Feature shared multi-decoder network using complementary learning for Photon counting CT ring artifact suppression.

Accuracy and precision of segmentation and quantification of wrist bone microarchitecture using photon-counting computed tomography ex vivo.

DuDo-RAC: Dual-domain optimization for ring artifact correction in photon counting CT.

Ring artifacts are a major obstacle that prevent the easy adoption of photon counting detectors (PCDs) into next-generation CT scanners. PCDs use a direct conversion semiconductor rather than an indirect conversion scintillator, but the stability of common direct conversion semiconductors (CdTe, CZT) remains limited. Small changes in sensitivity between neighboring pixels are amplified in CT reconstruction and result in ring artifacts in PCD CT images. To reduce these artifacts, we propose that pixels be dynamically autocalibrated to each other during the scan. This is done by measuring line integrals with two neighboring detector pixels and building a model of the differences between these two pixels. This can be done practically using flying focal spot, which minimizes the temporal offset between the two datasets. In this work, we demonstrate the proof of principle with a detector shift. The two resulting sinograms are used in an optimization problem that estimates gain factors on a per-pixel level. Preliminary results in a chicken thigh specimen and resolution phantom show strong reductions in ring artifact by about 75%.


Photon-counting detectors (PCDs) for CT imaging use energy thresholds to simultaneously acquire projections at multiple energies, making them suitable for spectral imaging and material decomposition. Unfortunately, setting multiple energy thresholds results in noisy analytical reconstructions due to low photon counts in high-energy bins. Iterative reconstruction provides high quality photon-counting CT (PCCT) images but requires enormous computation time for 5D (3D + energy + time) in vivo cardiac imaging. 

Approach. 
We recently introduced UnetU, a deep learning (DL) approach that accurately denoises axial slices from 4D (3D + energy) PCCT reconstructions at various acquisition settings. In this study, we explore UnetU configurations for 5D cardiac PCCT denoising, focusing on singular value decomposition (SVD) modifications along the energy and time dimensions and alternate network architectures such as 3D U-net, FastDVDNet, and Swin Transformer UNet. We compare our networks to multi-energy non-local means (ME NLM), an established PCCT denoising algorithm. 

Main results. 
Our evaluation, using real mouse data and the digital MOBY phantom, revealed that all DL methods were more than 16 times faster than iterative reconstruction. DL denoising with SVD along the energy dimension was most effective, consistently providing low root mean square error and spatio-temporal reduced reference entropic difference, alongside strong qualitative agreement with iterative reconstruction. This superiority was attributed to lower effective rank along the energy dimension than the time dimension in 5D cardiac PCCT reconstructions. ME NLM sometimes outperformed DL with time SVD or time and energy SVD, but lagged behind iterative reconstruction and DL with energy SVD. Among alternate DL architectures with energy SVD, none consistently outperformed UnetU Energy (2D). 

Significance. 
Our study establishes UnetU Energy as an accurate and efficient method for 5D cardiac PCCT denoising, offering a 32-fold speed increase from iterative reconstruction. This advancement sets a new benchmark for DL applications in cardiovascular imaging.

Ring artifacts usually appear in photon counting computed tomography (PCCT) images, which may compromise image quality and cause non-uniformity bias. This study proposed a fast ring artifacts removal method by exploring the correlation from projections with different views for PCCT. This method has three advantages. First, our method only employ mean projection of current scan to correct projections without additional scans. Second, our method can correct the inconsistency of all detector pixels simultaneously without locating the inconsistent response pixels. Third, it can preserve reconstructed image details well without extra computational cost. Both numerical and preclinical experiments demonstrate the proposed method can suppress the ring artifacts very well than the competitors.

