%0 Journal Article
%A Pawar, Kamlesh
%A Chen, Zhaolin
%A Shah, N. J.
%A Egan, Gary. F.
%T A Deep Learning Framework for Transforming Image Reconstruction Into Pixel Classification
%J IEEE access
%V 7
%@ 2169-3536
%C New York, NY
%I IEEE
%M FZJ-2020-00036
%P 177690 - 177702
%D 2019
%X A deep learning framework is presented that transforms the task of MR image reconstruction from randomly undersampled k-space data into pixel classification. A DL network was trained to remove incoherent undersampling artifacts from MR images. The underlying, fully sampled, target image was represented as a discrete quantized image. The quantization step enables the design of a convolutional neural network (CNN) that can classify each pixel in the input image to a discrete quantized level. The reconstructed image quality of the proposed DL classification model was compared with conventional compressed sensing (CS) and a DL regression model. The reconstructed images using the DL classification model outperformed the state-of-the-art compressed sensing and DL regression models with a similar number of parameters assessed using quantitative measures. The experiments reveal that the proposed deep learning method is robust to noise and is able to reconstruct high-quality images in low SNR scenarios where conventional CS reconstructions and DL regression networks perform poorly. A generic design framework for transforming MR image reconstruction into pixel classification is developed. The proposed method can be easily incorporated into other DL-based image reconstruction methods.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000509483800029
%R 10.1109/ACCESS.2019.2959037
%U https://juser.fz-juelich.de/record/868442