Abstract
Ultra-low-field MRI makes neonatal brain imaging deploy-
able in low-resource settings, but its low SNR, lack of shielding, and
long scan duration make it especially prone to acquisition artifacts, mo-
tivating automated quality control. We address the LISA 2026 Task 1a
challenge: multi-label severity grading (0/1/2) of seven common image
artifacts on ULF $T_2$ weighted volumes. We identify that a number of backbones
may be successfully paired with a classification MLP, but that no single
backbone is uniformly best across artifacts. To improve performance, we
evaluate routing complementary foundation model teachers through a
per-artifact gate, as well as distilling the teachers into a single in-domain
ViT-S student (LoFi-RADIO) over an unlabeled low-field MRI corpus.
Both of these strategies improve the weighted composite. The distilled
backbone matches or exceeds the gate and has the added advantage of
not requiring deployment of multiple large foundation models at infer-
ence.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/LISA_020.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=6cYTv4mYdF
BibTex
@InProceedings{MarJon_LoFi_MICCAISAT2026,
author = { Martin, Jonathan B. AND Kurmi, Yashwant AND Sappo, Charlotte R.},
title = { { LoFi RADIO: A Distilled In-Domain Backbone Applied for Artifact-Severity Grading of Ultra-Low-Field Neonatal Brain MRI } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17257},
month = {pending},
page = {pending}
}