ObjectiveTo investigate the image quality of a low-dose dental imaging protocol in the first clinical photon-counting computed tomography (PCCT) system in comparison to a normal-dose acquisition in a digital volume tomography (DVT) system. Materials and methodsClinical PCCT systems offer an increased spatial resolution compared to previous generations of clinical systems. Their spatial resolution is in the order of dental DVT systems. Resolution-matched acquisitions of ten porcine jaws were performed in a PCCT (Naeotom Alpha, Siemens Healthineers) and in a DVT (Orthophos XL, Dentsply Sirona). PCCT images were acquired with 90 kV at a dose of 1 mGy CTDI16 cm. DVT used 85 kV at 4 mGy. Image reconstruction was performed using the standard algorithms of each system to a voxel size of 160 × 160 × 200 µm. The dose-normalized contrast-to-noise ratio (CNRD) was measured between dentine and enamel and dentine and bone. Two readers evaluated overall diagnostic quality of images and quality of relevant structures such as root channels and dentine. ResultsCNRD is higher in all PCCT acquisitions. CNRD is 37 % higher for the contrast dentine-enamel and 31 % higher for the dentine-bone contrast (p < 0.05). Overall diagnostic image quality was higher for PCCT over DVT (p < 0.02 and p < 0.04 for readers 1 and 2). Quality scores for anatomical structures were higher in PCCT compared to DVT (all p < 0.05). Inter- and intrareader reproducibility were acceptable (all ICC>0.64). ConclusionsPCCT provides an increased image quality over DVT even at a lower dose level and might enable complex dental imaging protocols in the future. Clinical significanceThe evolution of photon-counting technology and it's optimization will increasingly move dental imaging towards standardized 3D visualizations providing both minimal radiation exposure and high diagnostic accuracy.

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.

ObjectiveSpectral imaging in photon-counting CT (PCCT) allows selective quantification of desired structures and coupled with high spatial resolution could deliver the desired imaging parameters for calcified coronary plaque characterisation. Objective...

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.

PurposeTo characterize invasion-associated CT features in pulmonary subsolid nodules using low-dose ultrahigh-resolution (UHR) photon-counting CT (PCCT) images and evaluate UHR’s diagnostic superiority over standard high-resolution (HR) images.MethodsPatients with subsolid lung adenocarcinoma were recruited for chest scan on PCCT to obtain UHR and standard HR images between November 2023 and May 2024. Nodule characteristics were visually assessed and histogram features were extracted from each nodule. Image quality and radiation dose at previous energy-integrating detector CT (EID-CT) of 30 patients were compared with those of PCCT. Differences between UHR and standard HR, PCCT and EID-CT were compared using paired McNemar-test or paired Wilcox-test.ResultsOne hundred and eighty-four patients with 203 subsolid nodules were collected including 77 precursors, 77 minimally invasive adenocarcinoma (MIA) and 49 IA. UHR significantly outperformed standard HR in revealing CT findings including larger nodular diameter and solid-component diameter, more frequency of heterogeneous attenuation, lobulation, bubble-like sign, air bronchogram, pleural indentation and vascular sign (all P < 0.05). Additionally, UHR images exhibited significantly greater value in histogram-derived parameters compared to standard HR images (all P < 0.05), except for “Median,” “Minimum.” Furthermore, the radiation dose in PCCT was half of that in EID-CT (effective dose: 1.32 ± 0.27 vs. 3.85 ± 1.65/mSv, P < 0.001. CDTIvol: 2.97 ± 0.53 vs. 6.90 ± 2.97/mGy, P < 0.001), with image quality significantly better in PCCT.ConclusionThe UHR protocol on PCCT provides a magnified perspective to reveal CT characteristics of invasive growth in subsolid LUAD, previously undetectable on standard HR images, achieving halved radiation dose and better image quality than EID-CT.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11547-025-02057-0.

In Computed Tomography (CT) imaging, the ring artifacts caused by the inconsistent detector response can significantly degrade the reconstructed images, having negative impacts on the subsequent applications. The new generation of CT systems based on photon-counting detectors are affected by ring artifacts more severely. The flexibility and variety of detector responses make it difficult to build a well-defined model to characterize the ring artifacts. In this context, this study proposes the global dependency-enhanced dual-domain parallel neural network for Ring Artifact Removal (RAR). First, based on the fact that the features of ring artifacts are different in Cartesian and Polar coordinates, the parallel architecture is adopted to construct the deep neural network so that it can extract and exploit the latent features from different domains to improve the performance of ring artifact removal. Besides, the ring artifacts are globally relevant whether in Cartesian or Polar coordinate systems, but convolutional neural networks show inherent shortcomings in modeling long-range dependency. To tackle this problem, this study introduces the novel Mamba mechanism to achieve a global receptive field without incurring high computational complexity. It enables effective capture of the long-range dependency, thereby enhancing the model performance in image restoration and artifact reduction. The experiments on the simulated data validate the effectiveness of the dual-domain parallel neural network and the Mamba mechanism, and the results on two unseen real datasets demonstrate the promising performance of the proposed RAR algorithm in eliminating ring artifacts and recovering image details.

