Dr. Picha Shunhavanich Profile

Dr. Picha Shunhavanich

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Publications (8)

Proceedings Article | 7 April 2023 Poster + Paper

Improvements in dose efficiency with high resolution scan modes in photon counting CT

Picha Shunhavanich, Kishore Rajendran, Mingdong Fan, Cynthia McCollough, Shuai Leng, Lifeng Yu, Scott Hsieh

Proceedings Volume 12463, 124633O (2023) https://doi.org/10.1117/12.2654334

KEYWORDS: Computed tomography, Scanners, Spatial resolution, Photon counting, Image quality standards, Image sharpness

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Proceedings Article | 7 April 2023 Presentation + Paper

A dense search challenge phantom fabricated with pixel-based 3D printing for precise detectability assessment

Scott Hsieh, Kai Mei, Nadav Shapira, Picha Shunhavanich, J. Webster Stayman, Cynthia McCollough, Grace Gang, Shuai Leng, Michael Geagen, Lifeng Yu, Peter Noël

Proceedings Volume 12463, 124631D (2023) https://doi.org/10.1117/12.2654336

KEYWORDS: 3D printing, Target detection, Scanners, Reconstruction algorithms, Computed tomography

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SPIE Journal Paper | 25 June 2019

Implementation of a piecewise-linear dynamic attenuator

Picha Shunhavanich, Nathaniel Robert Bennett, Scott Hsieh, Norbert Pelc

JMI, Vol. 6, Issue 02, 023502, (June 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.2.023502

KEYWORDS: Attenuators, Modulation, Sensors, Signal attenuation, Chest, Scanners, Computed tomography, Actuators, Fluctuations and noise, Abdomen

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Proceedings Article | 9 March 2018 Paper

Implementation of a piecewise-linear dynamic attenuator

Picha Shunhavanich, N. Robert Bennett, Scott Hsieh, Norbert Pelc

Proceedings Volume 10573, 105730T (2018) https://doi.org/10.1117/12.2293525

KEYWORDS: Photon counting, Modulation, Signal attenuation, Chest, Sensors, Abdomen, Attenuators, Fluctuations and noise, Scanners

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Proceedings Article | 9 March 2017 Paper

Sensitivity analysis of pulse pileup model parameter in photon counting detectors

Picha Shunhavanich, Norbert Pelc

Proceedings Volume 10132, 101323M (2017) https://doi.org/10.1117/12.2255665

KEYWORDS: Photon counting, Sensors, Monte Carlo methods, Performance modeling, Computer simulations, Calibration, Diagnostics, Calcium, Gadolinium, Error analysis, Distortion, Statistical analysis

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Proceedings Article | 25 March 2016 Paper

Lossless compression of projection data from photon counting detectors

Picha Shunhavanich, Norbert Pelc

Proceedings Volume 9783, 97831J (2016) https://doi.org/10.1117/12.2217231

KEYWORDS: Photon counting, Sensors, Data modeling, Computer simulations, Data compression, Image compression, Data acquisition, Statistical analysis, Edge detection, Quantization, Computer programming, Head

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With many attractive attributes, photon counting detectors with many energy bins are being considered for clinical CT systems. In practice, a large amount of projection data acquired for multiple energy bins must be transferred in real time through slip rings and data storage subsystems, causing a bandwidth bottleneck problem. The higher resolution of these detectors and the need for faster acquisition additionally contribute to this issue. In this work, we introduce a new approach to lossless compression, specifically for projection data from photon counting detectors, by utilizing the dependencies in the multi-energy data. The proposed predictor estimates the value of a projection data sample as a weighted average of its neighboring samples and an approximation from other energy bins, and the prediction residuals are then encoded. Context modeling using three or four quantized local gradients is also employed to detect edge characteristics of the data. Using three simulated phantoms including a head phantom, compression of 2.3:1-2.4:1 was achieved. The proposed predictor using zero, three, and four gradient contexts was compared to JPEG-LS and the ideal predictor (noiseless projection data). Among our proposed predictors, three-gradient context is preferred with a compression ratio from Golomb coding 7% higher than JPEG-LS and only 3% lower than the ideal predictor. In encoder efficiency, the Golomb code with the proposed three-gradient contexts has higher compression than block floating point. We also propose a lossy compression scheme, which quantizes the prediction residuals with scalar uniform quantization using quantization boundaries that limit the ratio of quantization error variance to quantum noise variance. Applying our proposed predictor with three-gradient context, the lossy compression achieved a compression ratio of 3.3:1 but inserted a 2.1% standard deviation of error compared to that of quantum noise in reconstructed images. From the initial simulation results, the proposed algorithm shows good control over the bits needed to represent multienergy projection data.

Proceedings Article | 18 March 2015 Paper

First results from a prototype dynamic attenuator system

Scott Hsieh, Mark Peng, Christopher May, Picha Shunhavanich, Norbert Pelc

Proceedings Volume 9412, 94121N (2015) https://doi.org/10.1117/12.2081482

KEYWORDS: Attenuators, Prototyping, Image quality, Calibration, X-rays, Dynamical systems, Sensors, Rapid manufacturing, Collimation, Fluctuations and noise

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Proceedings Article | 18 March 2015 Paper

Fluid-filled dynamic bowtie filter: a feasibility study

Picha Shunhavanich, Scott Hsieh, Norbert Pelc

Proceedings Volume 9412, 94121L (2015) https://doi.org/10.1117/12.2081673

KEYWORDS: Signal attenuation, Attenuators, Fluid dynamics, Fluctuations and noise, Sensors, Detection and tracking algorithms, Image quality, Modulation, Liquids, X-rays

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Showing 5 of 8 publications

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