10 Mcps photon-counting X-ray computed tomography system utilizing a LSO-MPPC-module detector

X-ray photons are detected using a Lu2(SiO4)O [LSO] single-crystal scintillator with a decay time of 40 ns and a multipixel photon counter (MPPC). The photocurrent from the MPPC is amplified by a high-speed current–voltage amplifier with an 80 MHz-gain-band operational amplifier, and the 200-ns-width event pulses are sent to a multichannel analyzer (MCA) to measure X-ray spectra. The MPPC was driven in the pre-Geiger mode at a bias voltage of 70.7 V and a temperature of 23 °C. Photon-counting computed tomography (PC-CT) is accomplished by repeated linear scans and rotations of an object, and projection curves of the object are obtained by linear scanning at a tube current of 1.5 mA. In PC-CT, the event pulse height is dispersed using a 7-ns-delay comparator. The exposure time for obtaining a tomogram is 10 min with scan steps of 0.5 mm and rotation steps of 1.0°. At a tube voltage of 80 kV, the maximum count rate is 250 kcps. We carry out PC-CT using iodine media and confirm the energy-dispersive effect with changes in the lower level voltage of event pulses using a comparator.

Demonstration of multiple contrast agent imaging for the next generation color X-ray CT

Electron density estimation using MPPC-based photon-counting CT for radiation therapy planning

Objective. Photon counting CT (PCCT) has been a research focus in the last two decades. Recent studies and advancements have demonstrated that systems using semiconductor-based photon counting detectors (PCDs) have the potential to provide better contrast, noise and spatial resolution performance compared to conventional scintillator-based systems. With multi-energy threshold detection, PCD can simultaneously provide the photon energy measurement and enable material decomposition for spectral imaging. In this work, we report a performance evaluation of our first CdZnTe-based prototype full-size PCCT system through various phantom imaging studies. Approach. This prototype system supports a 500 mm scan field-of-view and 10 mm z-coverage at isocenter. Phantom scans were acquired using 120 kVp from 50 to 400 mAs to assess the imaging performance on: CT number accuracy, uniformity, noise, spatial resolution, material differentiation and quantification. Main results. Both qualitative and quantitative evaluations show that PCCT, under the tested conditions, has superior imaging performance with lower noise and improved spatial resolution compared to conventional energy integrating detector (EID)-CT. Using projection domain material decomposition approach with multiple energy bin measurements, PCCT virtual monoenergetic images have lower noise, and good accuracy in quantifying iodine and calcium concentrations. These results lead to increased contrast-to-noise ratio (CNR) for both high and low contrast study objects compared to EID-CT at matched dose and spatial resolution. PCCT can also generate super-high resolution images using much smaller detector pixel size than EID-CT and greatly improve image spatial resolution. Significance. Improved spatial resolution and quantification accuracy with reduced image noise of the PCCT images can potentially lead to better diagnosis at reduced radiation dose compared to conventional EID-CT. Increased CNR achieved by PCCT suggests potential reduction in iodine contrast media load, resulting in better patient safety and reduced cost.

Recently, photon-counting computed tomography (PCCT) has been extensively studied as a promising modality for spectral imaging. PCCT offers some benefits over conventional CT, such as energy-resolved X-ray information and reduced radiation dose owing to its ability to count and discriminate each X-ray photon based on its energy. However, PCCT images in certain energy bands tend to suffer from artifacts and image noise owing to insufficient photon counts. These problems reduce the accuracy and quality of PCCT images, thereby limiting the full potential of energy-resolved imaging. Recently, many studies have been conducted on the application of deep learning to conventional CT for the reduction of image noise and other improvements, which are considered promising. However, the application of deep learning to PCCT has not been thoroughly investigated. In this study, we investigated the effectiveness of deep learning in improving PCCT images and explored a new approach for utilizing energy-resolved PCCT information. For this purpose, we conducted experiments using our PCCT system and acquired data by scanning phantoms and mice. Deep learning was then applied to the data, and the images were evaluated in terms of noise and material-decomposed imaging. Consequently, image noise and artifacts are significantly suppressed by the application of deep learning. In particular, more effective noise reduction was achieved with a model that could refer to and utilize energy information. Improvements were also confirmed in the accuracy of contrast agent discrimination in phantom imaging, as well as in the visualization of accumulated contrast agents in organs in mouse imaging. Consequently, deep learning effectively incorporating energy information was shown to significantly improve the PCCT image quality, demonstrating a new potential for energy information in PCCT.