Photon-counting computed tomography (PCCT) is a promising new technology with the potential to fundamentally change today's workflows in the daily routine and to provide new quantitative imaging information to improve clinical decision-making and patient management. The content of this review is based on an unrestricted literature search on PubMed and Google Scholar using the search terms "Photon-Counting CT", "Photon-Counting detector", "spectral CT", "Computed Tomography" as well as on the authors' experience. The fundamental difference with respect to the currently established energy-integrating CT detectors is that PCCT allows counting of every single photon at the detector level. Based on the identified literature, PCCT phantom measurements and initial clinical studies have demonstrated that the new technology allows improved spatial resolution, reduced image noise, and new possibilities for advanced quantitative image postprocessing. For clinical practice, the potential benefits include fewer beam hardening artifacts, radiation dose reduction, and the use of new contrast agents. In this review, we will discuss basic technical principles and potential clinical benefits and demonstrate first clinical use cases. · Photon-counting computed tomography (PCCT) has been implemented in the clinical routine. · Compared to energy-integrating detector CT, PCCT allows the reduction of electronic image noise. · PCCT provides increased spatial resolution and a higher contrast-to-noise ratio. · The novel detector technology allows the quantification of spectral information. · Stein T, Rau A, Russe MF et al. Photon-Counting Computed Tomography - Basic Principles, Potenzial Benefits, and Initial Clinical Experience. Fortschr Röntgenstr 2023; 195: 691 - 698.

Raw-data–based material decomposition in spectral CT using photon–counting energy–selective detectors relies on a precise forward model that predicts a count–rate given intersection lengths for each material. This re- quires extensive system–specific measurements or calibration techniques. Existing calibrations either estimate a detected spectrum and are able to account for spectrally distorted assumptions or correct the predicted count rate using a correction function and can accommodate for count rate–dependent effects such as pulse pileup. We propose a calibration method that uses transmission measurements to optimize a correction function that, unlike existing methods, depends both on the photon energy and the count rate. It is thus able to correct for both kinds of distortions. In a simulated material decomposition into water and iodine, the error was reduced by 96 % compared to the best performing reference method if only pulse pileup was present and reduced by 23 % if additionally spectral distortions were taken into account. In phantom measurements using a Dectris SANTIS prototype detector, the proposed method allowed to reduce the error by 29 % compared to the best performing reference method. Artifacts were below the noise level for the proposed method, while the reference methods either showed an offset in the water region or ring artifacts.

Validating Radiology Artificial Intelligence Model Performance on Photon-Counting CT Images Using Large Language Models for Ground Truth Extraction.

Objective.Lung nodule appearance may provide prognostic information, as the presence of spiculation increases the suspicion of a nodule being cancerous. Spiculations can be quantified using morphological radiomics features extracted from CT images. Radiomics features can be affected by the acquisition parameters and scanner technologies; thus, it is essential to identify imaging conditions that provide reliable measurements, particularly for emerging technologies like photon-counting CT (PCCT). This study aimed to systematically quantify the effect of imaging parameters on the radiomics measurements using a virtual imaging trial (VIT) platform, and further verify the findings with human clinical data.Approach.The VIT utilized nine virtual patients, each with three 6 mm nodules of varying spiculations. The virtual patients were run through a validated CT simulator (DukeSim) to acquire images at three dose levels (CTDIvol = 2.85, 5.69, and 11.38 mGy) with a clinical energy-integrating CT and a PCCT. The acquired projection images were reconstructed using multiple slice thicknesses, kernels, and matrix sizes. The reconstructed images were processed to extract morphological features using three segmentation methods. The features were clustered into three broad type categories. Features extracted from the acquired CT images were compared to their corresponding ground truth values, across all imaging conditions.Main results.Among all imaging conditions, slice thickness had the greatest effect on the radiomics measurements. When the thickest slices were used, the coefficient of variation increased by [1.19%-9.66%] in the energy integrating CT images, and [3.94%-24.43%] in the PCCT images. For both scanners, varying the kernel sharpness and dose affected the radiomics measurements insignificantly, while pixel size and segmentation method were observed to have stronger effects. Under varying imaging conditions, the trends and magnitude of radiomics features measurements were coherent with virtual trial results.Significance.The findings stress the importance of choosing optimal reconstruction settings for radiomics extraction to achieve precise feature quantifications.