X-ray Scatter Correction Research Articles

Performance and usefulness evaluation of a software-based scatter correction technique for mammographic images

Although physical grids improve contrast in radiographic images by reducing scattered radiation, various artifacts such as grid shadow, moire, and cutoff result in increased patient doses. To overcome these problems, this study evaluates the applicability and usefulness of a material thickness-based scatter-correction technique for mammography. Specifically, this study aims to compare and evaluate the performance of mammography using the proposed software-based scatter correction framework and a physical grid. The proposed technique enables scatter correction based on pre-calculated parameters of a thickness-based scatter kernel at a water slab phantom and an empirical quantity of scatter components in a mammographic system. In the Monte Carlo simulation and experiment, the proposed framework displayed an intensity profile and full width at half maximum that closely approximated those seen in the physical grid. In addition, by applying the proposed framework to the ACR phantom, it was verified that all structures, including specks, were distinctly distinguished. The results demonstrate that the X-ray scatter-correction method with a software-based framework for mammography is applicable to the field of diagnostic imaging, as this approach yields image quality equivalent to that achieved with physical grids while also enabling a reduction in radiation doses for patients.

X-Ray Scatter Correction by Deep Semi-supervised Learning of Simulated Projections with Beam-hole Array

In nondestructive testing and evaluation using cone-beam X-ray computed tomography (CBCT), X-ray scattering is a major cause of degrading the quality of CT images. To correct the negative effects of scattering, recent studies focused on the deep convolutional neural network (DCNN) for fast and accurate correction, and they trained the DCNNs using training data obtained by X-ray simulation. However, such approaches have a drawback where accurately simulating the physical behavior of Xrays requires considerable computational time. Moreover, it is unclear whether the simulated training data are effective to real measured data. To address these problems, we propose a new scatter correction method based on deep semi-supervised learning that can be applied to real measurements. Our training data are incomplete transmission images in which primary X-ray intensities are obtained for a part of pixels. We introduce a data augmentation method based on X-ray radiology, which allows us to significantly reduce training data in our semi-supervised learning. Through experiments using simulated data, this paper shows that our semi-supervised method using a few training data works as effectively as a baseline fully supervised method.

Spectral Signatures of X-ray Scatter Using Energy-Resolving Photon-Counting Detectors.

Energy-resolving photon-counting detectors (PCDs) separate photons from a polychromatic X-ray source into a number of separate energy bins. This spectral information from PCDs would allow advancements in X-ray imaging, such as improving image contrast, quantitative imaging, and material identification and characterization. However, aspects like detector spectral distortions and scattered photons from the object can impede these advantages if left unaccounted for. Scattered X-ray photons act as noise in an image and reduce image contrast, thereby significantly hindering PCD utility. In this paper, we explore and outline several important characteristics of spectral X-ray scatter with examples of soft-material imaging (such as cancer imaging in mammography or explosives detection in airport security). Our results showed critical spectral signatures of scattered photons that depend on a few adjustable experimental factors. Additionally, energy bins over a large portion of the spectrum exhibit lower scatter-to-primary ratio in comparison to what would be expected when using a conventional energy-integrating detector. These important findings allow flexible choice of scatter-correction methods and energy-bin utilization when using PCDs. Our findings also propel the development of efficient spectral X-ray scatter correction methods for a wide range of PCD-based applications.

Scatter Correction in Cone Beam CT for Metal Additive Manufacturing Components

This paper provides an x-ray scatter correction method for cone beam computed tomography (CT) to reduce cupping artifacts and image inhomogeneity of metal additive manufacturing (AM) components. Firstly, projections in 360° were obtained by a cone beam CT system. Secondly, the corresponding virtual CT system was built on Geant4 to obtain scatter photons. Different from previous studies, the geometry of the metal AM component was set by importing a CAD model of the component into Geant4, which can not only assure the accuracy of geometry but also simplify the definition of the geometry. Finally, the corresponding scatter photons were subtracted from the experimental projections in 360° to obtain corrected projections. Corrected reconstruction images were acquired via an FDK algorithm. In the corrected images, the average sum of squares of deviation of regions of interest was about 79.5% of that in the uncorrected images. Corrected images showed that cupping-shaped artifacts and image inhomogeneity were effectively reduced.

