Diffusion-weighted imaging (DWI) is frequently used in the field of diagnostic medicine to detect various human diseases. In DWI, noise suppression is very important for achieving high detection accuracy of diseases.
In this study, we develop a deep convolutional neural network (Deep-CNN) noise reduction algorithm and evaluate its effectiveness in DWI by performing both simulations and real experiments with a 1.5- and a 3.0-T MRI
system. The results validate the proposed Deep-CNN algorithm for DWI. Compared with previously developed non-local means (NLM) algorithms, the proposed Deep-CNN algorithm achieves superior quantitative results.
In conclusion, the quantitative results verify that the proposed Deep-CNN algorithm has higher noise reduction efficiency and image visibility than previously developed algorithms for DWI.