An experimental method to correct low-frequency concentric artifacts in photon counting CT

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

BACKGROUND AND OBJECTIVE

Due to the inconsistent response of photon counting detectors (PCDs) pixels to X-rays, there is an obvious presence of low-frequency ring artifacts in CT reconstructed images. Traditional CT ring artifact correction methods are ineffective in correcting low-frequency ring artifacts. Although the pixel-wise polynomial correction method can correct low-frequency ring artifacts, there may still remain residual artifacts due to the inaccuracy in the coefficient measurement and the inability of polynomial functions to perfectly model the relationship between the thickness and post-log raw data. To resolve such problems, this work proposes a high and low frequency ring artifact correction method based on dual-domain optimization (DuDo-RAC).

METHODS

This method is independent of spectral information and training data, making it suitable for various energy thresholds. Its principle is to model the inconsistent response as pixel-wise polynomial functions, with the coefficients for each pixel being determined via a dual-domain optimization framework. Since ring artifacts manifest as stripes after polar transformations, smoothing operations are utilized to further weaken the residual ring artifacts after the pre-correction process. Furthermore, a multi-resolution gradient loss function is designed to iteratively optimize the polynomial correction coefficients for a better assessment of ring removal performance.

RESULTS

The results have demonstrated that the proposed method can effectively correct the high and low frequency ring artifacts in PCD-CT images while preserving the image structure and details.

CONCLUSION

DuDo-RAC proposed in this study obtains effective ring artifact correction results in PCD-CT images.

Photon-counting in dual-contrast-enhanced computed tomography: a proof-of-concept quantitative biomechanical assessment of articular cartilage

This proof-of-concept study explores quantitative imaging of articular cartilage using photon-counting detector computed tomography (PCD-CT) with a dual-contrast agent approach, comparing it to clinical dual-energy CT (DECT). The diffusion of cationic iodinated CA4 + and non-ionic gadolinium-based gadoteridol contrast agents into ex vivo bovine medial tibial plateau cartilage was tracked over 72 h. Continuous maps of the contrast agents' diffusion were created, and correlations with biomechanical indentation parameters (equilibrium and instantaneous moduli, and relaxation time constants) were examined at 28 specific locations. Cartilage at each location was analyzed as full-thickness to ensure a fair comparison, and calibration-based material decomposition was employed for concentration estimation. Both DECT and PCD-CT exhibit strong correlations between CA4 + content and biomechanical parameters, with PCD-CT showing superior significance, especially at later time points. DECT lacks significant correlations with gadoteridol-related parameters, while PCD-CT identifies noteworthy correlations between gadoteridol diffusion and biomechanical parameters. In summary, the experimental PCD-CT setup demonstrates superior accuracy and sensitivity in concentration estimation, suggesting its potential as a more effective tool for quantitatively assessing articular cartilage condition compared to a conventional clinical DECT scanner.

Feasibility study of photon-counting CT for material identification based on YSO/SiPM detector: A proof of concept

BACKGROUND

Current photon-counting computed tomography (CT) systems utilize semiconductor detectors, such as cadmium telluride (CdTe), cadmium zinc telluride (CZT), and silicon (Si), which convert x-ray photons directly into charge pulses. An alternative approach is indirect detection, which involves Yttrium Orthosilicate (YSO) scintillators coupled with silicon photomultipliers (SiPMs). This presents an attractive and cost-effective option due to its low cost, high detection efficiency, low dark count rate, and high sensor gain.

OBJECTIVE

This study aims to establish a comprehensive quantitative imaging framework for three-energy-bin proof-of-concept photon-counting CT based on YSO/SiPM detectors developed in our group using multi-voltage threshold (MVT) digitizers and assess the feasibility of this spectral CT for material identification.

METHODS

We developed a proof-of-concept YSO/SiPM-based benchtop spectral CT system and established a pipeline for three-energy-bin photon-counting CT projection-domain processing. The empirical A-table method was employed for basis material decomposition, and the quantitative imaging performance of the spectral CT system was assessed. This evaluation included the synthesis errors of virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves. The validity of employing A-table methods for material identification in three-energy-bin spectral CT was confirmed through both simulations and experimental studies.

RESULTS

In both noise-free and noisy simulations, the thickness estimation experiments and quantitative imaging results demonstrated high accuracy. In the thickness estimation experiment using the practical spectral CT system, the mean absolute error for the estimated thickness of the decomposed Al basis material was 0.014 ± 0.010 mm, with a mean relative error of 0.66% ± 0.42%. Similarly, for the decomposed polymethyl methacrylate (PMMA) basis material, the mean absolute error in thickness estimation was 0.064 ± 0.058 mm, with a mean relative error of 0.70% ± 0.38%. Additionally, employing the equivalent thickness of the basis material allowed for accurate synthesis of 70 keV virtual monoenergetic images (relative error 1.85% ± 1.26%), electron density (relative error 1.81% ± 0.97%), and effective atomic number (relative error 2.64% ± 1.26%) of the tested materials. In addition, the average synthesis error of the linear attenuation coefficient curves in the energy range from 40 to 150 keV was 1.89% ± 1.07%.

