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Li C, Fan X, Aronson JP, Hong J, Khan T, Paulsen KD. Model-based image updating in deep brain stimulation with assimilation of deep brain sparse data. Med Phys 2023; 50:7904-7920. [PMID: 37418478 DOI: 10.1002/mp.16578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 04/06/2023] [Accepted: 05/01/2023] [Indexed: 07/09/2023] Open
Abstract
BACKGROUND Accuracy 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. PURPOSE We extended a model-based image updating scheme to address intraoperative brain shift in DBS surgery and improved its accuracy in deep brain. METHODS We 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). RESULTS In 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). CONCLUSIONS With 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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Affiliation(s)
- Chen Li
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA
| | - Xiaoyao Fan
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA
| | - Joshua P Aronson
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
- Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
| | - Jennifer Hong
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
- Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
| | - Tahsin Khan
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA
| | - Keith D Paulsen
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
- Norris Cotton Cancer Center, Lebanon, New Hampshire, USA
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Lipková J, Menze B, Wiestler B, Koumoutsakos P, Lowengrub JS. Modelling glioma progression, mass effect and intracranial pressure in patient anatomy. J R Soc Interface 2022; 19:20210922. [PMID: 35317645 PMCID: PMC8941421 DOI: 10.1098/rsif.2021.0922] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/21/2022] [Indexed: 02/06/2023] Open
Abstract
Increased intracranial pressure is the source of most critical symptoms in patients with glioma, and often the main cause of death. Clinical interventions could benefit from non-invasive estimates of the pressure distribution in the patient's parenchyma provided by computational models. However, existing glioma models do not simulate the pressure distribution and they rely on a large number of model parameters, which complicates their calibration from available patient data. Here we present a novel model for glioma growth, pressure distribution and corresponding brain deformation. The distinct feature of our approach is that the pressure is directly derived from tumour dynamics and patient-specific anatomy, providing non-invasive insights into the patient's state. The model predictions allow estimation of critical conditions such as intracranial hypertension, brain midline shift or neurological and cognitive impairments. A diffuse-domain formalism is employed to allow for efficient numerical implementation of the model in the patient-specific brain anatomy. The model is tested on synthetic and clinical cases. To facilitate clinical deployment, a high-performance computing implementation of the model has been publicly released.
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Affiliation(s)
- Jana Lipková
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zürich, Zürich, Switzerland
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Petros Koumoutsakos
- Computational Science and Engineering Lab, ETH Zürich, Zürich, Switzerland
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - John S. Lowengrub
- Department of Mathematics, University of California, Irvine, CA, USA
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, Chao Family Comprehensive Cancer Center, University of California, Irvine, CA, USA
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Sebek J, Taeprasartsit P, Wibowo H, Beard WL, Bortel R, Prakash P. Microwave ablation of lung tumors: A probabilistic approach for simulation-based treatment planning. Med Phys 2021; 48:3991-4003. [PMID: 33964020 DOI: 10.1002/mp.14923] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 04/16/2021] [Accepted: 04/17/2021] [Indexed: 12/16/2022] Open
Abstract
PURPOSE Microwave ablation (MWA) is a clinically established modality for treatment of lung tumors. A challenge with existing application of MWA, however, is local tumor progression, potentially due to failure to establish an adequate treatment margin. This study presents a robust simulation-based treatment planning methodology to assist operators in comparatively assessing thermal profiles and likelihood of achieving a specified minimum margin as a function of candidate applied energy parameters. METHODS We employed a biophysical simulation-based probabilistic treatment planning methodology to evaluate the likelihood of achieving a specified minimum margin for candidate treatment parameters (i.e., applied power and ablation duration for a given applicator position within a tumor). A set of simulations with varying tissue properties was evaluated for each considered combination of power and ablation duration, and for four different scenarios of contrast in tissue biophysical properties between tumor and normal lung. A treatment planning graph was then assembled, where distributions of achieved minimum ablation zone margins and collateral damage volumes can be assessed for candidate applied power and treatment duration combinations. For each chosen power and time combination, the operator can also visualize the histogram of ablation zone boundaries overlaid on the tumor and target volumes. We assembled treatment planning graphs for generic 1, 2, and 2.5 cm diameter spherically shaped tumors and also illustrated the impact of tissue heterogeneity on delivered treatment plans and resulting ablation histograms. Finally, we illustrated the treatment planning methodology on two example patient-specific cases of tumors with irregular shapes. RESULTS The assembled treatment planning graphs indicate that 30 W, 6 min ablations achieve a 5-mm minimum margin across all simulated cases for 1-cm diameter spherical tumors, and 70 W, 10 min ablations achieve a 3-mm minimum margin across 90% of simulations for a 2.5-cm diameter spherical tumor. Different scenarios of tissue heterogeneity between tumor and lung tissue revealed 2 min overall difference in ablation duration, in order to reliably achieve a 4-mm minimum margin or larger each time for 2-cm diameter spherical tumor. CONCLUSIONS An approach for simulation-based treatment planning for microwave ablation of lung tumors is illustrated to account for the impact of specific geometry of the treatment site, tissue property uncertainty, and heterogeneity between the tumor and normal lung.
