Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
660-04-002 ISMRM Abstract

Deep-learning model for improving B0-induced geometric distortion in clinically acquired Whole-Body Diffusion-Weighted MRI

Primary: Acquisition & Reconstruction - AI methods
Secondary: Analysis Methods - Image Registration and Alignment
660-04-002 · AI: Anything Synthetic or Correcting Artifacts · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row A
Keywords: Deep learning Diffusion-Weighted Imaging Whole-Body MRI B0 Inhomogeneity Field mapping
Accepted
Antonio Candito 1, Jessica M Winfield1,2, Sam Keaveney1,2, Alison Macdonald2, Christina Messiou1,3, Dow-Mu Koh1,3, Matthew D Blackledge1
1Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
2MRI Unit, The Royal Marsden Hospital NHS Foundation Trust, London, United Kingdom
3Department of Radiology, The Royal Marsden NHS Foundation Trust, London, United Kingdom
Presenting Author: Antonio Candito

Synopsis

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References

1. R. Perez-lopez et al., Multiparametric Magnetic Resonance Imaging of Prostate Cancer Bone Disease Correlation with Bone Biopsy Histological and Molecular Features. Invest. Radiol, 2018. 53(2): p. 96-102 DOI: 10.1097/RLI.0000000000000415 [doi]
2. R. Donners R et al., Multiparametric bone MRI targeting aides lesion selection for CT-guided sclerotic bone biopsies in metastatic castrate resistant prostate cancer. Cancer Imaging, 2023. 23(1): p. 2-9 DOI: 10.1186/s40644-023-00644-w [doi]
3. A. R. Padhani et al., Rationale for Modernising Imaging in Advanced Prostate Cancer. Eur Urol Focus, 2017. 3(2): p. 223–239 DOI: 10.1016/j.euf.2016.06.018 [doi]
4. Candito et al., A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on whole-body diffusion-weighted MRI (WB-DWI). Comput. Methods Programs Biomed., 2025. 272:  p. 1-15 DOI: 10.1016/j.cmpb.2025.109043 [doi]
5. L. A. Digma et al., Correcting B0 inhomogeneity-induced distortions in whole-body diffusion MRI of bone. Sci Rep, 2022. 12(265): p. 1-9 DOI: 10.1038/s41598-021-04467-2 [doi]
6. M. Jenkinson et al., FSL. Neuroimage, 2011. 62(2): p. 782-190 DOI: 10.1016/j.neuroimage.2011.09.015 [doi]
7. J. M. Winfield et al., Whole-body MRI: a practical guide for imaging patients with malignant bone disease. Clin Radiol, 2021. 76(10): p. 715-727 DOI: 10.1016/j.crad.2021.04.001 [doi]
8. E. Kerfoot et al. Left-Ventricle Quantification Using Residual U-Net. Lecture Notes in Computer Sciencevol. LNCS, 2019. p. 371–380 DOI: 10.1007/978-3-030-12029-0 [doi]
9. A. Candito et al. Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets. Bioengineering, 2024. 11(2): p. 1-17 DOI: 10.3390/bioengineering11020130 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/660-04-002