CONCLUSIONS

Both simulation and experimental results demonstrate the accurate generation of 70 keV virtual monoenergetic images, electron density, and effective atomic number images using the A-table method. Quantitative imaging results indicate that the YSO/SiPM-based photon-counting detector is capable of accurately reconstructing virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves, thereby achieving precise material identification.

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.

High-resolution hybrid micro-CT imaging pipeline for mouse brain region segmentation and volumetric morphometry

BACKGROUND

Brain region segmentation and morphometry in humanized apolipoprotein E (APOE) mouse models with a human NOS2 background (HN) contribute to Alzheimer's disease (AD) research by demonstrating how various risk factors affect the brain. Photon-counting detector (PCD) micro-CT provides faster scan times than MRI, with superior contrast and spatial resolution to energy-integrating detector (EID) micro-CT. This paper presents a pipeline for mouse brain imaging, segmentation, and morphometry from PCD micro-CT.

METHODS

We used brains of 26 mice from 3 genotypes (APOE22HN, APOE33HN, APOE44HN). The pipeline included PCD and EID micro-CT scanning, hybrid (PCD and EID) iterative reconstruction, and brain region segmentation using the Small Animal Multivariate Brain Analysis (SAMBA) tool. We applied SAMBA to transfer brain region labels from our new PCD CT atlas to individual PCD brains via diffeomorphic registration. Region-based and voxel-based analyses were used for comparisons by genotype and sex.

RESULTS

Together, PCD and EID scanning take ~5 hours to produce images with a voxel size of 22 μm, which is faster than MRI protocols for mouse brain morphometry with voxel size above 40 μm. Hybrid iterative reconstruction generates PCD images with minimal artifacts and higher spatial resolution and contrast than EID images. Our PCD atlas is qualitatively and quantitatively similar to the prior MRI atlas and successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant volume differences in 26 regions, including parts of the entorhinal cortex and cingulate cortex. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus, a region where atrophy is associated with AD.

CONCLUSIONS

This work establishes a pipeline for mouse brain analysis using PCD CT, from staining to imaging and labeling brain images. Our results validate the effectiveness of the approach, setting a foundation for research on AD mouse models while reducing scanning durations.

Multi-energy CT material decomposition using graph model improved CNN

In spectral CT imaging, the coefficient image of the basis material obtained by the material decomposition technique can estimate the tissue composition, and its accuracy directly affects the disease diagnosis. Although the precision of material decomposition is increased by employing convolutional neural networks (CNN), extracting the non-local features from the CT image is restricted using the traditional CNN convolution operator. A graph model built by multi-scale non-local self-similar patterns is introduced into multi-material decomposition (MMD). We proposed a novel MMD method based on graph edge-conditioned convolution U-net (GECCU-net) to enhance material image quality. The GECCU-net focuses on developing a multi-scale encoder. At the network coding stage, three paths are applied to capture comprehensive image features. The local and non-local feature aggregation (LNFA) blocks are designed to integrate the local and non-local features from different paths. The graph edge-conditioned convolution based on non-Euclidean space excavates the non-local features. A hybrid loss function is defined to accommodate multi-scale input images and avoid over-smoothing of results. The proposed network is compared quantitatively with base CNN models on the simulated and real datasets. The material images generated by GECCU-net have less noise and artifacts while retaining more information on tissue. The Structural SIMilarity (SSIM) of the obtained abdomen and chest water maps reaches 0.9976 and 0.9990, respectively, and the RMSE reduces to 0.1218 and 0.4903 g/cm3. The proposed method can improve MMD performance and has potential applications.

