Region-of-interest undersampled MRI reconstruction: A deep convolutional neural network approach

Abstract

Compressive sensing enables fast magnetic resonance imaging (MRI) reconstruction with undersampled k-space data. However, in most existing MRI reconstruction models, the whole MR image is targeted and reconstructed without taking specific tissue regions into consideration. This may fails to emphasize the reconstruction accuracy on important and region-of-interest (ROI) tissues for diagnosis. In some ROI-based MRI reconstruction models, the ROI mask is extracted by human experts in advance, which is laborious when the MRI datasets are too large. In this paper, we propose a deep neural network architecture for ROI MRI reconstruction called ROIRecNet to improve reconstruction accuracy of the ROI regions in under-sampled MRI. In the model, we obtain the ROI masks by feeding an initially reconstructed MRI from a pre-trained MRI reconstruction network (RecNet) to a pre-trained MRI segmentation network (ROINet). Then we fine-tune the RecNet with a binary weighted ℓ2 loss function using the produced ROI mask. The resulting ROIRecNet can offer more focus on the ROI. We test the model on the MRBrainS13 dataset with different brain tissues being ROIs. The experiment shows the proposed ROIRecNet can significantly improve the reconstruction quality of the region of interest.