CT Projection Research Articles

Reconstruction of blood flow velocity with deep learning information fusion from spectral ct projections and vessel geometry

In this work, we investigate a new deep learning reconstruction method of blood flow velocity within deformed vessels from contrast enhanced X-ray projections and vessel geometry. The principle of the method is to perform linear or nonlinear dimension reductions on the Radon projections and on the mesh of the vessel. These low dimensional projections are then fused to obtain the velocity field in the vessel. The accuracy of the reconstruction method is proved using various neural network architectures with realistic unsteady blood flows. The approach leverages the vessel geometry information and outperforms the simple PCA-net.

Optimal weighting strategies for maximizing contrast-to-noise ratio in photon counting CT images.

Photon counting detectors (PCDs) with energy discrimination capabilities have the potential to generate grayscale CT images with improved contrast-to-noise ratio (CNR) through optimal weighting of their spectral measurements. This study evaluates the CNR performance of grayscale CT projections and images generated from spectral measurements of PCDs using three energy-weighting strategies: pre-log weighting, post-log weighting, and material decomposition (MD) weighting. This study provides the expressions of optimal weights and maximum achievable CNR of these energy-weighting strategies, which only require the knowledge of detected bin counts and do not require information of PCD energy responses or imaging techniques. We defined and solved a generalized eigenvalue problem to obtain the maximum achievable CNR in the projection domain for low-contrast tasks using three energy-weighting strategies: pre-log weighting (weighted sum of energy bin counts), post-log weighting (weighted sum of line integrals), and MD weighting (weighted sum of basis material thicknesses, which is equivalent to virtual monoenergetic images [VMIs]). These expressions only contain energy bin counts from PCD measurements. We used a realistic PCD energy response model to simulate the detected bin counts and conducted Monte Carlo simulations of different contrast tasks and phantoms to evaluate the projection- and image-domain CNR performance of these energy-weighting strategies. Additionally, the total counts method (a special case of pre-log weighting with unity weights) was included for comparison. We also conducted Gammex head and body phantom scans on an edge-on-irradiated silicon PCCT prototype to evaluate the image-domain CNR performance of these energy-weighting strategies. The results show that pre-log, post-log, and MD weighting strategies generate approximately equal projection-domain maximum achievable CNR, with a difference of less than 2%, and outperform the total counts method. These three energy-weighting strategies also generate approximately equal image-domain maximum CNR when the contrast task is located at the center of a homogeneous phantom. Pre-log weighting generates the highest image-domain CNR for an off-center contrast task location or inhomogeneous phantoms while also outperforming the total counts method. We derived the expression of projection-domain maximum achievable CNR using three energy-weighting strategies. Our results suggest that using pre-log weighting strategies enables fast grayscale CT image generation with high CNR from spectral PCD measurements for inhomogeneous phantoms and off-center region of interests (ROIs).

Time varying flat field correction of X-ray microtomography with an improved deep-learning method.

In X-ray microtomography, the flat field image is usually needed to normalize the collected sample projections. Owing to the high brightness of the synchrotron radiation facility, dynamic CT imaging of in-situ or in-operando processes is broadly employed for the investigation of three-dimensional microstructure evolution. However, the fast, continuous data acquisition and the heavy, bulky in-situ devices usually prevent the easy collection of accurate flat field images, which means that conventional flat field correction is hard to efficiently correct the artefacts of X-ray microtomography. We report a deep-learning-based artefact correction method for X-ray microtomography, which uses flat field generated from each CT projection by an improved pix2pixHD model. Experimental results demonstrated that the proposed method has a significant advantage over the conventional method and available deep-learning-based flat field correction method for the flat field correction of projection images. The CT imaging results show that the proposed method efficiently reduces the systematic error during the intensity normalization process, and the CT reconstruction is improved significantly. Therefore, the method developed in this paper is applicable for the flat field correction of dynamic CT. Furthermore, experiments with a set of low Z material samples verified the generalization of the deep-learning-based method for a variety of samples never used for network training. In conclusion, the method developed in this paper is practicable for the flat field correction of in-situ CT imaging of dynamic processes and is also applicable to new samples as long as the neural network model is effectively trained.

A Denoising Diffusion Probabilistic Model for Metal Artifact Reduction in CT.

