Multimodal MRI Acceleration via Deep Cascading Networks with Peer-Layer-Wise Dense Connections

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.