<i>GACELLE:</i> GPU-accelerated tools for model parameter estimation and image reconstruction.

ABSTRACT: Quantitative MRI (qMRI) offers tissue-specific biomarkers that can be tracked over time or compared across populations; however, its adoption in clinical research is hindered by significant computational demands of parameter estimation. Images acquired at high spatial resolution and/or requiring fitting for multiple parameters often require lengthy processing time, constraining their use in routine pipelines and slowing methodological innovation and clinical translation. We present GACELLE, an open source, GPU-accelerated framework for high-throughput qMRI analysis. GACELLE unifies a stochastic gradient descent optimiser (askadam.m) and a stochastic sampler (mcmc.m) under a common interface in MATLAB, enabling fast parameter mapping, improved estimation robustne

SUBMITTER: Chan KS 

PROVIDER: S-EPMC12676372 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature