Dr. Chen Li Profile

Dr. Chen Li

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Publications (12)

Proceedings Article | 5 May 2023 Poster + Paper

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

Kristen Chen, Chen Li, Joshua Aronson, Xiaoyao Fan, Keith Paulsen

Proceedings Volume 12466, 124662L (2023) https://doi.org/10.1117/12.2654450

KEYWORDS: Brain, Artificial neural networks, Education and training, Data modeling, Machine learning, Neuroimaging, Deformation, Finite element methods, Brain stimulation, Performance modeling

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Proceedings Article | 3 April 2023 Presentation + Paper

SIFT-based cortical surface shift estimation for intraoperative image updating

Xiaoyao Fan, Kristen Chen, William Warner, Ryan Duke, Chen Li, Linton Evans, Keith Paulsen

Proceedings Volume 12466, 1246613 (2023) https://doi.org/10.1117/12.2654258

KEYWORDS: Image registration, Resection, Surgery, Optical flow, Image processing, Brain, Deformation, Tumors, Neuroimaging

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Proceedings Article | 3 April 2023 Presentation + Paper

Towards accounting for intraoperative spine motion: a simulation study of registration between stereovision surfaces

William Warner, Xiaoyao Fan, Ryan Duke, Kristen Chen, Haley Stoner, Chen Li, Shaoju Wu, Songbai Ji, Sohail Mirza, Keith Paulsen

Proceedings Volume 12466, 124661H (2023) https://doi.org/10.1117/12.2654396

KEYWORDS: Spine, Printed circuit board testing, Image registration, Surgery, Anatomy, Point clouds, Data acquisition, Computed tomography, MATLAB, Imaging systems

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Proceedings Article | 4 April 2022 Poster + Paper

Estimating shift at brain surface in deep brain stimulation using machine learning based methods

Kristen Chen, Chen Li, Xiaoyao Fan, Tahsin Khan, Joshua Aronson, Keith Paulsen

Proceedings Volume 12034, 120342D (2022) https://doi.org/10.1117/12.2613221

KEYWORDS: Brain, Data modeling, Neuroimaging, Computed tomography, Machine learning, Natural surfaces, Brain stimulation, Artificial neural networks, Neurons, Image segmentation

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Proceedings Article | 17 February 2021 Poster + Presentation + Paper

Optimal boundary conditions for model based brain shift simulation in deep brain stimulation surgery

Chen Li, Xiaoyao Fan, Joshua Aronson, Keith Paulsen

Proceedings Volume 11598, 115982J (2021) https://doi.org/10.1117/12.2582315

KEYWORDS: Brain, Surgery, Brain stimulation, Data modeling, Ultrasonography, Skull, Lead, Fluid dynamics, Electrodes, Data acquisition

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Proceedings Article | 16 March 2020 Paper

Knee orientation detection in MR scout scans using 3D U-net

Chen Li, Parmeet Bhatia, Yu Zhao

Proceedings Volume 11314, 113143O (2020) https://doi.org/10.1117/12.2551390

KEYWORDS: 3D image processing, Image segmentation, 3D modeling, Magnetic resonance imaging, Tissues, 3D scanning, Visualization, Matrices, Image registration, Reconstruction algorithms

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Purpose: Detecting knee orientation automatically from scout scans with high speed and accuracy is essential to a successful workflow of MR knee imaging. Although traditional methods of image processing such as rigid image registration and object detection are potentially available solutions, they are sensitive to image noise such as missing features due to the metal implants and anatomical variability in knee size and tissue composition. Method: In this study, a segmentation-based approach was proposed to calculate a 3-D transformation matrix that defined 3-D knee orientation using low-res MR scout scans. Specifically, 3-D U-net was used to segment a plane that was parallel to the knee meniscus plane and reconstruct the plane norm as one of the vectors (v₁) needed for a 3d transformation matrix. A separate model of 3-D U-net was then trained to segment another plane that was perpendicular to the meniscus and reconstruct the plane norm as v₂. A linear 3-D transformation matrix was then obtained for each patient case in 14 testing subjects that were initially manually rotated in small (group S) and large (group L) degrees for training. Angle corrected images were also visually compared against their corresponding ground truth. Results: The average of v₁ and v₂ error in group S were 5.62° and 5.12° , respectively, whereas the error average of these two vectors were 6.65° and 8.25° , respectively for group L. The standard deviation for v₁ and v₂ in group S and L were 2.51° , 2.84° , 5.65° , and 7.65° , respectively. The Dice similarity coefficient (DSC) of reconstructed v₁ and v₂ planes were 0.78, 0.70, 0.71, and 0.65 for group S and L. The qualitative assessment further showed consistent knee representation after correction for knees with heavy distortion and fatty tissue. Conclusion: Initial results suggest that our approach has the potential to automatically correct for small knee rotations commonly seen in clinical setting and is robust even under stress test for knees with anatomical structures (e.g. fatty tissue) that were even absent in the training data set and that appear heavily distorted.

