Scatter Artifacts Research Articles

Learning Scatter Artifact Correction in Cone-Beam X-Ray CT Using Incomplete Projections with Beam Hole Array

X-ray cone-beam computed tomography (CBCT) is a powerful tool for nondestructive testing and evaluation, yet the CT image quality can be compromised by artifact due to X-ray scattering within dense materials such as metals. This problem leads to the need for hardware- and software-based scatter artifact correction to enhance the image quality. Recently, deep learning techniques have merged as a promising approach to obtain scatter-free images efficiently. However, these deep learning techniques rely heavily on training data, often gathered through simulation. Simulated CT images, unfortunately, do not accurately reproduce the real properties of objects, and physically accurate X-ray simulation still requires significant computation time, hindering the collection of a large number of CT images. To address these problems, we propose a deep learning framework for scatter artifact correction using projections obtained solely by real CT scanning. To this end, we utilize a beam-hole array (BHA) to block the X-rays deviating from the primary beam path, thereby capturing scatter-free X-ray intensity at certain detector pixels. As the BHA shadows a large portion of detector pixels, we incorporate several regularization losses to enhance the training process. Furthermore, we introduce radiographic data augmentation to mitigate the need for long scanning time, which is a concern as CT devices equipped with BHA require two series of CT scans. Experimental validation showed that the proposed framework outperforms a baseline method that learns simulated projections where the entire image is visible and does not contain scattering artifacts.

Correction of scatter in CBCT with a new grid design

Objective. This study aims at developing a simple and rapid Compton scatter correction approach for cone-beam CT (CBCT) imaging. Approach. In this work, a new Compton scatter estimation model is established based on two distinct CBCT scans: one measures the full primary and scatter signals without anti-scatter grid (ASG), and the other measures a portion of primary and scatter signals with ASG. To accelerate the entire data acquisition speed, a half anti-scatter grid (h-ASG) that covers half of the full detector surface is proposed. As a result, the distribution of scattered x-ray photons could be estimated from a single CBCT scan. Physical phantom experiments are conducted to validate the performance of the newly proposed scatter correction approach. Main results. Results demonstrate that the proposed half grid approach can quickly and precisely estimate the distribution of scattered x-ray photons from only one single CBCT scan, resulting in a significant reduction of shading artifacts. In addition, it is found that the h-ASG approach is less sensitive to the grid transmission fractions, grid ratio and object size, indicating a robust performance of the new method. Significance. In the future, the Compton scatter artifacts can be quickly corrected using a half grid in CBCT imaging.

A rotating beam-blocker method for cone beam CT scatter correction.

Cone beam CT (CBCT) is widely utilized in clinics. However, the scatter artifact degrades the CBCT image quality, hampering the expansion of CBCT applications. Recently, beam-blocker methods have been used for CBCT scatter correction and proved their high cost-effectiveness. A rotating beam-blocker (RBB) method for CBCT scatter correction was proposed to complete scatter correction and image reconstruction within a single scan in both full- and half-fan scan scenarios. The RBB consisted of two open regions and two blocked regions, and was designed as a centrosymmetric structure. The open and blocked projections could be alternatively obtained within one single rotation. The open projections were corrected with the scatter signal calculated from the blocked projections, and then used to reconstruct the 3D image via the Feldkamp-Davis-Kress algorithm. The performance of the RBB method was evaluated on head and pelvis phantoms in scenarios with and without a bowtie filter. The images obtained from nine repeated scans in each scenario were used to calculate the evaluation metrics including the CT number error, spatial nonuniformity (SNU) and contrast-to-noise ratio (CNR). For the head phantom, the CT number error was decreased to<5 after scatter correction from>200 HU before correction when scanned without a bowtie filter, and to<4 from>160 HU when scanned with a full bowtie filter. For the pelvis phantom, the CT number error was reduced to<12 after scatter correction from>250 HU before correction when scanned without a bowtie filter, and to<10 from>190 HU when scanned with a half bowtie filter. After scatter correction, the uniformity and contrast were both improved, resulting in an SNU of>79% decrease and CNR of>2 times increase, respectively. High-quality CBCT images could be obtained in a single scan after using the proposed RBB method for scatter correction, enabling more accurate image guidance for surgery and radiation therapy applications. With almost no time delay between the successive open and blocked projections, the RBB method could eliminate the motion-induced anatomical mismatches between the corresponding open and blocked projections and could find particular usefulness in thoracic and abdominal imaging.

