Abstract
To explore the application value of quantitative susceptibility mapping (QSM) based on Laplace algorithm in the diagnosis of Parkinson’s disease, 48 Parkinson’s disease patients admitted to our hospital were included as the research objects. They were randomly divided into control group (24 cases) and experimental group (24 cases). All patients underwent quantitative magnetic susceptibility imaging scan. In the experimental group, the improved Laplace algorithm was used for QSM diagnosis, while in the control group, conventional QSM diagnosis was used. Through calculations of precision, recall, dice similarity coefficient, intersection-over-union (IoU), and area under the curve (AUC), the quality improvement effect of the improved Laplace algorithm for QSM image was assessed. Then, the diagnostic accuracy of the algorithm was verified by comparing with the results of QSM image diagnosis in Parkinson’s patients without algorithm processing. The results showed that compared with the traditional Laplace algorithm, the improved Laplace algorithm can considerably reduce the image noise level ( P < 0.05 ). The dice, IoU, precision, and recall rate of image quality evaluation indicator were considerably improved ( P < 0.05 ), and the AUC reached 0.896. There were no significant differences in fraction anisotropy (FA) and mean diffusivity (MD) between the two groups ( P > 0.05 ) and no significant differences in magnetic susceptibility of brain nuclei between the two groups ( P > 0.05 ). However, they all showed high magnetic susceptibility in the substantia nigra region of the brain. Compared with the control group, the diagnostic accuracy of the experimental group was 97.5 ± 1.23%, which was considerably higher than that of the control group (86.5 ± 3.56%) ( P < 0.05 ). In short, the image quality of QSM based on Laplace improved algorithm was greatly improved, and the diagnostic accuracy of PD was also greatly improved, which was worthy of promotion in the field of clinical QSM imaging diagnosis of PD.
Highlights
Parkinson’s disease (PD), known as palsy tremor, is a neurodegenerative disease common in middle-aged and elderly people
Compared with the quantitative susceptibility mapping (QSM) image that were not processed by the Laplace algorithm, the quality of QSM images processed by the conventional Laplace algorithm and the improved Laplace algorithm was considerably improved, and the sharpness of brain edges was greatly improved
By comparing the results with the traditional QSM image diagnosis results of the control group, the adoption potential summary, various image reconstructions and image optimizations based on the Laplace algorithm have very good application effects and have broad research prospects in the future
Summary
Parkinson’s disease (PD), known as palsy tremor, is a neurodegenerative disease common in middle-aged and elderly people. Static tremor, bradykinesia, myotonia, and postural balance disorders are the main characteristics. QSM can describe the distribution of magnetic susceptibility in the imaging site, indirectly reflecting the content of iron in the body, blood vessels, and tissue structure. It is a good method for the diagnosis and research of PD [11, 12]. QSM relies on phase information to measure the magnetic characteristics of tissues and has been successfully applied to the measurement of iron deposition, blood oxygen saturation, differentiation of bleeding, and calcification. QSM currently has problems such as phase artifact interference, and region of interest (ROI) need to be manually drawn, which wastes time and has errors [14]
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