Improved Quantitative Susceptibility Mapping under Laplace Algorithm in Diagnosis of Parkinson’s Disease

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) a...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Guangxi Chen, Liang Zeng, Liu Yang, Yixian Yu, Panli Sun, Tao Yao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/0f394b19c0154314bda6267a42e4c2ad
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0f394b19c0154314bda6267a42e4c2ad
record_format	dspace
spelling	oai:doaj.org-article:0f394b19c0154314bda6267a42e4c2ad2021-11-22T01:09:55ZImproved Quantitative Susceptibility Mapping under Laplace Algorithm in Diagnosis of Parkinson’s Disease1875-919X10.1155/2021/8210526https://doaj.org/article/0f394b19c0154314bda6267a42e4c2ad2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8210526https://doaj.org/toc/1875-919XTo 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.Guangxi ChenLiang ZengLiu YangYixian YuPanli SunTao YaoHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Guangxi Chen Liang Zeng Liu Yang Yixian Yu Panli Sun Tao Yao Improved Quantitative Susceptibility Mapping under Laplace Algorithm in Diagnosis of Parkinson’s Disease
description	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.
format	article
author	Guangxi Chen Liang Zeng Liu Yang Yixian Yu Panli Sun Tao Yao
author_facet	Guangxi Chen Liang Zeng Liu Yang Yixian Yu Panli Sun Tao Yao
author_sort	Guangxi Chen
title	Improved Quantitative Susceptibility Mapping under Laplace Algorithm in Diagnosis of Parkinson’s Disease
title_short	Improved Quantitative Susceptibility Mapping under Laplace Algorithm in Diagnosis of Parkinson’s Disease
title_full	Improved Quantitative Susceptibility Mapping under Laplace Algorithm in Diagnosis of Parkinson’s Disease
title_fullStr	Improved Quantitative Susceptibility Mapping under Laplace Algorithm in Diagnosis of Parkinson’s Disease
title_full_unstemmed	Improved Quantitative Susceptibility Mapping under Laplace Algorithm in Diagnosis of Parkinson’s Disease
title_sort	improved quantitative susceptibility mapping under laplace algorithm in diagnosis of parkinson’s disease
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/0f394b19c0154314bda6267a42e4c2ad
work_keys_str_mv	AT guangxichen improvedquantitativesusceptibilitymappingunderlaplacealgorithmindiagnosisofparkinsonsdisease AT liangzeng improvedquantitativesusceptibilitymappingunderlaplacealgorithmindiagnosisofparkinsonsdisease AT liuyang improvedquantitativesusceptibilitymappingunderlaplacealgorithmindiagnosisofparkinsonsdisease AT yixianyu improvedquantitativesusceptibilitymappingunderlaplacealgorithmindiagnosisofparkinsonsdisease AT panlisun improvedquantitativesusceptibilitymappingunderlaplacealgorithmindiagnosisofparkinsonsdisease AT taoyao improvedquantitativesusceptibilitymappingunderlaplacealgorithmindiagnosisofparkinsonsdisease
_version_	1718418382981693440

Improved Quantitative Susceptibility Mapping under Laplace Algorithm in Diagnosis of Parkinson’s Disease

Ejemplares similares