Virtual scatter modulation for X-ray CT scatter correction using primary modulator.

A new scatter estimation algorithm with a concept of virtual scatter modulation for X-ray scatter correction using primary modulator is proposed to reduce the aliasing errors in the estimated scatter. Virtual scatter modulation can be realized through dividing the measured primary-modulated image by the measured modulation function. After the division, the aggravation of the aliasing of primary due to the non-uniformity of the modulation function is largely transferred to that of scatter. Since scatter in general has less high frequencies than primary does, the aggravation of its aliasing is expected to be weaker, and therefore the overall aliasing can be reduced. A CatPhan©600 phantom and an anthropomorphic thorax phantom are scanned on a tabletop X-ray cone-beam computed tomography system to validate our proposed algorithm. On the Catphan phantom, the oscillations that are clearly observed in the central region of the Catphan scatter profile estimated using the original primary-modulation algorithm, are mostly eliminated with the proposed scatter modulation algorithm, leading to less residual artifacts and better CT number uniformity in the reconstructed image. Compared with 38.9 HU of CT nonuniformity in a selected uniform region when the primary-modulation algorithm is used, the new algorithm significantly reduces it to 4.5 HU, reaching the same level of uniformity as the ground truth reference. On the thorax phantom, overall better CT number uniformity is also achieved.

X-ray scatter correction for dedicated cone beam breast CT using a forward-projection model.

The quality of dedicated cone-beam breast CT (CBBCT) imaging is fundamentally limited by x-ray scatter contamination due to the large irradiation volume. In this paper, we propose a scatter correction method for CBBCT using a novel forward-projection model with high correction efficacy and reliability. We first coarsely segment the uncorrected, first-pass, reconstructed CBBCT images into binary-object maps and assign the segmented fibroglandular and adipose tissue with the correct attenuation coefficients based on the mean x-ray energy. The modified CBBCT are treated as the prior images toward scatter correction. Primary signals are first estimated via forward projection on the modified CBBCT. To avoid errors caused by inaccurate segmentation, only sparse samples of estimated primary are selected for scatter estimation. A Fourier-Transform based algorithm, herein referred to as local filtration hereafter, is developed to efficiently estimate the global scatter distribution on the detector. The scatter-corrected images are obtained by removing the estimated scatter distribution from measured projection data. We evaluate the method performance on six patients with different breast sizes and shapes representing the general population. The results show that the proposed method effectively reduces the image spatial non-uniformity from 8.27 to 1.91% for coronal views and from 6.50 to 3.00% for sagittal views. The contrast-to-deviation ratio is improved by an average factor of 1.41. Comparisons on the image details reveal that the proposed scatter correction successfully preserves fine structures of fibroglandular tissues that are lost in the segmentation process. We propose a highly practical and efficient scatter correction algorithm for CBBCT via a forward-projection model. The method is attractive in clinical CBBCT imaging as it is readily implementable on a clinical system without modifications in current imaging protocols or system hardware.

X-ray scatter correction for multi-source interior computed tomography.

