Photon-Counting Computed Tomography - Basic Principles, Potenzial Benefits, and Initial Clinical Experience.

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.

Objectives:To evaluate the clinical performance of a cadmium-zinc-telluride- (CZT-) based photon-counting computed tomography (PCCT) system for low-dose lung cancer screening (LCS-LDCT) using patient-specific 3D-printed lung phantoms, and to compare its image quality and radiomics consistency with a conventional energy-integrating detector CT (EIDCT) system.Methods:Six 3D-printed lung phantoms, derived from patient CT datasets and representing various lesion types (solid, part-solid, and ground-glass), were imaged on PCCT and EIDCT scanners at matched dose levels (1.6 – 20.4 mGy). Quantitative image metrics, Hounsfield unit (HU) accuracy, image noise, and contrast-to-noise ratio (CNR), were assessed across dose levels. Radiomic features were extracted for each lesion and analyzed via principal component analysis to quantify feature consistency (within-cluster distance) and lesion type separability.Results:PCCT demonstrated significantly lower image noise and higher CNR compared with EIDCT, particularly at lower dose levels. HU values were consistent across doses for both systems, with reduced variability in PCCT (coefficient of variation < 0.004). Radiomics analysis revealed tighter clustering (reduced within-cluster distances) and comparable lesion type separability between PCCT and EIDCT, indicating enhanced feature stability and lesion differentiation. Qualitative review confirmed superior lesion conspicuity and margin delineation with PCCT.Conclusions:CZT-based PCCT outperforms conventional EIDCT in quantitative and qualitative imaging metrics for LCS-LDCT, enabling superior image quality and radiomics reproducibility at reduced radiation doses. These findings support the clinical translation of PCCT for lung cancer screening and radiomics-based lesion characterization.

Quarter-millimeter spectral coronary stent imaging with photon-counting CT: Initial experience

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.

The purpose of this study is to determine if a universal 120-kV ultra-high pitch and virtual monoenergetic images (VMIs) protocol on the photon-counting computed tomography (PCCT) system can provide sufficient image quality for pediatric abdominal imaging, regardless of size, compared with protocols using a size-dependent kV and dual-source flash mode on the energy-integrating CT (EICT) system. One solid water insert and 3 iodine (2, 5, 10 mg I/mL) inserts were attached or inserted into phantoms of variable sizes, simulating the abdomens of a newborn, 5-year-old, 10-year-old, and adult-sized pediatric patients. Each phantom setting was scanned on an EICT using clinical size-specific kV dual-source protocols with a pitch of 3.0. The scans were performed with fixed scanning parameters, and the CTDI vol values of full dose were 0.30, 0.71, 1.05, and 7.40 mGy for newborn to adult size, respectively. In addition, half dose scans were acquired on EICT. Each phantom was then scanned on a PCCT (Siemens Alpha) using a universal 120-kV protocol with the same full dose and half dose as determined above on the EICT scanner. All other parameters matched to EICT settings. Virtual monoenergetic images were generated from PCCT scans between 40 and 80 keV with a 5-keV interval. Image quality metrics were compared between PCCT VMIs and EICT, including image noise (measured as standard deviation of solid water), contrast-to-noise ratio (CNR) (measured at iodine inserts with solid water as background), and noise power spectrum (measured in uniform phantom regions). Noise at a PCCT VMI of 70 keV (7.0 ± 0.6 HU for newborn, 14.7 ± 1.6 HU for adult) is comparable ( P > 0.05, t test) or significantly lower ( P < 0.05, t test) compared with EICT (7.8 ± 0.8 HU for newborn, 15.3 ± 1.5 HU for adult). Iodine CNR from PCCT VMI at 50 keV (50.8 ± 8.4 for newborn, 27.3 ± 2.8 for adult) is comparable ( P > 0.05, t test) or significantly higher ( P < 0.05, t test) to the corresponding EICT measurements (57.5 ± 6.7 for newborn, 13.8 ± 1.7 for adult). The noise power spectrum curve shape of PCCT VMI is similar to EICT, despite PCCT VMI exhibiting higher noise at low keV levels. The universal PCCT 120 kV with ultra-high pitch and postprocessed VMIs demonstrated equivalent or improved performance in noise (70 keV) and iodine CNR (50 keV) for pediatric abdominal CT, compared with size-specific kV images on the EICT.