The presence of metal objects leads to corrupted CT projection measurements, resulting in metal artifacts in the reconstructed CT images. AI promises to offer improved solutions to estimate missing sinogram data for metal artifact reduction (MAR), as previously shown with convolutional neural networks (CNNs) and generative adversarial networks (GANs). Recently, denoising diffusion probabilistic models (DDPM) have shown great promise in image generation tasks, potentially outperforming GANs. In this study, a DDPM-based approach is proposed for inpainting of missing sinogram data for improved MAR. The proposed model is unconditionally trained, free from information on metal objects, which can potentially enhance its generalization capabilities across different types of metal implants compared to conditionally trained approaches. The performance of the proposed technique was evaluated and compared to the state-of-the-art normalized MAR (NMAR) approach as well as to CNN-based and GAN-based MAR approaches. The DDPM-based approach provided significantly higher SSIM and PSNR, as compared to NMAR (SSIM: p [Formula: see text]; PSNR: p [Formula: see text]), the CNN (SSIM: p [Formula: see text]; PSNR: p [Formula: see text]) and the GAN (SSIM: p [Formula: see text]; PSNR: p <0.05) methods. The DDPM-MAR technique was further evaluated based on clinically relevant image quality metrics on clinical CT images with virtually introduced metal objects and metal artifacts, demonstrating superior quality relative to the other three models. In general, the AI-based techniques showed improved MAR performance compared to the non-AI-based NMAR approach. The proposed methodology shows promise in enhancing the effectiveness of MAR, and therefore improving the diagnostic accuracy of CT.

Noise characterization analysis of dynamic dual-energy CT and its advantage in suppressing statistical noise

Objective.Multi-energy CT conducted by photon-counting detector has a wide range of applications, especially in multiple contrast agents imaging. However, static multi-energy (SME) CT imaging suffers from higher statistical noise because of increased energy bins with static energy thresholds. Our team has proposed a dynamic dual-energy (DDE) CT detector model and the corresponding iterative reconstruction algorithm to solve this problem. However, rigorous and detailed analysis of the statistical noise characterization in this DDE CT was lacked.Approach.Starting from the properties of the Poisson random variable, this paper analyzes the noise characterization of the DDE CT and compares it with the SME CT. It is proved that the multi-energy CT projections and reconstruction images calculated from the proposed DDE CT algorithm have less statistical noise than that of the SME CT.Main results.Simulations and experiments verify that the expectations of the multi-energy CT projections calculated from DDE CT are the same as those of the SME projections. Still, the variance of the former is smaller. We further analyze the convergence of the iterative DDE CT algorithm through simulations and prove that the derived noise characterization can be realized under different CT imaging configurations.Significance.The low statistical noise characteristics demonstrate the value of DDE CT imaging technology.

An efficient dual-domain deep learning network for sparse-view CT reconstruction

Background and ObjectiveWe develop an efficient deep-learning based dual-domain reconstruction method for sparse-view CT reconstruction with small training parameters and comparable running time. We aim to investigate the model's capability and its clinical value by performing objective and subjective quality assessments using clinical CT projection data acquired on commercial scanners. MethodsWe designed two lightweight networks, namely Sino-Net and Img-Net, to restore the projection and image signal from the DD-Net reconstructed images in the projection and image domains, respectively. The proposed network has small training parameters and comparable running time among dual-domain based reconstruction networks and is easy to train (end-to-end). We prospectively collected clinical thoraco-abdominal CT projection data acquired on a Siemens Biograph 128 Edge CT scanner to train and validate the proposed network. Further, we quantitatively evaluated the CT Hounsfield unit (HU) values on 21 organs and anatomic structures, such as the liver, aorta, and ribcage. We also analyzed the noise properties and compared the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) of the reconstructed images. Besides, two radiologists conducted the subjective qualitative evaluation including the confidence and conspicuity of anatomic structures, and the overall image quality using a 1–5 likert scoring system. ResultsObjective and subjective evaluation showed that the proposed algorithm achieves competitive results in eliminating noise and artifacts, restoring fine structure details, and recovering edges and contours of anatomic structures using 384 views (1/6 sparse rate). The proposed method exhibited good computational cost performance on clinical projection data. ConclusionThis work presents an efficient dual-domain learning network for sparse-view CT reconstruction on raw projection data from a commercial scanner. The study also provides insights for designing an organ-based image quality assessment pipeline for sparse-view reconstruction tasks, potentially benefiting organ-specific dose reduction by sparse-view imaging.

