Streak Artifacts Research Articles

Enhancing image quality in circular-view photoacoustic tomography using randomized detection points

Abstract Circular-view (circular scan) photoacoustic computed tomography (PACT) with low-density detection points (DPs) is an efficient, high-speed, and inexpensive modality with numerous (pre-) clinical applications. However, as the number of DPs decreases (decrease in A-lines), some unwanted streak artifacts appear in the reconstructed PACT image. Here, we present an approach to address the mentioned challenge and enhance image contrast. In this method, several low-resolution-images (LRIs) are reconstructed by employing a few DPs' data with randomized locations. These LRIs are used in computing an artifact score matrix (ASM) to identify the location of artifacts. Three numerical (two vasculatures and human brain), two experimental (triangle and complex leaf), and an in vivo (a rat brain) studies were conducted to evaluate the efficacy of the proposed method (applying the computed ASM to the final reconstructed photoacoustic image). Our findings show that the proposed method outperforms conventional methods and offers better image quality. The signal-to-noise ratio (SNR) and structural similarity index values (SSIM) of the proposed method are quantitatively 20 dB and 25% better than the conventional method, respectively. Furthermore, compared to the conventional method, the proposed method has an artifact standard deviation that is 50 times lower.

Improved ECG-gated cardiac cine imaging with variable initial value based tiny golden-angle radial trajectory.

Breath-held electrocardiogram-gated cardiac cine imaging (ECG-CINE), as the gold standard for assessing cardiac function in magnetic resonance imaging (MRI), is prone to motion artifacts. Conventional golden-angle (CGA) sampling has emerged as a promising technique for mitigating motion effects in real-time cardiac cine imaging. However, in ECG-CINE, the irregular re-binning of radial k-space profiles based on CGA can exacerbate k-space non-uniformity, resulting in severe streaking artifacts. The recently introduced segmented golden-angle ratio (SGA) scheme aims to solve this problem; nevertheless, it sacrifices the desired motion insensitivity. The study aims to develop a more efficient k-space sampling scheme for ECG-CINE that guarantees both improved motion insensitivity and optimized k-space coverage. Theoretically, to enhance motion insensitivity, it is essential that the single-frame radial k-space profiles acquired within each heartbeat (HB) span as close to a full 360-degree range as possible. Meanwhile, to ensure uniform data coverage, the sequentially acquired k-space profiles need to be evenly distributed both within each HB and across multiple HBs. In this study, we propose a Variable Initial value-based tiny Golden-Angle radial trajectory (VIGA) to achieve these two goals. Specifically, VIGA is a two-step approach: First, the tiny golden-angle ratio is applied to the k-space profiles within each HB to maintain motion insensitivity and k-space uniformity as in CGA. Second, a golden ratio of the golden angle used within each HB is applied to the initial k-space profiles across adjacent HBs to optimize coverage further. We validated the proposed VIGA method through numerical simulations, phantom experiments, and prospective and retrospective in vivo cardiac cine experiments. Numerical simulations revealed that the k-space uniformity of CGA is highly dependent on the number of spokes per HB, whereas VIGA and SGA maintained nearly optimal k-space coverage regardless of this parameter. Both phantom and prospective studies demonstrated that VIGA outperforms CGA when the number of spokes per HB is suboptimal, and surpasses SGA in conditions with residual respiratory motion. The standard deviation of gradient scores indicates statistical significance between CGA and VIGA under free-breathing conditions (p=0.039) and between SGA and VIGA under all conditions tested (Free-breathing, 200 spokes/HB: p=0.028; Breath-holding, 200 spokes/HB: p=0.008; Free-breathing, 200 spokes/HB: p=0.013; Breath-holding, 200 spokes/HB: p=0.011). Retrospective results demonstrated that doctor ratings for SGA were lower than those for VIGA, and the ratings for systole images using VIGA were significantly higher than those using CGA (2.55±0.45 vs. 3.29±0.52; p=0.04). A novel and efficient k-space sampling scheme, named VIGA, was proposed to improve k-space uniformity and motion insensitivity. VIGA facilitates robust image quality in both prospective and retrospective cardiac cine imaging, demonstrating its potential as a clinically viable alternative to CGA and SGA.

High-resolution volumetric dynamic magnetic resonance imaging of the wrist using an 8-channel flexible receive coil.

