https://openworks.mdanderson.org/sumexp22/1051/thumbnail.jp

Brock, Kristy K, PhD

Cazoulat, Guillaume, PhD

Chen, Melissa, MD

Lesage, Anne-Cecile J, PhD

Nilforoush, Ali Reza

Weinberg, Jeffrey S, MD

English

OpenWorks @ MD Anderson

Biomechanical Modeling of Brain Shift During NeurosurgeryAli R. Nilforoush,1,2 Anne-Cecile J. Lesage, PhD2, Melissa Chen, MD3, Guillaume Cazoulat, PhD2, Jeffrey S. Weinberg, MD4, Kristy K. Brock, PhD21 CPRIT-CURE Summer Undergraduate Research Program, The University of Texas MD Anderson Cancer Center2 Image Guided Cancer Therapy Research Program, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center3 Department of Neuroradiology, The University of Texas MD Anderson Cancer Center4 Department of Neurosurgery, The University of Texas MD Anderson Cancer CenterConclusion• Poroelastic model is more accurate than viscoelastico Supports prediction that including more physicalparameters increases simulation accuracy• Poroelastic also took less time due to nature of its formula –elastic modulus does not vary with time whereas visco does• Currently working on improving run time and accuracyo Pre-process mesh making takes >4 hours, FEAprocessing takes 4-12 hourso FEM code runs into some bugs due to minor errors inmesh modeling• Future works: developing deep learning to automatesimulation process and translate work to clinicAcknowledgementsBrain imaging data used in this work was provided by the RESECT study. This work wassupported by the Image Guided Cancer Therapy (IGCT) Research Program at MD AndersonCancer Center. ARN was supported by CPRIT-CURE Summer Program. For furtherinformation, please contact Ali Nilforoush at nilforoushali@gmail.comIntroduction• Craniotomies for glioma tumor resection are oftenchallenging as brain shifting (due to fluid loss, gravity,pressure changes, edema, etc.) causes a mismatch whenintra-operative anatomical positions are compared to pre-operative imaging• Low accuracy in pre-op planning due to uncertainty in brainshifts increases the difficulty of the procedure and the riskof harm to healthy tissue and further complications• Biomechanics and Finite Element Modeling (FEM) can beutilized to accurately simulate brain shifts (i.e., sagging orswelling) for improved surgical planningReferences1) Lesage et al, 2020, IPEM, “Viscoelastic biomechanical models to predict inward brain-shift using public benchmark data”2) Sun et al, 2013, Journal of Translational Engineering in Health and Medicine, “NearReal-Time Computer Assisted Surgery for Brain Shift Correction Using BiomechanicalModels”3) Xiao et al, 2017, AAPM, “REtroSpective Evaluation of Cerebral Tumors (RESECT): Aclinical database of pre-operative MRI and intra-operative ultrasound in low-gradeglioma surgeries”4) Poulios et al., 2022, open-source software, GetFEM5) Chen et al, 2017, IEEE Xplore, “A simple brain shift estimation for neuronavigationbased on finite element method and hydrostatics”6) Bom et al, 2018, Acustica e Vibracoes, “Arduino-based Head-tracking device: assemblyand application in acoustics”7) Dreamstime, royalty-free, https://www.dreamstime.com/measuring-cups-different-fluid-levels-measuring-cups-scale-different-fluid-levels-flat-design-simple-image1898376348) Cohen-Gadol, 2022, The Neurological Atlas, “Brain Tumor: What the Patient Needs toKnow”Materials & Methods• Brain shift simulated is due to the craniotomy prior totumor resection through the following steps (Fig. 2):• FEMs were created from a 3D mesh generated usingseveral modeling software systems (RayStation, SimLab,HyperMesh, and various C scripts)• Two material models were used in the FEM computation:o Viscoelastic – material with combination of viscousand elastic properties (i.e., gelatin). Models saggingo Poroelastic – material is porous and elastic (i.e.,sponge). Takes into account more complex physics(brain drainage), and predicted to produce moreaccurate simulations. Models sagging, shrinking andswelling• In addition, varying levels of cranio-spinal fluid (CSF) andhead orientation were investigated (Fig. 3)• Finite element analysis (FEA) simulations were computedusing open-source software GetFEM on the MD AndersonSeaDragon super-computer• FEA results were visualized on ParaView• Accuracy of the model is determined using targetregistration error (TRE) based on pre-op MR images with~15 set landmarks and compared to a grand truth based onintra-op ultrasound (iUS) of the same patient (see Fig 3)B. SwellingFig. 1. Illustration of brain shift from (5) Chen et al, 2017, IEEE XploreFig. 2. Flowchart depicting general methodology of simulating brain shift in three phases: pre-processing (mesh generation), processing (FEM), and post-processing(optimization).Fig. 3. Flowchart displaying atlas simulation process based on known and unknown parameters. Based on grand truth data, atlas is optimized with a linear combination formula,producing a deformed image. POI types color coded as: pre-op (blue), intra-op (green), simulated (purple). Illustrations from references (6), (7), and (8).Fig. 4,5. Box and whisker plots of RESECT cases 17 and 25 (sagging). Target registration error (TRE) is the distance between the grand truth intra-op POI and the simulatedpoint. Viscoelastic and poroelastic optimized target registration errors of all and 7 POIs are compared to initial TRE (original pre-op landmark without simulation). Optimized TREwith 7 POIs (about half of all) are reported to demonstrate continued accuracy of model with fewer data. Poroelastic model was found to produce lower TREs than viscoelastic.Using less POIs produced slightly less accurate and precise TREs.Fig. 6. Box and whisker plot of RESECT case 23 (swelling). Poroelastic optimized targetregistration errors are compared to initial TRE (original pre-op landmark without simulation).Optimized TRE with 7 POIs (about half of all) are also reported to demonstrate accuracy ofmodel with fewer data. Having fewer POI TREs impacted accuracy assessment more thanwith sagging.Optimizing atlas to generate simulated POI with lowest TREWrite GetFEM scripts (poro/visco) for each run3D mesh formation2D mesh generationof brain and componentsCreate atlas of boundary conditions based on parametersEdit and clean meninges meshMR Segmentation Run FEA Simulation (ParaView visualization)ResultsA. Sagging: Poroelastic vs. Viscoelastic

Biomechanical Modeling of Brain Shift During Neurosurgery

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Biomechanical Modeling of Brain Shift During Neurosurgery

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