Paper Info
Title
Multimodal MRI Acceleration via Deep Cascading Networks with Peer-Layer-Wise Dense Connections
Abstract
Medical diagnosis benefits from multimodal Magnetic Resonance Imaging (MRI). However, multimodal MRI has an inherently slow acquisition process. For acceleration, recent studies explored using a fully-sampled side modality (fSM) as a guidance to reconstruct the fully-sampled query modalities (fQMs) from their undersampled k-space data via convolutional neural networks. However, even aided by fSM, the reconstruction of fQMs from highly undersampled QM data (uQM) is still suffering from aliasing artifacts. To enhance reconstruction quality, we suggest to fully use both uQM and fSM via a deep cascading network, which adopts an iterative Reconstruction-And-Refinement (iRAR) structure. The main limitation of the iRAR structure is that its intermediate reconstruction operators impede the feature flow across subnets and thus leads to short-term memory. We therefore propose two typical Peer-layer-wise Dense Connections (PDC), namely, inner PDC (iPDC) and end PDC (ePDC), to achieve long-term memory. Extensive experiments on different query modalities under different acceleration rates demonstrate that the deep cascading network equipped with iPDC and ePDC consistently outperforms the state-of-the-art methods and can preserve anatomical structure faithfully up to 12-fold acceleration.
Year
DOI
Venue
2021
10.1007/978-3-030-87231-1_32
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VI
Keywords
DocType
Volume
MRI acceleration, Guidance-based reconstruction methods, Deep cascading networks, Peer-layer-wise dense connections
Conference
12906
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Xiao-Xin Li182.80
Zhijie Chen2709.84
Xin-Jie Lou300.34
Junwei Yang400.34
Yong Chen501.01
Dinggang Shen67837611.27