To realize novel photon-counting energy-dispersive X-ray computed tomography (CT), we have developed a low-dose CT scanner using a detector consisting of a cerium-doped yttrium aluminum perovskite [YAP(Ce)] crystal and a small photomultiplier tube (PMT). X-ray photons are absorbed by the YAP(Ce) crystal, and negative outputs are produced from the PMT. The PMT outputs are amplified by an inverse voltage-to-voltage amplifier, and the event pulses are counted by the counter with a low threshold energy of 20 keV. First, almost all the photons are counted without the photon-energy dependence, since the scintillation-photon number produced by one X-ray photon is proportional to the photon energy. Second, the energy-dispersive imaging is performed using the self-beam hardening by the object. The maximum photon count of the projection data is determined after the air absorption, and the effective photon energy increases with increasing digital-amplification factor at the constant maximum count. In the triple-energy CT, the X-ray beam diameter was 0.5 mm, and the spatial resolutions were approximately 0.3×0.3 mm2 . The exposure time for DE-CT was 9.8 min at a total rotation angle of 180°.

Conventional X-ray fluorescence camera with a cadmium-telluride detector and its application to cancer diagnosis

Application of an oscillation-type linear cadmium telluride detector to enhanced gadolinium K-edge computed tomography

The performance of photon-counting CT (PCCT) depends on how detector energy thresholds or bins (collectively referred to as energy settings) are defined. This study aimed to identify optimal energy settings for various PCCT technologies to enhance spectral separability and spatial detectability of materials relevant to diagnostic imaging. We modeled vendor-neutral PCCT scanners with cadmium telluride (CdTe), cadmium zinc telluride (CZT), and silicon (Si) based detectors using an in-silico imaging framework. The scanner models incorporated scanner geometries and spatio-energetic detector responses to account for inter-pixel and inter-energy crosstalk and noise correlation. Using these models, we imaged two sizes of a cylindrical phantom containing inserts with various concentrations of calcium, iodine, and gadolinium across various energy settings and two dose levels. Other settings were held constant, including tube voltage (120 kV), pitch (1), gantry rotation speed (0.5 rot/second), and reconstruction settings. Image quality was quantified in terms of separability index (s') and contrast-to-noise ratio (CNR). The scanner-specific optimal energy settings were determined by ranking the measured s' values using a rank-sum method and evaluating them with the Friedman test. The optimal energy settings varied primarily with the phantom size and material pairs to separate (Ca-I, Ca-Gd, I-Gd) rather than the dose level. When aggregated across all imaging tasks and conditions, the optimal energy settings were 30-65 keV and 20-35-50-70 keV for two-and four-threshold CdTe-, 20-35-50-70 keV for four-threshold CZT-, and 5-35-50-80-120 keV for four-bin Si-based PCCT systems. Optimization of energy settings significantly improved material decomposition accuracy for Ca, I, and Gd across PCCT systems, particularly under low-concentration conditions, reducing percent differences from as high as 422.0% to within 17.0%. This work presents a simulation-based framework for optimizing energy settings of photon-counting detectors used in PCCT scanners, providing clinically translatable insights towards clinical adoption for accurate and standardized spectral imaging.

A simple x-ray computed tomography (CT) system utilizing a cadmium telluride detector and its application to enhanced iodine K-edge angiography are described. The CT system is of the first generation type and consists of an x-ray generator, a turn table, a translation unit, a motor drive unit, a cadmium telluride detector, an interface unit for the detector, and a personal computer (PC). Tomography was performed by the repetition of the translation and rotation. Narrow-photon-energy bremsstrahlung x-rays with a peak photon energy of approximately 35 keV is very useful for performing enhanced K-edge angiography because these rays are absorbed effectively by iodine-based contrast media with a K-edge of 33.2 keV. The tube voltage, the tube current, and the aluminum filter thickness were 60 kV, 1.5 mA, and 3.0 mm, respectively. Holes filled with iodine media in phantoms are visible with high contrasts, and the CT system can be applied to photon-counting and fluorescent x-ray CT systems.

An energy-discrimination K-edge x-ray computed tomography (CT) system is useful for controlling the image contrast of a target region by selecting both the photon energy and the energy width. The CT system has an oscillation-type linear cadmium telluride (CdTe) detectror. CT is performed by repeated linear scans and rotations of an object. Penetrating x-ray photons from the object are detected by a CdTe detector, and event signals of x-ray photons are produced using charge-sensitive and shaping amplifiers. Both photon energy and energy width are selected out using a multichannel analyzer, and the number of photons is counted by a counter card. In energy-discrimination CT, the tube voltage and tube current were 80 kV and 20 μA, respectively, and the x-ray intensity was 1.92 μGy/s at a distance of 1.0 m from the source and a tube voltage of 80 kV. The energy-discrimination CT was carried out by selecting x-ray photon energies.