Two-view topogram-based anatomy-guided CT reconstruction for prospective risk minimization

To facilitate a prospective estimation of the effective dose of an CT scan prior to the actual scanning in order to use sophisticated patient risk minimizing methods, a prospective spatial dose estimation and the known anatomical structures are required. To this end, a CT reconstruction method is required to reconstruct CT volumes from as few projections as possible, i.e. by using the topograms, with anatomical structures as correct as possible. In this work, an optimized CT reconstruction model based on a generative adversarial network (GAN) is proposed. The GAN is trained to reconstruct 3D volumes from an anterior-posterior and a lateral CT projection. To enhance anatomical structures, a pre-trained organ segmentation network and the 3D perceptual loss are applied during the training phase, so that the model can then generate both organ-enhanced CT volume and organ segmentation masks. The proposed method can reconstruct CT volumes with PSNR of 26.49, RMSE of 196.17, and SSIM of 0.64, compared to 26.21, 201.55 and 0.63 using the baseline method. In terms of the anatomical structure, the proposed method effectively enhances the organ shapes and boundaries and allows for a straight-forward identification of the relevant anatomical structures. We note that conventional reconstruction metrics fail to indicate the enhancement of anatomical structures. In addition to such metrics, the evaluation is expanded with assessing the organ segmentation performance. The average organ dice of the proposed method is 0.71 compared with 0.63 for the baseline model, indicating the enhancement of anatomical structures.

Tomographic analysis of segregation behavior of hollow glass microspheres in lightweight cementitious composites

Lightweight concrete must contain lightweight aggregates that are more susceptible to segregation than traditional concrete. Concrete has increasingly used lightweight fillers but research on segregation analysis is relatively scarce. To systematically investigate the segregation of hollow glass microspheres (HGMs), lightweight cementitious composites (LWCC) with a water-to-cement (w/c) ratio of 0.25–0.5 were investigated. The 3D structure of HGMs in the LWCC was observed with micro-computed tomography (μ-CT), and a rapid CT projection analysis was proposed to evaluate the segregation. The results showed that a slight segregation tendency of HGMs was observed from 0.45, while severe floating segregation occurred at 0.5 of w/c. The flowability of the LWCC increased with the increase of the w/c ratio, but it could not anticipate the segregation behavior. Meanwhile, 2D CT projection image could quickly evaluate the segregation phenomenon so it was proposed that it can be utilized for evaluating potential segregation of cement-based materials.

Denoising of CT Projections Exploiting Spectral and Angular Information for Multispectral Computed Tomography

Denoising of CT Projections Exploiting Spectral and Angular Information for Multispectral Computed Tomography

Quantitative analysis of small coronary artery calcium detectability with an accurate simulation and validation on a clinical CT scanner.

The absence of coronary artery calcium (CAC) measured via CT is associated with very favorable prognosis, and current guidelines recommend low-density lipoprotein cholesterol (LDL-c) lowering therapy for individuals with any CAC. This motivates early detection of small granules of CAC; however, calcium scan sensitivity for detecting very low levels of calcium has not been quantified. In this work, the size limit of detectability of small calcium hydroxyapatite (CaHA) granules with clinical CAC scanning was assessed using validated simulations. CT projections of digital 3D mathematical phantoms containing small CaHA granules were simulated analytically; images were reconstructed using a filter designed to reproduce the point spread function of a specific commercial scanner, and a relationship of HU number versus diameter was derived. These simulation results were validated with experimental measurements of HU versus diameter from phantoms containing small granules of CaHA on a GE Revolution CT scanner in the clinic; ground truth measurements of the CaHA granule diameters were obtained using a Zeiss Xradia 510 Versa high-resolution 3D micro-CT imaging system. Using experimental measurements on the clinical CT scanner, detectability was quantified with a detectability index (d') using a non-prewhitened matched filter. The effect of changes to reconstruction slice thickness and reconstruction kernel on granule detectability was evaluated. Under typical clinical calcium scanning and reconstruction conditions, the minimum detectable diameter of a simulated spherical calcium granule with a clinically relevant CaHA density was 0.76mm. The minimum detectable volume was 2.4 times smaller on images reconstructed at a slice thickness of 0.625mm compared to 2.5mm. The detectability index d' increased by a factor of 1.7 when images were reconstructed with 0.625mm slices compared to 2.5mm slices. d' did not change when images were reconstructed with the high-resolution BONE filter compared to the less sharp STANDARD resolution filter on the GE Revolution CT. We have quantified detectability versus size of small calcium granules at the resolution limit of a widely available clinical CT scanner. Detectability increased significantly with reduced slice thickness and did not change with a sharper reconstruction kernel. The simulation can be used to calculate the trade-off between dose and CAC detectability.

