Pattern-Matching Unit for Medical Applications

We explore the application of concepts developed in high-energy physics (HEP) in a field of high social impact, i.e., advanced medical data analysis. More specifically, we focus on shortening the reconstruction times of a multi-parametric quantitative magnetic resonance imaging (MRI) technique: magnetic resonance fingerprinting (MRF). This technique has the potential to replace multiple qualitative MRI acquisitions with a single reproducible measurement for increased sensitivity and efficiency of the examination. In MRF, a fast acquisition is followed by a pattern-matching (PM) task, where signal responses are matched to entries from a dictionary of simulated, physically feasible responses, yielding multiple tissue parameters simultaneously. Each voxel signal response in the volume is compared through scalar products with all dictionary entries to choose the best measurement reproduction. MRF is limited by the PM processing time, which scales exponentially with the dictionary dimensionality, i.e., with the number of tissue parameters to be reconstructed. In the context of HEP, we developed a powerful, compact, embedded system, optimized for extremely fast PM. This system executes real-time particle trajectory (track) reconstruction for online event selection in the HEP experiments, exploiting maximum parallelism and pipelining. Track reconstruction is executed in two steps. The associative memory (AM) ASIC first implements a PM algorithm by recognizing track candidates at low resolution. The second step, which is implemented into field programmable gate arrays (FPGAs), refines the AM output finding the track parameters at full resolution. We propose to use this system to achieve a faster reconstruction time in MRF. This article proposes an adaptation of the HEP system for medical imaging and shows some preliminary results.