The schemes of multi-source interior computed tomography (CT) have shown promise for ultra-fast, organ-oriented, and low-dose dynamic imaging. Besides forward scattering, x-ray cross scattering from multiple x-ray sources activated simultaneously can further degrade image quality. Here, we investigate the overall x-ray scattering artifact in a recently proposed multi-source interior CT architecture, and present two methods for scatter correction. Compared to single-source global CT, scattering in multi-source interior CT architecture is affected by two new factors: cross scattering from simultaneously activated multiple x-ray sources and region-of-interest (ROI) oriented interior CT mode. The scatter artifact in the multi-source interior CT architecture was evaluated through both numerical simulation and physical experimentation, and compared to that from conventional single-source global CT. Monte Carlo simulation was conducted with a modified numerical CATphan® 600 phantom. Physical experiments were performed in an in-house developed CT imaging platform with a custom-built phantom. The simulation and experiments were carried out on the single-source CT architecture and the multi-source CT architecture, respectively in the global CT mode and the interior CT mode for comparison. To correct the scattering artifact, two new methods were presented. The first is a beam-stopper-array (BSA)-based method, which enables an online correction of forward scattering and cross scattering simultaneously. The second is a source-trigger-sequence (STS)-based method dedicated to cross-scatter correction. It enables on-the-fly measurements of the cross scattering signals at a few pre-selected views. The CT image quality was quantitatively evaluated in terms of contrast-to-noise ratio (CNR) and CT number deviation before and after the scatter correction. X-ray cross scattering degraded image quality in both the simulation and experiments. Before the scatter correction, the multi-source interior CT mode yielded a reduction of CNR at the ROIs by up to 68.5% and 50.7% in the simulation and experiments, respectively. The stationary BSA-based method significantly improved CNR and CT number accuracy in the images from multi-source interior CT mode, by reducing the negative effects from both forward scattering and cross scattering. The STS-based method enabled multi-source interior CT mode to provide comparable image quality to that with the single-source interior CT mode, by correcting the artifact from cross scattering. The remaining forward scattering artifact can be corrected with the fast adaptive scatter kernel superposition (FASKS) technique. With the proposed scatter correction methods, the CT number error at the ROIs was reduced to less than 37 HU in both simulation and experiments, respectively. Cross scattering, in addition to forward scattering, can cause significant image quality degradation in the multi-source interior CT architecture. However, image quality can be significantly improved with the proposed scatter correction methods.

Analysis of primary modulation based x-ray scatter correction methods.

The purpose of this study is to provide an overview of primary modulation based scatter estimation techniques and evaluate their performance as well as to propose improvements. There have been published two distinct approaches to estimate scatter with primary modulators; however they have not been compared, so far. This work seeks to quantify their performance (focusing on how accurately they can estimate a smooth scatter from primary modulated images with real textures and signal to noise ratios) along with that of proposed improved variants. The influence of the parameters of the described algorithms on the estimation error is also investigated. Simulations were carried out using primary components derived from real chest DTS projections. After modulation, artificial scatter distributions were added to these projections, and the difference between the estimated and the artificial scatter distributions was quantified. Two existing and four proposed methods were assessed. The performance of the scatter estimators was examined under noisy and noiseless circumstances and with scatter distributions of different bandwidths. The proposed methods (SSE, SSENP, ADASE) solve the problem of varying effective transmission factors in the case of cell-image based methods, and they include an improved gradient based method (LSGSE) as well, which estimates scatter distribution more accurately. It was found that the gradient based algorithms outperformed the cell-image based algorithms under noiseless conditions, while the cell-image based solutions proved to be insensitive to noise. The proposed improved methods reached considerably lower scatter estimation errors than the existing ones. Modulator cell sampling windows of different sizes and shapes were examined. Although much lower effective transmission factors (higher contrast between the attenuated and unattenuated cells) can be achieved using narrow windows, in our case the best estimation results are provided by windows that cover about half of the pixels of a modulator cell. The effect of detector backscatter on scatter estimation was also studied, and a solution was proposed to incorporate this type of scatter into the primary modulation framework. Considering all the above results, the authors suggest the use of LSGE for scatter estimation with primary modulation.

X-ray scatter correction method for dedicated breast computed tomography: improvements and initial patient testing

A previously proposed x-ray scatter correction method for dedicated breast computed tomography was further developed and implemented so as to allow for initial patient testing. The method involves the acquisition of a complete second set of breast CT projections covering 360° with a perforated tungsten plate in the path of the x-ray beam. To make patient testing feasible, a wirelessly controlled electronic positioner for the tungsten plate was designed and added to a breast CT system. Other improvements to the algorithm were implemented, including automated exclusion of non-valid primary estimate points and the use of a different approximation method to estimate the full scatter signal. To evaluate the effectiveness of the algorithm, evaluation of the resulting image quality was performed with a breast phantom and with nine patient images. The improvements in the algorithm resulted in the avoidance of introduction of artifacts, especially at the object borders, which was an issue in the previous implementation in some cases. Both contrast, in terms of signal difference and signal difference-to-noise ratio were improved with the proposed method, as opposed to with the correction algorithm incorporated in the system, which does not recover contrast. Patient image evaluation also showed enhanced contrast, better cupping correction, and more consistent voxel values for the different tissues. The algorithm also reduces artifacts present in reconstructions of non-regularly shaped breasts. With the implemented hardware and software improvements, the proposed method can be reliably used during patient breast CT imaging, resulting in improvement of image quality, no introduction of artifacts, and in some cases reduction of artifacts already present. The impact of the algorithm on actual clinical performance for detection, diagnosis and other clinical tasks in breast imaging remains to be evaluated.