Dual-source photon-counting CT: impact of residual cross-scatter on quantitative spectral results.

Dual-source photon-counting CT combines the high temporal resolution and high pitch of dual-source CT with the material quantification capabilities of photon-counting CT. It, however, results in cross-scatter that increases in severity with increased patient size and collimation. This cross-scatter must be corrected to ensure the removal of scatter artifacts and improve quantitative accuracy. To evaluate residual cross-scatter of a first-generation dual-source photon-counting CT and the effect of phantom size, collimation, and radiation dose, a phantom was scanned in single- and dual-source modes with and without its extension ring at three collimations and three radiation doses. Virtual monoenergetic images (VMI) at 50 keV, VMI 150 keV, and iodine density maps were reconstructed to determine variation between acquisition parameters in single- and dual-source modes. Additionally, differences relative to single-source acquisitions and to single-source and small collimation acquisitions were calculated to reflect residual cross-scatter with and without matched collimation. At VMI 50 keV, inserts exhibited accuracy and similar variation between single- and dual-source modes, averaging 5.4 ± 2.6 and 6.2 ± 2.5 HU, respectively, across phantom size, collimation, and radiation dose. Differences relative to single-source measured 5.1 ± 8.5 and 0.4 ± 4.2 HU while differences relative to single-source and small collimation acquisitions were 6.4 ± 10.8 HU and -0.5 ± 3.9 HU for VMI 50 and 150 keV, respectively. This minimal residual cross-scatter increases confidence in the quantitative accuracy of spectral results necessary for clinical applications of dual-source photon-counting CT with motion, such as cardiac imaging.

A carbon nanotube x-ray source array designed for a new multisource cone beam computed tomography scanner

Cone beam computed tomography (CBCT) is known to suffer from strong scatter and cone beam artifacts. The purpose of this study is to develop and characterize a rapidly scanning carbon nanotube (CNT) field emission x-ray source array to enable a multisource CBCT (ms-CBCT) image acquisition scheme which has been demonstrated to overcome these limitations. A CNT x-ray source array with eight evenly spaced focal spots was designed and fabricated for a medium field of view ms-CBCT for maxillofacial imaging. An external multisource collimator was used to confine the radiation from each focal spot to a narrow cone angle. For ms-CBCT imaging, the array was placed in the axial direction and rapidly scanned while rotating continuously around the object with a flat panel detector. The x-ray beam profile, temporal and spatial resolutions, energy and dose rate were characterized and evaluated for maxillofacial imaging. The CNT x-ray source array achieved a consistent focal spot size of 1.10 ± 0.04 mm × 0.84 ± 0.03 mm and individual beam cone angle of 2.4° ± 0.08 after collimation. The x-ray beams were rapidly switched with a rising and damping times of 0.21 ms and 0.19 ms, respectively. Under the designed operating condition of 110 kVp and 15 mA, a dose rate of 8245 μGy s−1 was obtained at the detector surface with the inherent Al filtration and 2312 μGy s−1 with an additional 0.3 mm Cu filter. There was negligible change of the x-ray dose rate over many operating cycles. A ms-CBCT scan of an adult head phantom was completed in 14.4 s total exposure time for the imaging dose in the range of that of a clinical CBCT scanner. A spatially distributed CNT x-ray source array was designed and fabricated. It has enabled a new multisource CBCT to overcome some of the main inherent limitations of the conventional CBCT.

