Image quality of photon-counting detector CT for visualization of maxillofacial anatomy in comparison with energy-integrating detector CT and intraoperative C-arm CBCT.

BACKGROUND: Accurate diagnostic imaging is crucial for managing facial fractures, which are a common global occurrence. This study aimed to compare the image quality of Photon Counting Detector CT (PCD-CT) with state-of-the-art Energy Integrating Detector CT (EID-CT) and intraoperative C-arm CBCT (CBCT) in visualizing maxillofacial anatomy using a cadaveric sheep head model. METHODS: Three fresh sheep heads were used, with surgical interventions simulating metal implants in two of them. The specimens were imaged using PCD-CT, EID-CT, and CBCT, following which quantitative assessments of signal-to-noise ratio, sharpness, and artifacts were conducted. A visual grading study was performed by six observers, using criteria focusing on the mandible, orbit, and soft tissues. Statistical analyses included Friedman tests for comparing modalities and Kendall's W and Gwet's AC1 for assessing inter- and intrarater agreement. RESULTS: PCD-CT demonstrated a significantly higher signal-to-noise ratio (p = 0.03) and bone sharpness (p < 0.001) compared to CBCT. In visual grading, PCD-CT outperformed CBCT, but not EID-CT, particularly in delineating mandibular and orbital structures. EID-CT and PCD-CT showed slightly more severe hypodense artifacts (p = 0.01) but were comparable in streak artifact presentation. The interrater and intrarater agreements indicated consistent evaluations across and within observers. CONCLUSION: PCD-CT exhibits superior image quality over CBCT in key parameters essential for maxillofacial imaging, while no apparent improvement was shown compared to state-of-the-art EID-CT. PCD-CT offers enhanced visualization of critical anatomical structures, suggesting its potential as a preferred modality in managing maxillofacial trauma. The findings in this study align with limited existing research on PCD-CT, underscoring its promise for advanced diagnostic imaging in maxillofacial applications.

Study on the impact of bowtie filter on photon-counting CT imaging.

OBJECTIVE: The aim of this study was to investigate the impact of the bowtie filter on the image quality of the photon-counting detector (PCD) based CT imaging. Approach: Numerical simulations were conducted to investigate the impact of bowtie filters on image uniformity using two water phantoms, with tube potentials ranging from 60 to 140 kVp with a step of 5 kVp. Subsequently, benchtop PCD-CT imaging experiments were performed to verify the observations from the numerical simulations. Additionally, various correction methods were validated through these experiments. Main results: It was found that the use of a bowtie filter significantly alters the uniformity of PCD-CT images, depending on the size of the object and the X-ray spectrum. Two notable effects were observed: the capping effect and the flattening effect. Furthermore, it was demonstrated that the conventional beam hardening correction method could effectively mitigate such non-uniformity in PCD-CT images, provided that dedicated calibration parameters were used. Significance: It was demonstrated that the incorporation of a bowtie filter results in varied image artifacts in PCD-CT imaging under different conditions. Certain image correction methods can effectively mitigate and reduce these artifacts, thereby enhancing the overall quality of PCD-CT images.

Improved Discriminability of Severe Lung Injury and Atelectasis in Thoracic Trauma at Low keV Virtual Monoenergetic Images from Photon-Counting Detector CT.

Objectives: To evaluate the value of virtual monoenergetic images (VMI) from photon-counting detector CT (PCD-CT) for discriminability of severe lung injury and atelectasis in polytraumatized patients. Materials & Methods: Contrast-enhanced PCD-CT examinations of 20 polytraumatized patients with severe thoracic trauma were included in this retrospective study. Spectral PCD-CT data were reconstructed using a noise-optimized virtual monoenergetic imaging (VMI) algorithm with calculated VMIs ranging from 40 to 120 keV at 10 keV increments. Injury-to-atelectasis contrast-to-noise ratio (CNR) was calculated and compared at each energy level based on CT number measurements in severely injured as well as atelectatic lung areas. Three radiologists assessed subjective discriminability, noise perception, and overall image quality. Results: CT values for atelectasis decreased as photon energy increased from 40 keV to 120 keV (mean Hounsfield units (HU): 69 at 40 keV; 342 at 120 keV), whereas CT values for severe lung injury remained near-constant from 40 keV to 120 keV (mean HU: 42 at 40 keV; 44 at 120 keV) with significant differences at each keV level (p < 0.001). The optimal injury-to-atelectasis CNR was observed at 40 keV in comparison with the remaining energy levels (p < 0.001) except for 50 keV (p > 0.05). In line with this, VMIs at 40 keV were rated best regarding subjective discriminability. VMIs at 60-70 keV, however, provided the highest subjective observer parameters regarding subjective image noise as well as image quality. Conclusions: Discriminability between severely injured and atelectatic lung areas after thoracic trauma can be substantially improved by virtual monoenergetic imaging from PCD-CT with superior contrast and visual discriminability at 40-50 keV.

