Dr. Jin Zhang Profile

Dr. Jin Zhang

Director of Engineering, Photon Counting Division at Varex Imaging Corp

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Publications (3)

Proceedings Article | 15 February 2021 Presentation + Paper

Dual energy chest x-ray for improved COVID-19 detection using a dual-layer flat-panel detector: simulation and phantom studies

Linxi Shi, N. Robert Bennett, Minghui Lu, Mingshan Sun, Jin Zhang, Josh Star-Lack, Emily Tsai, H. Henry Guo, Adam Wang

Proceedings Volume 11595, 1159528 (2021) https://doi.org/10.1117/12.2581317

KEYWORDS: Chest imaging, Sensors, Dual energy imaging, Chest, Opacity, Visualization, Switching, Sensor performance, Radiography, Bone

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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.

Proceedings Article | 4 March 2019 Presentation + Paper

Dual energy imaging with a dual-layer flat panel detector

Minghui Lu, Adam Wang, Edward Shapiro, Amy Shiroma, Jin Zhang, Joachim Steiger, Josh Star-Lack

Proceedings Volume 10948, 1094815 (2019) https://doi.org/10.1117/12.2513499

KEYWORDS: Sensors, Dual energy imaging, Modulation transfer functions, Bone, X-rays, Scintillators, Calibration, Imaging systems, X-ray detectors, X-ray imaging

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Dual Energy (DE) imaging has been widely used in digital radiography and fluoroscopy, as has dual energy CT for various medical applications. In this study, the imaging performance of a dynamic dual-layer a-Si flat panel detector (FPD) prototype was characterized for dual energy imaging tasks. Dual energy cone beam CT (DE CBCT) scans were acquired and used to perform material decomposition in the projection domain, followed by reconstruction to generate material specific and virtual monoenergetic (VM) images. The dual-layer FPD prototype was built on a Varex XRD 4343RF detector by adding a 200 μm thick CsI scintillator and a-Si panel of 150 μm pixel size on top as a low energy detector. A 1 mm copper filter was added as a middle layer to increase energy separation with the bottom layer as a high energy detector. The imaging performance, such as Modulation Transfer Function (MTF), Conversion Factor (CF), and Detector Quantum Efficiency (DQE) of both the top and bottom detector layers were characterized and compared with those of the standard single layer XRD4343 RF detector. Several tissue equivalent cylinders (solid water, liquid water, bone, acrylic, polyethylene, etc.) were placed on a rotating stand, and two separate 450-projection CBCT scans were performed under continuous 120 kV and 80 kV X-ray beams. After an empirical material decomposition calibration, water and bone images were generated for each projection, and their respective volumes were reconstructed using Varex’s CBCT Software Tools (CST 2.0). A VM image, which maximized the contrast-to-noise ratio of water to polyethylene, was generated based on the water and bone images. The MTF at 1.0 lp/mm from the low energy detector was 32% and 22% higher than the high energy detector and the standard detector, respectively; the DQE of both high and low energy detectors is much lower than that of the standard XRD 4343RF detector. The CNR of water to polyethylene from the VM image improved by 50% over that from the low energy image alone at 120 kV, and by 80% at 80 kV. This study demonstrates the feasibility of using a dual-layer FPD in applications such as DE CBCT for contrast enhancement and material decomposition. Further evaluations are underway.

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