2026 ISMRM Workshop on Data Sampling and Image Reconstruction
11 January 2026 – 14 January 2026 · Sedona, AZ, United States of America
301-03-001 Workshop Abstract

Harmonizing Neuroimaging Acquisition and Reconstruction

Accepted
Berkin Bilgic 1,2, Jon-Fredrik Nielsen3, Maxim Zaitsev4, Yogesh Rathi1,5
1Harvard Medical School, Boston, United States of America
2Athinoula A. Martinos Center for Biomedical Imaging, Cambridge, United States of America
3Department of Radiology, University of Michigan, Ann Arbor, MI., United States of America
4Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg, Germany
5Brigham and Women's Hospital, Boston, United States of America
Presenting Author: Berkin Bilgic
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for 2026 ISMRM Workshop on Data Sampling and Image Reconstruction are available to registered attendees. Registered attendees must check in at the meeting before content unlocks.

To request or purchase access, contact the ISMRM Central Office at support@ismrm.org.

Log in

References

1. Casey BJ, Cannonier T, Conley MI, et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev Cogn Neurosci. 2018;32:43-54.
2. Jordan CJ, Weiss SRB, Howlett KD, Freund MP. Introduction to the Special Issue on “Informing Longitudinal Studies on the Effects of Maternal Stress and Substance Use on Child Development: Planning for the HEALthy Brain and Child Development (HBCD) Study.” Adversity and Resilience Science. 2020;1(4):217-221.
3. Alexander LM, Escalera J, Ai L, et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data. 2017;4:170181.
4. Mirzaalian H, Ning L, Savadjiev P, et al. Inter-site and inter-scanner diffusion MRI data harmonization. Neuroimage. 2016;135:311-323.
5. Cetin Karayumak S, Bouix S, Ning L, et al. Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters. Neuroimage. 2019;184:180-200.
6. Maier O, Baete SH, Fyrdahl A, et al. CG-SENSE revisited: Results from the first ISMRM reproducibility challenge. Magn Reson Med. 2021;85(4):1821-1839.
7. Poldrack RA, Baker CI, Durnez J, et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci. 2017;18(2):115-126.
8. Melonakos ED, Shenton ME, Rathi Y, Terry DP, Bouix S, Kubicki M. Voxel-based morphometry (VBM) studies in schizophrenia-can white matter changes be reliably detected with VBM? Psychiatry Res. 2011;193(2):65-70.
9. Gountouna VE, Job DE, McIntosh AM, et al. Functional Magnetic Resonance Imaging (fMRI) reproducibility and variance components across visits and scanning sites with a finger tapping task. Neuroimage. 2010;49(1):552-560.
10. Kubicki M, McCarley R, Westin CF, et al. A review of diffusion tensor imaging studies in schizophrenia. Journal of psychiatric research. 2005;41(1-2):15.
11. Vista AI. Vista.ai. https://vista.ai/. Published August 24, 2025. Accessed November 12, 2025.
12. Artiges A, Saimbhi AS, Castillo-Passi C, Lattanzi R, Block KT. mtrk—A flexible environment for developing open-source MRI pulse sequences. Magnetic Resonance in Medicine. September 2025. doi:10.1002/mrm.70067 [doi]
13. Konstandin S, Günther M, Hoinkiss DC. gammaSTAR: A framework for the development of dynamic, real-time capable MR sequences. Magnetic Resonance in Medicine. 2025;94(4):1485-1499.
14. BART Toolbox. https://mrirecon.github.io/bart/. Accessed September 30, 2022.
15. Hansen MS, Sørensen TS. Gadgetron: An open source framework for medical image reconstruction. Magnetic Resonance in Medicine. 2013;69(6):1768-1776.
16. GitHub - JeffFessler/MIRT.jl: MIRT: Michigan Image Reconstruction Toolbox (Julia version). GitHub. https://github.com/JeffFessler/MIRT.jl. Accessed November 12, 2025.
17. SigPy — sigpy 0.1.25 documentation. https://sigpy.readthedocs.io/en/latest/. Accessed November 12, 2025.
18. Fujita S, Gagoski B, Nielsen JF, et al. Vendor-agnostic 3D multiparametric relaxometry improves cross-platform reproducibility. Magnetic Resonance in Medicine. 2025;94(3):937-948.
19. Gaspar AS, Silva NA, Ferreira AM, Nunes RG. Repeatability of Open-MOLLI: An open-source inversion recovery myocardial T1 mapping sequence for fast prototyping. Magnetic Resonance in Medicine. 2024;92(2):741-750.
20. Pothoof J, Wehkamp N, Fessler J, Zaitsev M, Noll D, Nielsen, J-F Open-Source, Multi-Vendor B0 Shimming Protocol. Proceedings of ISMRM 2025 p.3479
21. Liu Q, Ning L, Shaik IA, et al. Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI. Magn Reson Med. 2024;92(1):246-256.

Cite this abstract