The X-ray imaging field is currently undergoing a period of rapid technological innovation in diagnostic imaging equipment. An important recent development is the advent of new X-ray detectors, i.e., photon-counting detectors (PCD), which have been introduced in recent clinical prototype systems, called PCD computed tomography (PCD-CT) or photon-counting CT (PCCT) or spectral photon-counting CT (SPCCT) systems. PCD allows a pixel up to 200 microns pixels at iso-center, which is much smaller than that can be obtained with conventional energy integrating detectors (EID). PCDs have also a higher dose efficiency than EID mainly because of electronic noise suppression. In addition, the energy-resolving capabilities of these detectors allow generating spectral basis imaging, such as the mono-energetic images or the water/iodine material images as well as the K-edge imaging of a contrast agent based on atoms of high atomic number. In recent years, studies have therefore been conducted to determine the potential of PCD-CT as an alternative to conventional CT for chest imaging.

Photon-counting computed tomography (PCCT) is a significant technological advancement in musculoskeletal imaging. Unlike traditional CT detectors, which are energy-integrating detectors, PCCT uses direct-conversion technology, or photon-counting detectors. This enables ultra-high spatial resolution, systematic spectral imaging, and effective electronic noise reduction without increasing radiation exposure. This review article illustrates the potential benefit of PCCT in clinical practice across a broad spectrum of musculoskeletal disorders. PCCT is expected to improve the detection and characterization of fractures, infections, inflammatory and degenerative arthropathies, bone marrow disorders, tumors, congenital bone diseases, and postoperative complications. It will also assist with interventional procedures. PCCT holds great promise for opportunistic imaging and artificial intelligence-driven analytics in musculoskeletal radiology.

The introduction of photon-counting detector CT (PCD-CT) marks a remarkable leap in innovation in CT imaging. The new detector technology allows X-rays to be converted directly into an electrical signal without an intermediate step via a scintillation layer and allows the energy of individual photons to be measured. Initial data show high spatial resolution, complete elimination of electronic noise, and steady availability of spectral image data sets. In particular, the new technology shows promise with respect to the imaging of osseous structures. Recently, PCD-CT was implemented in the clinical routine. The aim of this review was to summarize recent studies and to show our first experiences with photon-counting detector technology in the field of musculoskeletal radiology.We performed a literature search using Medline and included a total of 90 articles and reviews that covered recent experimental and clinical experiences with the new technology.In this review, we focus on (1) spatial resolution and delineation of fine anatomic structures, (2) reduction of radiation dose, (3) electronic noise, (4) techniques for metal artifact reduction, and (5) possibilities of spectral imaging. This article provides insight into our first experiences with photon-counting detector technology and shows results and images from experimental and clinical studies. · This review summarizes recent experimental and clinical studies in the field of photon-counting detector CT and musculoskeletal radiology.. · The potential of photon-counting detector technology in the field of musculoskeletal radiology includes improved spatial resolution, reduction in radiation dose, metal artifact reduction, and spectral imaging.. · PCD-CT enables imaging at lower radiation doses while maintaining or even enhancing spatial resolution, crucial for reducing patient exposure, especially in repeated or prolonged imaging scenarios.. · It offers promising results in reducing metal artifacts commonly encountered in orthopedic or dental implants, enhancing the interpretability of adjacent structures in postoperative and follow-up imaging.. · With its ability to routinely acquire spectral data, PCD-CT scans allow for material classification, such as detecting urate crystals in suspected gout or visualizing bone marrow edema, potentially reducing reliance on MRI in certain cases.. Bette S, Risch F, Becker J et al. Photon-counting detector CT - first experiences in the field of musculoskeletal radiology. Fortschr Röntgenstr 2024; DOI 10.1055/a-2312-6914.

Background Detection of hepatic metastases at CT is a daily task in radiology departments that influences medical and surgical treatment strategies for oncology patients. Purpose To compare simulated photon-counting CT (PCCT) with energy-integrating detector (EID) CT for the detection of small liver lesions. Materials and Methods In this reader study (July to December 2023), a virtual imaging framework was used with 50 anthropomorphic phantoms and 183 generated liver lesions (one to six lesions per phantom, 0.4-1.5 cm in diameter). Virtual CT platforms simulated PCCT and EID CT scanners. Phantoms were virtually scanned using routine (6 mGy) and low-dose (1.5 mGy) conditions and reconstructed with three kernels. A subset of 300 scans (150 PCCT vs EID CT pairs) were selected. Four radiologists independently reviewed all scans to mark liver lesions, assigned confidence scores for detection, and rated scan quality. Analysis was performed on a per-lesion basis to determine sensitivity for several variables and on a per-scan basis for scan quality. The McNemar test, two-sided paired t tests, and mixed-effects logistic regression models were fitted; P < .05 was considered indicative of statistically significant difference. Results Consensus reader sensitivity in detecting lesions was 82.1% (451 of 549) for PCCT versus 77.6% (426 of 549) for EID CT (P < .001), with a mean sensitivity gain of 4.3 percentage points ± 1.3 (P < .001 to P = .02 per reader). Readers had better subjective confidence for lesions at PCCT (mean score, 61.5 ± 22 vs 56.1 ± 24 [on a 101-point scale]; P < .001). Sensitivity was lower for lesions smaller than 1 cm, with more pronounced difference between PCCT and EID CT (74.0% [271 of 366] vs 67.2% [246 of 366]; P < .001). At the lower dose level, PCCT showed higher sensitivity than EID CT (68.9% [168 of 244] vs 61.1% [149 of 244]; P < .001) for subcentimeter lesions. In a multivariable model, PCCT was independently associated with increased odds of lesion detection (odds ratio, 1.55; P < .001). Image quality was slightly higher for PCCT (mean score, 55.3 vs 50.6 [on a 101-point scale]; P < .001). Conclusion Compared with EID CT, PCCT showed better sensitivity in the detection of small liver lesions. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Menu in this issue.