Real-time imaging is useful for the evaluation of wrist instability. However, currently available real-time magnetic resonance imaging (MRI) methods are limited due to their 2D nature or provide insufficient temporal resolution and image quality for quantitative kinematic analysis. This work introduces a novel approach for volumetric dynamic MRI of the wrist joint during active motion and demonstrates the feasibility of tracking carpal bone motion. A flexible 8-element 3T wrist receive coil and 3D-printed support platform for guiding motion were designed for dynamic wrist imaging. 2D real-time images were acquired using a fat-saturated FLASH sequence with radial sampling and reconstructed with the GRASP algorithm. Corresponding volumetric dynamic wrist images were obtained by assembling 2D real-time images into 3D snapshots using autodetected MRI-visible markers for slice alignment. The proposed method was demonstrated for radial-ulnar deviation on five healthy volunteers. The flexible wrist coil provided high SNR while allowing a wide range of wrist movements. 2D real-time wrist images were acquired with a temporal resolution of 48ms/frame with negligible streaking artifacts. Carpal bones and metacarpal bones were properly aligned in the assembled dynamic volumes for all five subjects. The excellent bone-to-tissue contrast enabled accurate segmentation of the individual carpal bones on the assembled dynamic volumes. This work introduces a novel wrist coil and demonstrates that real-time volumetric dynamic examination of the moving wrist is feasible. The achieved image quality and high temporal resolution could enable automatic segmentation of carpal bones and quantitative kinematic assessment for evaluating wrist instability.

Contrast Kinetics in CT Coronary Angiography

AbstractComputed tomography coronary angiography (CTCA) is a technically demanding radiological investigation that requires adequate opacification of coronary arteries at peak aortic enhancement phase, with minimal or no contrast in the superior vena cava and right-sided cardiac chambers to avoid streak artifacts of dense contrast. Therefore, it is prudent to know about contrast media, contrast kinetics, and contrast injection protocols. This article attempts to describe the essentials of various aspects of contrast media that should be considered for CTCA along with an in-depth analysis of contrast kinetics that every radiologist should know for obtaining adequate opacification of coronary arteries.

TMAA-net: tensor-domain multi-planal anti-aliasing network for sparse-view CT image reconstruction

Objective.Among various deep-network-based sparse-view CT image reconstruction studies, the sinogram upscaling network has been predominantly employed to synthesize additional view information. However, the performance of the sinogram-based network is limited in terms of removing aliasing streak artifacts and recovering low-contrast small structures. In this study, we used a view-by-view back-projection (VVBP) tensor-domain network to overcome such limitations of the sinogram-based approaches.Approach.The proposed method offers advantages of addressing the aliasing artifacts directly in the 3D tensor domain over the 2D sinogram. In the tensor-domain network, the multi-planal anti-aliasing modules were used to remove artifacts within the coronal and sagittal tensor planes. In addition, the data-fidelity-based refinement module was also implemented to successively process output images of the tensor network to recover image sharpness and textures.Main result.The proposed method showed outperformance in terms of removing aliasing artifacts and recovering low-contrast details compared to other state-of-the-art sinogram-based networks. The performance was validated for both numerical and clinical projection data in a circular fan-beam CT configuration.Significance.We observed that view-by-view aliasing artifacts in sparse-view CT exhibit distinct patterns within the tensor planes, making them effectively removable in high-dimensional representations. Additionally, we demonstrated that the co-domain characteristics of tensor space processing offer higher generalization performance for aliasing artifact removal compared to conventional sinogram-domain processing.

Hybrid Reconstruction Approach for Polychromatic Computed Tomography in Highly Limited-Data Scenarios.

Conventional strategies aimed at mitigating beam-hardening artifacts in computed tomography (CT) can be categorized into two main approaches: (1) postprocessing following conventional reconstruction and (2) iterative reconstruction incorporating a beam-hardening model. While the former fails in low-dose and/or limited-data cases, the latter substantially increases computational cost. Although deep learning-based methods have been proposed for several cases of limited-data CT, few works in the literature have dealt with beam-hardening artifacts, and none have addressed the problems caused by randomly selected projections and a highly limited span. We propose the deep learning-based prior image constrained (PICDL) framework, a hybrid method used to yield CT images free from beam-hardening artifacts in different limited-data scenarios based on the combination of a modified version of the Prior Image Constrained Compressed Sensing (PICCS) algorithm that incorporates the L2 norm (L2-PICCS) with a prior image generated from a preliminary FDK reconstruction with a deep learning (DL) algorithm. The model is based on a modification of the U-Net architecture, incorporating ResNet-34 as a replacement of the original encoder. Evaluation with rodent head studies in a small-animal CT scanner showed that the proposed method was able to correct beam-hardening artifacts, recover patient contours, and compensate streak and deformation artifacts in scenarios with a limited span and a limited number of projections randomly selected. Hallucinations present in the prior image caused by the deep learning model were eliminated, while the target information was effectively recovered by the L2-PICCS algorithm.

