TY - GEN
T1 - High-Fidelity MRI Reconstruction with the Densely Connected Network Cascade and Feature Residual Data Consistency Priors
AU - Liu, Jingshuai
AU - Qin, Chen
AU - Yaghoobi Vaighan, Mehrdad
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022/9/22
Y1 - 2022/9/22
N2 - Since its advent in the last century, magnetic resonance imaging (MRI)
provides a radiation-free diagnosis tool and has revolutionized medical
imaging. Compressed sensing (CS) methods leverage the sparsity prior of
signals to reconstruct clean images from under-sampled measurements and
accelerate the acquisition process. However, it is challenging to reduce
strong aliasing artifacts caused by under-sampling and produce
high-quality reconstructions with fine details. In this paper, we
propose a novel GAN-based framework to recover the under-sampled images,
which is characterized by a novel data consistency block and a densely
connected network cascade used to improve the model performance in
visual inspection and evaluation metrics. The role of each proposed
block has been challenged in the ablation study, in terms of
reconstruction quality metrics, using texture-rich FastMRI Knee image
dataset.
AB - Since its advent in the last century, magnetic resonance imaging (MRI)
provides a radiation-free diagnosis tool and has revolutionized medical
imaging. Compressed sensing (CS) methods leverage the sparsity prior of
signals to reconstruct clean images from under-sampled measurements and
accelerate the acquisition process. However, it is challenging to reduce
strong aliasing artifacts caused by under-sampling and produce
high-quality reconstructions with fine details. In this paper, we
propose a novel GAN-based framework to recover the under-sampled images,
which is characterized by a novel data consistency block and a densely
connected network cascade used to improve the model performance in
visual inspection and evaluation metrics. The role of each proposed
block has been challenged in the ablation study, in terms of
reconstruction quality metrics, using texture-rich FastMRI Knee image
dataset.
KW - MRI Reconstruction
KW - GAN-Based framework
KW - dense network connections
KW - data consistency
U2 - 10.1007/978-3-031-17247-2_4
DO - 10.1007/978-3-031-17247-2_4
M3 - Conference contribution
VL - 13587
T3 - Lecture Notes in Computer Science
SP - 34
EP - 43
BT - Machine Learning for Medical Image Reconstruction
PB - Springer
T2 - 5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022
Y2 - 22 September 2022
ER -