Objective. While photon-counting computed tomography (PCCT) improves image quality and reduces radiation dose, artifacts induced by cardiac and respiratory motion is still a challenge. The purpose of this work is to evaluate the potential of an image-domain motion-artifact-correction method based on a deep-learning model that incorporates spectral information (material basis images). Approach. We simulated PCCT imaging of five XCAT phantoms, and used these for training two deep neural networks—one with and one without spectral information—to map two motion-corrupted virtual monoenergetic images to corresponding motion-free images. Using images from another simulated XCAT phantom, we calculated the CT number error on five regions of interest and 10 segmented organs. The method was also evaluated visually on clinical cardiac PCCT images. Stretch quantification of endocardial engraved zones was used to calculate regional wall motion and mechanical delay. The results were compared with the motion-free image using a paired t-test. Main results. Out of 45 regions and organs, the CT number accuracy is improved in 41 regions (91%). Among these, the best accuracy is obtained with spectral information in 25 regions (61%). Both models, in particular the one with spectral information, improves visual image quality in simulated and clinical images. The model significantly ( P < 0.01) improved estimation of the regional wall motion and assessment of mechanical delay of the left ventricle, but no significant difference was observed between models with and without spectral information. Significance. Our approach, validated on simulated datasets, shows that quantitative cardiac CT imaging can be improved by deep-learning motion correction and that spectral information substantially improves performance.

With the increasing popularity of photon-counting detectors in X-ray computed tomography, and with virtual clinical trials playing an increasingly important role in the evaluation of imaging systems, it is vital to have accurate simulation models for photon-counting imaging systems. Physical effects such as charge sharing, K-fluorescence and Compton scattering give rise to spatiotemporal correlations between different energy bins in different pixels, and existing methods for simulating such correlations accurately with multi-pixel correlation lengths are too computationally costly to be practical for full-size CT acquisition simulations. In this work, we propose a fast, accurate method for simulating correlated Poisson noise in photon-counting detectors based on the Anscombe transformation. We use Cholesky factorization to generate correlated Gaussian random numbers and then apply the inverse Anscombe transformation to map these into approximately Poisson-distributed counts. We show that any desired correlation structure of the counts can be obtained by adjusting the mean and covariance matrix used for the Gaussian random number generation. We evaluate this method in a simulation study with both a one-dimensional “toy” model with a single energy bin and a one-dimensional “realistic” model¹ , by using the chi-square statistic to assess the accuracy of the marginal probability distributions and pairwise joint probability distributions. The computational speed is compared to brute-force generation of Poisson random numbers. Our results show that the proposed method achieves reasonable accuracy in approximating the Poisson distribution, with > 80% lower chi-squared than a Gaussian approximation and that it can decrease the computation time to 1% of the time required for direct Poisson generation.

Respiratory motion remains a significant challenge in computed tomography (CT), especially in thoracic imaging. Realistic motion modeling is essential to reduce artifacts and facilitate accurate diagnostics; however, acquiring the high-quality 4DCT data necessary for such modeling typically requires long scan times, which expose patients to high doses of ionizing radiation. As a result, public access to comprehensive 4DCT respiratory data is scarce. To address this problem, this report introduces a novel framework for synthesizing realistic and structurally coherent 4DCT respiratory numerical phantoms of the thoracic region from static CT images using latent diffusion models. Specifically, we propose a two-stage architecture which includes: 1. convolutional autoencoders for encoding both prior anatomical volumes and deformable vector fields (DVFs) into compact latent representations, and 2. a Res-UNet-based diffusion model used to generate latent DVFs conditioned on a full 3DCT volume prior and sampled Gaussian noise. To enforce structural coherence of the generated DVFs, a combined loss function was used incorporating bending energy and diffusion regularization. Our results show that the proposed approach can generate plausible and diverse DVFs via Denoising Diffusion Implicit Model (DDIM) sampling, enabling effective simulation of respiratory motion without requiring repeated patient scans. The results from this work can, among other things, have important implications for the training and evaluation of deep-learning-based motion compensation methods. This project’s code and pretrained model weights are available at https://github.com/Axreub/ldm-respiratory-motion.

Energy-resolving photon-counting CT promises improved material-separation capabilities compared to conventional CT. However, accurate separation of iodine and calcium, which is especially important for imaging atherosclerotic plaques, remains a challenge. In this proof-of-concept study, we present a deep-learning based method that takes a pair of basis images from a photon-counting CT as input and produces a map of the iodine distribution where the contamination from other materials such as calcium is minimized, and demonstrate its performance on clinical images of the carotid arteries. As training data, we used 13 pairs of image slices of the neck from a silicon-based photon-counting spectral CT, with one non-contrast and one contrast-enhanced slice in each pair. To generate a ground-truth iodine maps as training labels, 40 keV virtual monoenergetic non-contrast images were registered to align with the corresponding 40 keV contrast-enhanced images, and the difference between those two image slices was used to generate an iodine concentration map. We trained a ResUNet++ deep convolutional neural network using water-iodine basis image pairs resulting from a two-basis material decomposition as inputs and the iodine concentration map obtained from subtraction as label. The resulting method was evaluated on a previously unseen photon-counting CT image slice of the neck from the same patient. Our results show that the trained network correctly highlights the image features containing iodinated contrast agent and quantifies the concentration accurately. The contamination from calcium and other tissues is significantly reduced compared to the original iodine basis image. Our results demonstrate that the proposed method can successfully separate iodine from calcium and other tissues on clinical silicon-based photon-counting CT, with important potential implications for imaging of atherosclerosis.

