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• Clinical Article •
Clinical diagnostic value of multimodal quantitative MRI in the combined evaluation of Alzheimer's disease
WU Yaqi  WEI Dingtai  CHEN Qiuyan  LI Zhaoyang  ZHENG Yushan  PENG Xiaolan 

Cite this article as: WU Y Q, WEI D T, CHEN Q Y, et al. Clinical diagnostic value of multimodal quantitative MRI in the combined evaluation of Alzheimer's disease[J]. Chin J Magn Reson Imaging, 2026, 17(10): 44-54. DOI:10.12015/issn.1674-8034.2026.10.006.


[Abstract] Objective To explore the value of amide proton transfer (APT) imaging, quantitative susceptibility mapping (QSM) and ultra-high b-values diffusion-weighted imaging (DWI_uh) in the clinical diagnosis of Alzheimer's disease (AD).Materials and Methods A total of 61 participants were enrolled in this study, including 17 in the AD group, 20 in the mild cognitive impairment (MCI) group, and 24 in the normal control (NC) group. Analysis of covariance (ANCOVA) was used to compare the differences in magnetization transfer rate asymmetry (MTRasym), ultra-high b-value apparent diffusion coefficient (ADC_uh), and QSM values among the three groups. Partial correlation analysis was performed to assess the relationships between the imaging parameters, as well as between each parameter and the Mini-Mental State Examination (MMSE) score. Receiver operating characteristic (ROC) curve analysis was applied to assess the diagnostic performance of MTRasym, QSM and ADC_uh, using the area under the curve (AUC), sensitivity and specificity as evaluation metrics. The parameter with the highest AUC among the three was selected for binary multivariable logistic regression analysis to compare the diagnostic efficacy of the combined three-parameter model versus the single-parameter models.Results Compared with the NC and MCI groups, the AD group showed increased MTRasym, QSM, and ADC_uh values in multiple brain regions (all P < 0.05). The MTRasym, QSM, and ADC_uh values in multiple brain regions were negatively correlated with MMSE scores. Specifically, MTRasym values exhibited significant negative correlations in the bilateral frontal white matter, bilateral temporal white matter, bilateral substantia nigra, bilateral amygdala, bilateral hippocampus, right putamen, and right thalamus, with more prominent correlations observed in the right frontal white matter (r = -0.452, P < 0.001), right substantia nigra (r = -0.435, P < 0.001), and right putamen (r = -0.480, P < 0.001). QSM values showed significant negative correlations in the bilateral substantia nigra, bilateral amygdala, bilateral putamen, bilateral thalamus, and bilateral hippocampus. ADC_uh values were negatively correlated in the bilateral hippocampus, left temporal white matter, and left thalamus, with the most significant correlations found in the right hippocampus (r = -0.486, P < 0.001) and left hippocampus (r = -0.512, P < 0.001). ROC curve analysis demonstrated that MTRasym and QSM parameters could effectively distinguish NC from MCI, NC from AD, and MCI from AD. ADC_uh only differentiated NC from AD and MCI from AD, with no statistically significant difference between the NC and MCI groups. In the differential diagnosis between NC and AD, the QSM value in the left amygdala yielded the best diagnostic performance (AUC = 0.987). Three parameters including MTRasym of the right frontal white matter, QSM of the left amygdala and ADC_uh of the right hippocampus were incorporated to establish a binary logistic regression combined diagnostic model for differentiating NC from AD. The combined model yielded an AUC of 0.988 (95% CI: 0.961 to 1.000, P < 0.001), with an optimal cut-off value of 0.441 6, a sensitivity of 100.0% and a specificity of 95.8%. Its AUC was close to that of the single left amygdala QSM model (AUC = 0.987), indicating no statistically significant difference in diagnostic performance between the two models.Conclusions Measurements of MTRasym, QSM, and ADC_uh in specific brain regions facilitate the differential diagnosis of AD, with QSM values of the left amygdala exhibiting optimal diagnostic efficiency. The multi-parameter combined model achieves favorable diagnostic performance for AD, whereas its efficacy is comparable to that of the optimal single-parameter model without statistically significant difference. Integrating information from multiple pathological dimensions, this model still holds promise as a combined imaging biomarker for AD. The integrated application of multiple quantitative magnetic resonance imaging techniques provides valuable evidence for the clinical auxiliary diagnosis of AD.
[Keywords] Alzheimer's disease；mild cognitive impairment；amide proton transfer；quantitative magnetic susceptibility mapping；ultra-high b-value diffusion-weighted imaging；magnetic resonance imaging

WU Yaqi1, 2, 3   WEI Dingtai1, 2   CHEN Qiuyan1, 2   LI Zhaoyang1, 2   ZHENG Yushan1, 2, 3   PENG Xiaolan1, 2*  

1 Ningde Clinical Medical College of Fujian Medical University, Ningde 352100, China

2 Department of Radiology, Ningde Municipal Hospital, Ningde 352100, China

3 Department of Imaging, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou 363000, China

Corresponding author: PENG X L, E-mail: 764320071@qq.com

Conflicts of interest   None.

Received  2026-01-05
Accepted  2026-08-07
DOI: 10.12015/issn.1674-8034.2026.10.006
Cite this article as: WU Y Q, WEI D T, CHEN Q Y, et al. Clinical diagnostic value of multimodal quantitative MRI in the combined evaluation of Alzheimer's disease[J]. Chin J Magn Reson Imaging, 2026, 17(10): 44-54. DOI:10.12015/issn.1674-8034.2026.10.006.

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