Ultra-high resolution spectral photon-counting CT outperforms dual layer CT for lung imaging: Results of a phantom study.

PURPOSE: The purpose of this study was to compare lung image quality obtained with ultra-high resolution (UHR) spectral photon-counting CT (SPCCT) with that of dual-layer CT (DLCT), at standard and low dose levels using an image quality phantom and an anthropomorphic lung phantom. METHODS: An image quality phantom was scanned using a clinical SPCCT prototype and an 8 cm collimation DLCT from the same manufacturer at 10 mGy. Additional acquisitions at 6 mGy were performed with SPCCT only. Images were reconstructed with dedicated high-frequency reconstruction kernels, slice thickness between 0.58 and 0.67 mm, and matrix between 5122 and 10242 mm, using a hybrid iterative algorithm at level 6. Noise power spectrum (NPS), task-based transfer function (TTF) for iodine and air inserts, and detectability index (d') were assessed for ground-glass and solid nodules of 2 mm to simulate highly detailed lung lesions. Subjective analysis of an anthropomorphic lung phantom was performed by two radiologists using a five-point quality score. RESULTS: At 10 mGy, noise magnitude was reduced by 29.1 % with SPCCT images compared to DLCT images for all parameters (27.1 ± 11.0 [standard deviation (SD)] HU vs. 38.2 ± 1.0 [SD] HU, respectively). At 6 mGy with SPCCT images, noise magnitude was reduced by 8.9 % compared to DLCT images at 10 mGy (34.8 ± 14.1 [SD] HU vs. 38.2 ± 1.0 [SD] HU, respectively). At 10 mGy and 6 mGy, average NPS spatial frequency (fav) was greater for SPCCT images (0.75 ± 0.17 [SD] mm-1) compared to DLCT images at 10 mGy (0.55 ± 0.04 [SD] mm-1) while remaining constant from 10 to 6 mGy. At 10 mGy, TTF at 50 % (f50) was greater for SPCCT images (0.92 ± 0.08 [SD] mm-1) compared to DLCT images (0.67 ± 0.06 [SD] mm-1) for both inserts. At 6 mGy, f50 decreased by 1.1 % for SPCCT images, while remaining greater compared to DLCT images at 10 mGy (0.91 ± 0.06 [SD] mm-1 vs. 0.67 ± 0.06 [SD] mm-1, respectively). At both dose levels, d' were greater for SPCCT images compared to DLCT for all clinical tasks. Subjective analysis performed by two radiologists revealed a greater median image quality for SPCCT (5; Q1, 4; Q3, 5) compared to DLCT images (3; Q1, 3; Q3, 3). CONCLUSION: UHR SPCCT outperforms DLCT in terms of image quality for lung imaging. In addition, UHR SPCCT contributes to a 40 % reduction in radiation dose compared to DLCT.

Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography.

PURPOSE: The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA). MATERIALS AND METHODS: Consecutive patients with suspected CAD who underwent PC-CCTA between January 2022 and December 2023 were included in this retrospective, single-center study. Non-ultra-high resolution (UHR) PC-CCTA images were analyzed by artificial intelligence using two deep learning models (CorEx, Spimed-AI), and compared to human expert reader assessment using UHR PC-CCTA images. Diagnostic performance for global CAD assessment (at least one significant stenosis ≥ 50 %) was estimated at patient and vessel levels. RESULTS: A total of 140 patients (96 men, 44 women) with a median age of 60 years (first quartile, 51; third quartile, 68) were evaluated. Significant CAD on UHR PC-CCTA was present in 36/140 patients (25.7 %). The sensitivity, specificity, accuracy, positive predictive value), and negative predictive value of deep learning-based CAD were 97.2 %, 81.7 %, 85.7 %, 64.8 %, and 98.9 %, respectively, at the patient level and 96.6 %, 86.7 %, 88.1 %, 53.8 %, and 99.4 %, respectively, at the vessel level. The area under the receiver operating characteristic curve was 0.90 (95 % CI: 0.83-0.94) at the patient level and 0.92 (95 % CI: 0.89-0.94) at the vessel level. CONCLUSION: Automated deep learning shows remarkable performance for the diagnosis of significant CAD on non-UHR PC-CCTA images. AI pre-reading may be of supportive value to the human reader in daily clinical practice to target and validate coronary artery stenosis using UHR PC-CCTA.