X-Ray Scatter Correction on Soft Tissue Images for Portable Cone Beam CT.

Soft tissue images from portable cone beam computed tomography (CBCT) scanners can be used for diagnosis and detection of tumor, cancer, intracerebral hemorrhage, and so forth. Due to large field of view, X-ray scattering which is the main cause of artifacts degrades image quality, such as cupping artifacts, CT number inaccuracy, and low contrast, especially on soft tissue images. In this work, we propose the X-ray scatter correction method for improving soft tissue images. The X-ray scatter correction scheme to estimate X-ray scatter signals is based on the deconvolution technique using the maximum likelihood estimation maximization (MLEM) method. The scatter kernels are obtained by simulating the PMMA sheet on the Monte Carlo simulation (MCS) software. In the experiment, we used the QRM phantom to quantitatively compare with fan-beam CT (FBCT) data in terms of CT number values, contrast to noise ratio, cupping artifacts, and low contrast detectability. Moreover, the PH3 angiography phantom was also used to mimic human soft tissues in the brain. The reconstructed images with our proposed scatter correction show significant improvement on image quality. Thus the proposed scatter correction technique has high potential to detect soft tissues in the brain.

TU-F-18C-03: X-Ray Scatter Correction in Breast CT: Advances and Patient Testing

Purpose: To further develop and perform patient testing of an x-ray scatter correction algorithm for dedicated breast computed tomography (BCT). Methods: A previously proposed algorithm for x-ray scatter signal reduction in BCT imaging was modified and tested with a phantom and on patients. A wireless electronic positioner system was designed and added to the BCT system that positions a tungsten plate in and out of the x-ray beam. The interpolation used by the algorithm was replaced with a radial basis function-based algorithm, with automated exclusion of non-valid sampled points due to patient motion or other factors. A 3D adaptive noise reduction filter was also introduced to reduce the impact of scatter quantum noise post-reconstruction. The impact on image quality of the improved algorithm was evaluated using a breast phantom and seven patient breasts, using quantitative metrics such signal difference (SD) and signal difference-to-noise ratios (SDNR) and qualitatively using image profiles. Results: The improvements in the algorithm resulted in a more robust interpolation step, with no introduction of image artifacts, especially at the imaged object boundaries, which was an issue in the previous implementation. Qualitative evaluation of the reconstructed slices and corresponding profiles show excellent homogeneity of both the background andmore » the higher density features throughout the whole imaged object, as well as increased accuracy in the Hounsfield Units (HU) values of the tissues. Profiles also demonstrate substantial increase in both SD and SDNR between glandular and adipose regions compared to both the uncorrected and system-corrected images. Conclusion: The improved scatter correction algorithm can be reliably used during patient BCT acquisitions with no introduction of artifacts, resulting in substantial improvement in image quality. Its impact on actual clinical performance needs to be evaluated in the future. Research Agreement, Koning Corp., Hologic Inc., Barco Corp Consultant Agreement, Fujifilm Medical Systems.« less

X‐ray scatter correction in breast tomosynthesis with a precomputed scatter map library