Analysis of Artefacts in Endodontically Treated Teeth on Cone Beam Computed (CBCT) Images and their Impact on Diagnostic Value

The aim of this paper was to investigate the incidence of artefacts produced by endodontically treated teeth on CBCT images and their impacts the diagnostic value of the image for endodontic purposes. A retrospective analysis of 57 root filled teeth from 40 CBCT scans from UCLAN dental clinic between 2016-2020 was undertaken. Each tooth was split into crown and root section with the incidence of all known artefacts recorded by the binary yes/no variable. Subjective assessment into the diagnostic acceptability of the image in relation to endodontic indications stated in the European Society of Endodontics guidance was done. Age, gender, field of view, tooth type, location, coronal restoration of subject tooth and adjacent teeth were recorded. Statistical analysis (SPSS v28, IBM system) was used to assess the relationship between study factors, artefact expression and the effects on diagnostic quality. Scatter artefacts were present in 100% of samples with Beam Hardening being present in 94.7% of samples. Image Noise, Motion and Aliasing (distortion) artefacts reported an incidence ranging between 0-17.5%. The significance level was set at p&lt;0.05. Of the study characteristics Beam Hardening was affected by tooth type/location, coronal restoration of subject tooth and adjacent tooth whereas Motion and Aliasing artefacts were affected by the number of roots and tooth type/location respectively. Beam Hardening, Image Noise, Motion and Aliasing artefacts were shown to have a statistically significant association with the diagnostic acceptability of the scans of root filled teeth for endodontic purposes especially in the axial and sagittal views. Beam Hardening had the most significant impact on diagnostic quality. This is the first study to show artefact expression is much more prevalent than previously reported and has strong ability to affect the diagnostic acceptability of CBCT scans of root filled teeth taken for endodontic purpose.

Energy-guided diffusion model for CBCT-to-CT synthesis

Cone Beam Computed Tomography (CBCT) plays a crucial role in Image-Guided Radiation Therapy (IGRT), providing essential assurance of accuracy in radiation treatment by monitoring changes in anatomical structures during the treatment process. However, CBCT images often face interference from scatter noise and artifacts, posing a significant challenge when relying solely on CBCT for precise dose calculation and accurate tissue localization. There is an urgent need to enhance the quality of CBCT images, enabling a more practical application in IGRT. This study introduces EGDiff, a novel framework based on the diffusion model, designed to address the challenges posed by scatter noise and artifacts in CBCT images. In our approach, we employ a forward diffusion process by adding Gaussian noise to CT images, followed by a reverse denoising process using ResUNet with an attention mechanism to predict noise intensity, ultimately synthesizing CBCT-to-CT images. Additionally, we design an energy-guided function to retain domain-independent features and discard domain-specific features during the denoising process, enhancing the effectiveness of CBCT-CT generation. We conduct numerous experiments on the thorax dataset and pancreas dataset. The results demonstrate that EGDiff performs better on the thoracic tumor dataset with SSIM of 0.850, MAE of 26.87 HU, PSNR of 19.83 dB, and NCC of 0.874. EGDiff outperforms SoTA CBCT-to-CT synthesis methods on the pancreas dataset with SSIM of 0.754, MAE of 32.19 HU, PSNR of 19.35 dB, and NCC of 0.846. By improving the accuracy and reliability of CBCT images, EGDiff can enhance the precision of radiation therapy, minimize radiation exposure to healthy tissues, and ultimately contribute to more effective and personalized cancer treatment strategies.

