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 (11)

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Special Issue on Photon-Counting: Detectors and Applications

Photon Counting: Detectors and Applications

Patrick La Riviere, Mini Das

Journal of Medical Imaging, Vol. 11, Issue S1, S12801, (December 2024) https://doi.org/10.1117/1.JMI.11.S1.S12801

TOPICS: Sensors, Photon counting, Biomedical applications, Industrial applications, X-ray computed tomography, Tissues, Spectral resolution, Scanners, Contrast agents, X-rays

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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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Iterative clustering material decomposition aided by empirical spectral correction for photon counting detectors in micro-CT

J. Carlos Rodriguez Luna, Mini Das

Journal of Medical Imaging, Vol. 11, Issue S1, S12810, (December 2024) https://doi.org/10.1117/1.JMI.11.S1.S12810

TOPICS: Polymethylmethacrylate, Voxels, Sensors, Attenuation correction, Windows, Signal attenuation, Calibration, Attenuation, Mixtures, Tissues

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Purpose

Photon counting detectors offer promising advancements in computed tomography (CT) imaging by enabling the quantification and three-dimensional imaging of contrast agents and tissue types through simultaneous multi-energy projections from broad X-ray spectra. However, the accuracy of these decomposition methods hinges on precise composite spectral attenuation values that one must reconstruct from spectral micro-CT. Errors in such estimations could be due to effects such as beam hardening, object scatter, or detector sensor–related spectral distortions such as fluorescence. Even if accurate spectral correction is done, multi-material separation within a volume remains a challenge. Increasing the number of energy bins in material decomposition problems often comes with a significant noise penalty but with minimal decomposition benefits.

Approach

We begin with an empirical spectral correction method executed in the tomographic domain that accounts for distortions in estimated spectral attenuation for each voxel. This is followed by our proposed iterative clustering material decomposition (ICMD) where clustering of voxels is used to reduce the number of basis materials to be resolved for each cluster. Using a larger number of energy bins for the clustering step shows distinct advantages in excellent classification to a larger number of clusters with accurate cluster centers when compared with the National Institute of Standards and Technology attenuation values. The decomposition step is applied to each cluster separately where each cluster has fewer basis materials compared with the entire volume. This is shown to reduce the need for the number of energy bins required in each decomposition step for the clusters. This approach significantly increases the total number of materials that can be decomposed within the volume with high accuracy and with excellent noise properties.

Results

Utilizing a (cadmium telluride 1-mm-thick sensor) Medipix detector with a 55-μm pitch, we demonstrate the quantitatively accurate decomposition of several materials in a phantom study, where the sample includes mixtures of soft materials such as water and poly-methyl methacrylate along with contrast-enhancing materials. We show improved accuracy and lower noise when all five energy bins were used to yield effective classification of voxels into multiple accurate fundamental clusters which was followed by the decomposition step applied to each cluster using just two energy bins. We also show an example of biological sample imaging and separating three distinct types of tissue in mice: muscle, fat, and bone. Our experimental results show that the combination of effective and practical spectral correction and high-dimensional data clustering enhances decomposition accuracy and reduces noise in micro-CT.

Conclusions

This ICMD allows for quantitative separation of multiple materials including mixtures and also effectively separates multi-contrast agents.

Utility of photon-counting detectors for MV-kV dual-energy computed tomography imaging

Giavanna Jadick, Maya Ventura, Patrick La Rivière

Journal of Medical Imaging, Vol. 11, Issue S1, S12811, (December 2024) https://doi.org/10.1117/1.JMI.11.S1.S12811

TOPICS: Sensors, Bone, Signal to noise ratio, Photons, Imaging systems, Radiotherapy, Tissues, X-ray computed tomography, Simulations, Photodetectors

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