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

BackgroundAccurate 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. MethodsThree 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. ResultsPCD-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. ConclusionPCD-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.

Dynamic contrast-enhanced MRA of the aorta using a Golden-angle RAdial Sparse Parallel (GRASP) sequence: comparison with conventional time-resolved cartesian MRA (TWIST)

PurposeTo compare the application of two contrast-enhanced time-resolved magnetic resonance angiography sequences on an aortic disease patient cohort: the conventional Cartesian-sampling-based, Time-resolved angiography With Interleaved Stochastic Trajectories (TWIST) sequence, and the radial-sampling-based Golden-angle RAdial Sparse Parallel (GRASP) sequence. TWIST is highly sensitive to patient movement, which can lead to blurring and reduced sharpness of vascular structures, particularly in dynamic regions like the aorta. Such motion artifacts can compromise diagnostic accuracy. Radial-sampling-based techniques are less sensitive to motion than cartesian sampling and are expected to improve the image quality in body parts subjected to motion.Methods30 patients (60.9 ± 16.1y.o.) with various aortic diseases underwent a 1.5T magnetic resonance angiography examination. Assessment of image quality in the ascending aorta (AA), descending aorta (DA), and abdominal aorta (AbA) on a 4-point Likert scale (1 = excellent, 4 = non-diagnostic) as well as max. aortic diameters (Dmax) were performed. T-test and multilevel mixed-effect proportional-odds models were used for the image analysis.ResultsGRASP offered superior depiction of vascular structures in terms of vascular contrast for qualitative analysis (TWIST, reader 1: 1.6 ± 0.5; reader 2: 1.9 ± 0.4; reader 3: 1.1 ± 0.4; GRASP, reader 1: 1.5 ± 0.5; reader 2: 1.4 ± 0.5; reader 3: 1.0 ± 0.2) and vessel sharpness for qualitative (TWIST, reader 1: 1.9 ± 0.6; reader 2: 1.6 ± 0.6; reader 3: 2.0 ± 0.3; GRASP, reader 1: 1.4 ± 0.6; reader 2: 1.2 ± 0.4; reader 3: 1.3 ± 0.6) and quantitative analysis (TWIST, AA = 0.12 ± 0.04, DA = 0.12 ± 0.03, AbA = 0.11 ± 0.03; GRASP, AA = 0.20 ± 0.05, DA = 0.22 ± 0.06, AbA=0.20 ± 0.05). Streaking artefacts of GRASP were more visible compared to TWIST (TWIST, reader 1: 2.2 ± 0.6; reader 2: 1.9 ± 0.3; reader 3: 2.0 ± 0.5; GRASP, reader 1: 2.6 ± 0.6; reader 2: 2.3 ± 0.5; reader 3: 2.8 ± 0.6). Aortic Dmax comparison among the sequence showed no clinical relevance.ConclusionGRASP outperformed TWIST in SNR, vessel sharpness, and reduction in image blurring; streaking artefacts were stronger with GRASP, but did not affect diagnostic image quality.

Blind CT Image Quality Assessment Using DDPM-Derived Content and Transformer-Based Evaluator.

Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evaluate perceptual quality in alignment with what radiologists perceive, which plays an important role in advancing low-dose CT reconstruction techniques. An intriguing direction involves developing BIQA methods that mimic the operational characteristic of the human visual system (HVS). The internal generative mechanism (IGM) theory reveals that the HVS actively deduces primary content to enhance comprehension. In this study, we introduce an innovative BIQA metric that emulates the active inference process of IGM. Initially, an active inference module, implemented as a denoising diffusion probabilistic model (DDPM), is constructed to anticipate the primary content. Then, the dissimilarity map is derived by assessing the interrelation between the distorted image and its primary content. Subsequently, the distorted image and dissimilarity map are combined into a multi-channel image, which is inputted into a transformer-based image quality evaluator. By leveraging the DDPM-derived primary content, our approach achieves competitive performance on a low-dose CT dataset.