Computed Tomography Artifact Reduction Employing a Convolutional Neural Network Within the Context of Dimensional Metrology

Abstract Utilizing accurate, nondestructive testing methods to improve quality control and reduce manufacturing errors has gained prominence in light of industry development in various fields. Industrial computed tomography (CT) scanning carries considerable weight among all conventional methods because of their unique features, such as providing a three-dimensional specimen model. Due to the prevalence of metals with high linear attenuation coefficients in industrial applications, beam hardening and scatter artifacts are two of the most prevalent artifacts in any reconstructed volume. Other notable artifacts include those with a nonideal focal spot and conical beam radiation. These artifacts may manifest as a distortion of gray value peaks, systematic discrepancies, blurring-like cupping, and streaking in reconstructed images, degrading volume reconstruction quality. In this paper, the effect of these artifacts is illustrated and mitigated by adopting our proposed method, a combination of conventional and contemporary techniques, including the use of a pretrained convolutional neural network (CNN). Five tests are replicated in different geometric parameters to perform a geometric configuration analysis, indicating how effective the proposed approach is at encountering different geometric situations. The results demonstrate that the proposed method has substantially achieved its goal of improving the accuracy of dimensional metrology performed on our phantom.

Scatter Corrected Cone-Beam CT for Radiomics in SBRT: A Feasibility Study

The brilliant therapeutic effect and longer patient survival time has made stereotactic body radiation therapy (SBRT) the major technology in radiation therapy. In the therapy procedure, cone-beam computed tomography (CBCT) imaging is routinely performed for patient set-up and positioning verification. CBCT images also have valuable information about day-to-day changes of the lesion during the treatment procedure, which may have huge potential influence in the therapy outcome. we perform scatter correction on CBCT and analyze the radiomics features from scatter corrected CBCT to explore its feasibility for radiomics analysis in SBRT. The dataset consisted of 12 patients with two-stage abdomen CBCT and planning CT (pCT) images. Firstly, the scatter artifacts are suppressed using a customized ultrafast Monte Carlo simulation-based scatter correction algorithm. The regions of interest (ROIs) are contoured randomly in CBCT, pCT and uncorrected CBCT images to extract the radiomics features. A total number of 271 radiomics features which include 59 unfiltered and 212 wavelet-filtered features are calculated. The Spearman's correlation coefficients (CCs) are calculated for statistical analysis. The percentages of strongly-correlated features (PSCFs) (CC>0.8) are improved by 11.8% and 9.9% in two stages respectively after the scatter correction. Specifically, for the features after applying low-pass filter in both x and y directions (LL-wavelet-filtered features), the PRFs increase from 17.0%, 18.9% to 37.7% and 34.0%, respectively. As the results shows, effective scatter correction can improve the correlation between CBCT and pCT features. The LL-wavelet-filtered features are improved most significantly. The features extracted from CBCT images have the possibility to be applied in radiomics.

Scattering correction for samples with cylindrical domains measured with polarized infrared spectroscopy

Scattering artifacts are one of the most common effects distorting transmission spectra in Fourier-Transform Infrared spectroscopy. Their increased impact, strongly diminishing the quantitative and qualitative power of IR spectroscopy, is especially observed for structures with a size comparable to the radiation wavelength. To tackle this problem, a wide range of preprocessing techniques based on the Extended Multiplicative Scattering Correction method was developed, using physical properties to remove scattering presence in the spectra. However, until recently those algorithms were mostly focused on spherically shaped samples, for example, cells. Here, an algorithm for samples with cylindrical domains is described, with additional implementation of a linearly polarized light case, which is crucial for the growing field of polarized IR imaging and spectroscopy. An open-source code with GPU based implementation is provided, with a calculation time of several seconds per spectrum. Optimizations done to improve the throughput of this algorithm allow the application of this method into the standard preprocessing pipeline of small datasets.

An unsupervised dual contrastive learning framework for scatter correction in cone-beam CT image

