Comparison of the Agatston score acquired with photon-counting detector CT and energy-integrating detector CT: ex vivo study of cadaveric hearts

The purpose of this study was to compare the correlation and agreement between AS derived from either an energy-integrating detector CT (EID-CT) or a photon-counting detector CT (PCD-CT). Reproducibility was also compared. In total, 26 calcified coronary lesions (from five cadaveric hearts) were identified for inclusion. The hearts were positioned in a chest phantom and scanned in both an EID-CT and a prototype PCD-CT. The EID-CT and PCD-CT acquisition and reconstruction parameters were matched. To evaluate the reproducibility, the phantom was manually repositioned, and an additional scan was performed using both methods. The EID-CT reconstructions were performed using the dedicated calcium score kernel Sa36. The PCD-CT reconstructions were performed with a vendor-recommended kernel (Qr36). Several monoenergetic energy levels (50–150 keV) were evaluated to find the closest match with the EID-CT scans. A semi-automatic evaluation of calcium score was performed on a post-processing multimodality workplace. The best match with Sa36 was PCD-CT Qr36 images, at a monoenergetic level of 72 keV. Statistical analyses showed excellent correlation and agreement. The correlation and agreement with regards to the Agatston score (AS) between the two methods, for each position as well as between the two positions for each method, were assessed with the Spearman´s rank correlation. The correlation coefficient, rho, was 0.98 and 0.97 respectively 0.99 and 0.98. The corresponding agreements were investigated by means of Bland–Altman plots. High correlation and agreement was observed between the AS derived from the EID-CT and a PCD-CT. Both methods also demonstrated excellent reproducibility.

Multi bin energy-sensitive micro-CT using large area photon-counting detectors Timepix

X-ray micro-CT has become a popular and widely used tool for the purposes of scientific research. Although the current state-of-the-art micro-CT is on a high technology level, it still has some known limitations. One of the relevant issues is an inability to clearly identify and quantify specific materials. The mentioned drawback can be solved by the energy-sensitive CT approach. Dual-energy CT, which is already frequently used in human medicine, offers the identification of two different materials; for example, it differentiates an intravenous contrast agent from bone or it can indicate the composition of urinary stones. Resolving a larger number of material components within a single object is beyond the capabilities of dual-energy CT. Such an approach requires a higher number of measurements using different photon energies. A possible solution for multi bin, or so-called spectral CT, is the application of photon-counting detectors. Photon counting technology offers an integrated circuitry capable of resolving the energy of incoming photons in each pixel. Therefore, it is possible to collect data in user-defined energy windows. This contribution evaluates the applicability of the large-area photon-counting detector Timepix for multi bin energy-sensitive micro-CT. It presents an experimental phantom study focused on the simultaneous K-edge-based identification and quantification of multiple contrast agents within a single object. The paper describes the collection of multiple energy bins using the Timepix detector operated in the photon counting mode, explains the data processing, and demonstrates the results obtained from an in-house implemented basis material decomposition algorithm.

A Geant4 tool for edge-illumination X-ray phase-contrast imaging

The PEPI project is developing a new experimental facility integrating a chromatic photon-counting detector within an edge-illumination (EI) phase-contrast setup. In this context, a novel Geant4-based simulation tool has been introduced with the aim of defining the optimal design of the experimental setup. The code includes a custom X-ray refraction process and allows simulating the whole EI system, comprising a polychromatic and extended source, absorbing masks, substrates, their movement during acquisition, and X-ray detection. In this paper, a realistic spectral detector model is introduced and its energy response validated against experimental data acquired with synchrotron radiation at energies between 26 and 50 keV. Moreover, refraction and transmission images of a plastic phantom are reconstructed from simulation data and successfully compared with theoretical predictions. Finally, an optimization study aiming at finding the effect of the X-ray focal spot size (i.e. spatial coherence) on image quality is presented; the results suggest that, in the considered configuration, the system can tolerate source sizes up to 30 μm, while, for a fixed exposure time, the best signal-to-noise ratio in refraction images is found for source sizes in the order of 10 to 15 μm.

Visualization of bone details in a novel photon-counting dual-source CT scanner—comparison with energy-integrating CT

Objectives Photon-counting detector CT (PCD-CT) promises a leap in spatial resolution due to smaller detector pixel sizes than implemented in energy-integrating detector CTs (EID-CT). Our objective was to compare the visualization of smallest bone details between PCD-CT and EID-CT using a mouse as a specimen. Materials and methods Two euthanized mice were scanned at a 20-slice EID-CT and a dual-source PCD-CT in single-pixel mode at various CTDIVol values. Image noise and signal-to-noise ratio (SNR) were evaluated using repeated ROI measurements. Edge sharpness of bones was compared by the maximal slope within CT value plots along sampling lines intersecting predefined bones of the spine. Two readers evaluated bone detail visualization at four regions of the spine on a three-point Likert scale at various CTDIVol’s. Two radiologists selected the series with better detail visualization among each of 20 SNR-matched pairs of EID-CT and PCD-CT series. Results In CTDIVol-matched scans, PCD-CT series showed significantly lower image noise (NoiseCTDI=5 mGy: 16.27 ± 1.39 vs. 23.46 ± 0.96 HU, p < 0.01), higher SNR (SNRCTDI=5 mGy: 20.57 ± 1.89 vs. 14.00 ± 0.66, p < 0.01), and higher edge sharpness (Edge Slopelumbar spine: 981 ± 160 vs. 608 ± 146 HU/mm, p < 0.01) than EID-CT series. Two radiologists considered the delineation of bone details as feasible at consistently lower CTDIVol values at PCD-CT than at EID-CT. In comparison of SNR-matched reconstructions, PCD-CT series were still considered superior in almost all cases. Conclusions In this head-to-head comparison, PCD-CT showed superior objective and subjective image quality characteristics over EID-CT for the delineation of tiniest bone details. Even in SNR-matched pairs (acquired at different CTDIVol’s), PCD-CT was strongly preferred by radiologists. Key Points • In dose-matched scans, photon-counting detector CT series showed significantly less image noise, higher signal-to-noise ratio, and higher edge sharpness than energy-integrating detector CT series. • Human observers considered the delineation of tiny bone details as feasible at much lower dose levels in photon-counting detector CT than in energy-integrating detector CT. • In direct comparison of series matched for signal-to-noise ratio, photon-counting detector CT series were considered superior in almost all cases.

