Dr. Arnold Gomez Profile

Dr. Arnold Gomez

at Johns Hopkins Univ School of Medicine

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Publications (4)

Proceedings Article | 15 February 2021 Presentation + Paper

Reconstruction and refinement of crossing muscle fibers in the human tongue

Muhan Shao, Arnold Gomez, Jiachen Zhuo, Xiao Liang, Maureen Stone, Aaron Carass, Jerry Prince

Proceedings Volume 11596, 115961K (2021) https://doi.org/10.1117/12.2582287

KEYWORDS: Tongue, Diffusion weighted imaging, Spherical lenses, High angular resolution diffusion imaging, Convolutional neural networks, Brain, Anisotropic diffusion

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Proceedings Article | 12 March 2018 Presentation + Paper

Inverse biomechanical modeling of the tongue via machine learning and synthetic training data

Aniket Tolpadi, Maureen Stone, Aaron Carass, Jerry Prince, Arnold Gomez

Proceedings Volume 10576, 1057606 (2018) https://doi.org/10.1117/12.2296927

KEYWORDS: Data modeling, Tongue, Machine learning, Magnetic resonance imaging, Wavelets, Calibration, Motion measurement, Tissues, Motion estimation, Computer simulations

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Proceedings Article | 2 March 2018 Presentation + Paper

A novel filtering approach for 3D harmonic phase analysis of tagged MRI

Xiaokai Wang, Maureen Stone, Jerry Prince, Arnold Gomez

Proceedings Volume 10574, 1057414 (2018) https://doi.org/10.1117/12.2293643

KEYWORDS: Linear filtering, Error analysis, Image filtering, Tongue, Digital filtering, Motion analysis, Magnetic resonance imaging, Motion estimation, Electronic filtering, Tissues

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Proceedings Article | 13 March 2017 Presentation + Paper

Test suite for image-based motion estimation of the brain and tongue

Jordan Ramsey, Jerry Prince, Arnold Gomez

Proceedings Volume 10137, 101371G (2017) https://doi.org/10.1117/12.2254626

KEYWORDS: Motion estimation, Brain, Tongue, Magnetic resonance imaging, Head, Neuroimaging, Motion models, Motion measurement, Error analysis, Monte Carlo methods

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Noninvasive analysis of motion has important uses as qualitative markers for organ function and to validate biomechanical computer simulations relative to experimental observations. Tagged MRI is considered the gold standard for noninvasive tissue motion estimation in the heart, and this has inspired multiple studies focusing on other organs, including the brain under mild acceleration and the tongue during speech. As with other motion estimation approaches, using tagged MRI to measure 3D motion includes several preprocessing steps that affect the quality and accuracy of estimation. Benchmarks, or test suites, are datasets of known geometries and displacements that act as tools to tune tracking parameters or to compare different motion estimation approaches. Because motion estimation was originally developed to study the heart, existing test suites focus on cardiac motion. However, many fundamental differences exist between the heart and other organs, such that parameter tuning (or other optimization) with respect to a cardiac database may not be appropriate. Therefore, the objective of this research was to design and construct motion benchmarks by adopting an "image synthesis" test suite to study brain deformation due to mild rotational accelerations, and a benchmark to model motion of the tongue during speech. To obtain a realistic representation of mechanical behavior, kinematics were obtained from finite-element (FE) models. These results were combined with an approximation of the acquisition process of tagged MRI (including tag generation, slice thickness, and inconsistent motion repetition). To demonstrate an application of the presented methodology, the effect of motion inconsistency on synthetic measurements of head- brain rotation and deformation was evaluated. The results indicated that acquisition inconsistency is roughly proportional to head rotation estimation error. Furthermore, when evaluating non-rigid deformation, the results suggest that inconsistent motion can yield "ghost" shear strains, which are a function of slice acquisition viability as opposed to a true physical deformation.

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