Model-based image updating in deep brain stimulation with assimilation of deep brain sparse data.
Model-based image updating in deep brain stimulation with assimilation of deep brain sparse data.
复制标题
深部脑刺激中基于模型的图像更新与深部脑稀疏数据的同化。
DOI:
10.1002/mp.16578
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发表时间:
2023
期刊:
影响因子:
3.8
通讯作者:
Paulsen,KeithD
中科院分区:
文献类型:
--
作者:
Li,Chen;Fan,Xiaoyao;Aronson,JoshuaP;Hong,Jennifer;Khan,Tahsin;Paulsen,KeithD
BackgroundAccuracy of electrode placement for deep brain stimulation (DBS) is critical to achieving desired surgical outcomes and impacts the efficacy of treating neurodegenerative diseases. Intraoperative brain shift degrades the accuracy of surgical navigation based on preoperative images.PurposeWe extended a model‐based image updating scheme to address intraoperative brain shift in DBS surgery and improved its accuracy in deep brain.MethodsWe evaluated 10 patients, retrospectively, who underwent bilateral DBS surgery and classified them into groups of large and small deformation based on a 2 mm subsurface movement threshold and brain shift index of 5%. In each case, sparse brain deformation data were used to estimate whole brain displacements and deform preoperative CT (preCT) to generate updated CT (uCT). Accuracy of uCT was assessed using target registration errors (TREs) at the Anterior Commissure (AC), Posterior Commissure (PC), and four calcification points in the sub‐ventricular area by comparing their locations in uCT with their ground truth counterparts in postoperative CT (postCT).ResultsIn the large deformation group, TREs were reduced from 2.5 mm in preCT to 1.2 mm in uCT (53% compensation); in the small deformation group, errors were reduced from 1.25 to 0.74 mm (41%). Average reduction of TREs at AC, PC and pineal gland were significant, statistically (p⩽ 0.01).ConclusionsWith more rigorous validation of model results, this study confirms the feasibility of improving the accuracy of model‐based image updating in compensating for intraoperative brain shift during DBS procedures by assimilating deep brain sparse data.
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影响因子:
2.8
作者:
Kremer, Naomi I.;Oterdoom, D. L. Marinus;van Dijk, J. Marc C.
通讯作者:
van Dijk, J. Marc C.
DOI:
--
发表时间:
2005
期刊:
Neurosurgical focus [electronic resource].
影响因子:
--
作者:
McClelland3rd,Shearwood;Ford,Blair;Senatus,PatrickB;Winfield,LindaM;Du,YunlingE;Pullman,SethL;Yu,Qiping;Frucht,StevenJ;McKhann2nd,GuyM;Goodman,RobertR
通讯作者:
Goodman,RobertR
影响因子:
1.7
作者:
Furlanetti, Luciano;Hasegawa, Harutomo;Ashkan, Keyoumars
通讯作者:
Ashkan, Keyoumars
DOI:
10.1117/12.2582315
发表时间:
2021
期刊:
Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling
影响因子:
--
作者:
Chen Li;Xiaoyao Fan;J. Aronson;K. Paulsen
通讯作者:
K. Paulsen
影响因子:
4.7
作者:
Chen Li;Xiaoyao Fan;J. Aronson;K. Paulsen
通讯作者:
K. Paulsen