To develop and evaluate the impact on lesion conspicuity of a software-based x-ray scatter correction algorithm for digital breast tomosynthesis (DBT) imaging into which a precomputed library of x-ray scatter maps is incorporated. A previously developed model of compressed breast shapes undergoing mammography based on principal component analysis (PCA) was used to assemble 540 simulated breast volumes, of different shapes and sizes, undergoing DBT. A Monte Carlo (MC) simulation was used to generate the cranio-caudal (CC) view DBT x-ray scatter maps of these volumes, which were then assembled into a library. This library was incorporated into a previously developed software-based x-ray scatter correction, and the performance of this improved algorithm was evaluated with an observer study of 40 patient cases previously classified as BI-RADS® 4 or 5, evenly divided between mass and microcalcification cases. Observers were presented with both the original images and the scatter corrected (SC) images side by side and asked to indicate their preference, on a scale from -5 to +5, in terms of lesion conspicuity and quality of diagnostic features. Scores were normalized such that a negative score indicates a preference for the original images, and a positive score indicates a preference for the SC images. The scatter map library removes the time-intensive MC simulation from the application of the scatter correction algorithm. While only one in four observers preferred the SC DBT images as a whole (combined mean score = 0.169 ± 0.37, p > 0.39), all observers exhibited a preference for the SC images when the lesion examined was a mass (1.06 ± 0.45, p < 0.0001). When the lesion examined consisted of microcalcification clusters, the observers exhibited a preference for the uncorrected images (-0.725 ± 0.51, p < 0.009). The incorporation of the x-ray scatter map library into the scatter correction algorithm improves the efficiency of the algorithm. The observer study presented here is also the first test of the scatter correction algorithm with patient images and human observers, and demonstrates its potential to improve the clinical performance of DBT.

Flexible Retrospective Phase Stepping in X-Ray Scatter Correction and Phase Contrast Imaging Using Structured Illumination

The development of phase contrast methods for diagnostic x-ray imaging is inspired by the potential of seeing the internal structures of the human body without the need to deposit any harmful radiation. An efficient class of x-ray phase contrast imaging and scatter correction methods share the idea of using structured illumination in the form of a periodic fringe pattern created with gratings or grids. They measure the scatter and distortion of the x-ray wavefront through the attenuation and deformation of the fringe pattern via a phase stepping process. Phase stepping describes image acquisition at regular phase intervals by shifting a grating in uniform steps. However, in practical conditions the actual phase intervals can vary from step to step and also spatially. Particularly with the advent of electromagnetic phase stepping without physical movement of a grating, the phase intervals are dependent upon the focal plane of interest. We describe a demodulation algorithm for phase stepping at arbitrary and position-dependent (APD) phase intervals without assuming a priori knowledge of the phase steps. The algorithm retrospectively determines the spatial distribution of the phase intervals by a Fourier transform method. With this ability, grating-based x-ray imaging becomes more adaptable and robust for broader applications.

Peripheral Quantitative CT (pQCT) Using a Dedicated Extremity Cone-Beam CT Scanner.

We describe the initial assessment of the peripheral quantitative CT (pQCT) imaging capabilities of a cone-beam CT (CBCT) scanner dedicated to musculoskeletal extremity imaging. The aim is to accurately measure and quantify bone and joint morphology using information automatically acquired with each CBCT scan, thereby reducing the need for a separate pQCT exam. A prototype CBCT scanner providing isotropic, sub-millimeter spatial resolution and soft-tissue contrast resolution comparable or superior to standard multi-detector CT (MDCT) has been developed for extremity imaging, including the capability for weight-bearing exams and multi-mode (radiography, fluoroscopy, and volumetric) imaging. Assessment of pQCT performance included measurement of bone mineral density (BMD), morphometric parameters of subchondral bone architecture, and joint space analysis. Measurements employed phantoms, cadavers, and patients from an ongoing pilot study imaged with the CBCT prototype (at various acquisition, calibration, and reconstruction techniques) in comparison to MDCT (using pQCT protocols for analysis of BMD) and micro-CT (for analysis of subchondral morphometry). The CBCT extremity scanner yielded BMD measurement within ±2-3% error in both phantom studies and cadaver extremity specimens. Subchondral bone architecture (bone volume fraction, trabecular thickness, degree of anisotropy, and structure model index) exhibited good correlation with gold standard micro-CT (error ~5%), surpassing the conventional limitations of spatial resolution in clinical MDCT scanners. Joint space analysis demonstrated the potential for sensitive 3D joint space mapping beyond that of qualitative radiographic scores in application to non-weight-bearing versus weight-bearing lower extremities and assessment of phalangeal joint space integrity in the upper extremities. The CBCT extremity scanner demonstrated promising initial results in accurate pQCT analysis from images acquired with each CBCT scan. Future studies will include improved x-ray scatter correction and image reconstruction techniques to further improve accuracy and to correlate pQCT metrics with known pathology.

