Model-Based Image Updating for Brain Shift in Deep Brain Stimulation Electrode Placement Surgery

Li, Chen; Fan, Xiaoyao; Hong, Jennifer; Roberts, David W.; Aronson, Joshua P.; Paulsen, Keith D.

doi:10.1109/tbme.2020.2990669

Cited by 5 publications

(21 citation statements)

References 41 publications

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“…Previously, Lunn et al applied an inverse scheme by incorporating intra-op data and demonstrated an average of 35% improvement over solving a forward brain model alone, warranting driving model with intra-op sparse data to estimate unknown forcing conditions for improved prediction. 30 Our previous work 16 used BCs for opencranial procedures and only incorporated cortical surface sparse data. Results showed that brain shift was compensated effectively at AC (meeting the clinical goal of shift < 2 mm), but left unaltered at PC and pineal gland.…”

Section: Discussionmentioning

confidence: 99%

“…A similar problem was encountered in our previous study where the model was driven by sparse brain surface data in which case shift at the anterior commissure (AC) was corrected successfully whereas errors at the posterior commissure (PC) and the pineal gland were unaltered. 16 In this study, we extended our image updating framework to assimilate sparse data from deep brain and demonstrate improved performance in correcting brain shift at six deep brain landmarks during DBS lead placement surgery. In particular, we extracted deep brain displacements by registering corresponding ventricles in preoperative CT (preCT) to postoperative CT (postCT).…”

Section: Introductionmentioning

confidence: 98%

“…Lately, approaches based on biomechanical brain models have been explored to correct for brain shift in DBS surgery using iMR and CT data for validation 15,16 . Adapted from Biot's consolidation theory, one finite element model deforms brain tissue under assumed loading and boundary conditions (BCs) with assimilation of intraoperative measurements via an inverse scheme 17–20 .…”

Section: Introductionmentioning

confidence: 99%

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Model‐based image updating in deep brain stimulation with assimilation of deep brain sparse data

Fan

Aronson

et al. 2023

Medical Physics

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Model‐based image updating in deep brain stimulation with assimilation of deep brain sparse data

Fan

Aronson

et al. 2023

Medical Physics

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“…Previously, we implemented a finite-element (FEM) bio-mechanical model to compute whole brain displacement fields to update preoperative images to compensate for brain shift [5][6][7][8][9] . We have incorporated both surface and deep brain sparse data to drive the bio-mechanical model.…”

Section: Introductionmentioning

confidence: 99%

“…The surface sparse data was extracted by registering corresponding brain surfaces from a postoperative CT (postCT) to its corresponding preoperative CT (preCT). The deep brain sparse data was extracted by non-rigidly registering the lateral ventricle of the postCT with the preCT ventricle using Demon's algorithm 6,8 .…”

Section: Introductionmentioning

confidence: 99%

Estimating shift at deep brain targets in deep brain stimulation: a comparison between a machine learning approach and a biomechanical model

Chen

Aronson

et al. 2023

Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

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The success of deep brain stimulation (DBS) is dependent on the accurate placement of electrodes in the operating room (OR). However, due to intraoperative brain shift, the accuracy of pre-operative scans and pre-surgical planning are often degraded. To compensate for brain shift, we created a finite element bio-mechanical brain model that updates preoperative images by assimilating intraoperative sparse data from the brain surface or deep brain targets. Additionally, we constructed an artificial neural network (ANN) that leveraged a large number of ventricle nodal displacements to estimate brain shift. The machine learning method showed potential in incorporating ventricle sparse data to accurately compute shift at the brain surface. Thus, in this paper, we propose using this machine learning model to estimate brain atrophy at deep brain targets such as the anterior commissure (AC) and the posterior commissure (PC). The ANN consists of an input layer with 9 hand-engineered features, such as the distance between the deep brain target and the ventricle node, two hidden layers and an output layer. This model was trained using 8 patient cases and tested on 2 patient cases.

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A particle filter approach to dynamic kidney pose estimation in robotic surgical exposure

et al. 2022

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Model-Based Image Updating for Brain Shift in Deep Brain Stimulation Electrode Placement Surgery

Cited by 5 publications

References 41 publications

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

Estimating shift at deep brain targets in deep brain stimulation: a comparison between a machine learning approach and a biomechanical model

A particle filter approach to dynamic kidney pose estimation in robotic surgical exposure

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