Imaging in France: 2024 Update.

Radiology in France has made major advances in recent years through innovations in research and clinical practice. French institutions have developed innovative imaging techniques and artificial intelligence applications in the field of diagnostic imaging and interventional radiology. These include, but are not limited to, a more precise diagnosis of cancer and other diseases, research in dual-energy and photon-counting computed tomography, new applications of artificial intelligence, and advanced treatments in the field of interventional radiology. This article aims to explore the major research initiatives and technological advances that are shaping the landscape of radiology in France. By highlighting key contributions in diagnostic imaging, artificial intelligence, and interventional radiology, we provide a comprehensive overview of how these innovations are improving patient outcomes, enhancing diagnostic accuracy, and expanding the possibilities for minimally invasive therapies. As the field continues to evolve, France's position at the forefront of radiological research ensures that these innovations will play a central role in addressing current healthcare challenges and improving patient care on a global scale.

Targeted K-Edge Nanoprobes From Praseodymium and Hafnium for Ratiometric Tracking of Dual Biomarkers using Spectral Photon Counting CT.

Utilizing metal nanoprobes with unique K-edge identities to visualize complementary biological activities simultaneously can provide valuable information about complex biological processes. This study describes the design and preparation of an innovative pair of K-edge metal nanoprobes and demonstrates the feasibility of their simultaneous quantitative detection using spectral photon-counting computed tomography (SPCCT). Glycosaminoglycan (GAG) capped nanoparticles (ca. 15-20 nm) targeting two distinct components of the cartilage tissue, namely, aggrecan (acan) and aggrecanase (acanase) are designed and synthesized. These targeted nanoparticles comprised of praseodymium (Pr) and hafnium (Hf), with well-separated K-edge energies, enable simultaneous and ratiometric imaging of dual biomarkers in cartilage tissue. Following extensive physico-chemical characterization of the ligand-targeted particles, the feasibility of homing dual biomarkers in vitro is demonstrated. The material discrimination and simultaneous quantification of these targeted particles are also achieved and corroborated with inductively coupled plasmon spectroscopy. For the first time, the use of praseodymium is reported as a contrast agent for SPCCT imaging and demonstrates the ability to pair it with hafnium nanoprobes for multicontrast imaging of diseases. Importantly, the potential for ratiometric molecular imaging and tracking of osteoarthritis (OA) progression is shown with SPCCT K-edge based imaging approach.

The Quantification of Bone Mineral Density Using Photon Counting Computed Tomography and its Implications for Detecting Bone Remodelling.

High-Resolution peripheral quantitative CT (HR-pQCT) has become standard practice when quantifying volumetric bone mineral density (vBMD) in vivo. Yet, it is only accessible to peripheral sites, with small fields of view and lengthy scanning times. This limits general applicability in clinical workflows. The goal of this study was to assess the potential of Photon Counting CT (PCCT) in quantitative bone imaging. Using the European Forearm Phantom, PCCT was calibrated to hydroxy-apatite (HA) density. Eight cadaveric forearms were scanned twice with PCCT, and once with HR-pQCT. The dominant forearm of two volunteers was scanned twice with PCCT. In each scan the carpals were delineated. At bone-level, accuracy was assessed with a paired measurement of total vBMD (Tt.vBMD) calculated with PCCT and HR-pQCT. At voxel-level, repeatability was assessed by image registration and voxel-wise subtraction of the ex vivo PCCT scans. In an ideal scenario, this difference would be zero; any deviation was interpreted as falsely detected remodelling. For clinical usage, the least detectable remodelling was determined by finding a threshold in the PCCT difference image that resulted in a classification of bone formation and resorption below acceptable noise levels (<0.5%). The paired measurement of Tt.vBMD had a Pearson correlation of 0.986. Compared to HR-pQCT, PCCT showed a bias of 7.46 mgHA/cm3. At voxel-level, the repeated PCCT scans showed a bias of 17.66 mgHA/cm3 and standard error of 96.23 mgHA/cm3. Least detectable remodelling was found to be 250 mgHA/cm3, for which 0.37% of the voxels was incorrectly classified as newly added or resorbed bone. In vivo, this volume increased to 0.97%. Based on the cadaver data we conclude that PCCT can be used to quantify vBMD and bone turnover. We provided proof of principle that this technique is also accurate in vivo, hence, that it has high potential for clinical applications.