Single LED-based 46-m underwater wireless optical communication enabled by a multi-pixel photon counter with digital output

X-ray photon counting was performed using a silicon X-ray diode (Si-XD) at a tube current of 2.0 mA and tube voltages ranging from 50 to 70 kV. The Si-XD is a high-sensitivity Si photodiode selected for detecting X-ray photons, and Xray photons are directly detected using the Si-XD without a scintillator. Photocurrent from the diode is amplified using charge-sensitive and shaping amplifiers. To investigate the X-ray-electric conversion, we performed the event-pulseheight (EPH) analysis using a multichannel analyzer. Photon-counting computed tomography (PC-CT) is accomplished by repeated linear scans and rotations of an object, and projection curves of the object are obtained by the linear scan. The exposure time for obtaining a tomogram was 10 min at a scan step of 0.5 mm and a rotation step of 1.0°. In PC-CT at a tube voltage of 70 kV, the image contrast of iodine media fell with increasing lower-level voltage of the event pulse using a comparator.

Photon-counting computed tomography (PCCT) has recently emerged into clinical use; however, its optimum imaging protocols and added benefits remains unknown in terms of providing more accurate lung density quantification compared to energy-integrating computed tomography (EICT) scanners. To systematically assess the performance of a clinical PCCT scanner for lung density quantifications and compare it against EICT. This cross-sectional study involved a retrospective analysis of subjects scanned (August-December 2021) using a clinical PCCT system. The influence of altering reconstruction parameters was studied (reconstruction kernel, pixel size, slice thickness). A virtual CT dataset of anthropomorphic virtual subjects was acquired to demonstrate the correspondence of findings to clinical dataset, and to perform systematic imaging experiments, not possible using human subjects. The virtual subjects were imaged using a validated, scanner-specific CT simulator of a PCCT and two EICT (defined as EICT A and B) scanners. The images were evaluated using mean absolute error (MAE) of lung and emphysema density against their corresponding ground truth. Clinical and virtual PCCT datasets showed similar trends, with sharper kernels and smaller voxel sizes increasing percentage of low-attenuation areas below -950 HU (LAA-950) by up to 15.7±6.9% and 11.8±5.5%, respectively. Under the conditions studied, higher doses, thinner slices, smaller pixel sizes, iterative reconstructions, and quantitative kernels with medium sharpness resulted in lower lung MAE values. While using these settings for PCCT, changes in the dose level (13 to 1.3 mGy), slice thickness (0.4 to 1.5mm), pixel size (0.49 to 0.98mm), reconstruction technique (70keV-VMI to wFBP), and kernel (Qr48 to Qr60) increased lung MAE by 15.3±2.0, 1.4±0.6, 2.2±0.3, 4.2±0.8, and 9.1±1.6 HU, respectively. At the optimum settings identified per scanner, PCCT images exhibited lower lung and emphysema MAE than those of EICT scanners (by 2.6±1.0 and 9.6±3.4 HU, compared to EICT A, and by 4.8±0.8 and 7.4±2.3 HU, compared to EICT B). The accuracy of lung density measurements was correlated with subjects' mean lung density (p<0.05), measured by PCCT at optimum setting under the conditions studied. Photon-counting CT demonstrated superior performance in density quantifications, with its influences of imaging parameters in line with energy-integrating CT scanners. The technology offers improvement in lung quantifications, thus demonstrating potential toward more objective assessment of respiratory conditions.

Background: Computed tomography (CT) is the standard of reference for diagnosis and follow-up in aortic dissection (AD). Localizing the entry and identifying false and true lumen are as important as differing post-treatment changes from contrast media extravasations. Photon-counting detector CT (PCDCT) allows for virtual monoenergetic (VME) reconstructions, which can augment contrast media effects on lower energy levels, and for virtual non-contrast (VNC) reconstructions. The aim of this study was to analyze the influence of VME reconstructions on contrast media effects in different dissection compartments as well as compare true and VNC series in AD patients. Methods: We retrospectively analyzed PCDCT datasets from 28 patients with aortic dissections, with different dissection types and different treatment statuses. Attenuation and standard deviation values of the ascending and descending aorta, as well as CT values of the false lumen, were measured. These measurements were obtained from VME images at energy levels ranging from 40 to 190 keV in 10 keV increments, as well as from non-contrast (NC) and VNC reconstructions. The signal-to-noise ratio (SNR) was calculated. Additionally, subjective values for dissection assessability and native aspects of the images were acquired for different reconstructions. Results: CT values decreased with higher energy levels in VME imaging. Ascending aorta showed higher attenuation values than descending aorta, which was higher than false lumen (e.g., at 70 keV ascending 357 [310; 419] HU, descending 346 [305; 401] HU and false lumen 298 [248; 363] HU). These differences increased on lower VME reconstructions with statistical significance for the comparisons of ascending and descending aorta with the false lumen on all energy levels. In line with this, SNR showed highest values for ascending aorta compared to descending aorta and false lumen on all energy levels. For NC comparisons, VNC and VME at 190 keV reconstructions showed higher CT values than NC reconstructions (e.g., overall data NC 48 [42; 55] HU, VNC 66 [57; 73] HU, 190 keV 97 [89; 105] HU). Subjective ratings were worse with VNC than with NC images. Conclusions: VME reconstructions on lower energy levels can be helpful in differentiating between true and false lumen in aortic dissections.