Dr. Ameet Jain Profile

Dr. Ameet Jain

at Philips Research North America

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Author

Publications (14)

Proceedings Article | 26 March 2011 Paper

Calibration of 3D ultrasound to an electromagnetic tracking system

Andrew Lang, Vijay Parthasarathy, Ameet Jain

Proceedings Volume 7968, 79680W (2011) https://doi.org/10.1117/12.878974

KEYWORDS: Calibration, Image segmentation, Sensors, Statistical analysis, Ultrasonography, Error analysis, Electromagnetism, Data acquisition, Imaging systems, 3D image processing

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Proceedings Article | 2 March 2011 Paper

Feature-based US to CT registration of the aortic root

Pencilla Lang, Elvis C. Chen, Gerard Guiraudon, Doug Jones, Daniel Bainbridge, Michael Chu, Maria Drangova, Noby Hata, Ameet Jain, Terry Peters

Proceedings Volume 7964, 79641G (2011) https://doi.org/10.1117/12.878386

KEYWORDS: Image registration, Computed tomography, Fluoroscopy, Ultrasonography, Heart, Visualization, Detection and tracking algorithms, 3D acquisition, Image segmentation, Reconstruction algorithms

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Proceedings Article | 24 February 2010 Paper

Absolute vs. relative error characterization of electromagnetic tracking accuracy

Mohammad Matinfar, Ganesh Narayanasamy, Luis Gutierrez, Raymond Chan, Ameet Jain

Proceedings Volume 7625, 762524 (2010) https://doi.org/10.1117/12.844326

KEYWORDS: Sensors, Calibration, Environmental sensing, Electromagnetism, Error analysis, Optical tracking, Visualization, X-rays, Navigation systems, Phase measurement

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Electromagnetic (EM) tracking systems are often used for real time navigation of medical tools in an Image Guided Therapy (IGT) system. They are specifically advantageous when the medical device requires tracking within the body of a patient where line of sight constraints prevent the use of conventional optical tracking. EM tracking systems are however very sensitive to electromagnetic field distortions. These distortions, arising from changes in the electromagnetic environment due to the presence of conductive ferromagnetic surgical tools or other medical equipment, limit the accuracy of EM tracking, in some cases potentially rendering tracking data unusable. We present a mapping method for the operating region over which EM tracking sensors are used, allowing for characterization of measurement errors, in turn providing physicians with visual feedback about measurement confidence or reliability of localization estimates. In this instance, we employ a calibration phantom to assess distortion within the operating field of the EM tracker and to display in real time the distribution of measurement errors, as well as the location and extent of the field associated with minimal spatial distortion. The accuracy is assessed relative to successive measurements. Error is computed for a reference point and consecutive measurement errors are displayed relative to the reference in order to characterize the accuracy in near-real-time. In an initial set-up phase, the phantom geometry is calibrated by registering the data from a multitude of EM sensors in a non-ferromagnetic ("clean") EM environment. The registration results in the locations of sensors with respect to each other and defines the geometry of the sensors in the phantom. In a measurement phase, the position and orientation data from all sensors are compared with the known geometry of the sensor spacing, and localization errors (displacement and orientation) are computed. Based on error thresholds provided by the operator, the spatial distribution of localization errors are clustered and dynamically displayed as separate confidence zones within the operating region of the EM tracker space.

Proceedings Article | 24 February 2010 Paper

3D TEE registration with MR for cardiac interventional applications

Jonghye Woo, Vijay Parthasarathy, Dalal Sandeep, Ameet Jain

Proceedings Volume 7625, 76252I (2010) https://doi.org/10.1117/12.845425

KEYWORDS: Image registration, Magnetic resonance imaging, 3D image processing, Image fusion, Image segmentation, Speckle, Visualization, Signal to noise ratio, Binary data, Image-guided intervention

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Proceedings Article | 31 March 2008 Paper

Dynamic dosimetry and edema detection in prostate brachytherapy: a complete system

