Automated Brain Segmentation in Accelerated T2-Weighted MRI: Effects of Deep Learning-Based Reconstruction
- 1AGH University of Krakow, 30-059 Krakow, Poland
Abstract
BackgroundAutomated brain volumetry is increasingly used for clinical and research assessment, but its outputs depend on acquisition and reconstruction settings. We evaluated how parallel-imaging acceleration and vendor deep-learning reconstruction (Siemens Deep Resolve) affect automated volumetry on T2-weighted turbo spin-echo MRI, and whether deep-learning reconstruction stabilises measurements across protocols.
MethodsFour healthy volunteers each underwent 14 T2-TSE-TRA acquisitions on a 1.5 T scanner, covering seven protocols (baseline, GRAPPA R = 2, 3, 4, and SMS x2, x3, x4), with each protocol acquired separately once with Deep Resolve off and once with Deep Resolve on, for 56 acquisitions in total. Six pipelines were applied: SynthSeg, OpenMAP-T2, and GOUHFI 2.0 for subcortical parcellation, and TotalSegmentator MRI, HD-BET, and NV-Segment-CTMR for whole-brain masking. A three-rater STAPLE consensus of six subcortical structures on the baseline acquisition served as the expert reference. Reference-relative accuracy (absolute percentage error), cross-protocol dispersion (coefficient of variation), and spatial reproducibility (Dice coefficient and 95th-percentile surface distance) were compared between reconstruction states.
ResultsDeep Resolve improved reference-relative accuracy most for GOUHFI 2.0 (pooled median absolute percentage error 28.4% to 21.9%), modestly for SynthSeg (16.2% to 15.5%), and had a mixed, structure-dependent effect for OpenMAP-T2. It approximately halved cross-protocol dispersion for all three parcellation tools (coefficient of variation 2.62% to 1.39% for SynthSeg, 4.42% to 2.14% for OpenMAP-T2, and 21.75% to 7.87% for GOUHFI) and improved spatial reproducibility in parallel. It eliminated the systematic GOUHFI under-segmentation seen at high acceleration under conventional reconstruction (median deviation -31.6% at GRAPPA R = 4; 14 failure events under conventional reconstruction, none with Deep Resolve). Whole-brain masking was robust in both states.
ConclusionsDeep Resolve improved the robustness of automated brain segmentation in accelerated T2-weighted MRI, reducing cross-protocol variability and improving spatial reproducibility, with the largest reduction in reference-relative error in the most acceleration-sensitive pipeline. The elimination of severe segmentation failures observed under conventional reconstruction highlights its potential to support reliable volumetry at higher acceleration. Reconstruction optimization therefore offers a practical route to more consistent quantitative measurements across acquisition protocols.
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BibTeX
@misc{lasek2026automated,
title = {{Automated Brain Segmentation in Accelerated T2-Weighted MRI: Effects of Deep Learning-Based Reconstruction}},
author = {J. LASEK and R. Obuchowicz and M. Strzelecki and I. Dwojak and W. Kanas and I. Obuchowicz and A. Krzyzak and J. Minkowski},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.08.26362537},
url = {https://www.medrxiv.org/content/10.64898/2026.09.08.26362537v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Automated Brain Segmentation in Accelerated T2-Weighted MRI: Effects of Deep Learning-Based Reconstruction
AU - J. LASEK
AU - R. Obuchowicz
AU - M. Strzelecki
AU - I. Dwojak
AU - W. Kanas
AU - I. Obuchowicz
AU - A. Krzyzak
AU - J. Minkowski
PY - 2026
DA - 2026/09/08
PB - medRxiv
DO - 10.64898/2026.09.08.26362537
UR - https://www.medrxiv.org/content/10.64898/2026.09.08.26362537v1
AB - BackgroundAutomated brain volumetry is increasingly used for clinical and research assessment, but its outputs depend on acquisition and reconstruction settings. We evaluated how parallel-imaging acceleration and vendor deep-learning reconstruction (Siemens Deep Resolve) affect automated volumetry on T2-weighted turbo spin-echo MRI, and whether deep-learning reconstruction stabilises measurements across protocols. MethodsFour healthy volunteers each underwent 14 T2-TSE-TRA acquisitions on a 1.5 T scanner, covering seven protocols (baseline, GRAPPA R = 2, 3, 4, and SMS x2, x3, x4), with each protocol acquired separately once with Deep Resolve off and once with Deep Resolve on, for 56 acquisitions in total. Six pipelines were applied: SynthSeg, OpenMAP-T2, and GOUHFI 2.0 for subcortical parcellation, and TotalSegmentator MRI, HD-BET, and NV-Segment-CTMR for whole-brain masking. A three-rater STAPLE consensus of six subcortical structures on the baseline acquisition served as the expert reference. Reference-relative accuracy (absolute percentage error), cross-protocol dispersion (coefficient of variation), and spatial reproducibility (Dice coefficient and 95th-percentile surface distance) were compared between reconstruction states. ResultsDeep Resolve improved reference-relative accuracy most for GOUHFI 2.0 (pooled median absolute percentage error 28.4% to 21.9%), modestly for SynthSeg (16.2% to 15.5%), and had a mixed, structure-dependent effect for OpenMAP-T2. It approximately halved cross-protocol dispersion for all three parcellation tools (coefficient of variation 2.62% to 1.39% for SynthSeg, 4.42% to 2.14% for OpenMAP-T2, and 21.75% to 7.87% for GOUHFI) and improved spatial reproducibility in parallel. It eliminated the systematic GOUHFI under-segmentation seen at high acceleration under conventional reconstruction (median deviation -31.6% at GRAPPA R = 4; 14 failure events under conventional reconstruction, none with Deep Resolve). Whole-brain masking was robust in both states. ConclusionsDeep Resolve improved the robustness of automated brain segmentation in accelerated T2-weighted MRI, reducing cross-protocol variability and improving spatial reproducibility, with the largest reduction in reference-relative error in the most acceleration-sensitive pipeline. The elimination of severe segmentation failures observed under conventional reconstruction highlights its potential to support reliable volumetry at higher acceleration. Reconstruction optimization therefore offers a practical route to more consistent quantitative measurements across acquisition protocols.
ER -