Volume 11 Issue S1 | Journal of Medical Imaging

Journal of Medical Imaging

VOL. 11 · NO. S1 | December 2024

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Special Issue on Photon-Counting: Detectors and Applications (8)

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Special Issue on Photon-Counting: Detectors and Applications

Framework for evaluating photon-counting detectors under pile-up conditions

David Leibold, Stefan J. van der Sar, Marlies C. Goorden, Dennis R. Schaart

Journal of Medical Imaging, Vol. 11, Issue S1, S12802, (May 2024) https://doi.org/10.1117/1.JMI.11.S1.S12802

TOPICS: Sensors, Photons, Photodetectors, Point spread functions, Electroluminescence, Spectral response, X-rays, Simulations, X-ray detectors, Detector arrays

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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

Journal of Medical Imaging, Vol. 11, Issue S1, S12803, (May 2024) https://doi.org/10.1117/1.JMI.11.S1.S12803

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

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Purpose

We 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.

Approach

We 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.

Results

With 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.

Conclusions

The 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.

Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network

Shaojie Chang, Jeffrey F. Marsh Jr., Emily K. Koons, Hao Gong, Cynthia H. McCollough, Shuai Leng

Journal of Medical Imaging, Vol. 11, Issue S1, S12804, (May 2024) https://doi.org/10.1117/1.JMI.11.S1.S12804

TOPICS: Denoising, Education and training, Infrared imaging, Computed tomography, Sensors, Photodetectors, Neural networks, Image quality, Image sharpness, Image processing

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Spectral optimization using fast kV switching and filtration for photon counting CT with realistic detector responses: a simulation study

Sen Wang, Yirong Yang, Debashish Pal, Zhye Yin, Jonathan Maltz, Norbert Pelc, Adam Wang

Journal of Medical Imaging, Vol. 11, Issue S1, S12805, (July 2024) https://doi.org/10.1117/1.JMI.11.S1.S12805

TOPICS: Fast packet switching, Tunable filters, Sensors, Calcium, Optical filters, Iodine, Gadolinium, Photon counting, Denoising, Silicon

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Impact of patient habitus and acquisition protocol on iodine quantification in dual-source photon-counting computed tomography

Leening Liu, Rizza Pua, Michael Dieckmeyer, Nadav Shapira, Pooyan Sahbaee, Grace Gang, Harold Litt, Peter Noël

Journal of Medical Imaging, Vol. 11, Issue S1, S12806, (July 2024) https://doi.org/10.1117/1.JMI.11.S1.S12806

TOPICS: Iodine, Computed tomography, Optical computing, Medical image reconstruction, Blood, Quantum protocols, Information operations, Diagnostics, Visualization, Tunable filters

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Number of energy windows for photon counting detectors: is more actually more?

Katsuyuki Taguchi

Journal of Medical Imaging, Vol. 11, Issue S1, S12807, (September 2024) https://doi.org/10.1117/1.JMI.11.S1.S12807

TOPICS: Windows, Sensors, Signal to noise ratio, Photon counting, Quantum noise, Education and training, Bone, Gold, X-rays, Pulse signals

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Contrast-to-noise ratio comparison between X-ray fluorescence emission tomography and computed tomography

Hadley DeBrosse, Giavanna Jadick, Ling Jian Meng, Patrick La Rivière

Journal of Medical Imaging, Vol. 11, Issue S1, S12808, (October 2024) https://doi.org/10.1117/1.JMI.11.S1.S12808

TOPICS: Gold, X-ray computed tomography, Sensors, Simulations, Metals, X-ray fluorescence spectroscopy, Tumors, Photons, Kidney, Image resolution

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Deep learning estimation of proton stopping power with photon-counting computed tomography: a virtual study

Karin Larsson, Dennis Hein, Ruihan Huang, Daniel Collin, Andrea Scotti, Erik Fredenberg, Jonas Andersson, Mats Persson

Journal of Medical Imaging, Vol. 11, Issue S1, S12809, (November 2024) https://doi.org/10.1117/1.JMI.11.S1.S12809

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