A. Jain, A. Deguet, I. Iordachita, G. Chintalapani, J. Blevins, Y. Le, E. Armour, C. Burdette, D. Song, G. Fichtinger

Proceedings Volume 6918, 69181Y (2008) https://doi.org/10.1117/12.772352

KEYWORDS: Prostate, X-rays, Image segmentation, X-ray imaging, Tissues, Calibration, Image registration, 3D modeling, 3D image reconstruction, Distortion

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Purpose: Brachytherapy (radioactive seed insertion) has emerged as one of the most effective treatment options for patients with prostate cancer, with the added benefit of a convenient outpatient procedure. The main limitation in contemporary brachytherapy is faulty seed placement, predominantly due to the presence of intra-operative edema (tissue expansion). Though currently not available, the capability to intra-operatively monitor the seed distribution, can make a significant improvement in cancer control. We present such a system here. Methods: Intra-operative measurement of edema in prostate brachytherapy requires localization of inserted radioactive seeds relative to the prostate. Seeds were reconstructed using a typical non-isocentric C-arm, and exported to a commercial brachytherapy delivery system. Technical obstacles for 3D reconstruction on a non-isocentric C-arm include pose-dependent C-arm calibration; distortion correction; pose estimation of C-arm images; seed reconstruction; and C-arm to TRUS registration. Results: In precision-machined hard phantoms with 40-100 seeds and soft tissue phantoms with 45-87 seeds, we correctly reconstructed the seed implant shape with an average 3D precision of 0.35 mm and 0.24 mm, respectively. In a DoD Phase-1 clinical trial on 6 patients with 48-82 planned seeds, we achieved intra-operative monitoring of seed distribution and dosimetry, correcting for dose inhomogeneities by inserting an average of 4.17 (1-9) additional seeds. Additionally, in each patient, the system automatically detected intra-operative seed migration induced due to edema (mean 3.84 mm, STD 2.13 mm, Max 16.19 mm). Conclusions: The proposed system is the first of a kind that makes intra-operative detection of edema (and subsequent re-optimization) possible on any typical non-isocentric C-arm, at negligible additional cost to the existing clinical installation. It achieves a significantly more homogeneous seed distribution, and has the potential to affect a paradigm shift in clinical practice. Large scale studies and commercialization are currently underway.

Proceedings Article | 22 March 2007 Paper

Statistical characterization of C-arm distortion with application to intra-operative distortion correction

Gouthami Chintalapani, Ameet Jain, Russell Taylor

Proceedings Volume 6509, 65092Y (2007) https://doi.org/10.1117/12.711605

KEYWORDS: Distortion, Principal component analysis, Sensors, Statistical analysis, Video, Data modeling, 3D modeling, Error analysis, Image intensifiers, 3D image processing

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Proceedings Article | 22 March 2007 Paper

C-arm calibration: is it really necessary?

Ameet Kumar Jain, Michael An, Nicha Chitphakdithai, Gouthami Chintalapani, Gabor Fichtinger

Proceedings Volume 6509, 65092U (2007) https://doi.org/10.1117/12.712301

KEYWORDS: Calibration, Distortion, Fluoroscopy, 3D modeling, X-ray imaging, X-rays, X-ray sources, Image registration, 3D image processing, Affine motion model

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Proceedings Article | 21 March 2007 Paper

A cheap and easy method for 3D C-arm reconstruction using elliptic curves

David Burkhardt, Ameet Jain, Gabor Fichtinger

Proceedings Volume 6509, 65090B (2007) https://doi.org/10.1117/12.712395

KEYWORDS: Reconstruction algorithms, Image segmentation, Optical tracking, Imaging systems, Clouds, Calibration, Image processing algorithms and systems, Fluoroscopy, Detection and tracking algorithms, 3D modeling

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Proceedings Article | 13 March 2006 Paper

Incorporating seed orientation in brachytherapy implant reconstruction

Yu Zhou, Ameet Jain, Gregory Chirikjian, Gabor Fichtinger

Proceedings Volume 6141, 61411H (2006) https://doi.org/10.1117/12.654171

KEYWORDS: Image segmentation, Reconstruction algorithms, Prostate, Fluoroscopy, 3D image processing, Calibration, X-ray sources, Data hiding, X-ray imaging, Detection and tracking algorithms

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Proceedings Article | 29 April 2005 Paper

