A Framework for SPICE MRSI Reconstruction Posterior Uncertainty Quantification
- 1University of Oxford
Abstract
Purpose: Low-rank reconstruction methods such as SPICE substantially improve Magnetic Resonance Spectroscopic Imaging (MRSI) reconstruction quality. However, SPICE does not provide an estimate of reconstruction uncertainty. A framework for posterior uncertainty estimation in SPICE reconstruction is proposed, hereafter referred to as the framework. Theory and Methods: A Laplace approximation is used to estimate the covariance of the reconstructed spatial coefficients. This covariance is propagated through the spectral basis and spectral fitting to obtain metabolite concentration uncertainty. Matrix-free Hessian--vector products and voxel-wise conjugate-gradient estimation are used to avoid explicit construction or inversion of the full Hessian, making the method tractable. Results: Validation using repeated in vivo 7 T MRSI acquisitions showed that the estimated posterior uncertainty exhibited spatial and spectral patterns similar to those of empirical repeated-scan uncertainty. Experiments further demonstrated how spatial regularization and k-space undersampling affect the estimated uncertainty. Conclusion: The framework provides a practical framework for uncertainty quantification when using SPICE reconstruction of MRSI. It could be adapted to other regularised low-rank reconstruction methods.
Related
- Representation-aware conformal prediction for reliable uncertainty quantification in drug-target affinity
- Uncertainty Quantification in Stochastic Dynamical Gene Regulatory Networks
- Uncertainty Quantification in Cardiac Model Personalisation from Ultrafast Ultrasound
- Uncertainty Quantification-Incorporated Ensemble Model for Personalized Prediction of Severe Radiotherapy-Induced Immunosuppression Among Esophageal Cancer Patients
- Bayesian Uncertainty Quantification of Breast-Cancer Pathology AI Agreement under Reader- and Annotation-Condition-Dependent Reference Labels
BibTeX
@misc{lyu2026framework,
title = {{A Framework for SPICE MRSI Reconstruction Posterior Uncertainty Quantification}},
author = {T. Lyu and S. Jbabdi and S. M. Finney and W. T. Clarke},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.05.764591},
url = {https://www.biorxiv.org/content/10.64898/2026.11.05.764591v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A Framework for SPICE MRSI Reconstruction Posterior Uncertainty Quantification
AU - T. Lyu
AU - S. Jbabdi
AU - S. M. Finney
AU - W. T. Clarke
PY - 2026
DA - 2026/11/05
PB - bioRxiv
DO - 10.64898/2026.11.05.764591
UR - https://www.biorxiv.org/content/10.64898/2026.11.05.764591v1
AB - Purpose: Low-rank reconstruction methods such as SPICE substantially improve Magnetic Resonance Spectroscopic Imaging (MRSI) reconstruction quality. However, SPICE does not provide an estimate of reconstruction uncertainty. A framework for posterior uncertainty estimation in SPICE reconstruction is proposed, hereafter referred to as the framework. Theory and Methods: A Laplace approximation is used to estimate the covariance of the reconstructed spatial coefficients. This covariance is propagated through the spectral basis and spectral fitting to obtain metabolite concentration uncertainty. Matrix-free Hessian--vector products and voxel-wise conjugate-gradient estimation are used to avoid explicit construction or inversion of the full Hessian, making the method tractable. Results: Validation using repeated in vivo 7 T MRSI acquisitions showed that the estimated posterior uncertainty exhibited spatial and spectral patterns similar to those of empirical repeated-scan uncertainty. Experiments further demonstrated how spatial regularization and k-space undersampling affect the estimated uncertainty. Conclusion: The framework provides a practical framework for uncertainty quantification when using SPICE reconstruction of MRSI. It could be adapted to other regularised low-rank reconstruction methods.
ER -