This study aims to compare the performance of first-generation dual-source photon-counting detector computed tomography (PCD-CT) to third-generation dual-source energy-integrating detector (EID-CT) regarding stent imaging in the femoral arterial runoff. Continuous extracorporeal perfusion was established in 1 human cadaver using an inguinal and infragenicular access and peristaltic pump. Seven peripheral stents were implanted into both superior femoral arteries by means of percutaneous angioplasty. Radiation dose-equivalent CT angiographies (high-/medium-/low-dose: 10/5/3 mGy) with constant tube voltage of 120 kVp, matching iterative reconstruction algorithm levels, and convolution kernels were used both with PCD-CT and EID-CT. In-stent lumen visibility, luminal and in-stent attenuation as well as contrast-to-noise ratio (CNR) were assessed via region of interest and diameter measurements. Results were compared using analyses of variance and regression analyses. Maximum in-stent lumen visibility achieved with PCD-CT was 94.48% ± 2.62%. The PCD-CT protocol with the lowest lumen visibility (BV40: 78.93% ± 4.67%) performed equal to the EID-CT protocol with the best lumen visibility (BV59: 79.49% ± 2.64%, P > 0.999). Photon-counting detector CT yielded superior CNR compared with EID-CT regardless of kernel and dose level ( P < 0.001). Maximum CNR was 48.8 ± 17.4 in PCD-CT versus 31.28 ± 5.7 in EID-CT (both BV40, high-dose). The theoretical dose reduction potential of PCD-CT over EID-CT was established at 88% (BV40), 83% (BV48/49), and 73% (BV59/60), respectively. In-stent attenuation was not significantly different from luminal attenuation outside stents in any protocol. With superior lumen visibility and CNR, PCD-CT allowed for noticeable dose reduction over EID-CT while maintaining image quality in a continuously perfused human cadaveric model.

Detailed visualization of bone microarchitecture is essential for assessment of wrist fractures in computed tomography (CT). This study aims to evaluate the imaging performance of a CT system with clinical cadmium telluride-based photon-counting detector (PCD-CT) compared with a third-generation dual-source CT scanner with energy-integrating detector technology (EID-CT). Both CT systems were used for the examination of 8 cadaveric wrists with radiation dose equivalent scan protocols (low-/standard-/full-dose imaging: CTDIvol = 1.50/5.80/8.67 mGy). All wrists were scanned with 2 different operating modes of the photon-counting CT (standard-resolution and ultra-high-resolution). After reformatting with comparable reconstruction parameters and convolution kernels, subjective evaluation of image quality was performed by 3 radiologists on a 7-point scale. For estimation of interrater reliability, we report the intraclass correlation coefficient (absolute agreement, 2-way random-effects model). Signal-to-noise and contrast-to-noise ratios were calculated to provide semiquantitative assessment of image quality. Subjective image quality of standard-dose PCD-CT examinations in ultra-high-resolution mode was superior compared with full-dose PCD-CT in standard-resolution mode (P = 0.016) and full-dose EID-CT (P = 0.040). No difference was ascertained between low-dose PCD-CT in ultra-high-resolution mode and standard-dose scans with either PCD-CT in standard-resolution mode (P = 0.108) or EID-CT (P = 0.470). Observer evaluation of standard-resolution PCD-CT and EID-CT delivered similar results in full- and standard-dose scans (P = 0.248/0.509). Intraclass correlation coefficient was 0.876 (95% confidence interval, 0.744-0.925; P < 0.001), indicating good reliability. Between dose equivalent studies, signal-to-noise and contrast-to-noise ratios were substantially higher in photon-counting CT examinations (all P's < 0.001). Superior visualization of fine anatomy is feasible with the clinical photon-counting CT system in cadaveric wrist scans. The ultra-high-resolution scan mode suggests potential for considerable dose reduction over energy-integrating dual-source CT.