Purpose:Cone-beam computed tomography (CBCT) is widely utilized in modern radiotherapy; however, CBCT images exhibit increased scatter artifacts compared to planning CT (pCT), compromising image quality and limiting further applications. Scatter correction is thus crucial for improving CBCT image quality. Methods:In this study, we proposed an unsupervised contrastive learning method for CBCT scatter correction. Initially, we transformed low-quality CBCT into high-quality synthetic pCT (spCT) and generated forward projections of CBCT and spCT. By computing the difference between these projections, we obtained a residual image containing image details and scatter artifacts. Image details primarily comprise high-frequency signals, while scatter artifacts consist mainly of low-frequency signals. We extracted the scatter projection signal by applying a low-pass filter to remove image details. The corrected CBCT (cCBCT) projection signal was obtained by subtracting the scatter artifacts projection signal from the original CBCT projection. Finally, we employed the FDK reconstruction algorithm to generate the cCBCT image. Results:To evaluate cCBCT image quality, we aligned the CBCT and pCT of six patients. In comparison to CBCT, cCBCT maintains anatomical consistency and significantly enhances CT number, spatial homogeneity, and artifact suppression. The mean absolute error (MAE) of the test data decreased from 88.0623 ± 26.6700 HU to 17.5086 ± 3.1785 HU. The MAE of fat regions of interest (ROIs) declined from 370.2980 ± 64.9730 HU to 8.5149 ± 1.8265 HU, and the error between their maximum and minimum CT numbers decreased from 572.7528 HU to 132.4648 HU. The MAE of muscle ROIs reduced from 354.7689 ± 25.0139 HU to 16.4475 ± 3.6812 HU. We also compared our proposed method with several conventional unsupervised synthetic image generation techniques, demonstrating superior performance. Conclusions:Our approach effectively enhances CBCT image quality and shows promising potential for future clinical adoption.

Artifact removal for unpaired thorax CBCT images using a feature fusion residual network and contextual loss.

Cone-beam computed tomography (CBCT) has become a more and more active cutting-edge topic in the international computed tomography (CT) research due to its advantages of fast scanning speed, high ray utilization rate and high precision. However, scatter artifacts affect the imaging performance of CBCT, which hinders its application seriously. Therefore, our study aimed to propose a novel algorithm for scatter artifacts suppression in thorax CBCT based on a feature fusion residual network (FFRN), where the contextual loss was introduced to adapt the unpaired datasets better. In the method we proposed, a FFRN with contextual loss was used to reduce CBCT artifacts in the region of chest. Unlike L1 or L2 loss, the contextual loss function makes input images which are not aligned strictly in space available, so we performed it on our unpaired datasets. The algorithm aims to reduce artifacts via studying the mapping between CBCT and CT images, where the CBCT images were set as the beginning while planning CT images as the end. The proposed method effectively removes artifacts in thorax CBCT, including shadow artifacts and cup artifacts which can be collectively referred to as uneven grayscale artifacts, in the CBCT image, and perform well in preserving details and maintaining the original shape. In addition, the average PSNR number of our proposed method achieved 27.7, which was higher than the methods this paper referred which indicated the significance of our method. What is revealed by the results is that our method provides a highly effective, rapid, and robust solution for the removal of scatter artifacts in thorax CBCT images. Moreover, as is shown in Table 1, our method has demonstrated better artifact reduction capability than other methods.

An iterative method for simultaneous reduction on beam-hardening and scatter artifacts in x-ray CT

Beam-hardening and scatter are two significant factors leading to contrast reduction and gray value inaccuracy in CT images. The cupping artifacts and obscure boundaries in reconstructed images are also caused mainly by both beam-hardening and scattering. It is difficult to get high-quality CT images with only one of them to make correction. In this paper, we proposed an x-ray CT polychromatic attenuation model with scatter effect, and an iterative method for simultaneous reduction on beam-hardening and scatter artifacts. In this model, the measurements of the detector comprise two parts: an attenuation term and a scatter term. The former is defined by an exponential rational fraction to fit the traditional attenuation process, and the latter is defined by a convolutional scatter intensity. The coefficients of the rational fraction in the attenuation term and the scatter term kernel are all calculated from a calibration phantom which is scanned to get corresponding equations. Based on the polychromatic attenuation model, we proposed an iterative artifacts reduction method combining deconvolution technique with linearized back-projection (iDLB method). This method makes the nonlinear polychromatic attenuation model become easily solvable by linearizing transformation, which simplifies the residuals allocation process. Experiments of both numerical simulation and practical data show the iDLB method has the ability to reduce beam-hardening and scatter artifacts simultaneously, improve the contrast of CT images, and it is highly parallelized for lower computational cost.

