We present PROSUB: PROgressive SUBsampling, a deep learning based, automated methodology that subsamples an oversampled data set (e.g. multi-channeled 3D images) with minimal loss of information. We build upon a recent dual-network approach that won the MICCAI MUlti-DIffusion (MUDI) quantitative MRI measurement sampling-reconstruction challenge, but suffers from deep learning training instability, by subsampling with a hard decision boundary. PROSUB uses the paradigm of recursive feature elimination (RFE) and progressively subsamples measurements during deep learning training, improving optimization stability. PROSUB also integrates a neural architecture search (NAS) paradigm, allowing the network architecture hyperparameters to respond to the subsampling process. We show PROSUB outperforms the winner of the MUDI MICCAI challenge, producing large improvements >18% MSE on the MUDI challenge sub-tasks and qualitative improvements on downstream processes useful for clinical applications. We also show the benefits of incorporating NAS and analyze the effect of PROSUB's components. As our method generalizes to other problems beyond MRI measurement selection-reconstruction, our code is https://github.com/sbb-gh/PROSUB
@article{arxiv.2203.09268,
title = {Progressive Subsampling for Oversampled Data - Application to Quantitative MRI},
author = {Stefano B. Blumberg and Hongxiang Lin and Francesco Grussu and Yukun Zhou and Matteo Figini and Daniel C. Alexander},
journal= {arXiv preprint arXiv:2203.09268},
year = {2022}
}
Comments
Accepted In: Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022