Objective models of compressed breast shapes undergoing mammography

To develop models of compressed breasts undergoing mammography based on objective analysis, that are capable of accurately representing breast shapes in acquired clinical images and generating new, clinically realistic shapes. An automated edge detection algorithm was used to catalogue the breast shapes of clinically acquired cranio-caudal (CC) and medio-lateral oblique (MLO) view mammograms from a large database of digital mammography images. Principal component analysis (PCA) was performed on these shapes to reduce the information contained within the shapes to a small number of linearly independent variables. The breast shape models, one of each view, were developed from the identified principal components, and their ability to reproduce the shape of breasts from an independent set of mammograms not used in the PCA, was assessed both visually and quantitatively by calculating the average distance error (ADE). The PCA breast shape models of the CC and MLO mammographic views based on six principal components, in which 99.2% and 98.0%, respectively, of the total variance of the dataset is contained, were found to be able to reproduce breast shapes with strong fidelity (CC view mean ADE = 0.90 mm, MLO view mean ADE = 1.43 mm) and to generate new clinically realistic shapes. The PCA models based on fewer principal components were also successful, but to a lesser degree, as the two-component model exhibited a mean ADE = 2.99 mm for the CC view, and a mean ADE = 4.63 mm for the MLO view. The four-component models exhibited a mean ADE = 1.47 mm for the CC view and a mean ADE = 2.14 mm for the MLO view. Paired t-tests of the ADE values of each image between models showed that these differences were statistically significant (max p-value = 0.0247). Visual examination of modeled breast shapes confirmed these results. Histograms of the PCA parameters associated with the six principal components were fitted with Gaussian distributions. The six-component model was also used to generate CC and MLO view mammogram breast shapes, using the mean PCA parameter values of these distributions and randomly generated values based on the fitted Gaussian distributions, which resemble clinically encountered breasts. A spreadsheet with the data necessary to apply this model is provided as the supplementary material. Our PCA models of breast shapes in both mammographic views successfully reproduce analyzed breast shapes and generate new clinically relevant shapes. This work can aid in research applications which incorporate breast shape modeling, such as x-ray scatter correction, dosimetry, and image registration.

SU-E-I-58: Objective Models of Breast Shape Undergoing Mammography and Tomosynthesis Using Principal Component Analysis.

To develop an objective model of the shape of the compressed breast undergoing mammographic or tomosynthesis acquisition. Automated thresholding and edge detection was performed on 984 anonymized digital mammograms (492 craniocaudal (CC) view mammograms and 492 medial lateral oblique (MLO) view mammograms), to extract the edge of each breast. Principal Component Analysis (PCA) was performed on these edge vectors to identify a limited set of parameters and eigenvectors that. These parameters and eigenvectors comprise a model that can be used to describe the breast shapes present in acquired mammograms and to generate realistic models of breasts undergoing acquisition. Sample breast shapes were then generated from this model and evaluated. The mammograms in the database were previously acquired for a separate study and authorized for use in further research. The PCA successfully identified two principal components and their corresponding eigenvectors, forming the basis for the breast shape model. The simulated breast shapes generated from the model are reasonable approximations of clinically acquired mammograms. Using PCA, we have obtained models of the compressed breast undergoing mammographic or tomosynthesis acquisition based on objective analysis of a large image database. Up to now, the breast in the CC view has been approximated as a semi-circular tube, while there has been no objectively-obtained model for the MLO view breast shape. Such models can be used for various breast imaging research applications, such as x-ray scatter estimation and correction, dosimetry estimates, and computer-aided detection and diagnosis.

TU-E-217BCD-02: An X-Ray Scatter Correction Method for Dedicated Breast Computed Tomography.