In quantitative computed tomography (QCT) , bone images have grey values that reflect the local bone mineral content within each voxel. Aggregated over large bone regions, a total bone mineral density can be calculated, which helps in identifying weak bones and fracture risk. At small scales, QCT can detect where bone is being formed, and thus the bone mineral content increases, and where bone is being removed, and thus the bone mineral content decreases. These measurements are typically done with high-resolution peripheral QCT (HR-pQCT). However, HR-pQCT can only scan small regions of the arms and legs, for which a long scanning time is needed. This makes it challenging to use HR-pQCT in a clinical context. Photon Counting CT (PCCT) is a new CT device that can scan bone with an image quality similar to HR-pQCT, yet it can scan faster and cover a larger area. Used at the large scale, our results indicate that PCCT and HR-pQCT can be used interchangeably for the quantification of bone mineral density in large bone regions. Used at small scales, our results indicate that both technologies can detect changes in bone mineral content with similar sensitivity. These results demonstrate that PCCT enables the use of these QCT analyses in a clinical context.

Performance of iodine quantification through high-pitch dual-source photon-counting CT: a phantom study.

PURPOSE: To investigate the feasibility and accuracy of iodine quantification using PCD-CT in standard-pitch and high-pitch scanning at different scan parameters in a phantom model. MATERIALS AND METHODS: Four inserts with known iodine concentrations (2, 5, 10, and 15 mg/mL) were placed in the removable CT phantom and scanned using high-pitch (3.2) and standard-pitch (0.8) modes on PCD-CT. Two tube voltages (120 and 140 kVp) and four radiation doses (1, 3, 5, and 10 mGy) were alternated. Each scan setting was repeated three times. Mean iodine concentration for each insert across three consecutive slices was recorded. Percentage absolute bias (PAB) was assessed for iodine quantification. RESULTS: A total of 96 acquisitions were conducted. In small phantom, the average for PAB was 2.96% (range: 1.75% ~ 4.56%) and 1.67% (range: 1.00% ~ 3.42%) for high-pitch and standard-pitch acquisitions, respectively. In large phantom, it was 3.72% (range: 1.75% ~ 5.97%) and 2.94% (range: 1.75% ~ 4.70%). Linear regression analysis revealed that only phantom size significantly influenced (P < 0.001) the accuracy of iodine quantification. CONCLUSION: The high-pitch scan mode in PCD-CT can be used to quantify iodine density with similar accuracy compared with standard pitch.

Imaging performance of a LaBr3:Ce scintillation detector for photon counting x-ray computed tomography: Simulation study.

BACKGROUND: Photon counting detectors (PCDs) for x-ray computed tomography (CT) are the future of CT imaging. At present, semiconductor-based PCDs such as cadmium telluride (CdTe), cadmium zinc telluride, and silicon have been either used or investigated for clinical PCD CT. Unfortunately, all of them have the same major challenges, namely high cost and limited spectral signal-to-noise ratio (SNR). Recent studies showed that some high-quality scintillators, such as lanthanum bromide doped with cerium (LaBr3:Ce), are less expensive and almost as fast as CdTe. PURPOSE: The objective of this study is to assess the performance of a LaBr3:Ce PCD for clinical x-ray CT. METHODS: We performed Monte Carlo simulations and compared the performance of 3 mm thick LaBr3:Ce and 2 mm thick CdTe for PCD CT with x-rays at 120 kVp and 20-1000 mA. The two PCDs were operated with either a threshold-subtract (TS) counting scheme or a direct energy binning (DB) counting scheme. The performance was assessed in terms of the accuracy of registered spectra, counting capability, and count-rate-dependent spectral imaging-task performance, for conventional CT imaging, water-bone material decomposition, and K-edge imaging with tungsten as the K-edge material. The performance for these imaging-tasks was quantified by nCRLB, that is, the Cramér-Rao lower bound on the variance of basis line-integral estimation, normalized by the corresponding value of CdTe at 20 mA. RESULTS: The spectrum recorded by CdTe was distorted significantly due to charge sharing, whereas the spectra recorded by LaBr3:Ce better matched the incident spectrum. The dead time, estimated by fitting a paralyzable detector model to the count-rate curves, was 20.7, 15.0, 37.2, and 13.0 ns for CdTe with TS, CdTe with DB, LaBr3:Ce with TS, and LaBr3:Ce with DB, respectively. Conventional CT imaging showed an adverse effect of reduced geometrical efficiency due to optical reflectors in LaBr3:Ce PCD. The nCRLBs (a lower value indicates a better SNR) for CdTe with TS, CdTe with DB, LaBr3:Ce with TS, LaBr3:Ce with DB, and the ideal PCD, were 1.00 ± 0.01, 1.00 ± 0.01, 1.18 ± 0.02, 1.18 ± 0.02, and 0.79 ± 0.01, respectively, at 20 mA. The nCRLBs for water-bone material decomposition, in the same order, were 1.00 ± 0.02, 1.00 ± 0.02, 0.85 ± 0.02, 0.85 ± 0.02, and 0.24 ± 0.02, respectively, at 20 mA; and 0.98 ± 0.02, 0.98 ± 0.02, 1.09 ± 0.02, 0.83 ± 0.02, and 0.24 ± 0.02, respectively, at 1000 mA. Finally, the nCRLBs for K-edge imaging, the most demanding task among the five, were 1.00 ± 0.02, 1.00 ± 0.02, 0.55 ± 0.02, 0.55 ± 0.02, and 0.13 ± 0.02, respectively, at 20 mA; and 2.45 ± 0.02, 2.29 ± 0.02, 3.12 ± 0.02, 2.11 ± 0.02, and 0.13 ± 0.02, respectively, at 1,000 mA. CONCLUSION: The Monte Carlo simulations showed that, compared to CdTe with either TS or DB, LaBr3:Ce with DB provided more accurate spectra, comparable or better counting capability, and superior spectral imaging-task performances, that is, water-bone material decomposition and K-edge imaging. CdTe had a better performance than LaBr3:Ce for the conventional CT imaging task due to its higher geometrical efficiency. LaBr3:Ce PCD with DB scheme may be an excellent alternative option for CdTe PCD.

