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Updated: Jun 21, 2026
Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury
Published on: August 14, 2019
Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for
Noriyuki Fujima1, Junichi Nakagawa2, Hiroyuki Kameda3
1Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, 060-8638, Japan. fujima@med.hokudai.ac.jp.
Objectives:
To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI).
Materials And Methods:
We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle.
Results:
Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (p < 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (p = 0.002 ~ p < 0.001).
Discussion:
DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.