Flow diverters are a crucial element in the treatment of intracranial aneurysms. However, the optimal non-invasive follow-up imaging modality, particularly for the detection of in-stent stenosis, remains uncertain. This study aims to compare the performance of photon-counting detector CT (PCD-CT) in ultra-high-resolution (UHR) mode with flat-panel CT (FP-CT) for the evaluation of intracranial flow diverters. A phantom model for intracranial vessels was used to evaluate 15 flow diverters of various sizes and designs. Imaging was performed using both PCD-CT and FP-CT. Qualitative assessment of the stent lumen was conducted by three experienced neuroradiologists using a 5-point Likert scale. Quantitative analysis included measurements of lumen area, contrast to noise ratio and signal to noise ratio. FP-CT provided a significantly larger assessable stent lumen than PCD-CT at all dose levels (p < 0.05), with no significant differences between PCD-CT dose levels (p = 0.999). Increasing PCD-CT dose did not improve lumen visualization. SNR and CNR increased with PCD-CT dose (p < 0.001), peaking at CTDI 20, but showed diminishing returns beyond CTDI 10. Flow diverter diameter correlated positively with SNR and CNR (p < 0.05). Subjective image quality improved with PCD-CT dose (p < 0.001) but showed no significant difference beyond 10 mGy (p > 0.05). FRED devices had the lowest ratings, independent of imaging modality (p = 0.80). Our study demonstrated that while FP-CT provided superior visualization of the flow diverter lumen in a head phantom vessel model, subjective assessability ratings were comparable between FP-CT and PCD-CT when evaluated by experienced readers. PCD-CT at a CTDIvol of 10 mGy offered the best balance between image quality and radiation dose, making it a viable alternative for post-interventional assessment of flow diverters.

Implemented with energy-integration x-ray detector, dual energy spectral CT has been playing increasingly important roles in the clinic for diagnostic imaging. Encouraged by the clinical value added by dual energy spectral CT in oncological, cardiovascular and neurovascular applications, the technological community is investing more resource and effort on photon-counting spectral CT that is anticipated to outperform its energy-integration counterpart in many advanced clinical applications. Based on the preliminary data of phantom and animal studies acquired using a prototype photoncounting spectral CT system, we revisit the underlying physics, mathematics, dimensionality of material space and selection of basis materials for spectral imaging. Focused on three-material decomposition for virtual monochromatic imaging/analysis, we investigate the feasibility and performance of spectral imaging without contrast agents in photoncounting CT, along with an in-depth analysis of the pros and cons of three-material decomposition over two-material decomposition for spectral imaging.

PurposeThe purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up. Materials and methodsConsecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis. PC-CCTA stenosis quantification (at 50% level) and FFRai (at 0.8 level) were predicted using two deep learning models (CorEx, Spimed-AI). Diagnostic performance for global CAD evaluation (at least one significant stenosis ≥ 50% or FFRai ≤ 0.8) was assessed. ResultsA total of 260 patients (138 men, 122 women) with a mean age of 78.7 ± 8.1 (standard deviation) years (age range: 51–93 years) were evaluated. Significant CAD on ICA was present in 126/260 patients (48.5%). Per-patient sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 96.0% (95% confidence interval [CI]: 91.0–98.7), 68.7% (95% CI: 60.1–76.4), 74.3 % (95% CI: 69.1–78.8), 94.8% (95% CI: 88.5–97.8), and 81.9% (95% CI: 76.7–86.4) for PC-CCTA, and 96.8% (95% CI: 92.1–99.1), 87.3% (95% CI: 80.5–92.4), 87.8% (95% CI: 82.2–91.8), 96.7% (95% CI: 91.7–98.7), and 91.9% (95% CI: 87.9–94.9) for FFRai. Area under the curve of FFRai was 0.92 (95% CI: 0.88–0.95) compared to 0.82 for PC-CCTA (95% CI: 0.77–0.87) (P < 0.001). FFRai-guidance could have prevented the need for ICA in 121 out of 260 patients (46.5%) vs. 97 out of 260 (37.3%) using PC-CCTA alone (P < 0.001). ConclusionDeep learning-based photon-counting FFRai evaluation improves the accuracy of PC-CCTA ≥ 50% stenosis detection, reduces the need for ICA, and may be incorporated into the clinical TAVR work-up for the assessment of CAD.

Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.