Dr. David Marshall Cash Profile

Dr. David Marshall Cash

at Vanderbilt Univ

SPIE Involvement:

Author

Publications (11)

Proceedings Article | 27 February 2018 Presentation + Paper

Gaussian processes with optimal kernel construction for neuro-degenerative clinical onset prediction

Liane Canas, Benjamin Yvernault, David Cash, Erika Molteni, Tom Veale, Tammie Benzinger, Sébastien Ourselin, Simon Mead, Marc Modat

Proceedings Volume 10575, 105750G (2018) https://doi.org/10.1117/12.2293242

KEYWORDS: Brain, Data modeling, Alzheimer's disease, Performance modeling, Image processing, Cognitive modeling, Feature extraction, Medical imaging, Neurology, Modeling

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SPIE Journal Paper | 19 September 2014 Open Access

Global image registration using a symmetric block-matching approach

Marc Modat, David Cash, Pankaj Daga, Gavin Winston, John Duncan, Sébastien Ourselin

JMI, Vol. 1, Issue 02, 024003, (September 2014) https://doi.org/10.1117/12.10.1117/1.JMI.1.2.024003

KEYWORDS: Image registration, Magnetic resonance imaging, Databases, Computed tomography, Positron emission tomography, Brain, Medical imaging, Medical research, Dementia, Neurology

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Proceedings Article | 21 March 2014 Paper

A symmetric block-matching framework for global registration

Marc Modat, David Cash, Pankaj Daga, Gawin Winston, John Duncan, Sébastien Ourselin

Proceedings Volume 9034, 90341D (2014) https://doi.org/10.1117/12.2043652

KEYWORDS: Image registration, Magnetic resonance imaging, Positron emission tomography, Databases, Computed tomography, Fourier transforms, Medical research, Brain, Medical imaging, Tissues

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Proceedings Article | 10 March 2006 Paper

Robust surface registration using salient anatomical features in image-guided liver surgery

Logan Clements, David Cash, William Chapman, Robert Galloway, Michael Miga

Proceedings Volume 6141, 61410E (2006) https://doi.org/10.1117/12.654209

KEYWORDS: Image registration, Liver, Lawrencium, Surgery, Computed tomography, Image segmentation, Image-guided intervention, Natural surfaces, Gold, Optical spheres

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Proceedings Article | 29 April 2005 Paper

Semiautomatic segmentation of textured laser range scans for use in image-guided procedures

Logan Clements, David Cash, Tuhin Sinha, Robert Galloway

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595814

KEYWORDS: Image segmentation, Lawrencium, Volume rendering, Clouds, Data acquisition, 3D image processing, Liver, Algorithm development, Surgery, Image processing algorithms and systems

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Proceedings Article | 5 May 2004 Paper

Identification of deformation using invariant surface information

David Cash, Tuhin Sinha, Cheng-Chun Chen, Benoit Dawant, William Chapman, Michael Miga, Robert Galloway

Proceedings Volume 5367, (2004) https://doi.org/10.1117/12.536707

KEYWORDS: Liver, Tumors, Rigid registration, Natural surfaces, Visualization, Surgery, Tomography, Data acquisition, Detection and tracking algorithms, Image registration

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Proceedings Article | 30 May 2003 Paper

Intraoperative registration of the liver for image-guided surgery using laser range scanning and deformable models

Michael Miga, David Cash, Zhujiang Cao, Robert Galloway, Benoit Dawant, William Chapman

Proceedings Volume 5029, (2003) https://doi.org/10.1117/12.480216

KEYWORDS: Liver, Surgery, Tumors, Image-guided intervention, Lawrencium, Image registration, Data modeling, Laser vision correction, Computed tomography, Visualization

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Proceedings Article | 30 May 2003 Paper

Incorporation of a laser range scanner into an image-guided surgical system

David Cash, Tuhin Sinha, William Chapman, Hiromi Terawaki, Michael Miga, Robert Galloway

Proceedings Volume 5029, (2003) https://doi.org/10.1117/12.480376

KEYWORDS: Liver, Scanners, Image registration, Calibration, Surgery, Data acquisition, Tumors, Rigid registration, Computed tomography, Laser range finders

