Mr. Jinesh J. Jain Profile

Jinesh J. Jain

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Proceedings Article | 3 March 2007 Paper

Automated arterial input function identification using iterative self organizing maps

Jinesh Jain, John Glass, Wilburn Reddick

Proceedings Volume 6512, 65120U (2007) https://doi.org/10.1117/12.708628

KEYWORDS: Arteries, Gold, Tissues, Magnetic resonance imaging, Blood circulation, Brain, Cerebral blood flow, Positron emission tomography, Blood, Deconvolution

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Quantification of cerebral blood flow and volume using dynamic-susceptibility contrast MRI relies on deconvolution with the arterial input function (AIF) - commonly estimated from signal changes in a major artery. Manual selection of AIF is user-dependent and typical selection in primary arteries leads to errors due to bolus delay and dispersion. An AIF sampled form the primary as well as the peripheral arteries should minimize these errors. We present a fully automated technique for the identification of the AIF by classifying the pixels in the imaging set into unique classes using a Kohonen self organizing map, followed by an iterative refinement of the previous selections. Validation was performed across 31 pediatric patients by comparison with manually identified AIF and a recently published automated AIF technique. Our technique consistently yielded higher bolus peak heights and over 50% increase in the area under the first pass, therefore lowering the values obtained for blood flow and volume. This technique provides a robust and accurate estimation of the arterial input function and can easily be adapted to extract the AIF locally, regionally or globally as suitable to the analysis.

Proceedings Article | 13 March 2006 Paper

Constrained triple-component T2 quantification in vivo

Jinesh Jain, Wilburn Reddick

Proceedings Volume 6143, 61431X (2006) https://doi.org/10.1117/12.653969

KEYWORDS: Signal to noise ratio, In vivo imaging, Brain, Optimization (mathematics), Brain mapping, Tissues, Physiology, Magnetic resonance imaging, Biological research, MATLAB

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T₂ relaxation decay curves from in vivo human brain tissue are rarely mono-exponential due to both physiology and partial volume averaging. We propose a tri-exponential model, parametric fitting of the T₂ relaxation curve, restricting the range for the T₂ in each compartment, and estimating the probability of the existence of each of the components on a voxel-by-voxel basis. The model quantifies the T₂ into three discrete compartments: Myelin (T₂ short = 20-50 ms), White / Gray Matter (T₂ middle = 50-120 ms), and CSF (T₂ long = 120-500 ms). A constrained nonlinear minimization technique using subspace trust-region methods was implemented. A voxel-by-voxel analysis was performed, and for any given voxel, the three T₂ components were forced to lie within each compartment. However, the magnitude for each of these components was allowed to take any non-negative value including zero. As a result, if any component were absent, its magnitude would be zero and hence not contribute to the fit. Results from the processing of six healthy normal adults, imaged on a 3T magnet with clinically viable imaging protocols, have been presented and are shown to be in excellent agreement with reported values. This technique is robust and accurate and may potentially be useful in aiding clinical diagnosis and follow-up of patients with white matter abnormalities.

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