Feasibility study of YSO/SiPM based detectors for virtual monochromatic image synthesis.

BACKGROUND: The development of photon-counting CT systems has focused on semiconductor detectors like cadmium zinc telluride (CZT) and cadmium telluride (CdTe). However, these detectors face high costs and charge-sharing issues, distorting the energy spectrum. Indirect detection using Yttrium Orthosilicate (YSO) scintillators with silicon photomultiplier (SiPM) offers a cost-effective alternative with high detection efficiency, low dark count rate, and high sensor gain. OBJECTIVE: This work aims to demonstrate the feasibility of the YSO/SiPM detector (DexScanner L103) based on the Multi-Voltage Threshold (MVT) sampling method as a photon-counting CT detector by evaluating the synthesis error of virtual monochromatic images. METHODS: In this study, we developed a proof-of-concept benchtop photon-counting CT system, and employed a direct method for empirical virtual monochromatic image synthesis (EVMIS) by polynomial fitting under the principle of least square deviation without X-ray spectral information. The accuracy of the empirical energy calibration techniques was evaluated by comparing the reconstructed and actual attenuation coefficients of calibration and test materials using mean relative error (MRE) and mean square error (MSE). RESULTS: In dual-material imaging experiments, the overall average synthesis error for three monoenergetic images of distinct materials is 2.53% ±2.43%. Similarly, in K-edge imaging experiments encompassing four materials, the overall average synthesis error for three monoenergetic images is 4.04% ±2.63%. In rat biological soft-tissue imaging experiments, we further predicted the densities of various rat tissues as follows: bone density is 1.41±0.07 g/cm3, adipose tissue density is 0.91±0.06 g/cm3, heart tissue density is 1.09±0.04 g/cm3, and lung tissue density is 0.32±0.07 g/cm3. Those results showed that the reconstructed virtual monochromatic images had good conformance for each material. CONCLUSION: This study indicates the SiPM-based photon-counting detector could be used for monochromatic image synthesis and is a promising method for developing spectral computed tomography systems.

Photon-counting CT for forensic death investigations-a glance into the future of virtual autopsy.

This article explores the potential of photon-counting computed tomography (CT) in forensic medicine for a range of forensic applications. Photon-counting CT surpasses conventional CT in several key areas. It boasts superior spatial and contrast resolution, enhanced image quality at low x-ray energies, and spectral imaging capabilities that enable more precise material differentiation. These advantages translate to superior visualization of bone structures, foreign bodies, and soft tissues in postmortem examinations. The article discusses the technical principles of photon-counting CT detectors and highlights its potential applications in forensic imaging, including high-resolution virtual autopsies, pediatric forensic CT, trauma analysis, and bone density measurements. Furthermore, advancements in vascular imaging and soft tissue contrast promise to propel CT-based death investigations to an even more prominent role. The article concludes by emphasizing the immense potential of this new technology in forensic medicine and anthropology.