Real-Time 4-D-Cone Beam CT Accurate Estimation Based on Single-Angle Projection via Dual-Attention Mechanism Residual Network

Recently rapid and efficient localization of lung tumors based on single cone beam CT (CBCT) projection has attracted great interest in the radiation oncology community. High quality 4D-CBCT benefits accurate monitoring of moving targets during radiation therapy (RT). However, conventional iterative 4D-CBCT reconstruction process is heavily time-consuming. To address these issues, a motion sensitive cascade model was proposed. The proposed cascade model is composed of dual attention mechanism together with residual network (DA-ResNet) and a principal component analysis (PCA) model. It maps single projection from different breathing phase to each phase 3D-CBCT for achieving real time 4D-CBCT. The dual attention mechanism focuses on the motion information of both low-level features and high-level features to improve accuracy and efficiency of the network. The PCA model ensures a real time motion representation scheme. Compared with state-of-the-art networks, the proposed method outperformed them in the quantification labels of mean absolute error (MAE), R-squared (R2), normalized cross correlation (NCC), and structural similarity index measure (SSIM). This experiment was verified both on simulation and clinical data to support the conclusion.

Towards pedagogical content knowledge learning trajectories: tracing elementary teachers’ infusion of computational thinking

ABSTRACT This paper documents the Making CT researcher-practitioner partnership, designed to support in-service elementary teachers in understanding and integrating computational thinking into their disciplinary teaching. Drawing from this collaborative work with teachers, over a sustained time, we describe the shifts in teachers’ integration of CT into their teaching, as well as shifts in their abilities to attend to children’s development of CT skills. In doing so, we have developed a unique construct – pedagogical content knowledge learning trajectories – which draws from literature on learning trajectories, the use of learning trajectories for teacher professional development, and domains of knowledge for teaching. Operationalizing our construct, we propose two specific learning trajectories – first, for integrating CT into disciplinary teaching, and second, for attending to students’ computational thinking – which we describe and illustrate using data from the Making CT teachers. This includes a description of professional learning experiences that supported teacher growth on each of the two learning trajectories, thus explicating the knowledge gained and classroom impact from participation in the Making CT project. Our learning trajectories can be used to plan for CT-specific teacher professional development and our pedagogical content knowledge learning trajectory construct can be applied to additional contexts.

Visualization and comparison of CT dose distribution between axial and helical acquisitions.

It is challenging to assess the accuracy of volume CT Dose Index (CTDIvol ) when the axial scan modes corresponding to a helical scan protocol are not available. An alternative approach was proposed to directly measure using helical acquisitions and relatively small differences (<20%) from CTDIvol were observed. To visually demonstrate the 3D dose distribution for both axial and helical CT acquisitions and quantitively compare and CTDIvol . 3D dose distribution within the standard CTDI phantoms (16 and 32cm diameter) from a single CT projection, Dp (x,y,z) was first generated using Monte Carlo simulation (GEANT4) with 9×108 photons per combination of tube voltage (80-140kV), collimation width (1-8cm), and z-axis location of the central ray of the x-ray beam, with a spatial resolution of 1 mm3 . These dose distributions from one single projection were analytically ensembled to simulate 3D dose volumes DA (x,y,z) and DH (x,y,z) for axial and helical scans, respectively, with different helical pitches (0.3-2) and scan lengths (100-150mm). 2D planar dose distributions were obtained by integrating the inside 100mm of the dose volumes. CTDIvol and were calculated using the planar dose data at corresponding pencil chamber locations and the percentage differences (PD) were reported. High spatial resolution 3D CT dose volumes were generated and visualized. PDs between and CTDIvol had strong dependency on scan length and peripheral chamber locations, with subtle dependency on collimation width and pitch. PDs were mostly within the range of±3% for a scan length of 150mm with four peripheral chamber locations. With a scan length covering the entire phantom length, directly measured from helical scans can serve as an alternative to CTDIvol only if all four peripheral locations were measured.

