@inproceedings{8de1572caae746cfb10adda1169dc54b,
title = "Morphological component analysis based compressed sensing technique on dynamic MRI reconstruction",
abstract = "Compressive sensing (CS) MRI have been developed to speed up data acquisition without significantly degrading image quality. This paper proposes a novel compressed sensing reconstruction method exploiting temporally complementary morphological characteristics. This method relies on some well-developed signal processing techniques: Morphological component analysis (MCA) and sparse derivatives. It also relies on well-developed MRI reconstruction techniques: Incoherent undersampling schemes and parallel imaging. Other MRI schemes were simulated to make comparsion with our MCA-based CS method. CS and parallel imaging has been merged together to highly increase acceleration rate. This work simulates this framework also. Performance of applying different temporal regularizations individually and hybrid signal models based on MCA with and without auxilary spatial regularization are all analyzed in this paper. Nonlinear conjugate gradient algorithm is applied to gain all signal components simultaneously.",
keywords = "Compressed Sensing, Morphological Component Analysis, Parallel Imaging, Sparse Derivatives",
author = "Lei Yin and Ivan Selesnick",
year = "2017",
month = feb,
day = "7",
doi = "10.1109/SPMB.2016.7846881",
language = "English (US)",
series = "2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings",
note = "2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 ; Conference date: 03-12-2016",
}