Simultaneous assessment of NAD(P)H and flavins with multispectral fluorescence lifetime imaging microscopy at a single excitation wavelength of 750 nm.

Significance: Autofluorescence characteristics of the reduced nicotinamide adenine dinucleotide and oxidized flavin cofactors are important for the evaluation of the metabolic status of the cells. The approaches that involve a detailed analysis of both spectral and time characteristics of the autofluorescence signals may provide additional insights into the biochemical processes in the cells and biological tissues and facilitate the transition of spectral fluorescence lifetime imaging into clinical applications. Aim: We present the experiments on multispectral fluorescence lifetime imaging with a detailed analysis of the fluorescence decays and spectral profiles of the reduced nicotinamide adenine dinucleotide and oxidized flavin under a single excitation wavelength aimed at understanding whether the use of multispectral detection is helpful for metabolic imaging of cancer cells. Approach: We use two-photon spectral fluorescence lifetime imaging microscopy. Starting from model solutions, we switched to cell cultures treated by metabolic inhibitors and then studied the metabolism of cells within tumor spheroids. Results: The use of a multispectral detector in combination with an excitation at a single wavelength of 750 nm allows the identification of fluorescence signals from three components: free and bound NAD(P)H, and flavins based on the global fitting procedure. Multispectral data make it possible to assess not only the lifetime but also the spectral shifts of emission of flavins caused by chemical perturbations. Altogether, the informative parameters of the developed approach are the ratio of free and bound NAD(P)H amplitudes, the decay time of bound NAD(P)H, the amplitude of flavin fluorescence signal, the fluorescence decay time of flavins, and the spectral shift of the emission signal of flavins. Hence, with multispectral fluorescence lifetime imaging, we get five independent parameters, of which three are related to flavins. Conclusions: The approach to probe the metabolic state of cells in culture and spheroids using excitation at a single wavelength of 750 nm and a fluorescence time-resolved spectral detection with the consequent global analysis of the data not only simplifies image acquisition protocol but also allows to disentangle the impacts of free and bound NAD(P)H, and flavin components evaluate changes in their fluorescence parameters (emission spectra and fluorescence lifetime) upon treating cells with metabolic inhibitors and sense metabolic heterogeneity within 3D tumor spheroids.

Abdominal Applications of Photon-Counting CT.

Photon-counting computed tomography (PCCT) has shown promising advancements in abdominal imaging in clinical use. Though more peer-reviewed primary literature is needed, this commentary explores PCCT's potential applications, focusing on enhancing diagnostic accuracy, optimizing radiation dose management, and improving patient care. PCCT offers improved spatial and contrast resolution, lower image noise, and reduced radiation dose. Increased spatial resolution provides better detail in abdominal imaging, aiding in the detection of small lesions and subtle pathological changes. However, this generates more images per scan, raising concerns about "image overload" in PACS, potentially leading to longer reading times and increased stress for radiologists. PCCT's improved contrast resolution enhances tissue differentiation, reducing the need for intravenous contrast agents. The technology's advanced tissue characterization provides several advantages, such as non-invasive and opportunistic liver disease evaluation and improved differentiation of renal and adrenal masses. PCCT's optimized radiation dose management is crucial for patients requiring frequent scans. Enhanced diagnostic accuracy through spectral information aids in tissue differentiation, improving confidence in diagnoses. Streamlined workflows, particularly in emergency settings, and oncologic imaging, are potential benefits, reducing the need for additional imaging studies. Future integration of PCCT into clinical practice requires collaboration, education, and research to fully harness its potential, ensuring optimized abdominal imaging and improved patient care.

UNet-based multi-organ segmentation in photon counting CT using virtual monoenergetic images.