Adaptive orthogonal directional total variation with kernel regression for CT image denoising.

Low-dose computed tomography (CT) has been successful in reducing radiation exposure for patients. However, the use of reconstructions from sparse angle sampling in low-dose CT often leads to severe streak artifacts in the reconstructed images. In order to address this issue and preserve image edge details, this study proposes an adaptive orthogonal directional total variation method with kernel regression. The CT reconstructed images are initially processed through kernel regression to obtain the N-term Taylor series, which serves as a local representation of the regression function. By expanding the series to the second order, we obtain the desired estimate of the regression function and localized information on the first and second derivatives. To mitigate the noise impact on these derivatives, kernel regression is performed again to update the first and second derivatives. Subsequently, the original reconstructed image, its local approximation, and the updated derivatives are summed using a weighting scheme to derive the image used for calculating orientation information. For further removal of stripe artifacts, the study introduces the adaptive orthogonal directional total variation (AODTV) method, which denoises along both the edge direction and the normal direction, guided by the previously obtained orientation. Both simulation and real experiments have obtained good results. The results of two real experiments show that the proposed method has obtained PSNR values of 34.5408 dB and 29.4634 dB, which are 1.2392-5.9333 dB and 2.828-6.7995 dB higher than the contrast denoising algorithm, respectively, indicating that the proposed method has good denoising performance. The study demonstrates the effectiveness of the method in eliminating strip artifacts and preserving the fine details of the images.

Retrospective denoising and sharpening effects on postmortem computed tomography images of formalin-fixed human cadavers for human anatomy education and research

BackgroundThe value of formalin-fixed postmortem computed tomography (PMCT) has significantly increased in human anatomy education and research. However, improving embalmed PMCT images with high noise, low contrast, and multiple artifacts is an important challenge in enhancing the quality of clinical images. Retrospective denoising methods for embalmed PMCT data are essential for anatomical education and research when new-generation CT scanners with denoising functions are unavailable. MethodsThe three currently available standalone software denoising methods, including image summation methods (Fused CT), noise-reduction system methods (iNoir), and iterative reconstruction (IR, SafeCT), were used to analyze the retrospective noise-reduction effect on 13 human formalin-fixed PMCT datasets. ResultsFused CT had some advantages; however, it was not useful for embalmed cadavers because of high noise, which doubled with the addition. iNoir and SafeCT improved the visual image quality of the noisy cadaver images. Statistical examination showed a 22–67 % and 18–56 % improvement in noise reduction in the non-artifact and metal artifact parts, respectively, depending on the methods and denoising intensities. However, this improvement was not statistically or morphologically significant in the presence of strong metal artifacts. In contrast, the scattered metal and streak artifacts peculiar to cadaveric images showed effective morphological and statistical improvement. ConclusionsRetrospective denoising of embalmed PMCT images showed significant morphological and statistical improvements in noise reduction and is feasible for current anatomy education and research.

Optimising 4D imaging of fast-oscillating structures using X-ray microtomography with retrospective gating

Imaging the internal architecture of fast-vibrating structures at micrometer scale and kilohertz frequencies poses great challenges for numerous applications, including the study of biological oscillators, mechanical testing of materials, and process engineering. Over the past decade, X-ray microtomography with retrospective gating has shown very promising advances in meeting these challenges. However, breakthroughs are still expected in acquisition and reconstruction procedures to keep improving the spatiotemporal resolution, and study the mechanics of fast-vibrating multiscale structures. Thereby, this works aims to improve this imaging technique by minimising streaking and motion blur artefacts through the optimisation of experimental parameters. For that purpose, we have coupled a numerical approach relying on tomography simulation with vibrating particles with known and ideal 3D geometry (micro-spheres or fibres) with experimental campaigns. These were carried out on soft composites, imaged in synchrotron X-ray beamlines while oscillating up to 400 Hz, thanks to a custom-developed vibromechanical device. This approach yields homogeneous angular sampling of projections and gives reliable predictions of image quality degradation due to motion blur. By overcoming several technical and scientific barriers limiting the feasibility and reproducibility of such investigations, we provide guidelines to enhance gated-CT 4D imaging for the analysis of heterogeneous, high-frequency oscillating materials.

