MRI Field-transfer Reconstruction with Limited Data: Regularization by Neural Style Transfer

Autor:	Shen, Guoyao, Zhu, Yancheng, Jara, Hernan, Andersson, Sean B., Farris, Chad W., Anderson, Stephan, Zhang, Xin
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Physics - Medical Physics
Druh dokumentu:	Working Paper
Popis:	Recent works have demonstrated success in MRI reconstruction using deep learning-based models. However, most reported approaches require training on a task-specific, large-scale dataset. Regularization by denoising (RED) is a general pipeline which embeds a denoiser as a prior for image reconstruction. The potential of RED has been demonstrated for multiple image-related tasks such as denoising, deblurring and super-resolution. In this work, we propose a regularization by neural style transfer (RNST) method to further leverage the priors from the neural transfer and denoising engine. This enables RNST to reconstruct a high-quality image from a noisy low-quality image with different image styles and limited data. We validate RNST with clinical MRI scans from 1.5T and 3T and show that RNST can significantly boost image quality. Our results highlight the capability of the RNST framework for MRI reconstruction and the potential for reconstruction tasks with limited data. Comment: 30 pages, 8 figures, 2 tables, 1 algorithm chart
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.10968 Zobrazit plný text záznamu View this record from Arxiv