Abstract
Dense multi-shell diffusion MRI provides rich q-space information but requires long acquisition times. We propose a spatial masked-set framework for sparse multi-shell diffusion MRI signal synthesis. The model treats observed measurements as an unordered set, uses a local $3 \times 3 \times 3$ neighborhood for spatial context, and predicts radial-order-6 SHORE coefficients for the center voxel. The coefficients can then be decoded analytically to synthesize signals at arbitrary q-space locations. Training combines shell-wise gradient dropping, dense signal supervision, and rotation-consistent SHORE targets so that sparse input signals remain aligned with their coefficient supervision under augmentation. We evaluate on held-out HCP100 white-matter voxels by retaining limited subsets of measured diffusion-weighted signals from the reference acquisition. The proposed method achieves lower signal NMSE than both analytical q-space models and a state-of-the-art continuous dMRI signal synthesis model designed for arbitrary input and output q-space sampling; in the $b=1000$ setting with 10 input gradients, it achieves $2.70\%$ NMSE, a $22.4\%$ relative reduction over this continuous model, and the lowest DTI principal-eigenvector angular error in the sparse regime. The implementation is available on https://github.com/xmindflow/SHOREPred.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/cdmri_002.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=fS2mzJdUuf
BibTex
@InProceedings{SadYou_Spatial_MICCAISAT2026,
author = { Sadegheih, Yousef AND Kumari, Pratibha AND Merhof, Dorit},
title = { { Spatial Masked-Set Learning for Sparse Multi-Shell Diffusion MRI Signal Synthesis } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17278},
month = {pending},
page = {pending}
}