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Affiliation(s)
- Jan Sebek
- Department of Electrical and Computer Engineering, Kansas State University Manhattan, KS, 66506, USA.,Department of Circuit Theory, Czech Technical University in Prague, Prague, Czech Republic
| | - Pinyo Taeprasartsit
- PhenoMapper, LLC, San Jose, CA, 95112, USA.,Department of Computing, Faculty of Science, Silpakorn University, Thailand
| | | | - Warren L Beard
- Department of Clinical Sciences, Kansas State University, Manhattan, KS, 66506, USA
| | - Radoslav Bortel
- Department of Circuit Theory, Czech Technical University in Prague, Prague, Czech Republic
| | - Punit Prakash
- Department of Electrical and Computer Engineering, Kansas State University Manhattan, KS, 66506, USA
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Luo M, Narasimhan S, Larson PS, Martin AJ, Konrad PE, Miga MI. Impact of brain shift on neural pathways in deep brain stimulation: a preliminary analysis via multi-physics finite element models. J Neural Eng 2021; 18. [PMID: 33740780 DOI: 10.1088/1741-2552/abf066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 03/19/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The effectiveness of deep brain stimulation (DBS) depends on electrode placement accuracy, which can be compromised by brain shift during surgery. While there have been efforts in assessing the impact of electrode misplacement due to brain shift using preop- and postop- imaging data, such analysis using preop- and intraop- imaging data via biophysical modeling has not been conducted. This work presents a preliminary study that applies a multi-physics analysis framework using finite element biomechanical and bioelectric models to examine the impact of realistic intraoperative shift on neural pathways determined by tractography. APPROACH The study examined six patients who had undergone interventional magnetic resonance (iMR)-guided DBS surgery. The modeling framework utilized a biomechanical approach to update preoperative MR to reflect shift-induced anatomical changes. Using this anatomically deformed image and its undeformed counterpart, bioelectric effects from shifting electrode leads could be simulated and neural activation differences were approximated. Specifically, for each configuration, volume of tissue activation (VTA) was computed and subsequently used for tractography estimation. Total tract volume and overlapping volume with motor regions as well as connectivity profile were compared. In addition, volumetric overlap between different fiber bundles among configurations was computed and correlated to estimated shift. MAIN RESULT The study found deformation-induced differences in tract volume, motor region overlap, and connectivity behavior, suggesting the impact of shift. There is a strong correlation (R=-0.83) between shift from intended target and intended neural pathway recruitment, where at threshold of ~2.94 mm, intended recruitment completely degrades. The determined threshold is consistent with and provides quantitative support to prior observations and literature that deviations of 2-3 mm are detrimental. SIGNIFICANCE The findings support and advance prior studies and understanding to illustrate the need to account for shift in DBS and the potentiality of computational modeling for estimating influence of shift on neural activation.
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Affiliation(s)
- Ma Luo
- Biomedical Engineering, Vanderbilt University, 5824 Stevenson Center, Nashville, Tennessee, 37232, UNITED STATES
| | - Saramati Narasimhan
- Department of Neurological Surgery, Vanderbilt University Medical Center, Village at Vanderbilt, 1500 21st Ave. South, Nashville, Tennessee, 37212, UNITED STATES
| | - Paul S Larson
- Department of Neurological Surgery, University of California San Francisco, Box 0112, 505 Parnassus Ave, Room M779, San Francisco, California, 94143, UNITED STATES
| | - Alastiar J Martin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, California, 94143, UNITED STATES
| | - Peter E Konrad
- Department of Neurosurgery, West Virginia University, PO Box 9183, Morgantown, West Virginia, 26506, UNITED STATES
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, 5901 Stevenson Center, Nashville, Tennessee, 37235, UNITED STATES
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