Advancing oxygen-enhanced MRI acquisition and modelling through dynamic ΔR1 and ΔR2* sensing
Abstract
Purpose: To develop a multiparametric mapping framework for quantifying fat- and water-specific longitudinal and effective transverse relaxation rates, as well as changes in these parameters induced by a gas challenge. Theory and Methods: The proposed framework, termed SR-Fat DESPOT (single-reference fat-water separated driven equilibrium single-pulse observation of T1), combines a two-component signal model with a single-reference variable flip angle (SR-VFA) approach. The signal model explicitly incorporates gas challenge-induced changes in relaxation rates. The single-reference acquisition strategy reduces data acquisition requirements while preserving sensitivity to both baseline parameters and dynamic changes. Sequence parameters were optimized using Cramer-Rao lower bound theory. SR-Fat DESPOT was evaluated using a modified open-surce abdominal numerical phantom and in two in vivo experiments: a single participant test-retest experiment and a pilot study with six participants. Results: In the abdominal numerical phantom, SR-Fat DESPOT was accurate for quantification of baseline relaxation rates with a normalized median absolute error (nMedAE) <3% for signal-to-noise ratio (SNR) >30 when comparing ground truth and estimates. SR-Fat DESPOT was also able to accurately recover median changes in relaxation rates, with differences within 0.001 s-1 when compared to ground truth. In vivo, SR-Fat DESPOT measured changes in relaxation rates for spleen, kidneys, liver, and muscle under 100% O2 breathing, consistent with reports in literature. Changes detected in the spleen and kidneys were statistically significant across participants. Conclusion: SR-Fat DESPOT can quantify changes in relaxation rates induced by a gas challenge while reducing data requirements compared to more conventional approaches used in MR-oximetry literature.
Related
- Modelling sexual partnership dynamics and population heterogeneities in agent-based dynamic network models
- Dimensionally consistent surrogate modelling through dimensional analysis and harmonic expansions
- Modelling lock-in of AI adoption through skill erosion
- From Heterogeneity to Function: Enhanced Modular Dynamic Range in Hierarchical Brain Networks
- Enhanced nanometer-scale linewidth metrology using dark-field through-focus scanning optical microscopy
BibTeX
@misc{pazmino2026advancing,
title = {{Advancing oxygen-enhanced MRI acquisition and modelling through dynamic $ΔR_1$ and $ΔR_2^*$ sensing}},
author = {Jorge Campos Pazmiño and Amiel Beausoleil-Morrison and Nathalie Panov and Véronique Fortier and Ives R. Levesque},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.30793},
eprint = {2610.30793},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.30793},
}
RIS
TY - UNPB
TI - Advancing oxygen-enhanced MRI acquisition and modelling through dynamic ΔR_1 and ΔR_2^* sensing
AU - Jorge Campos Pazmiño
AU - Amiel Beausoleil-Morrison
AU - Nathalie Panov
AU - Véronique Fortier
AU - Ives R. Levesque
PY - 2026
DA - 2026/10/22
PB - arXiv
DO - 10.48550/arxiv.2610.30793
UR - https://arxiv.org/abs/2610.30793
AB - Purpose: To develop a multiparametric mapping framework for quantifying fat- and water-specific longitudinal and effective transverse relaxation rates, as well as changes in these parameters induced by a gas challenge. Theory and Methods: The proposed framework, termed SR-Fat DESPOT (single-reference fat-water separated driven equilibrium single-pulse observation of T_1), combines a two-component signal model with a single-reference variable flip angle (SR-VFA) approach. The signal model explicitly incorporates gas challenge-induced changes in relaxation rates. The single-reference acquisition strategy reduces data acquisition requirements while preserving sensitivity to both baseline parameters and dynamic changes. Sequence parameters were optimized using Cramer-Rao lower bound theory. SR-Fat DESPOT was evaluated using a modified open-surce abdominal numerical phantom and in two in vivo experiments: a single participant test-retest experiment and a pilot study with six participants. Results: In the abdominal numerical phantom, SR-Fat DESPOT was accurate for quantification of baseline relaxation rates with a normalized median absolute error (nMedAE) <3% for signal-to-noise ratio (SNR) >30 when comparing ground truth and estimates. SR-Fat DESPOT was also able to accurately recover median changes in relaxation rates, with differences within 0.001 s^-1 when compared to ground truth. In vivo, SR-Fat DESPOT measured changes in relaxation rates for spleen, kidneys, liver, and muscle under 100% O2 breathing, consistent with reports in literature. Changes detected in the spleen and kidneys were statistically significant across participants. Conclusion: SR-Fat DESPOT can quantify changes in relaxation rates induced by a gas challenge while reducing data requirements compared to more conventional approaches used in MR-oximetry literature.
ER -