Fast algorithm for probabilistic bone edge detection (FAPBED)

Danilo Scepanovic, Joshua Kirshtein, Ameet Jain, Russell Taylor

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.596950

KEYWORDS: Bone, Image segmentation, Image registration, Natural surfaces, Computed tomography, Edge detection, Image processing, Absorption, Tissues, Detection and tracking algorithms

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The registration of preoperative CT to intra-operative reality systems is a crucial step in Computer Assisted Orthopedic Surgery (CAOS). The intra-operative sensors include 3D digitizers, fiducials, X-rays and Ultrasound (US). FAPBED is designed to process CT volumes for registration to tracked US data. Tracked US is advantageous because it is real time, noninvasive, and non-ionizing, but it is also known to have inherent inaccuracies which create the need to develop a framework that is robust to various uncertainties, and can be useful in US-CT registration. Furthermore, conventional registration methods depend on accurate and absolute segmentation. Our proposed probabilistic framework addresses the segmentation-registration duality, wherein exact segmentation is not a prerequisite to achieve accurate registration. In this paper, we develop a method for fast and automatic probabilistic bone surface (edge) detection in CT images. Various features that influence the likelihood of the surface at each spatial coordinate are combined using a simple probabilistic framework, which strikes a fair balance between a high-level understanding of features in an image and the low-level number crunching of standard image processing techniques. The algorithm evaluates different features for detecting the probability of a bone surface at each voxel, and compounds the results of these methods to yield a final, low-noise, probability map of bone surfaces in the volume. Such a probability map can then be used in conjunction with a similar map from tracked intra-operative US to achieve accurate registration. Eight sample pelvic CT scans were used to extract feature parameters and validate the final probability maps. An un-optimized fully automatic Matlab code runs in five minutes per CT volume on average, and was validated by comparison against hand-segmented gold standards. The mean probability assigned to nonzero surface points was 0.8, while nonzero non-surface points had a mean value of 0.38 indicating clear identification of surface points on average. The segmentation was also sufficiently crisp, with a full width at half maximum (FWHM) value of 1.51 voxels.

Proceedings Article | 12 April 2005 Paper

Matching and reconstruction of brachytherapy seeds using the Hungarian algorithm (MARSHAL)

Ameet Jain, Yu Zhou, Tabish Mustufa, E. Burdette, Gregory Chirikjian, Gabor Fichtinger

Proceedings Volume 5744, (2005) https://doi.org/10.1117/12.595825

KEYWORDS: Reconstruction algorithms, Image segmentation, Prostate, 3D image processing, X-rays, X-ray imaging, Algorithm development, 3D image reconstruction, Error analysis, Computing systems

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Proceedings Article | 12 April 2005 Paper

A robust fluoroscope tracking (FTRAC) fiducial

Ameet Jain, Tabish Mustufa, Yu Zhou, E. Burdette, Gregory Chirikjian, Gabor Fichtinger

Proceedings Volume 5744, (2005) https://doi.org/10.1117/12.595782

KEYWORDS: Image segmentation, Error analysis, Fluoroscopy, X-ray imaging, X-rays, Optical tracking, Surgery, 3D image reconstruction, 3D image processing, Calibration

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Proceedings Article | 28 April 2004 Paper

Understanding bone responses in B-mode ultrasound images and automatic bone surface extraction using a Bayesian probabilistic framework

Ameet Jain, Russell Taylor

Proceedings Volume 5373, (2004) https://doi.org/10.1117/12.535984

KEYWORDS: Bone, Image segmentation, Reflection, Image registration, Phase modulation, 3D image processing, Image processing algorithms and systems, Ultrasonography, Computed tomography, Surgery

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Proceedings Article | 23 May 2003 Paper

Rapid calibration method for registration and 3D tracking of ultrasound images using spatial localizer

Emad Boctor, Ameet Jain, Michael Choti, Russell Taylor, Gabor Fichtinger

Proceedings Volume 5035, (2003) https://doi.org/10.1117/12.479953

KEYWORDS: Calibration, Image processing, Ultrasonography, Reflectors, Transmitters, 3D image processing, Receivers, Matrices, Surgery, Visualization

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Showing 5 of 14 publications

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