Automated 3D Defect Detectionm based on Simulated Reference

Bringing the Computed Tomography (CT) technology into production lines brings many advantages due to the complete and detailed 3D characterization and localization of defects. But compared to laboratory systems, Inline-CT systems must meet several challenges, the most significant one is certainly the limited available time for the measurement procedure and data evaluation. Visual inspection of 3D data sets of complex parts within the production rate is almost impossible, so automated image evaluation algorithms are mandatory for that task. Fast measurement procedures often mean short integration times and a lower number of projections which are two factors that increase quantum noise and thus decrease image quality significantly. In addition to that, scatter artifacts also have a negative influence on the image quality. All these conditions constitute challenges to the image evaluation algorithm. The Fraunhofer EZRT has developed a method for automated defect detection in cast parts by using reference (that means flawless) parts for comparison. The advantage of this method is that image artifacts are considered, since they appear in both data sets in the same way and have almost no negative influence on the reliability of the evaluation result. Most recent work took this method one step further by using not real flawless parts, but instead using simulations to generate reference data sets in a very convenient and quick way for any number of different parts.

Multi-material blind beam hardening correction in near real-time based on non-linearity adjustment of projections

Beam hardening (BH) is one of the major artifacts that severely reduces the quality of computed tomography (CT) imaging. This BH artifact arises due to the polychromatic nature of the X-ray source and causes cupping and streak artifacts. This work aims to propose a fast and accurate BH correction method that requires no prior knowledge of the materials and corrects first and higher-order BH artifacts. This is achieved by performing a wide sweep of the material based on an experimentally measured look-up table to obtain the closest estimate of the material. Then, the non-linearity effect of the BH is corrected by adding the difference between the estimated monochromatic and the polychromatic simulated projections of the segmented image. The estimated polychromatic projection is accurately derived using the least square estimation (LSE) method by minimizing the difference between the experimental projection and the linear combination of simulated polychromatic projections. As a result, an accurate non-linearity correction term is derived that leads to an accurate BH correction result. The simulated projections in this work are performed using a multi-GPU-accelerated forward projection model which ensures a fast BH correction in near real-time. To evaluate the proposed BH correction method, we have conducted extensive experiments on real-world CT data. It is shown that the proposed method results in images with improved contrast-to-noise ratio (CNR) in comparison to the images corrected from only the scatter artifacts and the BH-corrected images using the state-of-the-art empirical BH correction method.

A chairside digital radiographic guide for registering digital casts to cone beam computed tomography scans with strong metallic artifacts

Accurate registration of digital casts and cone beam computed tomography (CBCT) scans with strong metallic artifacts is essential for the accuracy of guided implant surgery. This article describes a procedure for mapping digital casts onto CBCT scans containing significant scatter artifacts in the virtual implant planning stage. The technique uses a chairside segmented occlusal wing-like radiographic guide, which is constructed of digital splints fabricated using a desktop 3-dimensional printer and composite resin spheres as markers to accurately superimpose the bimaxillary digital scans onto the CBCT scans in a single procedure. This cost-effective technique is timesaving for clinicians and patients, and the digital information for implant planning can be collected in a single visit.

A correlated sampling-based Monte Carlo simulation for fast CBCT iterative scatter correction.

