Dr. Mingdong Fan Profile

Dr. Mingdong Fan

at Mayo Clinic

SPIE Involvement:

Author

Publications (8)

SPIE Journal Paper | 24 May 2024

Low-contrast detectability of photon-counting-detector CT at different scan modes and image types in comparison with energy-integrating-detector CT

Mingdong Fan, Zhongxing Zhou, Jarod Wellinghoff, Cynthia H. McCollough, Lifeng Yu

JMI, Vol. 11, Issue S1, S12803, (May 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.S1.S12803

KEYWORDS: Computed tomography, Infrared imaging, Image restoration, Photodetectors, Sensors, Scanners, Quality control, Image quality, Interference (communication), Image sharpness

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PurposeWe aim to compare the low-contrast detectability of a clinical whole-body photon-counting-detector (PCD)-CT at different scan modes and image types with an energy-integrating-detector (EID)-CT.ApproachWe used a channelized Hotelling observer (CHO) previously optimized for quality control purposes. An American College of Radiology CT accreditation phantom was scanned on both PCD-CT and EID-CT with 10 phantom positionings. For PCD-CT, images were generated using two scan modes, standard resolution (SR) and ultra-high-resolution (UHR); two image types, virtual monochromatic images at 70 keV and low-energy threshold (T3D); both filtered-back-projection (FBP) and iterative reconstruction (IR) reconstruction methods; and three reconstruction kernels. For each positioning, three repeated scans were acquired for each scan mode, image type, and CTDIvol of 6, 12, and 24 mGy. For EID-CT, images acquired from scans (10 positionings × 3 repeats × 3 doses) were reconstructed using the closest counterpart FBP and IR kernels. CHO was applied to calculate the index of detectability (d′) on both scanners.ResultsWith the smooth Br44 kernel, the d′ of UHR was mostly comparable with that of the SR mode (difference: −11.4% to 8.3%, p=0.020 to 0.956), and the T3D images had a higher d′ (difference: 0.7% to 25.6%) than 70 keV images on PCD-CT. Compared with the EID-CT, UHR-T3D of PCD-CT had non-inferior d′ (difference: −2.7% to 12.9%) with IR and non-superior d′ (difference: 0.8% to 11.2%) with FBP using the Br44 kernel. PCD-CT produced higher d′ than EID-CT by 61.8% to 247.1% with the sharper reconstruction kernels.ConclusionsThe comparison between PCD-CT and EID-CT was significantly influenced by the reconstruction method and kernel. With a smooth kernel that is typically used in low-contrast detection tasks, the PCD-CT demonstrated low-contrast detectability that was comparable to EID-CT with IR and showed no superiority when using FBP. With the use of sharper kernels, the PCD-CT significantly outperformed EID-CT in low-contrast detectability.

Proceedings Article | 3 April 2024 Poster

Automated web-based software for CT quality control testing of low-contrast detectability using model observers

Zhongxing Zhou, Jarod Wellinghoff, Mingdong Fan, Scott Hsieh, David Holmes, Cynthia McCollough, Lifeng Yu

Proceedings Volume 12925, 129252J (2024) https://doi.org/10.1117/12.3008777

KEYWORDS: Quality control, Image quality, Computed tomography, Software validation, Modulation transfer functions, Medical image reconstruction, Clinical research, Biomedical applications

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Proceedings Article | 2 April 2024 Poster + Paper

Image-domain material decomposition for dual-energy CT using a conditional diffusion model

Junbo Peng, Chih-Wei Chang, Mingdong Fan, Huiqiao Xie, Justin Roper, Richard L. Qiu, Tonghe Wang, Xiangyang Tang, Xiaofeng Yang

Proceedings Volume 12930, 1293022 (2024) https://doi.org/10.1117/12.3006941

KEYWORDS: Diffusion, Signal to noise ratio, Computed tomography, Tissues, Matrices, Education and training, Bone, Performance modeling, Image processing, Biomedical applications

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Proceedings Article | 1 April 2024 Poster + Paper

Metal artifact reduction in CT using unsupervised sinogram manifold learning

Junbo Peng, Chih-Wei Chang, Huiqiao Xie, Mingdong Fan, Tonghe Wang, Justin Roper, Richard L. Qiu, Xiangyang Tang, Xiaofeng Yang

Proceedings Volume 12925, 129252G (2024) https://doi.org/10.1117/12.3006947

KEYWORDS: Metals, Computed tomography, Data acquisition, Image restoration, Machine learning, CT reconstruction, Data modeling, X-ray computed tomography, Matrices, Photons

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