Dr. Ming Yan Profile

Dr. Ming Yan

at GE Global Research

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Publications (3)

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Proceedings Article | 18 March 2015 Paper

Personalized low dose CT via variable kVp

Hui Wang, Yannan Jin, Yangyang Yao, Mingye Wu, Ming Yan, Kun Tao, Zhye Yin, Bruno De Man

Proceedings Volume 9412, 94122X (2015) https://doi.org/10.1117/12.2082353

KEYWORDS: Calibration, Modulation, X-rays, Computed tomography, Signal attenuation, Bone, X-ray computed tomography, Tomography, Image quality, Quantum electronics

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Computerized Tomography (CT) is a powerful radiographic imaging technology but the health risk due to the exposure of x-ray radiation has drawn wide concern. In this study, we propose to use kVp modulation to reduce the radiation dose and achieve the personalized low dose CT. Two sets of simulation are performed to demonstrate the effectiveness of kVp modulation and the corresponding calibration. The first simulation used the helical body phantom (HBP) that is an elliptical water cylinder with high density bone inserts. The second simulation uses the NCAT phantom to emulate the practical use of kVp modulation approach with region of interest (ROI) selected in the cardiac region. The kVp modulation profile could be optimized view by view based on the knowledge of patient attenuation. A second order correction is applied to eliminate the beam hardening artifacts. To simplify the calibration process, we first generate the calibration vectors for a few representative spectra and then acquire other calibration vectors with interpolation. The simulation results demonstrate the beam hardening artifacts in the images with kVp modulation can be eliminated with proper beam hardening correction. The results also show that the simplification of calibration did not impair the image quality: the calibration with the simplified and the complete vectors both eliminate the artifacts effectively and the results are comparable. In summary, this study demonstrates the feasibility of kVp modulation and gives a practical way to calibrate the high order beam hardening artifacts.

Proceedings Article | 18 March 2015 Paper

Metal artifact reduction in tomosynthesis imaging

Zhaoxia Zhang, Ming Yan, Kun Tao, Xiao Xuan, John Sabol, Hao Lai

Proceedings Volume 9412, 94125A (2015) https://doi.org/10.1117/12.2080923

KEYWORDS: Metals, Image filtering, Image segmentation, Reconstruction algorithms, Bone, X-ray imaging, Image processing algorithms and systems, Tissues, Ions, Neodymium

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The utility of digital tomosynthesis has been shown for many clinical scenarios including post orthopedic surgery applications. However, two kinds of metal artifacts can influence diagnosis: undershooting and ripple. In this paper, we describe a novel metal artifact reduction (MAR) algorithm to reduce both of these artifacts within the filtered backprojection framework. First, metal areas that are prone to cause artifacts are identified in the raw projection images. These areas are filled with values similar to those in the local neighborhood. During the filtering step, the filled projection is free of undershooting due to the resulting smooth transition near the metal edge. Finally, the filled area is fused with the filtered raw projection data to recover the metal. Since the metal areas are recognized during the back projection step, anatomy and metal can be distinguished - reducing ripple artifacts. Phantom and clinical experiments were designed to quantitatively and qualitatively evaluate the algorithms. Based on phantom images with and without metal implants, the Artifact Spread Function (ASF) was used to quantify image quality in the ripple artifact area. The tail of the ASF with MAR decreases from in-plane to out-of-plane, implying a good artifact reduction, while the ASF without MAR remains high over a wider range. An intensity plot was utilized to analyze the edge of undershooting areas. The results illustrate that MAR reduces undershooting while preserving the edge and size of the metal. Clinical images evaluated by physicists and technologists agree with these quantitative results to further demonstrate the algorithm’s effectiveness.

Proceedings Article | 19 March 2014 Paper

Hybrid-model for computed tomography simulations and post-patient collimator design

Horace Xu, Kun Tao, Padmashree GK, Mingye Wu, Ximiao Cao, Yong Long, Ming Yan, Yangyang Yao, Bruno De Man

Proceedings Volume 9033, 90334R (2014) https://doi.org/10.1117/12.2043235

KEYWORDS: Monte Carlo methods, Modulation transfer functions, X-rays, Collimators, Computer simulations, Scintillators, X-ray imaging, Sensors, Computed tomography, Photon transport

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Ray-tracing based simulation methods are widely used in modeling X-ray propagation, detection and imaging. While most of the existing simulation methods rely on analytical modeling, a novel hybrid approach comprising of statistical modeling and analytical approaches, is proposed here. Our hybrid simulator is a unique combination of analytical modeling for evoking the fundamentals of X-ray transport through ray-tracing, and a look-up-table (LUT) based approach for integrating it with the Monte Carlo simulations that model optical photon-transport within scintillator. The LUT approach for scintillation-based X-ray detection invokes depth-dependent gain factors to account for intra-pixel absorption and light-transport, together with incident-angle dependent effects for inter-pixel X-ray absorption (parallax effect). The model simulates the post-patient collimator for scatter-rejection, as an X-ray shadow on scintillator, while handling its position with respect to the pixel boundary, by a smart over-sampling strategy for high efficiency. We have validated this simulator for computed tomography system-simulations, by using real data from GE Brivo CT385. The level of accuracy of image noise and spatial resolution is better than 98%. We have used the simulator for designing the post-patient collimator, and measured modulation transfer function (MTF) for different widths of the collimator plate. Validation and simulation study clearly demonstrates that the hybrid simulator is an accurate, reliable, efficient tool for realistic system-level simulations. It could be deployed for research, design and development purposes to model any scintillator-based X-ray imaging-system (2-dimensional and 3-dimensional), while being equally applicable for medical and industrial imaging.

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