Feasibility of reducing scan time based on deep learning reconstruction in magnetic resonance imaging: a phantom study

Abstract

Objective To explore the feasibility of deep learning reconstruction (DLR) in shortening the scanning time through water phantom experiments. Methods The control variable method based on phantom, was adopted to depict the curves of scanning time varying with the number of excitation (NEX) ，matrix，and resolution. The signal-to-noise ratio (SNR) and subjective image quality of different DLR with high, medium and low noise reduction levels (DLR_H, DLR_M, DLR_L) and traditional reconstruction (ConR) were analyzed, including the four-point assessment of sharpness and detail clarity, and distortion degree, and the curves of changes were depicted and the fitting curves were calculated. Results The positive correlation between NEX and resolution with MRI scanning time and SNR was consistent in ConR and DLR reconstructions with different noise reduction levels. Specifically, under the same NEX and resolution conditions, the SNR of ConR, DLR_L, DLR_M, and DLR_H increased sequentially. When the matrix was fixed at 512×512 while images with subjective evaluation score of 3 or 4 were taken as satisfactory ones, the images reconstructed by DLR can obtain satisfactory sharpness, distortion degree and detail display, and when NEX were 3, 4, 5 and 7 and 11, image details were displayed best while scanning time was significantly reduced. Meanwhile, the distortion of images achieved satisfactory results with NEX of 2, 4, 5, and 6. Also, satisfactory detail display was obtained when NEX was 3, 5, 7 and 11. All the above combinations of NEX and resolution saved scanning time from 31 to 244 seconds. Similarly, as the resolution increased, the image scores of the sharpness and detail display gradually increased and. distortion degree decreased. When NEX was fixed at 6, the images reconstructed by DLR_H, DLR_M, DLR_L and ConR obtained satisfactory sharpness ；When the matrix was 320×320, 384×384, 448×448 and 640×640,, the scanning time was 141 seconds, 141 seconds, 187 seconds and 232 seconds, respectively. DLR_H and DLR_M achieved a smaller distortion degree at the 512×512 matrix, while DLR_L and ConR required a higher imaging matrix and longer scanning time to obtain similar image quality. For detail clarity, DLR_H achieved satisfactory detail display when the matrix was 512×512, and the scanning time was less than that of DLR_M, DLR_L and ConR. Conclusions DLR, especially DLR_H, while reducing NEX and resolution to shorten the MRI scanning time, may not only maintain a satisfactory SNR and image detail clarity, but also achieve higher image clarity and lower distortion degree.

Keywords

• |deep learning reconstruction|number of excitations|image quality