Proceedings Article | 16 March 2020 Presentation + Paper

Brain deformation compensation for deep brain lead placement surgery: a comparison of simulations driven by surface vs deep brain sparse data

Chen Li, Xiaoyao Fan, Joshua Aronson, Keith Paulsen

Proceedings Volume 11315, 113150P (2020) https://doi.org/10.1117/12.2550048

KEYWORDS: Brain, Data modeling, Surgery, Neuroimaging, Data acquisition, Ultrasonography, Lead, Tissues, Natural surfaces, Computer simulations

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Accurate surgical placement of electrodes is essential to successful deep brain stimulation (DBS) for patients with neurodegenerative diseases such as Parkinson’s disease. However, the accuracy of pre-operative images used for surgical planning and guidance is often degraded by brain shift during surgery. To predict such intra-operative target deviation due to brain shift, we have developed a finite-element biomechanical model with the assimilation of intraoperative sparse data to compute a whole brain displacement field that updates preoperative images. Previously, modeling with the incorporation of surface sparse data achieved promising results at deep brain structures. However, access to surface data may be limited during a burr hole-based procedure where the size of exposed cortex is too small to acquire adequate intraoperative imaging data. In this paper, our biomechanical brain model was driven by deep brain sparse data that was extracted from lateral ventricles using a Demon’s algorithm and the simulation result was compared against the one resulted from modeling with surface data. Two patient cases were explored in this study where preoperative CT (preCT) and postoperative CT (postCT) were used for the simulation. In patient case one of large symmetrical brain shift, results show that model driven by deep brain sparse data reduced the target registration error(TRE) of preCT from 3.53 to 1.36 and from 1.79 to 1.17 mm at AC and PC, respectively, whereas results from modeling with surface data produced even lower TREs at 0.58 and 0.69mm correspondingly; However, in patient case two of large asymmetrical brain shift, modeling with deep brain sparse data yielded the lowest TRE of 0.68 from 1.73 mm. Results in this study suggest that both surface and deep brain sparse data are capable of reducing the TRE of preoperative images at deep brain landmarks. The success of modeling with the assimilation of deep brain sparse data alone shows the potential of implementing such method in the OR because sparse data at lateral ventricle can be acquired using ultrasound imaging.

Proceedings Article | 16 March 2020 Paper

Stereovision-updated image guidance in multi-level open spine surgery: short vs. long exposure

Xiaoyao Fan, Maxwell Durtschi, Chen Li, Linton Evans, Songbai Ji, Yunliang Cai, Sohail Mirza, Keith Paulsen

Proceedings Volume 11315, 1131529 (2020) https://doi.org/10.1117/12.2549646

KEYWORDS: Image registration, Image processing, Spine, Surgery, Computed tomography, Data acquisition, Navigation systems

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Proceedings Article | 8 March 2019 Presentation + Paper

A comparison of geometry- and feature-based sparse data extraction for model-based image updating in deep brain stimulation surgery

Chen Li, Xiaoyao Fan, Joshua Aronson, Keith Paulsen

Proceedings Volume 10951, 1095106 (2019) https://doi.org/10.1117/12.2513142

KEYWORDS: Data modeling, Brain, Surgery, Neuroimaging, Image registration, Natural surfaces, Feature extraction, Tissues, Model-based design, Brain stimulation

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Proceedings Article | 8 March 2019 Paper

Calibration of a hand-held stereovision system for image-guided spinal surgery

Xiaoyao Fan, Maxwell Durtschi, Chen Li, Songbai Ji, Sohail Mirza, Keith Paulsen

Proceedings Volume 10951, 109511X (2019) https://doi.org/10.1117/12.2512861

KEYWORDS: Calibration, Spine, Surgery, Cameras, Image-guided intervention, Image registration, Corner detection, Computed tomography, Navigation systems, Stereoscopic cameras

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Proceedings Article | 13 March 2018 Paper

Pre- to post-operative CT image registration to estimate cortical shift for image updating in deep brain stimulation

Chen Li, Xiaoyao Fan, Joshua Aronson, Keith Paulsen

Proceedings Volume 10576, 1057624 (2018) https://doi.org/10.1117/12.2293747

KEYWORDS: Brain, Volume rendering, Data modeling, Brain mapping, Image registration, Neuroimaging, Computed tomography, Surgery, 3D modeling, Brain stimulation

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Proceedings Article | 13 March 2018 Presentation + Paper

Image updating for brain deformation compensation: cross-validation with intraoperative ultrasound

Xiaoyao Fan, David Roberts, Jonathan Olson, Chen Li, Keith Paulsen

Proceedings Volume 10576, 105760S (2018) https://doi.org/10.1117/12.2293724

KEYWORDS: Brain, Data modeling, Neuroimaging, Finite element methods, Ultrasonography, Magnetic resonance imaging, Surgery, Data acquisition, Image registration, Image segmentation

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Showing 5 of 12 publications

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