BACKGROUND: Multi-organ segmentation aids in disease diagnosis, treatment, and radiotherapy. The recently emerged photon counting detector-based CT (PCCT) provides spectral information of the organs and the background tissue and may improve segmentation performance. PURPOSE: We propose UNet-based multi-organ segmentation in PCCT using virtual monoenergetic images (VMI) to exploit spectral information effectively. METHODS: The proposed method consists of the following steps: Noise reduction in bin-wise images, image-based material decomposition, generating VMIs, and deep learning-based segmentation. VMIs are synthesized for various x-ray energies using basis images. The UNet-based networks (3D UNet, Swin UNETR) were used for segmentation, and dice similarity coefficients (DSC) and 3D visualization of the segmented result were evaluation indicators. We validated the proposed method for the liver, pancreas, and spleen segmentation using abdominal phantoms from 55 subjects for dual- and quad-energy bins. We compared it to the conventional PCCT-based segmentation, which uses only the (noise-reduced) bin-wise images. The experiments were conducted on two cases by adjusting the dose levels. RESULTS: The proposed method improved the training stability for most cases. With the proposed method, the average DSC for the three organs slightly increased from 0.933 to 0.95, and the standard deviation decreased from 0.066 to 0.047, for example, in the low dose case (using VMIs v.s. bin-wise images from dual-energy bins; 3D UNet). CONCLUSIONS: The proposed method using VMIs improves training stability for multi-organ segmentation in PCCT, particularly when the number of energy bins is small.

Exploring charge sharing compensation using inter-pixel coincidence counters for photon counting detectors by deep-learning from local information.

OBJECTIVE: Photon counting detectors (PCDs) have well-acknowledged advantages in computed tomography (CT) imaging. However, charge sharing and other problems prevent PCDs from fully realizing the anticipated potential in diagnostic CT. PCDs with multi-energy inter-pixel coincidence counters (MEICC) have been proposed to provide particular information about charge sharing, thereby achieving lower Cramér-Rao Lower Bound (CRLB) than conventional PCDs when assessing its performance by estimating material thickness or virtual monochromatic attenuation integrals (VMAIs). This work explores charge sharing compensation using local spatial coincidence counter information for MEICC detectors through a deep-learning method. Approach: By analyzing the impact of charge sharing on photon count detection, we designed our network with a focus on individual pixels. Employing MEICC data of patches centered on POIs as input, we utilized local information for effective charge sharing compensation. The output was VMAI at different energies to address real detector issues without knowledge of primary counts. To achieve data diversity, a fast and online data generation method was proposed to provide adequate training data. A new loss function was introduced to reduce bias for training with high-noise data. The proposed method was validated by Monte Carlo (MC) simulation data for MEICC detectors that were compared with conventional PCDs. Main-Results: For conventional data as a reference, networks trained on low-noise data yielded results with a minimal bias (about 0.7%) compared with > 3% for the polynomial fitting method. The results of networks trained on high-noise data exhibited a slightly increased bias (about 1.3%) but a significantly reduced standard deviation (STD) and normalized root mean square error (NRMSE). The simulation study of the MEICC detector demonstrated superior compared to the conventional detector across all the metrics. Specifically, for both networks trained on high-noise and low-noise data, their biases were reduced to about 1% and 0.6%, respectively. Meanwhile, the results from a MEICC detector were of about 10% lower noise than a conventional detector. Moreover, an ablation study showed that the additional loss function on bias was beneficial for training on high-noise data. Significance: We demonstrated that a network-based method could utilize local information in PCDs effectively by patch-based learning to reduce the impact of charge sharing. MEICC detectors provide very valuable local spatial information by additional coincidence counters. Compared with MEICC detectors, conventional PCDs only have limited local spatial information for charge sharing compensation, resulting in higher bias and standard deviation in VMAI estimation with the same patch strategy. .

A framework to model charge sharing and pulse pileup for virtual imaging trials of photon-counting CT.

OBJECTIVE: This study describes the development, validation, and integration of a detector response model that accounts for the combined effects of x-ray crosstalk, charge sharing, and pulse pileup in photon-counting detectors. APPROACH: The x-ray photon transport was simulated using Geant4, followed by analytical charge sharing simulation in MATLAB. The analytical simulation models charge clouds with Gaussian-distributed charge densities, which are projected on a 3x3 pixel neighborhood of interaction location to compute detected counts. For pulse pileup, a prior analytical method for redistribution of energy-binned counts was implemented for delta pulses. The x-ray photon transport and charge sharing components were validated using experimental data acquired on the CdTe-based Pixirad-1/Pixie-III detector using monoenergetic beams at 26, 33, 37, and 50 keV. The pulse pileup implementation was verified with a comparable Monte Carlo simulation. The model output without pulse pileup was used to generate spatio-energetic response matrices for efficient simulation of scanner-specific photon-counting CT (PCCT) images with DukeSim, with pulse pileup modeled as a post-processing step on simulated projections. For analysis, images for the Gammex multi-energy phantom and the XCAT chest phantom were simulated at 120 kV, both with and without pulse pileup for a range of doses (27-1344 mAs). The XCAT images were evaluated qualitatively at 120 mAs, while images for the Gammex phantom were evaluated quantitatively for all doses using measurements of attenuation coefficients and Calcium concentrations. MAIN RESULTS: Reasonable agreement was observed between simulated and experimental spectra with Mean Absolute Percentage Error Values (MAPE) between 10%-31% across all incident energies and detector modes. The increased pulse pileup from increased dose affected attenuation coefficients and calcium concentrations, with an effect on calcium quantification as high as MAPE of 28%. SIGNIFICANCE: The presented approach demonstrates the viability of the model for enabling VITs to assess and optimize the clinical performance of PCCT.