Noise and blur removal from corrupted X-ray computed tomography scans: A multilevel and multiscale deep convolutional framework approach with synthetic training data (BAM SynthCOND)

Regardless of the experimental care practiced in acquiring X-ray computed tomography (XCT) data, artifacts might still exist, such as noise and blur. This is typical for fast XCT data acquisitions (e.g., in-situ investigations), or low-dose XCT. Such artifacts can complicate subsequent analysis of the data. Digital filters can moderately cure extensive artifacts. The selection of filter type, intensity, and order of application is not always straightforward. To tackle these problems, a complete sequential multilevel, multi-scale framework: BAM SynthCOND, employing newly designed deep convolutional neural networks (DCNNs), was formulated. Although data conditioning with neural networks is not uncommon, the main complication is that completely artifact-free XCT data for training do not exist. Thus, training data were acquired from an in-house developed library (BAM SynthMAT) capable of generating synthetic XCT material microstructures. Some novel DCNN architectures were introduced (2D/3D ACEnet_Denoise, 2D/3D ACEnet_Deblur) along with the concept of Assertive Contrast Enhancement (ACE) training, which boosts the performance of neural networks trained with continuous loss functions. The proposed methodology accomplished very good generalization from low resemblance synthetic training data. Indeed, denoising, sharpening (deblurring), and even ring artifact removal performance were achieved on experimental post-CT scans of challenging multiphase Al-Si Metal Matrix Composite (MMC) microstructures. The conditioning efficiencies were: 92% for combined denoising/sharpening, 99% for standalone denoising, and 95% for standalone sharpening. The results proved to be independent of the artifact intensity. We believe that the novel concepts and methodology developed in this work can be directly applied on the CT projections prior to reconstruction, or easily be extended to other imaging techniques such as: Microscopy, Neutron Tomography, Ultrasonics, etc.

Follow the hand that feeds you? The effects of non‐governmental cash transfers on citizenship

AbstractCash transfers (CT) are increasingly used as fundamental components of countries' poverty reduction and social protection strategies. Yet not much is known about how these interventions shape beneficiaries' relations with the state and citizenship in general, and even less evidence is available on how CTs distributed by non‐state actors affect the aforementioned variables. This paper analyses how a non‐governmental universal unconditional mobile CT (a small‐scale universal basic income experiment), handed out by a foreign NGO in a rural Ugandan village, influenced citizenship. The article zooms in on effects on recipients' perceptions of the state and interactions with government representatives, where both could potentially yield repercussions on the broader social contract. The study implements matching techniques and social network analysis to explore how the transfer influenced citizenship over time. Results suggest that the CT generated a positive effect on the non‐state actor's legitimacy, without necessarily causing a lasting impact on the legitimacy of the state. Major impacts were recorded in the treatment village's ‘call‐to‐action’ network, highlighting patterns of change in beneficiaries' relations with local duty bearers, such as the crowding out of (multiple) local leaders by an external actor linked to the CT project. These findings confirm the possibility of inducing unintended effects in local communities through CT initiatives implemented by non‐state actors.

Sparse-View Cone Beam CT Reconstruction Using Data-Consistent Supervised and Adversarial Learning From Scarce Training Data

Reconstruction of CT images from a limited set of projections through an object is important in several applications ranging from medical imaging to industrial settings. As the number of available projections decreases, traditional reconstruction techniques such as the FDK algorithm and model-based iterative reconstruction methods perform poorly. Recently, data-driven methods such as deep learning-based reconstruction have garnered a lot of attention in applications because they yield better performance when enough training data is available. However, even these methods have their limitations when there is a scarcity of available training data. This work focuses on image reconstruction in such settings, i.e., when both the number of available CT projections and the training data is extremely limited. We adopt a sequential reconstruction approach over several stages using an adversarially trained shallow network for ‘destreaking’ followed by a data-consistency update in each stage. To deal with the challenge of limited data, we use image subvolumes to train our method, and patch aggregation during testing. To deal with the computational challenge of learning on 3D datasets for 3D reconstruction, we use a hybrid 3D-to-2D mapping network for the ‘destreaking’ part. Comparisons to other methods over several test examples indicate that the proposed method has much potential, when both the number of projections and available training data are highly limited.