コンピュータ断層撮影（ＣＴ）における機械学習画像再構成のための１つ以上の信頼度表示を決定するための方法及びシステムが提供される。この方法は、（Ｓ１）エネルギー分解Ｘ線データを取得することと、（Ｓ２）少なくとも１つの機械学習システムに基づいてエネルギー分解Ｘ線データを処理して、少なくとも１つの再構成基底画像又はその画像特徴の事後確率分布の表現を生成することとを備える。本方法は更に、事後確率分布の表現に基づいて、前記少なくとも１つの再構成基底画像、又は前記少なくとも１つの再構成基底画像に由来する少なくとも１つの派生画像、又は前記少なくとも１つの再構成基底画像又は前記少なくとも１つの派生画像の画像特徴に対する１つ以上の信頼度表示を生成する（Ｓ３）ことを含む。【選択図】図６Ａ

Purpose: We aim to investigate the feasibility of developing a tabletop photon-counting micro-computed tomography (CT) device that can perform intraoperative virtual histopathology on tumor specimens, showing the demarcation between the tumor and surrounding tissue. By enabling fast imaging and tissue analysis during surgery, the micro-CT device would enhance the accuracy of tumor excision and thus minimize harm to the patient by reducing the need for re-operations. Approach: A simulation using a Python package called SpekPy is used to investigate the potential capabilities of a tabletop micro-CT device on tumor specimens.1 We use existing micro-CT systems as a model for the tube parameters (filters, voltage, power, and current), and we assume an ideal detector in order to understand the upper limit of detection capabilities. Results: The simulated data indicate that when the contrast-to-noise ratio (CNR) is normalized for time, higher tube voltage is optimal across all tissue thicknesses. In contrast, when the CNR is normalized for dose, lower tube voltage ranges are preferable for thinner tissues. Since shorter acquisition times are desirable in this application and dose is not a concern (as the tissue is not live), it is useful to know that the highest applied voltage will yield the highest CNR, and thus the best capability for tumor differentiation. Additionally, the data suggest that the device can distinguish features as small as 33 microns within soft tissue, facilitating precise assessment of tumor margins. Conclusions: The simulation demonstrates that a micro-CT device with these specifications is capable of effectively performing intraoperative tumor margin assessment.

Purpose: Evaluation of a new sensor for micrometer-resolution photon-counting CT. Approach: DAC-sweeps are performed using a commercial x-ray tube and are compared to simulations. An edge-scan using a 250 µm tungsten wafer without any interaction logic is also performed, as well as single interaction readout of the energy spectrum that is compared to simulations. Results: The edge-scan shows a line spread function with a full width at half maximum of 11.6 µm and a 5% modulation transfer function at 850 lp/cm. Conclusions: Fair agreement with simulations indicated that employing the interaction can further significantly improve spatial resolution.

【課題】改良されたＸ線検出器システムを提供する。【解決手段】Ｘ線源からのＸ線放射を検出するフォトンカウンティングＸ線検出器（２０）、及び前記Ｘ線検出器における光子相互作用の時間に関する情報と、前記Ｘ線検出器に対する前記Ｘ線源の位置に関する情報とに基づいて、前記Ｘ線検出器に入射する放射線に関する情報を決定する及び／又は取得する同時計数検出システム（６０）を含むＸ線検出器システム（５）を提供する。このようなＸ線検出器システムを含むＸ線イメージングシステム、並びに対応する同時計数検出システム及び対応する方法も提供する。【選択図】図２Ｂ

There is provided a method and corresponding system for image reconstruction based on energy-resolved x-ray data. The method comprises collecting (S1) at least one representation of energy-resolved x-ray data, and performing (S2) at least two basis material decompositions based on said at least one representation of energy-resolved x-ray data to generate at least two original basis image representation sets. The method further comprises obtaining or selecting (S3) at least two basis image representations from at least two of said original basis image representation sets, and processing (S4) said obtained or selected basis image representations by data processing based on machine learning to generate at least one representation of output image data.

Objective. We strive to overcome the challenges posed by ring artifacts in x-ray computed tomography (CT) by developing a novel approach for generating training data for deep learning-based methods. Training such networks require large, high quality, datasets that are often generated in the data domain, time-consuming and expensive. Our objective is to develop a technique for synthesizing realistic ring artifacts directly in the image domain, enabling scalable production of training data without relying on specific imaging system physics. Approach. We develop 'Syn2Real,' a computationally efficient pipeline that generates realistic ring artifacts directly in the image domain. To demonstrate the effectiveness of our approach, we train two versions of UNet, vanilla and a high capacity version with self-attention layers that we call UNetpp, with & ell;2 and perceptual losses, as well as a diffusion model, on energy-integrating CT images with and without these synthetic ring artifacts. Main Results. Despite being trained on conventional single-energy CT images, our models effectively correct ring artifacts across various monoenergetic images, at different energy levels and slice thicknesses, from a prototype photon-counting CT system. This generalizability validates the realism and versatility of our ring artifact generation process. Significance. Ring artifacts in x-ray CT pose a unique challenge to image quality and clinical utility. By focusing on data generation, our work provides a foundation for developing more robust and adaptable ring artifact correction methods for pre-clinical, clinical and other CT applications.