High-sensitivity and spatial resolution benchtop cone beam XFCT imaging system with pixelated photon counting detectors using enhanced multipixel events correction method.

OBJECTIVE: High atomic number element nanoparticles have shown potential in tumor diagnosis and therapy. X-ray fluorescence computed tomography (XFCT) technology enables quantitative imaging of high atomic number elements by specifically detecting characteristic X-ray signals. The potential for further biomedical applications of XFCT depends on balancing sensitivity, spatial resolution, and imaging speed in existing XFCT imaging systems. APPROACH: In this study, we utilized a high-energy resolution pixelated photon-counting detector for XFCT imaging. We tackled degradation caused by multi-pixel events in the photon-counting detector through energy and interaction position corrections. Sensitivity and spatial resolution imaging experiments were conducted using PMMA phantoms to validate the effectiveness of the multi-pixel events correction algorithm. MAIN RESULTS: After correction, the system's sensitivity and spatial resolution have both improved. Furthermore, XFCT/CBCT dual-modality imaging of gadolinium nanoparticles within mice subcutaneous tumor was successfully achieved. SIGNIFICANCE: These results demonstrate the preclinical research application potential of the XFCT/CBCT dual-modality imaging system in high atomic number nanoparticle-based tumor diagnosis and therapy.

Coronary stent imaging in photon counting computed tomography: improved imaging of in-stent stenoses in a phantom with optimized reconstruction kernels.

Objectives: Coronary CT angiography (CCTA) is becoming increasingly important in the workup of coronary artery disease. Imaging of stents and in-stent stenoses remains a challenge. This work investigates the assessability of in-stent stenoses in photon counting CT (PCCT) using ultra-high-resolution (UHR) imaging and optimized reconstruction kernels. Methods: In an established phantom, 6 stents with inserted hypodense stenoses were scanned in both standard resolution (SRM) and UHR in a clinical PCCT scanner (NAEOTOM Alpha, Siemens Healthineers, Germany). Reconstructions were made both with the clinically established and optimized kernels. The visible stent lumen and the extent of stenosis were quantitatively measured and compared with the angiographic reference standard. Also, region-of-interest (ROI)-based measurements and a qualitative assessment of image quality were performed. Results: The visible stent lumen and the extent of stenosis were measured more precisely in UHR compared to SRM (0.11 ± 0.19 vs 0.41 ± 0.22 mm, P < .001). The optimized kernel further improved the accuracy of the measurements and image quality in UHR (0.35 ± 0.23 vs 0.47 ± 0.19 mm, P < .001). Compared to angiography, stenoses were overestimated in PCCT, on average with an absolute difference of 18.20% ± 4.11%. Conclusions: Photon counting CCTA allows improved imaging of in-stent stenoses in a phantom using UHR imaging and optimized kernels. These results support the use of UHR and optimized kernels in clinical practice and further studies. Advances in knowledge: UHR imaging and optimized reconstruction kernels should be used in CCTA in the presence of cardiac stents.

Photon-counting computed tomography in radiology.

Photon-counting detector computed tomography (PCD-CT) devices have recently been introduced into practice, despite photon-counting detector technology having been studied for many years. PCD-CT devices are expected to provide advantages in dose reduction, tissue specificity, artifact-free imaging, and multi-contrast demonstration capacity. Noise reduction and increased spatial resolution are expected using PCD-CT, even under challenging scanning conditions. Some experimental or preliminary studies support this hypothesis. This pictorial review illustrates the features of PCD-CT systems, particularly in the interventional field. PCD-CT offers superior image quality and better lesion discrimination than conventional CT techniques for various conditions. PCD-CT shows significant improvements in many aspects of vascular imaging. It is still in its early stages, and several challenges have been identified. Also, PCD-CT devices have some important caveats. The average cost of these devices is 3 to 4 times higher than conventional CT units. This additional cost must be justified by improved clinical benefits or reduced clinical harms. Further investigations will be needed to resolve these issues.