In recent years, cone-beam computed tomography (CBCT) has played an important role in medical imaging. However, the applications of CBCT are limited due to the severe scatter contamination. Conventional Monte Carlo (MC) simulation can provide accurate scatter estimation for scatter correction, but the expensive computational cost has always been the bottleneck of MC method in clinical application. In this work, an MC simulation method combined with a variance reduction technique called correlated sampling is proposed for fast iterative scatter correction. Correlated sampling exploits correlation between similar simulation systems to reduce the variance of interest quantities. Specifically, conventional MC simulation is first performed on the scatter-contaminated CBCT to generate the initial scatter signal. In the subsequent correction iterations, scatter estimation is then updated by applying correlated MC sampling to the latest corrected CBCT images by reusing the random number sequences of the task-related photons in conventional MC. Afterward, the corrected projections obtained by subtracting the scatter estimation from raw projections are utilized for FDK reconstruction. These steps are repeated until an adequate scatter correction is obtained. The performance of the proposed framework is evaluated by the accuracy of the scatter estimation, the quality of corrected CBCT images and efficiency. Overall, the difference in mean absolute percentage error between scatter estimation with and without correlated sampling is 0.25% for full-fan case and 0.34% for half-fan case, respectively. In simulation studies, scatter artifacts are substantially eliminated, where the mean absolute error value is reduced from 15 to 2HU in full-fan case and from 53 to 13HU in half-fan case. Scatter-to-primary ratio is reduced to 0.02 for full-fan and 0.04 for half-fan, respectively. In phantom study, the contrast-to-noise ratio (CNR) is increased by a factor of 1.63, and the contrast is increased by a factor of 1.77. As for clinical studies, the CNR is improved by 11% and 14% for half-fan and full-fan, respectively. The contrast after correction is increased by 19% for half-fan and 44% for full-fan. Furthermore, root mean square error is also effectively reduced, especially from 78 to 4HU for full-fan. Experimental results demonstrate that the figure of merit is improved between 23 and 43 folds when using correlated sampling. The proposed method takes less than 25s for the whole iterative scatter correction process. The proposed correlated sampling-based MC simulation method can achieve fast and accurate scatter correction for CBCT, making it suitable for real-time clinical use.

Technical note: Characterization of novel iterative reconstructed cone beam CT images for dose tracking and adaptive radiotherapy on L-shape linacs.

Cone-beam computed tomography (CBCT) allows for patient setup and positioning, and potentially dose verification or adaptive replanning prior to each treatment delivery. Poor CBCT image quality due to scatter artifacts and patient motion has been a major limiting factor. A new image reconstruction algorithm was recently clinically implemented for improving image quality through iterative reconstruction (iCBCT). This study aims to characterize iCBCT image quality, establish image value (HU)-to-relative electron density (RED) calibration curves for dose calculation, and assess the dosimetric accuracy for different anatomical sites. Both conventional CBCT and iCBCT scans were acquired from a Varian TrueBeam On-Board Imager system. A Catphan 604 phantom was scanned to compare image quality between the traditional Feldkamp-Davis-Kress (FDK) and novel iterative reconstruction techniques. Computerized Imaging Reference Systems (CIRS) electron density phantom was used to construct site-specific HU-RED curves corresponding to various scan settings. The CIRS Dynamic Thorax phantom, Rando pelvis phantom, and BrainLab head phantom were used for assessing dosimetric accuracy calculated on iCBCT images, compared to that on traditional FDK-based CBCT images. All phantoms were scanned on a computed tomography (CT) to obtain baseline HU values for comparison. Test results obtained from Catphan showed statistically significant improvement with iCBCT, compared to FDK CBCT. Average HU differences from the baseline CT values were improved to within ±30HU for iCBCT, compared to FDK CBCT for phantom studies. Dose calculated on iCBCT for both phantoms and patient cases directly using baseline HU-RED calibration from CT showed 0.5%-2.0% accuracy from the baseline dose calculated on CT, which is comparable to doses calculated using site-specific HU-RED calibration curves. iCBCT provides improved image quality, improved HU accuracy compared to CT baseline, and has potential to provide online dose verification as part of the adaptive radiotherapy workflow directly using the baseline HU-RED calibration curve from CT.

