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.

Purpose: Proton radiation therapy may achieve precise dose delivery to the tumor while sparing non-cancerous surrounding tissue, owing to the distinct Bragg peaks of protons. Aligning the high-dose region with the tumor requires accurate estimates of the proton stopping power ratio (SPR) of patient tissues, commonly derived from computed tomography (CT) image data. Photon-counting detectors for CT have demonstrated advantages over their energy-integrating counterparts, such as improved quantitative imaging, higher spatial resolution, and filtering of electronic noise. We assessed the potential of photon-counting computed tomography (PCCT) for improving SPR estimation by training a deep neural network on a domain transform from PCCT images to SPR maps. Approach: The XCAT phantom was used to simulate PCCT images of the head with CatSim, as well as to compute corresponding ground truth SPR maps. The tube current was set to 260 mA, tube voltage to 120 kV, and number of view angles to 4000. The CT images and SPR maps were used as input and labels for training a U-Net. Results: Prediction of SPR with the network yielded average root mean square errors (RMSE) of 0.26% to 0.41%, which was an improvement on the RMSE for methods based on physical modeling developed for single-energy CT at 0.40% to 1.30% and dual-energy CT at 0.41% to 3.00%, performed on the simulated PCCT data. Conclusions: These early results show promise for using a combination of PCCT and deep learning for estimating SPR, which in extension demonstrates potential for reducing the beam range uncertainty in proton therapy.

Motion artifacts are among the most important factors degrading the diagnostic performance of x-ray CT images, in particular for photon-counting CT where these artifacts can degrade the higher spatial resolution and quantitative imaging capabilities. The purpose of this simulation study is to evaluate the capability of deep neural networks to correct for motion artifacts in spectral photon-counting cardiac CT, by generating motion-corrected virtual monoenergetic images at a range of different keVs. We used CatSim to generate synthetic training data by simulating motion-corrupted and motion-free CT imaging (100 kVp, 1 s rotation) of the dynamic XCAT phantom including heart and respiratory motion. In total 2160 image pairs were generated. We trained two different neural networks for the task of estimating motion-artifact-reduced images from motion-corrupted images: one based on UNet and one based on a Wasserstein generative adversarial network with a gradient penalty (WGAN-GP). To make these networks applicable to virtual monoenergetic images at different energies, we trained them with 40 keV and 70 keV monoenergetic images as inputs and used a loss function with two terms: 1) L1 -loss on soft tissue and bone basis images and 2) perceptual loss on 70 keV monoenergetic images. Our results show that the motion artifacts from 40 keV to 100 keV are reduced substantially. In conclusion, these results demonstrate the potential of image-domain deep neural networks to correct for motion artifacts in spectral cardiac CT images.

Purpose: Current photon-counting detectors are limited to a pixel size of 0.3 mm-1 mm, as decreasing the pixel size further generally introduces degraded dose efficiency and energy resolution from excessive charge sharing. In this work, we present experimental measurements of the first photon-counting detector prototype designed to leverage the charge sharing to estimate the photon interaction position, where simulations indicate a theoretical resolution of around 1 µm using a similar geometry. The goal of the measurements is to validate our Monte-Carlo simulation for further development. Approach: DAC sweeps are performed with an X-ray beam at specified locations on the sensor front, with the beam at 20 keV and 35 keV, as well as with different sensor biases with the beam at 35 keV. The experimental data are then compared to a Monte Carlo simulation combined with a charge transport model. In this first prototype wire bonds are used, and as such only a few channels are connected. Results: The experimental data agree generally well with the simulated data with the beam close to the electrodes, with the simulated data diverging from the experiments with the beam further away from the electrodes. The induced charge cloud signal exhibits a fairly linear dependency on the beam position, indicating that any estimation techniques will yield more precise position when the photon interacts further away from the electrodes, rather than closer. Conclusions: With the experimental data and the simulations agreeing generally well, together with the same software previously indicating a resolution of around 1 µm, we expect an ultra-high-resolution detector to be in reach, and are encouraged to continue development.