Image quality improvement of a one-step spectral CT reconstruction on a prototype photon-counting scanner

Objective. X-ray spectral computed tomography (CT) allows for material decomposition (MD). This study compared a one-step material decomposition MD algorithm with a two-step reconstruction MD algorithm using acquisitions of a prototype CT scanner with a photon-counting detector (PCD).Approach. MD and CT reconstruction may be done in two successive steps, i.e. decompose the data in material sinograms which are then reconstructed in material CT images, or jointly in a one-step algorithm. The one-step algorithm reconstructed material CT images by maximizing their Poisson log-likelihood in the projection domain with a spatial regularization in the image domain. The two-step algorithm maximized first the Poisson log-likelihood without regularization to decompose the data in material sinograms. These sinograms were then reconstructed into material CT images by least squares minimization, with the same spatial regularization as the one step algorithm. A phantom simulating the CT angiography clinical task was scanned and the data used to measure noise and spatial resolution properties. Low dose carotid CT angiographies of 4 patients were also reconstructed with both algorithms and analyzed by a radiologist. The image quality and diagnostic clinical task were evaluated with a clinical score.Main results. The phantom data processing demonstrated that the one-step algorithm had a better spatial resolution at the same noise level or a decreased noise value at matching spatial resolution. Regularization parameters leading to a fair comparison were selected for the patient data reconstruction. On the patient images, the one-step images received higher scores compared to the two-step algorithm for image quality and diagnostic.Significance. Both phantom and patient data demonstrated how a one-step algorithm improves spectral CT image quality over the implemented two-step algorithm but requires a longer computation time. At a low radiation dose, the one-step algorithm presented good to excellent clinical scores for all the spectral CT images.

Correcting statistical CT number biases without access to raw detector counts: Applications to high spatial resolution photon counting CT imaging

BACKGROUND

Due to the nonlinear nature of the logarithmic operation and the stochastic nature of photon counts (N), sinogram data of photon counting detector CT (PCD-CT) are intrinsically biased, which leads to statistical CT number biases. When raw counts are available, nearly unbiased statistical estimators for projection data were developed recently to address the CT number bias issue. However, for most clinical PCD-CT systems, users' access to raw detector counts is limited. Therefore, it remains a challenge for end users to address the CT number bias issue in clinical applications.

PURPOSE

To develop methods to correct statistical biases in PCD-CT without requiring access to raw PCD counts.

METHODS

(1) The sample variance of air-only post-log sinograms was used to estimate air-only detector counts,N ¯ 0 $\bar{N}_0$ . (2) If the post-log sinogram data, y, is available, then N of each detector pixel was estimated usingN = N ¯ 0 e - y $N = \bar{N}_0 \, \mathrm{e}^{-y}$ . Once N was estimated, a closed-form analytical bias correction was applied to the sinogram. (3) If a patient's post-log sinogram data are not archived, a forward projection of the bias-contaminated CT image was used to perform a first-order bias correction. Both the proposed sinogram domain- and image domain-based bias correction methods were validated using experimental PCD-CT data.

RESULTS

Experimental results demonstrated that both sinogram domain- and image domain-based bias correction methods enabled reduced-dose PCD-CT images to match the CT numbers of reference-standard images within [-5, 5] HU. In contrast, uncorrected reduced-dose PCD-CT images demonstrated biases ranging from -25 to 55 HU, depending on the material. No increase in image noise or spatial resolution degradation was observed using the proposed methods.

CONCLUSIONS

CT number bias issues can be effectively addressed using the proposed sinogram or image domain method in PCD-CT, allowing PCD-CT acquired at different radiation dose levels to have consistent CT numbers desired for quantitative imaging.

A C-arm photon counting CT prototype with volumetric coverage using multi-sweep step-and-shoot acquisitions

Objective.Existing clinical C-arm interventional systems use scintillator-based energy-integrating flat panel detectors (FPDs) to generate cone-beam CT (CBCT) images. Despite its volumetric coverage, FPD-CBCT does not provide sufficient low-contrast detectability desired for certain interventional procedures. The purpose of this work was to develop a C-arm photon counting detector (PCD) CT system with a step-and-shoot data acquisition method to further improve the tomographic imaging performance of interventional systems.Approach.As a proof-of-concept, a cadmium telluride-based 51 cm × 0.6 cm PCD was mounted in front of a FPD in an Artis Zee biplane system. A total of 10 C-arm sweeps (5 forward and 5 backward) were prescribed. A motorized patient table prototype was synchronized with the C-arm system such that it translates the object by a designated distance during the sub-second rest time in between gantry sweeps. To evaluate whether this multi-sweep step-and-shoot acquisition strategy can generate high-quality and volumetric PCD-CT images without geometric distortion artifacts, experiments were performed using physical phantoms, a human cadaver head, and anin vivoswine subject. Comparison with FPD-CT was made under matched narrow beam collimation and radiation dose conditions.Main results.Compared with FPD-CT images, PCD-CT images had lower noise and improved visualization of low-contrast lesion models, as well as improved visibility of small iodinated blood vessels. Fine structures were visualized more clearly by the PCD-CT than the highest-available resolution provided by FPD-CBCT and MDCT. No perceivable geometric distortion artifacts were observed in the multi-planar PCD-CT images.Significance.This work is the first demonstration of the feasibility of high-quality and multi-planar (volumetric) PCD-CT imaging with a rotating C-arm gantry.