Evaluation and comparison of a CdTe based photon counting detector with an energy integrating detector for X-ray phase sensitive imaging of breast cancer

PURPOSE: To compare imaging performance of a cadmium telluride (CdTe) based photon counting detector (PCD) with a CMOS based energy integrating detector (EID) for potential phase sensitive imaging of breast cancer. METHODS: A high energy inline phase sensitive imaging prototype consisting of a microfocus X-ray source with geometric magnification of 2 was employed. The pixel pitch of the PCD was 55μm, while 50μm for EID. The spatial resolution was quantitatively and qualitatively assessed through modulation transfer function (MTF) and bar pattern images. The edge enhancement visibility was assessed by measuring edge enhancement index (EEI) using the acrylic edge acquired images. A contrast detail (CD) phantom was utilized to compare detectability of simulated tumors, while an American College of Radiology (ACR) accredited phantom for mammography was used to compare detection of simulated calcification clusters. A custom-built phantom was employed to compare detection of fibrous structures. The PCD images were acquired at equal, and 30% less mean glandular dose (MGD) levels as of EID images. Observer studies along with contrast to noise ratio (CNR) and signal to noise ratio (SNR) analyses were performed for comparison of two detection systems. RESULTS: MTF curves and bar pattern images revealed an improvement of about 40% in the cutoff resolution with the PCD. The excellent spatial resolution offered by PCD system complemented superior detection of the diffraction fringes at boundaries of the acrylic edge and resulted in an EEI value of 3.64 as compared to 1.44 produced with EID image. At MGD levels (standard dose), observer studies along with CNR and SNR analyses revealed a substantial improvement of PCD acquired images in detection of simulated tumors, calcification clusters, and fibrous structures. At 30% less MGD, PCD images preserved image quality to yield equivalent (slightly better) detection as compared to the standard dose EID images. CONCLUSION: CdTe-based PCDs are technically feasible to image breast abnormalities (low/high contrast structures) at low radiation dose levels using the high energy inline phase sensitive imaging technique.

Feasibility study of portable multi-energy computed tomography with photon-counting detector for preclinical and clinical applications

In this study, preclinical experiments were performed with an in-house developed prototypal photon-counting detector computed tomography (PCD CT) system. The performance of the system was compared with the conventional energy-integrating detector (EID)-based CT, concerning the basic image quality biomarkers and the respective capacities for material separation. The pre- and the post-contrast axial images of a canine brain captured by the PCD CT and EID CT systems were found to be visually similar. Multi-energy images were acquired using the PCD CT system, and machine learning-based material decomposition was performed to segment the white and gray matters for the first time in soft tissue segmentation. Furthermore, to accommodate clinical applications that require high resolution acquisitions, a small, native, high-resolution (HR) detector was implemented on the PCD CT system, and its performance was evaluated based on animal experiments. The HR acquisition mode improved the spatial resolution and delineation of the fine structures in the canine’s nasal turbinates compared to the standard mode. Clinical applications that rely on high-spatial resolution expectedly will also benefit from this resolution-enhancing function. The results demonstrate the potential impact on the brain tissue segmentation, improved detection of the liver tumors, and capacity to reconstruct high-resolution images both preclinically and clinically.

Optimization of a CZT photon counting detector for contaminant detection

In the food industry, X-ray inspection systems are utilized to ensure packaged food is free from physical contaminants to maintain a high level of food safety for consumers. However, one of the challenges in the food industry is detecting small, low-density contaminants from packaged food. Cadmium zinc telluride (CZT) photon counting detectors (PCDs) can potentially alleviate this problem given its multi-energy bin capabilities, high spatial resolution and ability to eliminate electronic noise, which is superior to the conventional energy integrating detector (EID). However, the image quality from a CZT PCD can be further improved by applying an optimized energy bin weighting scheme that maximizes energy bin images that provide the largest image contrast and lowest image noise. Therefore, in this work, five contaminant materials embedded in an acrylic phantom were imaged using a CZT PCD while the phantom was in constant motion to mimic food products moving on a conveyor belt. Energy bin optimization was performed by applying an image-based weighting scheme and these results showed contrast-to-noise ratio (CNR) improvements ranging between 1.02–1.91 relative to an equivalent EID acquisition.