Accurate parameter estimation using scan-specific unsupervised deep learning for relaxometry and MR fingerprinting
Year: 2023
Type: conference-paper
Abstract: We propose an unsupervised convolutional neural network (CNN) for relaxation parameter estimation. This network incorporates signal relaxation and Bloch simulations while taking advantage of residual learning and spatial relations across neighboring voxels. Quantification accuracy and robustness to noise is shown to be significantly improved compared to standard parameter estimation methods in numerical simulations and in vivo data for multi-echo T2 and T2* mapping. The combination of the proposed network with subspace modeling and MR fingerprinting (MRF) from highly undersampled data permits high quality T1 and T2 mapping.
DOI: 10.58530/2022/4320
Authors: Mengze Gao, Huihui Ye, Tae Kim, Zijing Zhang, Seohee So, Berkin Bilgic
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4225529276