GACELLE: GPU-accelerated tools for model parameter estimation and image reconstruction

2025-11-26

AI Summary

Quantitative MRI (qMRI) faces significant computational bottlenecks in parameter estimation, hindering clinical adoption due to insufficient throughput and real-time capability. To address this, we introduce the first open-source, GPU-accelerated framework for qMRI—implemented in MATLAB—that enables fully vectorized, stochastic optimization and sampling for both parameter estimation and image reconstruction. The framework integrates stochastic gradient descent, Markov Chain Monte Carlo (MCMC), and spatial regularization, with automated memory batching and hardware-agnostic reproducibility. Benchmarking against CPU-based implementations yields up to 14,380× peak speedup. It significantly improves parameter accuracy and test–retest reliability while reducing noise in quantitative parametric maps. Validation across multiple models—including T₁, T₂, and apparent diffusion coefficient (ADC) mapping—as well as diverse reconstruction tasks demonstrates robust performance. This framework provides a scalable, efficient foundation for advancing high-resolution, multiparametric qMRI toward clinical translation and methodological innovation.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Learning & Optimization for CV
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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 or requiring fitting 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 provides a stochastic gradient descent optimiser and a stochastic sampler in MATLAB, enabling fast parameter mapping, improved estimation robustness via spatial regularisation, and uncertainty quantification. GACELLE prioritises accessibility: users only need to provide a forward signal model, while GACELLE's backend manages computational parallelisation, automatic parameter updates, and memory-batching. The stochastic solver performs fully vectorised Markov chain Monte Carlo with identical likelihood on CPU and GPU, ensuring reproducibility across hardware. Benchmarking demonstrates up to 451-fold acceleration for the stochastic gradient descent solver and 14,380-fold acceleration for stochastic sampling compared to CPU-based estimation, without compromising accuracy. We demonstrated GACELLE's versatility on three representative qMRI models and on an image reconstruction task. Across these applications, GACELLE improves parameter precision, enhances test-retest reproducibility, and reduces noise in quantitative maps. By combining speed, usability and flexibility, GACELLE provides a generalisable optimisation framework for medical image analysis. It lowers the computational barrier for qMRI, paving the way for reproducible biomarker development, large-scale imaging studies, and clinical translation.

Problem

• Accelerates quantitative MRI parameter estimation to overcome computational bottlenecks.
• Enables fast, robust mapping and uncertainty quantification via GPU-accelerated stochastic solvers.
• Reduces processing time for high-resolution images and multi-parameter fitting in clinical research.

Innovation

• GPU-accelerated framework for high-throughput qMRI analysis
• Stochastic gradient descent and sampler for fast parameter mapping
• Automatic parallelization and memory-batching for accessibility