Dr. Don Vernekohl Profile

Dr. Don Vernekohl

at Varex Imaging Corp

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Publications (3)

Proceedings Article | 18 October 2022 Open Access

Design and optimization of 3D VSHARP® scatter correction for industrial CBCT using the Linear Boltzmann Transport Equation

Kevin Holt, Devang Savaliya, Amy Shiroma, Martin Hu, David Nisius, Steve Hoelzer, Mingshan Sun, Don Vernekohl, Josh Star-Lack

Proceedings Volume 12304, 1230419 (2022) https://doi.org/10.1117/12.2647154

KEYWORDS: Sensors, Image segmentation, Image quality, Reconstruction algorithms, 3D image processing, Spatial resolution, Genetic algorithms, Aluminum, 3D acquisition, Physics

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We have developed VSHARP®, a suite of scatter correction solutions that have been incorporated into the commercially available cone-beam software development toolkit, CST (Varex Imaging, Salt Lake City, UT) enabling scatter correction to be applied as part of an entire CBCT reconstruction pipeline. The suite includes 2D VSHARP®, a deconvolution correction using asymmetric Gaussian kernels, 2D VSHARP-ML, a U-NET machine-learning correction, and 3D VSHARP®, a correction using a rapid finite-element Linear Boltzmann Transport Equation (LBTE) solver to estimate scatter in a manner similar to traditional stochastic Monte Carlo (MC) simulations. Of the three corrections, 3D VSHARP is the most accurate and flexible since it can be readily applied to arbitrary scanner geometries, protocols, and scan parts while the 2D VSHARP models may need to be regenerated for each configuration. On the other hand, 3D VSHARP is inherently slower since a minimum of two reconstruction passes are needed and the LBTE solver, while much faster than traditional MC, is still computationally intensive. The goal of this work was to minimize LBTE run times for (typically large) industrial datasets by optimizing parameter settings, particularly the choice of the sampling grid dimensions. This was achieved by applying a multi-objective genetic algorithm to find the Pareto front characterizing the tradeoff between speed and accuracy and identifying key operating points on the curve. Testing with 720 frames of 3720x3720 projection data to make a reconstruction volume of size 500x500x600, we found that excellent image quality can be obtained by using a coarse scatter grid size of 27x27x32 volume and 44x44 detector and a primary grid size of 246x246 x295 volume and 295x295 detector, both over 42 frames for a grand total of 21 seconds LBTE computation time. We show the Pareto characterization, as well as demonstrations of 3D VSHARP image quality with significantly reduced scatter-induced artifacts such as streaking and shading.

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

3D VSHARP®, a general-purpose CBCT scatter correction tool that uses the linear Boltzmann transport equation

Josh Star-Lack, Kevin Holt, Steve Hoelzer, Devang Savaliya, Amy Shiroma, Suman Shrestha, Eric Amusin, Don Vernekohl, David Nisius, Mingshan Sun

Proceedings Volume 11595, 115952X (2021) https://doi.org/10.1117/12.2582048

KEYWORDS: Algorithm development, 3D modeling, X-rays, X-ray detectors, X-ray computed tomography, Software development, Sensors, Photon transport, Image quality, Data acquisition

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Proceedings Article | 3 April 2019 Presentation + Paper

Comparison of CMOS and amorphous silicon detectors: determining the correct selection criteria, to optimize system performance for typical imaging tasks

Isaias Job, Arundhuti Ganguly, Don Vernekohl, Richard Weisfield, Elena Muñoz, Jin Zhang, Carlo Tognina, Rick Colbeth

Proceedings Volume 10948, 109480F (2019) https://doi.org/10.1117/12.2513500

KEYWORDS: Amorphous silicon, Sensors, CMOS sensors, Scintillators, X-rays, Modulation transfer functions, Imaging systems, X-ray imaging

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Complementary metal-oxide-semiconductors (CMOS) flat panel detectors (FPD) have steadily gained acceptance into medical imaging applications1-15. Selecting the proper detector technology for the imaging task requires optimization to balance the cost and the image quality. To facilitate this, fundamental detector performance of CMOS and a-Si panels were evaluated using the following quantitative imaging metrics: X-ray sensitivity, Noise Equivalent Dose (NED,) Noise Power Spectrum (NPS), Modulation Transfer Function (MTF), and Detective Quantum Efficiency (DQE). Imaging task measurements involved high-contrast and low-contrast resolution assessment. Varex FPDs evaluated for this study included: CMOS 3131 (150 μm pixel), a-Si 3030X (194 μm pixel), a-Si XRpad2 3025 (100 μm) and CMOS 2020 (100 μm pixel). Performance comparisons were organized by pixel size: large pixels, 150 μm CMOS and 194 μm a-Si, and small pixels, 100 μm in a-Si and CMOS technology. The results showed high dose DQE of the a-Si 3030X was about 10% higher than the CMOS 3131 between 0 - 1.8 cycles/mm, while beyond 1.8 cycles/mm, the CMOS performed better. The 3030X low dose DQE was higher than the 3131 between 0-1.3 cycles/mm, while the CMOS performance was higher beyond 1.3 cycles/mm. The high dose DQE of 100 μm a-Si was higher than the 100 μm CMOS for all frequencies. However, the low dose DQE of 100 μm CMOS was higher beyond 0.6 cycles/mm, while the 100 μm a-Si pixel had higher DQE only between 0 – 0.6 cycles/mm. Large pixel image quality (IQ) assessment favored a-Si pixel with 7% higher Contrast-to-Noise-Ratio (CNR) results for both high and low contrast-detail at 500 nGy. Small pixel CNR favored CMOS with ~38% better high contrast-detail and 12% greater low contrast-detail at ~500 nGy. Through these measurements that combine imaging metrics and image quality, we demonstrated a practical method for selecting the appropriate detector technology based on the requirements of the imaging applications.

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