Deep learning-based image reconstruction benefits DTI for depression severity assessment

Yuanyuan Cui¹Yihao Wang²Weiming Yuan³Youhan Zhang¹Yunmeng Wang¹Jiankun Dai⁴Yuxin Cheng¹Xin Zhang¹Hongbiao Sun¹Shuwen Dong¹Jinlin Wang¹Yonghai Bai²Shiyuan Liu^1*Yi Xiao^1*

• ¹Department of radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
• ²Department of Psychology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
• ³Qingdao Special Servicemen Recuperation Center of PLA Navy, Qingdao, China
• ⁴MR Research, GE Healthcare, Beijing, China

Objective: To evaluate whether deep learning-based image reconstruction (DLR) improves the accuracy of diffusion tensor imaging (DTI) measurements used to assess depression severity. Methods: A total of 52 patients diagnosed with depression in our hospital between March 2023 and July 2023 were enrolled in this study. The severity of depression was measured using the 9-item Patient Health Questionnaire (PHQ-9). Each patient underwent DTI scans. Two image sets were generated: one with the original DTI (ORI DTI) and one using DLR DTI. Tract-Based Spatial Statistics (TBSS) were used to compare the fractional anisotropy (FA) from DLR DTI versus ORI DTI, and between patients with mild-to-moderate versus severe depression. Multivariate logistic regression was carried out to find independent factors for discriminating mild-to-moderate from severe depression patients. Receiver operating characteristic (ROC) curve analysis the and areas under the curve (AUC) were used to assess the diagnostic performance. Results: There were 28 patients with mild-to-moderate depression and 24 with severe depression included. No significant differences were observed between the two groups in terms of gender (p=0.115), age (p=0.603), or educational background (p=0.148). Compared to patients with mild-to-moderate depression, those with severe depression showed lower FA values in the right corticospinal tract (CST) on ORI DTI. With DLR DTI, decreases in FA were found in the right CST, right anterior thalamic radiation, and left superior longitudinal fasciculus. The diagnostic model based on DLR DTI outperformed the ORI DTI model in assessing depression severity (AUC: 0.951 vs. 0.764, p<0.001). Conclusion: DLR DTI demonstrated greater sensitivity in detecting white matter (WM) abnormalities in patients with severe depression and provided better diagnostic performance in evaluating depression severity.