Linear diffusion noise boosted deep image prior for unsupervised sparse-view CT reconstruction

Objective. Deep learning has markedly enhanced the performance of sparse-view computed tomography reconstruction. However, the dependence of these methods on supervised training using high-quality paired datasets, and the necessity for retraining under varied physical acquisition conditions, constrain their generalizability across new imaging contexts and settings. Approach. To overcome these limitations, we propose an unsupervised approach grounded in the deep image prior framework. Our approach advances beyond the conventional single noise level input by incorporating multi-level linear diffusion noise, significantly mitigating the risk of overfitting. Furthermore, we embed non-local self-similarity as a deep implicit prior within a self-attention network structure, improving the model’s capability to identify and utilize repetitive patterns throughout the image. Additionally, leveraging imaging physics, gradient backpropagation is performed between the image domain and projection data space to optimize network weights. Main Results. Evaluations with both simulated and clinical cases demonstrate our method’s effective zero-shot adaptability across various projection views, highlighting its robustness and flexibility. Additionally, our approach effectively eliminates noise and streak artifacts while significantly restoring intricate image details. Significance. Our method aims to overcome the limitations in current supervised deep learning-based sparse-view CT reconstruction, offering improved generalizability and adaptability without the need for extensive paired training data.

Comparison of golden-angle radial sparse parallel (GRASP) and conventional cartesian sampling in 3D dynamic contrast-enhanced mri for bladder cancer: a preliminary study.

To compare the image quality, inter-reader agreement, and diagnostic capability for muscle-invasive bladder cancer (MIBC) of the reconstructed images in sections orthogonal to the bladder tumor obtained by 3D Dynamic contrast-enhanced (DCE)-MRI using the Golden-angle Radial Sparse Parallel (GRASP) technique with the images directly captured using the Cartesian sampling. This study involved 68 initial cases of bladder cancer examined with DCE-MRI (GRASP: n = 34, Cartesian: n = 34) at 3 Tesla. Four radiologists conducted qualitative evaluations (overall image quality, absence of motion artifact, absence of streak artifact, and tumor conspicuity) using a five-point Likert scale (5 = Excellent/None) and quantitative signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements. The areas under the receiver-operating characteristic curves (AUCs) for the Vesical Imaging-Reporting and Data System (VI-RADS) DCE score for MIBC assessment were calculated. Inter-reader agreement was also assessed. GRASP notably enhanced overall image quality (pooled score: GRASP 4 vs. Cartesian 3, P < 0.0001), tumor conspicuity (5 vs. 3, P < 0.05), SNR (Median 38.2 vs. 19.0, P < 0.0001), and CNR (7.9 vs. 6.0, P = 0.005), with fewer motion artifacts (5 vs. 3, P < 0.0001) and minor streak artifacts (5 vs. 5, P > 0.05). Although no significant differences were observed, the GRASP group tended to have higher AUCs for MIBC (pooled AUCs: 0.92 vs. 0.88) and showed a trend toward higher inter-reader agreement (pooled kappa-value: 0.70 vs. 0.63) compared to the Cartesian group. Using the GRASP for 3D DCE-MRI, the reconstructed images in sections orthogonal to the bladder tumor achieved higher image quality and improve the clinical work flow, compared to the images directly captured using the Cartesian. GRASP tended to have higher diagnostic ability for MIBC and showed a trend toward higher inter-reader agreement compared to the Cartesian.

Artificial intelligence iterative reconstruction in abdominal CT of patients with irregular arm positioning: a case-by-case evaluation.

Streak artifacts induced by irregular arm positioning have been an issue in diagnosing the abdomen. To illustrate the risk of misdiagnosis in abdominal computed tomography (CT) of patients with irregular arm positioning through a case-by-case evaluation and to test if it can be solved by the artificial intelligence iterative reconstruction (AIIR) algorithm. By reviewing 5220 cases of chest and thoracoabdominal CT, 64 patients with irregular arm positioning were enrolled, whose image data were reconstructed using AIIR in addition to routine hybrid iterative reconstruction (HIR). Lesion detection for livers, spleens, kidneys, gallbladders, and pancreas on AIIR images, performed by two radiologists, was compared with those on HIR images. Discrepancies arising from AIIR images included both cases with additional abnormalities and those with corrections made on previous detections. For cases with discrepancies, artifact scores for organs where discrepancies were found, and contrast-to-noise ratios (CNRs) of cysts with discrepancies were compared between two image sets. Additional abnormalities were detected for 15 cases: additional liver cirrhosis (n=2); additional gallbladder stone (n=1); additional cholecystitis (n=1), additional spleen nodule (n=1); additional kidney cysts (n=8); additional liver cysts (3); and additional spleen cyst (n=1). A spleen contusion was corrected for one case. All involved artifact scores were improved on AIIR images. CNRs of involved liver, kidney, and spleen cysts were improved by up to 539.7%, 538.5%, and 245.5%, respectively. Irregular arm positioning may induce a variety of misdiagnoses in abdominal CT, which is almost totally avoidable by the AIIR algorithm.

