Clinical application of 3D reconstruction and accurate volume measurement of white matter in patients with cognitive...

To quantitatively measure the volume of white matter hyperintensities (WMHs) in different parts of the brain in patients with different types of cognitive function and analyze the relationship between WMH volume and cognitive function to obtain a threshold WMH volume for the early detection and clinical assessment of cognitive dysfunction. The clinical data and magnetic resonance imaging (MRI) data of patients with WMHs indicated by cranial MR in the Department of General Medicine of Shandong Provincial Third Hospital were collected. The FLAIR sequence images of the patients were subsequently analyzed with computer automated detection technology. Through deep learning-based 3D reconstruction, the specific volumes of the patients' WMHs were obtained. Patients were divided into three groups according to the Fazekas scale score: Fazekas score 1, Fazekas score 2, and Fazekas score 3. The WMH volumes within each group were subsequently compared, and the correlations between the WMH volumes of the patients in each group and their Montreal Cognitive Assessment (MoCA) scores, Trail Making Test A (TMT-A) scores, Trail Making Test B (TMT-B) scores, age, duration of hypertension, duration of diabetes, basic information, etc., were analyzed. The patients were subsequently divided into a normal group (MoCA > 25) and a mild cognitive impairment group (18 < MoCA ≤ 25) on the basis of their MoCA scores. The WMH volumes in each group were then calculated separately. The cutoff values of the WMH volume for differentiating between the normal group and mild cognitive impairment group were obtained through receiver operating characteristic (ROC) curve analysis. The MoCA scores significantly differed among the three Fazekas score groups (r = - 0.5716, P < 0.0001). There were also statistically significant differences in the total volume of WMHs among the three groups (r = 0.7527, P < 0.0001). WMH volume was positively correlated with the TMT-A and TMT-B scores (r = 0.2345,  P< 0.05; r = 0.2404, P < 0.05) but negatively correlated with the MoCA score (r = - 0.4789, P < 0.0001). Moreover, WMH volume was positively associated with the duration of hypertension (F = 4.743, P < 0.05) but not with the duration of diabetes (F = 1.431, P = 0.2456). The cutoff value of WMH volume between the normal group and mild cognitive impairment group was 15.474900; at this value, the sensitivity of the WMH volume in discriminating the two groups was 0.808, and the specificity was 0.556. Automated detection technology can successfully be used to obtain the volume of WMHs in different parts of patients' brains. Since WMH volume is correlated with cognitive function scores, we can use MRI to identify and assess individuals who show potential early signs of cognitive dysfunction and administer early interventions. These findings provide potential preventive and therapeutic targets for the clinical diagnosis and treatment of cognitive dysfunction.