associated with vascular territories. It is useful for teaching advanced MR imaging methods.
14:12		Screen Number: 22 0958. A Means of Enforcing Image Alignment at Compile Time for Improved Safety when Processing Surgical or Radiotherapy Images View Presentation Video L. Reid, P. Starr, D. Wang, A. Lee, M. Morrison University of California San Francisco, San Francisco, United States Impact: Preventing image-orientation errors at compilation time reduces risks in clinical environments, and accelerates software development. The proposed pattern is readily implementable across many programming languages, and our framework making this available to all .Net languages, including Python via Iron Python.
14:14		Screen Number: 23 0959. INDI: open-source software for processing cardiac diffusion tensor imaging data View Presentation Video P. Ferreira, A. Di Biase, C. Munoz-Escobar, A. Scott, D. Pennell, S. Nielles-Vallespin Royal Brompton Hospital, London, United Kingdom Impact: INDI provides an open-source tool, promoting standardization and dissemination of cardiac diffusion processing tools to new research groups. It will support a large multi-center study including non-specialist centers and enable translation to widespread clinical adoption.
14:16		Screen Number: 24 0960. Microstructure.jl: a Julia Package for Probabilistic Microstructure Model Fitting with Diffusion MRI View Presentation Video T. Gong, A. Yendiki Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, United States Impact: An easy-to-use tool is provided to the community for microstructural mapping from diffusion MRI across biophysical models with high computation speed, fitting evaluation and uncertainty quantification, applicable to data feasible on typical research and higher-performance scanners.
14:18		Screen Number: 25 0961. EPISeg: Automatic Segmentation of Spinal Cord fMRI Data View Presentation Video R. Banerjee, M. Kaptan, A. Tinnermann, A. Khatibi, A. Dabbagh, C. Buechel, C. Kündig, C. Law, D. Pfyffer, D. Lythgoe, D. Tsivaka, D. Van De Ville, F. Eippert, F. Muhammad, G. Glover, G. David, G. Haynes, J. Haaker, J. Brooks, J. Doyon, J. Finsterbusch, K. Martucci, K. Hemmerling, M. Mobarak-Abadi, M. Hoggarth, M. Howard, M. Bright, N. Kinany, O. Kowalczyk, O. Lungu, P. Freund, R. Deshpande, R. Barry, S. Mackey, S. Vahdat, S. Schading, S. Medina, S. McMahon, S. Williams, T. Parrish, V. Marchand-Pauvert, Y. Dhaher, Y. Chen, Z. Smith, K. Weber II, B. De Leener, J. Cohen-Adad Stanford University School of Medicine, Palo Alto, United States Impact: EPISeg significantly enhances spinal fMRI research by enabling automated, accurate segmentations of EPI data, overcoming limitations of manual segmentation. Its integration into SCT broadens accessibility and reproducibility, facilitating robust group-level analyses essential for advancing studies of spinal processes and disorders.

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