Radiomic feature robustness in CT scans affected by fiducial marker induced streak artifacts for patients with hepatocellular carcinoma.

Stereotactic body radiation therapy for hepatocellular carcinoma necessitates the implantation of gold fiducial markers in the liver, resulting in artifacts on computed tomography (CT) scans, which affect radiomic feature values. This report aims to assess the effect of these artifacts on radiomic features and how removing CT slices affects radiomic features extracted from 3D regions of interest (ROI). First, the range variation in 38 tumor contours unaffected by artifacts was assessed after sequentially and randomly removing 25%, 50%, 75% of slices. Subsequently, the agreement of feature values before and after removing ROI slices containing artifacts from 186 patients' CT scans was assessed with Lin's concordance correlation coefficient (CCC) and a Wilcoxon signed-rank test. In artifact-free tumor volumes, at least 71% of features remain robust with up to 50% of slices removed, while 56% remains robust with up to 75% of slices removed. When comparing contours before and after removing slices containing artifacts, around a third of features in the tumor contour and surrounding area remain robust (CCC>0.9), compared to 44% in the healthy liver. Concerning the tumor, 13% (Gray Level Size Zone Matrix) to 61% (first order) of the features remain robust (CCC>0.9). Over 90% of features differ significantly as assessed by Wilcoxon signed-rank test, however. This study demonstrates that removing slices containing artifacts is a feasible solution for the CT fiducial problem in this patient population and provides insight into which features are affected.

Polyenergetic reconstruction mitigates streak artifacts by dual source imaging in chest photon counting detector computed tomography

ObjectiveThis study aims to assess the efficacy of polyenergetic reconstruction methods in reducing streak artifacts caused by dual source imaging in Photon Counting Detector Computed Tomography (PCD-CT) imaging, thereby improving image quality and diagnostic accuracy. MethodsA retrospective cohort study was conducted, involving 50 patients who underwent chest Computed Tomography Angiography with PCD-CT, focusing on those with streak artifacts. Quantitative and qualitative analyses were performed on images reconstructed using monoenergetic and polyenergetic techniques. Quantitative evaluations measured the attenuation of tracheal air density in regions affected by streak artifacts, while qualitative assessments employed a modified Likert scale to rate image quality. Statistical analyses included Wilcoxon's signed-rank tests and Spearman's correlation, alongside assessments of inter-rater reliability. ResultsThere was significantly lower attenuation of tracheal air density on the polyenergetic reconstructions (Median − 1010 ± 62 HU vs −930 ± 110 HU; P < 0.001), and significantly decreased variation on the polyenergetic reconstructions (Median 65.2 ± 79.5 HU vs 38.8 ± 33.9 HU; P < 0.001). The median modified-Likert scale were significantly better for the polyenergetic reconstructions (median modified-Likert 4 ± 0.5 vs 2.5 ± 1; P < 0.001). The inter-rater agreement was substantial and not significantly different between reconstructions (Gwet's ACPolyenergetic = 0.78 vs Gwet's ACVMI = 0.775). ConclusionPolyenergetic reconstruction significantly mitigates streak artifacts in PCD-CT imaging, enhancing quantitative and qualitative image quality. This advancement addresses a known limitation of current PCD-CT reconstruction techniques, offering a promising approach to improving diagnostic reliability and accuracy in clinical practice. We demonstrate that future software implementations can resolve this artifact.

Streak artefact removal in x-ray dark-field computed tomography using a convolutional neural network.

