A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors

Autor:	Dar, Salman Ul Hassan, Yurt, Mahmut, Çukur, Tolga
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	Deep neural networks (DNNs) have recently found emerging use in accelerated MRI reconstruction. DNNs typically learn data-driven priors from large datasets constituting pairs of undersampled and fully-sampled acquisitions. Acquiring such large datasets, however, might be impractical. To mitigate this limitation, we propose a few-shot learning approach for accelerated MRI that merges subject-driven priors obtained via physical signal models with data-driven priors obtained from a few training samples. Demonstrations on brain MR images from the NYU fastMRI dataset indicate that the proposed approach requires just a few samples to outperform traditional parallel imaging and DNN algorithms. Comment: Accepted for presentation at the 29th Annual Meeting of the International Society of Magnetic Resonance in Medicine (ISMRM)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.07790 Zobrazit plný text záznamu View this record from Arxiv