POD method for acceleration of blood flow reconstruction in a vessel with contrast enhanced X-ray CT

ABSTRACT The reconstruction of blood velocity in a vessel from contrast-enhanced X-ray CT projections is a complex inverse problem. In this work, we investigate an inversion approach based on the adjoint method with a partial differential equation as constraint. The proper orthogonal decomposition method is used to accelerate the reconstruction with reduced basis for the velocity, the density and the adjoint method. The effectiveness of the reconstruction method is shown with simulated realistic stationary and non-stationary blood flows in a vessel.

Lung Tumor Motion and its Impact on Deep Learning Prediction of Local Recurrence

<h3>Purpose/Objective(s)</h3> In order for deep learning (DL) models to successfully predict local recurrence of lung tumors from image-based features, uncertainties of the model must be quantified. One potential source of uncertainty for the DL model is lung tumor motion. We aimed to evaluate the association of motion magnitude with the prediction accuracy of the DL model. We hypothesized that knowledge of motion will help quantify the uncertainties of prediction and guide the use of individualized risk predictions for lung radiotherapy. <h3>Materials/Methods</h3> Physician delineated gross tumor volume (GTV) of 19 patients were propagated to their corresponding 10 breathing phases of the 4DCT images using a commercial treatment planning system. A programming environment code was used to extract the contours and determine the centroid and volume bounded by the contours. Motion was determined from the displacement of the GTV centroid. The Euclidean distance was calculated and used as an aggregate measure of tumor motion magnitude. The 4DCT, free-breathing CT (FBCT) and maximum-intensity projection (MIP) images of the 18 patients were used as input to a pre-trained DL model. The 8-layer DL model was previously trained to predict local recurrence using FBCT images of 933 lung patients. Correlation between motion magnitude and prediction accuracy was calculated for 4DCT images in each respiratory phase, FBCT and MIP images. <h3>Results</h3> Tumor movement with a mean exertion of 1 cm [IQR: 0.72-1.53] was observed from the cohort with 47.4 % of patients subjected to abdominal compression, and 57.9% of patients had peripheral lung tumors. The standard deviation of DL scores obtained from each 4DCT image correlated with the magnitude of tumor motion (R² =0.45, p=0.005). Higher uncertainty in DL scores was attributed to the larger tumor motion. The average DL score obtained from the 10 phases of the 4DCT images of each patient was (i) highly associated with the DL scores obtained from the FBCT images (r = 0.99; p<0.0001), and (ii) closest to the DL score obtained with the end-inspiration images. In comparing the prediction performance across image acquisition types, MIP images yields a higher DL score compared to FBCT (<i>p</i><0.0001) or 4DCT images (<i>p</i> =<0.0001). <h3>Conclusion</h3> The impact of tumor motion on the range of DL scores for prediction of local recurrence was reported for the first time. Standard deviations of the DL scores increased with motion magnitude. However, when DL score was measured and averaged across the 10 phases of the 4DCT images, DL score correlated well with those obtained from the FBCT images. This preliminary finding suggests that despite the increased variation with larger motion, the DL score, when averaged across the sinusoidal breathing trajectory, correlates with the DL score obtained from the FBCT images that were used for treatment planning. However, caution should be exercised when using MIP images for local recurrence prediction since a higher DL score than the FBCT images used for planning will be observed.

Diagnostic Performance in Low- and High-Contrast Tasks of an Image-Based Denoising Algorithm Applied to Radiation Dose-Reduced Multiphase Abdominal CT Examinations.