Computed tomography (CT) relies on the attenuation of x-rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X-ray dark-field (DF) imaging is a recently developed technology that utilizes x-ray optical gratings to enable small-angle scattering as an alternative contrast mechanism. The DF signal provides structural information about the micromorphology of an object, complementary to the conventional attenuation signal. A first human-scale x-ray DF CT has been developed by our group. Despite specialized processing algorithms, reconstructed images remain affected by streaking artifacts, which often hinder image interpretation. In recent years, convolutional neural networks have gained popularity in the field of CT reconstruction, amongst others for streak artefactremoval. Reducing streak artifacts is essential for the optimization of image quality in DF CT, and artefact free images are a prerequisite for potential future clinical application. The purpose of this paper is to demonstrate the feasibility of CNN post-processing for artefact reduction in x-ray DF CT and how multi-rotation scans can serve as a pathway for trainingdata. We employed a supervised deep-learning approach using a three-dimensional dual-frame UNet in order to remove streak artifacts. Required training data were obtained from the experimental x-ray DF CT prototype at our institute. Two different operating modes were used to generate input and corresponding ground truth data sets. Clinically relevant scans at dose-compatible radiation levels were used as input data, and extended scans with substantially fewer artifacts were used as ground truth data. The latter is neither dose-, nor time-compatible and, therefore, unfeasible for clinical imaging ofpatients. The trained CNN was able to greatly reduce streak artifacts in DF CT images. The network was tested against images with entirely different, previously unseen image characteristics. In all cases, CNN processing substantially increased the image quality, which was quantitatively confirmed by increased image quality metrics. Fine details are preserved during processing, despite the output images appearing smoother than the ground truthimages. Our results showcase the potential of a neural network to reduce streak artifacts in x-ray DF CT. The image quality is successfully enhanced in dose-compatible x-ray DF CT, which plays an essential role for the adoption of x-ray DF CT into modern clinicalradiology.

One-step inverse generation network for sparse-view dual-energy CT reconstruction and material imaging

Objective. Sparse-view dual-energy spectral computed tomography (DECT) imaging is a challenging inverse problem. Due to the incompleteness of the collected data, the presence of streak artifacts can result in the degradation of reconstructed spectral images. The subsequent material decomposition task in DECT can further lead to the amplification of artifacts and noise. Approach. To address this problem, we propose a novel one-step inverse generation network (OIGN) for sparse-view dual-energy CT imaging, which can achieve simultaneous imaging of spectral images and materials. The entire OIGN consists of five sub-networks that form four modules, including the pre-reconstruction module, the pre-decomposition module, and the following residual filtering module and residual decomposition module. The residual feedback mechanism is introduced to synchronize the optimization of spectral CT images and materials. Main results. Numerical simulation experiments show that the OIGN has better performance on both reconstruction and material decomposition than other state-of-the-art spectral CT imaging algorithms. OIGN also demonstrates high imaging efficiency by completing two high-quality imaging tasks in just 50 seconds. Additionally, anti-noise testing is conducted to evaluate the robustness of OIGN. Significance. These findings have great potential in high-quality multi-task spectral CT imaging in clinical diagnosis.

DdeNet: A dual-domain end-to-end network combining Pale-Transformer and Laplacian convolution for sparse view CT reconstruction

Sparse view (SV) computed tomography (CT) is a clinical diagnostic technique aimed at reducing radiation dose to the human body from X-rays. However, SVCT reconstructed using conventional filtered back-projection (FBP) algorithms will produce severe streak artifacts. In recent years, convolutional neural networks (CNNs) have demonstrated considerable success in addressing the inverse problem of SVCT reconstruction. However, the local convolutional operations of CNNs ignores long-range dependencies of contextual feature information, thereby limiting their capability in feature extraction. To address this limitation, we propose a dual-domain end-to-end network (DdeNet) combining Pale-Transformer and Laplacian convolution for SVCT reconstruction. Initially, in the projection domain, a UNet is employed to restore the interpolated sinogram. Subsequently, in the image domain, we introduce Pale-Transformer and Laplacian convolution to design a dual-stream feature fusion network for finely restoring the CT images reconstructed by FBP. The dual-stream feature fusion network comprises an image restoration network (IRN) module and an edge enhancement network (EEN) module. The depthwise separable convolution based modified pale-shaped self-attention mechanism in Pale-Transformer captures global feature information of the CT image in the IRN, while the Laplacian convolution and multi-scale convolution block with a squeeze and excitation mechanism in the EEN further extract and restore edge feature information of the CT image. Finally, the feature information output from IRN and EEN is fused along the channel dimension and then convolved to obtain the final reconstructed CT image. Experimental results demonstrate that DdeNet exhibits superior generalization and reconstruction performance compared to the previous CNN or Transformer-based reconstruction methods.