How to test blindsight without light scatter artefacts?

light scatter artefacts are a methodological problem in testing residual visual capacities (RVCs), for instance blindsight, in patients with homonymous visual field defects (HVFDs). The term light scatter artefact describes the phenomenon that light from targets directed towards the HVFD can stray into the sighted visual field. This might enable an observer to respond correctly to information directed at her blind field despite the fact that she is unable to process that information in the blind field itself. In this manuscript, we present a review of the relevance of light scatter in visual neuroscience, discuss factors that influence the impact of light scatter and evaluate means to test for light scatter artefacts. Furthermore, we present findings from an empirical study that was aimed at developing tests for RVCs that are free of light scatter artefacts. Previous studies on light scatter only used small sample sizes and equipment that is no longer in use. Hence, their results cannot be generalized to future experiments making it necessary to run laborious light scatter tests for every new study on RVCs. To avoid this, we hereby start a pool of stimuli and paradigms which demonstrably do not elicit light scatter artefacts. To this end, we investigated 21 healthy young participants in three frequently used RVC-paradigms: (1) temporal 2AFC task, (2) movement direction discrimination, and (3) redundant target paradigm. For each paradigm, we applied the blind-spot method. But first, we had to establish that our testing paradigm was sufficiently sensitive to detect light scatter artefacts. For this, we used conditions that are known to produce strong light scatter and a paradigm that is very sensitive to such effects. Specifically, we presented white targets on a black background in a dark room. The stimuli were presented to observers’ blind spot. To check for light scatter artefacts, we used a target-detection task in a temporal 2AFC format. We obtained clear light scatter artefacts. Participants produced reliably above-chance detection performance under these conditions. The other two luminance conditions, measured in an illuminated room, did not produce light scatter artefacts. Accuracy in the temporal 2AFC task was at chance level for white targets on a grey background at the blind-spot position. Additionally, black targets on a grey background avoided light scatter artefacts in all three RVC-paradigms. In future, researchers can apply these stimulus and illumination conditions when using one of the three above paradigms in their studies. Using these conditions, they will be able to avoid light scatter artefacts without having to perform their own blind-spot tests.

Empirical scatter correction: CBCT scatter artifact reduction without prior information.

The image quality of cone beam CT (CBCT) scans severely suffers from scattered radiation if no countermeasures are taken. Scatter artifacts may induce cupping and streak artifacts and lead to a reduced image contrast and wrong CT values of the reconstructed volumes. Established software-based approaches for a correction of scattered radiation typically rely on prior knowledge of the CT system, scan parameters, the scanned object, or all of theaforementioned. This study proposes a simple and effective postprocessing software-based correction method of scatter artifacts in CBCT scans without specific priorknowledge. We propose the empirical scatter correction (ESC), which generates scatter-like basis images from each projection image by convolution operations. A linear combination of these basis images is subtracted from the original projection image. The logarithm is taken and an FDK reconstruction is performed. The coefficients needed for the linear combination are determined automatically by a downhill simplex algorithm such that the resulting reconstructed images show no scatter artifacts. We demonstrate the potential of ESC by correcting simulated volumes with Monte Carlo scatter artifacts, a head phantom scan performed on our table-top CBCT, and a pelvis scan from a Varian Edge CBCTscanner. ESC is able to improve the image quality of CBCT scans, which is shown on the basis of our simulations and on measured data. For a simulated head CT, the CT value difference to the scatter-free reference image was as low as -6 HU after using ESC, whereas the uncorrected data deviated by more than -200 HU from the reference data. Simulations of thorax and abdomen CT scans show that although scatter artifacts are not fully removed, anatomical features which were hard to discover prior to the correction become clearly visible and better segmentable with ESC. Similar results are obtained in the phantom measurement, where a comparison to a slit scan of our head phantom shows only small differences. The CT values in soft tissue are improved in this measurement, as well. In soft tissue areas with severe scatter artifacts, the CT values agree well with those of the slit scan (difference to slit scan: 35HU corrected and -289 HU uncorrected). Scatter artifacts in measured patient data can also be reduced using the proposed ESC. The results are comparable to those achieved with designated correction algorithms installed on the Varian Edge CBCT system. ESC allows to reduce artifacts caused by patient scatter solely based on the projectiondata.