BACKGROUND. Anatomic redundancy between phases can be used to achieve denoising of multiphase CT examinations. A limitation of iterative reconstruction (IR) techniques is that they generally require use of CT projection data. A frequency-split multi-band-filtration algorithm applies denoising to the multiphase CT images themselves. This method does not require knowledge of the acquisition process or integration into the reconstruction system of the scanner, and it can be implemented as a supplement to commercially available IR algorithms. OBJECTIVE. The purpose of the present study is to compare radiologists' performance for low-contrast and high-contrast diagnostic tasks (i.e., tasks for which differences in CT attenuation between the imaging target and its anatomic background are subtle or large, respectively) evaluated on multiphase abdominal CT between routine-dose images and radiation dose-reduced images processed by a frequency-split multiband-filtration denoising algorithm. METHODS. This retrospective single-center study included 47 patients who underwent multiphase contrast-enhanced CT for known or suspected liver metastases (a low-contrast task) and 45 patients who underwent multiphase contrast-enhanced CT for pancreatic cancer staging (a high-contrast task). Radiation dose-reduced images corresponding to dose reduction of 50% or more were created using a validated noise insertion technique and then underwent denoising using the frequency-split multi-band-filtration algorithm. Images were independently evaluated in multiple sessions by different groups of abdominal radiologists for each task (three readers in the low-contrast arm and four readers in the high-contrast arm). The noninferiority of denoised radiation dose-reduced images to routine-dose images was assessed using the jackknife alternative free-response ROC (JAFROC) figure-of-merit (FOM; limit of noninferiority, -0.10) for liver metastases detection and using the Cohen kappa statistic and reader confidence scores (100-point scale) for pancreatic cancer vascular invasion. RESULTS. For liver metastases detection, the JAFROC FOM for denoised radiation dose-reduced images was 0.644 (95% CI, 0.510-0.778), and that for routine-dose images was 0.668 (95% CI, 0.543-0.792; estimated difference, -0.024 [95% CI, -0.084 to 0.037]). Intraobserver agreement for pancreatic cancer vascular invasion was substantial to near perfect when the two image sets were compared (κ = 0.53-1.00); the 95% CIs of all differences in confidence scores between image sets contained zero. CONCLUSION. Multiphase contrast-enhanced abdominal CT images with a radiation dose reduction of 50% or greater that undergo denoising by a frequency-split multiband-filtration algorithm yield performance similar to that of routine-dose images for detection of liver metastases and vascular staging of pancreatic cancer. CLINICAL IMPACT. The image-based denoising algorithm facilitates radiation dose reduction of multiphase examinations for both low- and high-contrast diagnostic tasks without requiring manufacturer-specific hardware or software.

Effect of cone-beam CT detector size and projection geometry on scatter-induced image noise

<h3>Objective</h3> The aim of this study was to evaluate the effect of detector size with different projection geometries on scatter-induced image noise in cone beam computed tomography (CBCT). <h3>Study Design</h3> A polymethyl methacrylate phantom containing air and aluminum underwent CBCT scanning positioned in the center of the FOV. Images were acquired with the PaX-i3D Green Premium (Vatech, Korea), which has a narrow vertical detector with off-axis projection geometry and multiple consecutive rotations for small and medium FOVs; the OP300 3D Maxio (Instrumentarium Dental, Tuusula, Finland), with aligned projection geometry for small FOV, and off-axis geometry for medium FOV; and the Promax 3D Max (Planmeca, Helsinki, Finland), which has aligned geometry for small and medium FOVs. Scans were performed using small and medium FOVs, combining tube voltages and tube currents to maintain a fixed normalized dose-area product. Signal difference-to-noise ratio (SDNR) was calculated on two-dimensional projection images and compared using one-way ANOVA with post hoc Tukey tests (α =0.05) between CBCT units. <h3>Results</h3> For small FOVs, Promax showed the highest SDNR compared to OP300 and PaX-i3D, with the difference being statistically significant (p < 0.0001). OP300 exhibited significantly higher SDNR values than PaX-i3D (p < 0.0001). For medium FOVs, Promax had significantly higher SDNR than OP300 and PaX-i3D (p < 0.0001), and no difference was discovered between OP300 and PaX-i3D. Therefore, Promax had the lowest scatter-induced image noise since it has aligned projection geometry in small and medium FOV sizes. For OP300, which has an off-axis geometry, the scatter-induced image noise with the medium FOV was comparable to PaX-i3D. <h3>Conclusion</h3> Reducing the active area of the detector does not result in fewer scattered photons reaching the detector, as it needs more rotations to capture all the projections. Moreover, in off-axis projections there is a lateral off-set of the detector, which results in the center of the FOV being positioned in the edges of the projection images. <b>Statement of Ethical Review</b> Ethical review was sought and study was exempted from ethical review