@inproceedings{14333e7e96ba46b6a098ebd6d7e460bf,
title = "A Myocardial T1-Mapping Framework with Recurrent and U-Net Convolutional Neural Networks",
abstract = "Noise and aliasing artifacts arise in various accelerated cardiac magnetic resonance (CMR) imaging applications. In accelerated myocardial T1-mapping, the traditional three-parameter based nonlinear regression may not provide accurate estimates due to sensitivity to noise. A deep neural network-based framework is proposed to address this issue. The DeepT1 framework consists of recurrent and U-net convolution networks to produce a single output map from the noisy and incomplete measurements. The results show that DeepT1 provides noise-robust estimates compared to the traditional pixel-wise three parameter fitting.",
keywords = "Cardiac Magnetic Resonance Imaging, Deep Learning, Image Reconstruction, T1-mapping",
author = "Haris Jeelani and Yang Yang and Ruixi Zhou and Kramer, {Christopher M.} and Michael Salerno and Weller, {Daniel S.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 ; Conference date: 03-04-2020 Through 07-04-2020",
year = "2020",
month = apr,
doi = "10.1109/ISBI45749.2020.9098459",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1941--1944",
booktitle = "ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging",
address = "United States",
}