To reduce the impact of x-ray scatter in dedicated breast computed tomography (BCT) images. The inclusion of x-ray scatter in BCT projections results in cupping artifacts, loss of contrast, and quantitative inaccuracies. To correct for this, an additional set of BCT projections is acquired with a tungsten plate placed between the x-ray source and the patient breast. The tungsten plate includes a two-dimensional grid of perforations to generate an array of pencil beams. Due to the limited area illuminated by the x-ray pencil beams, an array of signals of primary x-rays only is obtained. At the pencil beam locations, the difference between the plate projections and the standard projections is an estimate of the scatter present in the latter. These estimates are interpolated to obtain scatter-only estimates of the whole images, which are subtracted from the standard projections, resulting in BCT projections with primary signal only, which are then reconstructed. To reduce the impact of the quantum noise of the scatter signal, the resulting reconstructions are noise filtered. Monte Carlo simulations were performed to estimate the amount of scatter included in the pencil beams and the dose from these additional projections. The algorithm was tested using breast phantoms on a BCT clinical prototype system. The maximum scatter signal in the pencil beams is 2.2% (mean of 0.7%) of the total signal, so the pencil beams are an excellent estimate of the primary-only signal. The additional projections Result in only 0.4% of the glandular dose of the standard projections. The homogeneity of the resulting phantom images, the signal difference between adipose and glandular tissue, reconstruction accuracy, and contrast-to-noise ratios were improved with this algorithm. The proposed algorithm has the potential to substantially improve BCT image quality with practically no additional dose to the patient breast.

Algorithm for X-ray Scatter, Beam-Hardening, and Beam Profile Correction in Diagnostic (Kilovoltage) and Treatment (Megavoltage) Cone Beam CT

Quantitative reconstruction of cone beam X-ray computed tomography (CT) datasets requires accurate modeling of scatter, beam-hardening, beam profile, and detector response. Typically, commercial imaging systems use fast empirical corrections that are designed to reduce visible artifacts due to incomplete modeling of the image formation process. In contrast, Monte Carlo (MC) methods are much more accurate but are relatively slow. Scatter kernel superposition (SKS) methods offer a balance between accuracy and computational practicality. We show how a single SKS algorithm can be employed to correct both kilovoltage (kV) energy (diagnostic) and megavoltage (MV) energy (treatment) X-ray images. Using MC models of kV and MV imaging systems, we map intensities recorded on an amorphous silicon flat panel detector to water-equivalent thicknesses (WETs). Scattergrams are derived from acquired projection images using scatter kernels indexed by the local WET values and are then iteratively refined using a scatter magnitude bounding scheme that allows the algorithm to accommodate the very high scatter-to-primary ratios encountered in kV imaging. The algorithm recovers radiological thicknesses to within 9% of the true value at both kV and megavolt energies. Nonuniformity in CT reconstructions of homogeneous phantoms is reduced by an average of 76% over a wide range of beam energies and phantom geometries.

A new method for x-ray scatter correction: first assessment on a cone-beam CT experimental setup

Cone-beam computed tomography (CBCT) enables three-dimensional imaging with isotropic resolution and a shorter acquisition time compared to a helical CT scanner. Because a larger object volume is exposed for each projection, scatter levels are much higher than in collimated fan-beam systems, resulting in cupping artifacts, streaks and quantification inaccuracies. In this paper, a general method to correct for scatter in CBCT, without supplementary on-line acquisition, is presented. This method is based on scatter calibration through off-line acquisition combined with on-line analytical transformation based on physical equations, to adapt calibration to the object observed. The method was tested on a PMMA phantom and on an anthropomorphic thorax phantom. The results were validated by comparison to simulation for the PMMA phantom and by comparison to scans obtained on a commercial multi-slice CT scanner for the thorax phantom. Finally, the improvements achieved with the new method were compared to those obtained using a standard beam-stop method. The new method provided results that closely agreed with the simulation and with the conventional CT scanner, eliminating cupping artifacts and significantly improving quantification. Compared to the beam-stop method, lower x-ray doses and shorter acquisition times were needed, both divided by a factor of 9 for the same scatter estimation accuracy.