TY  - JOUR
AU  - Pawar, Kamlesh
AU  - Chen, Zhaolin
AU  - Shah, N. J.
AU  - Egan, Gary. F.
TI  - A Deep Learning Framework for Transforming Image Reconstruction Into Pixel Classification
JO  - IEEE access
VL  - 7
SN  - 2169-3536
CY  - New York, NY
PB  - IEEE
M1  - FZJ-2020-00036
SP  - 177690 - 177702
PY  - 2019
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000509483800029
DO  - DOI:10.1109/ACCESS.2019.2959037
UR  - https://juser.fz-juelich.de/record/868442
ER  -