Prof. Brian A. Hargreaves Profile

Prof. Brian A. Hargreaves

at Stanford Univ

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Publications (3)

Proceedings Article | 16 March 2023 Poster + Paper

Development of augmented reality technology for surgical resection accuracy via improved visualization

Anh Doan, Marc Fischer, Thea Walsh, Samantha Koire, Bruce Daniel, Brian Hargreaves, Marianne Black

Proceedings Volume 12449, 124491N (2023) https://doi.org/10.1117/12.2648463

KEYWORDS: Resection, Autoregressive models, Augmented reality, Surgery, Visualization, Motion models, Deformation, Head-mounted displays, Eye models, 3D tracking

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Proceedings Article | 16 March 2020 Paper

Comparison of head pose tracking methods for mixed-reality neuronavigation for transcranial magnetic stimulation

Supriya Sathyanarayana, Christoph Leuze, Brian Hargreaves, Bruce Daniel, Gordon Wetzstein, Amit Etkin, Mahendra Bhati, Jennifer McNab

Proceedings Volume 11315, 113150L (2020) https://doi.org/10.1117/12.2547917

KEYWORDS: Head, Cameras, Calibration, Mixed reality, Brain

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Purpose: Repetitive Transcranial Magnetic Stimulation (rTMS) is an important treatment option for medication resistant depression. It uses an electromagnetic coil that needs to be positioned accurately at a specific location and angle next to the head such that specific brain areas are stimulated. Existing image-guided neuronavigation systems allow accurate targeting but add cost, training and setup times, preventing their wide-spread use in the clinic. Mixed-reality neuronavigation can help mitigate these issues and thereby enable more widespread use of image-based neuronavigation by providing a much more intuitive and streamlined visualization of the target. A mixed-reality neuronavigation system requires two core functionalities: 1) tracking of the patient's head and 2) visualization of targeting-related information. Here we focus on the head tracking functionality and compare three different head tracking methods for a mixed-reality neuronavigation system. Methods: We integrated three head tracking methods into the mixed reality neuronavigation framework and measured their accuracy. Specifically, we experimented with (a) marker-based tracking with a mixed reality headset (optical see-through head-mounted display (OST-HMD)) camera, (b) marker-based tracking with a world-anchored camera and (c) markerless RGB-depth (RGB-D) tracking with a world-anchored camera. To measure the accuracy of each approach, we measured the distance between real-world and virtual target points on a mannequin head. Results: The mean tracking error for the initial head pose and the head rotated by 10° and 30° for the three methods respectively was: (a) 3.54±1.10 mm, 3.79±1.78 mm and 4.08±1.88 mm, (b) 3.97±1.41 mm, 6.01±2.51 mm and 6.84±3.48 mm, (c) 3.16±2.26 mm, 4.46±2.30 mm and 5.83±3.70 mm. Conclusion: For the initial head pose, all three methods achieved the required accuracy of < 5 mm for TMS treatment. For smaller head rotations of 10°, only the marker-based (a) and markerless method (c) delivered sufficient accuracy for TMS treatment. For larger head rotations of 30°, only the marker-based method (a) achieved sufficient accuracy. While the markerless method (c) did not provide sufficient accuracy for TMS at the larger head rotations, it offers significant advantages such as occlusion-handling and stability and could potentially meet the accuracy requirements with further methodological refinements.

Proceedings Article | 13 March 2019 Paper

Breast dispersion imaging using undersampled rapid dynamic contrast-enhanced MRI

Linxi Shi, Subashini Srinivasan, Brian Hargreaves, Bruce Daniel

Proceedings Volume 10950, 109502H (2019) https://doi.org/10.1117/12.2513059

KEYWORDS: Tumors, Tumor growth modeling, Tissues, Breast, Magnetic resonance imaging, Performance modeling, Mammography, Cancer, Blood

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