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Laser range scanners provide rapid and accurate non-contact methods for acquiring 3D surface data, offering many advntages over other techniques currently available during surgery. The range scanner was incorporated into our image-guided surgery system to augment registration and deformation compensation. A rigid body, embedded with IR diodes, was attached to the scanner for tracking in physical space with an optical localization system. The relationship between the scanner's coordinate system and the tracked rigid body was determined using a calibration phantom. Tracking of the scanner using the calibration phantom resulted in an error of 1.4±0.8 mm. Once tracked, data acquired intraoperatively from the range scanner data is registered with preoperative tomographic volumes using the Iterative Closest Point algorithm. Sensitivity studies were performed to ensure that this algorithm effectively reached a global minimum. In cases where tissue deformation is significant, rigid registrations can lead to inaccuracy during surgical navigation. Methods of non-rigid compensation may be necessary, and an initial study using a linearly elastic finite element model is presented. Differences between intraoperative and preoperative surfaces after rigid registration are used to formulate boundary conditions, and the resulting displacement field deforms the preoperative image volume. To test this protocol, a phantom was built, consisting of fiducial points and a silicone liver model. Range scan and CT data were captured both before and after deforming the organ. The pre-deformed images, after registration and modeling, were compared to post-deformation, although there is a noticeable improvement by implementing the finite element model. To improve accuracy, more elaborate surface registration and deformation compensation strategies will be investigated. To improve accuracy, more elaborate surface registration and deformation compensation strategies will be investigated. The ragne scanner is an innovative, uncumbersome, and relatively inexpensive method of collecting intraoperative data. It has been integrated into our image-guided surgical system and software with virtually no overhead.

Proceedings Article | 30 May 2003 Paper

Laser range scanning for cortical surface characterization during neurosurgery

Tuhin Sinha, David Cash, Robert Weil, Robert Galloway, Michael Miga

Proceedings Volume 5029, (2003) https://doi.org/10.1117/12.480342

KEYWORDS: Clouds, Lawrencium, Brain, Image registration, Data acquisition, Surgery, Neuroimaging, Tumors, Laser range finders, Detection and tracking algorithms

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Proceedings Article | 17 May 2002 Paper

Fast accurate surface acquisition using a laser range scanner for image-guided liver surgery

David Cash, Tuhin Sinha, William Chapman, Robert Galloway, Michael Miga

Proceedings Volume 4681, (2002) https://doi.org/10.1117/12.466910

KEYWORDS: Scanners, Image registration, Optical spheres, Liver, Computed tomography, Data acquisition, Surgery, Natural surfaces, Image segmentation, Laser range finders

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Many image guided surgical (IGS) procedures use surface representations of anatomical features to provide a registration between physical space and image space. While anatomical fiducial registration techniques can often be compromised by soft tissue deformation, laser-scanning systems offer a non-contact method for accurately resolving geometric surfaces. This digitization approach is particularly well suited for liver resection surgery, where registration is accomplished by aligning the segmented preoperative liver image volume with the intraoperative presentation of the organ. Further, this three-dimensional technology affords a method of rapid surface data acquisition that can be used for the measurement and compensation of tissue deformation. A phantom was constructed to assess the laser scanner's capability of acquiring accurate shape information and robust surface registration. The phantom contained a wealth of point and surface geometry that could be identified in both a CT image volume and a range scan image. Point based registrations were calculated and served as a reference transformation for comparison of the results from surface registration using the iterative closest point (ICP) algorithm. The laser range scanner used the optical principle of triangulation to obtain a dense point set of surface data with a grid spacing of 0.6 mm. The scanner was able to localize and track fiducials on the order of 0.1mm. Phantom experiments demonstrated the ability to perform point-based registrations to CT with a Fiducial Registration Error of 0.5 mm. The scanner accurately resolves geometric surface information and achieved surface-based registrations within a mean distance residual of 0.4 mm. The principal sources of error were systemic misrepresentations of the position by the range scanner, which can be accounted for through calibration or other procedures. The laser scanner system was successfully used to register images to sub-millimetric accuracy. Future work involves acquisition of liver surfaces during surgical procedures for use in registration and deformation measurements.

Proceedings Article | 28 May 2001 Paper

Surgically appropriate maximum intensity projections: quantization of vessel depiction and incorporation into surgical navigation

David Cash, Steven Hartmann, Robert Galloway

Proceedings Volume 4319, (2001) https://doi.org/10.1117/12.428057

KEYWORDS: Tomography, Angiography, Head, Image segmentation, Surgery, Mathematical modeling, Image registration, Quantization, Injuries, Computed tomography

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Showing 5 of 11 publications

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