FDK Algorithm Research Articles

Geometric artifacts reduction for cone-beam CT via L0-norm minimization without dedicated phantoms.

For cone-beam computed tomography (CBCT), transversal shifts of the rotation center exist inevitably, which will result in geometric artifacts in CT images. In this work, we propose a novel geometric calibration method for CBCT, which can also be used in micro-CT. The symmetry property of the sinogram is used for the first calibration, and then L0-norm of the gradient image from the reconstructed image is used as the cost function to be minimized for the second calibration. An iterative search method is adopted to pursue the local minimum of the L0-norm minimization problem. The transversal shift value is updated with affirmatory step size within a search range determined by the first calibration. In addition, graphic processing unit (GPU)-based FDK algorithm and acceleration techniques are designed to accelerate the calibration process of the presented new method. In simulation experiments, the mean absolute difference (MAD) and the standard deviation (SD) of the transversal shift value were less than 0.2 pixels between the noise-free and noisy projection images, which indicated highly accurate calibration applying the new calibration method. In real data experiments, the smaller entropies of the corrected images also indicated that higher resolution image was acquired using the corrected projection data and the textures were well protected. Study results also support the feasibility of applying the proposed method to other imaging modalities.

Optimal dose reduction algorithm using an attenuation-based tube current modulation method for cone-beam CT imaging.

To reduce the radiation dose given to patients, a tube current modulation (TCM) method has been widely used in diagnostic CT systems. However, the TCM method has not yet been applied to a kV-CBCT system on a LINAC machine. The purpose of this study is to investigate if a TCM method would be desirable in a kV-CBCT system for image-guided radiation therapy (IGRT) or not. We have developed an attenuation–based TCM method using prior knowledge from planning CT images of patients. The TCM method can provide optimized dose reductions without degrading image quality for kV-CBCT imaging. Here, we investigate whether or not our suggested TCM method is desirable to use in kV-CBCT systems to confirm and revise the exact position of a patient for IGRT. Patients go through diagnostic CT scans for RT planning; therefore, using information from prior CT images can enable estimations of the total X-ray attenuation through a patient’s body in a CBCT setting for radiation treatment. Having this planning CT image allows to use the proposed TCM method in RT. The proposed TCM method provides a minimal amount of current for each projection, as well as total current, required to reconstruct the current modulated CBCT image with an image quality similar to that of CBCT. After applying a calculated TCM current for each projection, projection images were acquired and the current modulated CBCT image was reconstructed using a FDK algorithm. To validate the proposed approach, we used a numerical XCAT phantom and a real ATOM phantom and evaluated the performance of the proposed method via visual and quantitative image quality metrics. The organ dose due to imaging radiation was calculated in both cases and compared using the GATE simulation toolkit. As shown in the quantitative evaluation, normalized noise and SSIM values of the TCM were similar to those of conventional CBCT images. In addition, the proposed TCM method yielded comparable image quality to that of conventional CBCT images for both simulations and experimental studies as organ doses were decreased. We have successfully demonstrated the feasibility and dosimetric merit of a prototypical TCM method for kV-CBCT via simulations and experimental study. The results indicate that the proposed TCM method and overall framework can be a viable option for CBCT imaging that utilizes an optimal dose reduction without degrading image quality. Thus, this method reduces the probability for side effects due to radiation exposure.

Head motion correction based on filtered backprojection for x-ray CT imaging.

For head x-ray CT imaging, the head needs to remain motionless during the scan. In clinical practice, however, head motion is sometimes unavoidable depending on the patient. The motion can occur abruptly during the scan and can be unpredictable. It thereby causes motion artifacts such as tissue blurring or doubled edges around the skull area. To mitigate this problem, we propose a 3D head motion estimation (ME) and compensation algorithm based on filtered backprojection. If a patient moves his or her head during the scan, a motion-corrupted sinogram is obtained. Modeling the head motion as a 3D rigid transformation, we develop a motion-compensated (MC) reconstruction algorithm based on the FDK algorithm. To determine the head motion of a rigid transformation, we propose two optimization-based ME schemes depending on the degree of head motion, both of which are performed by updating motion parameters and the corresponding MC reconstructed image alternatively until the proposed cost function is minimized for the MC reconstructed image. In particular, to improve the robustness in the case of large motion, we propose attaching a fiducial marker to the head so that more reliable motion parameters can be initialized by determining the marker position, before the optimization. To evaluate the proposed algorithm, a numerical phantom with realistic, continuous, and smoothly varying motion, and a moving physical phantom are used with a gantry rotation time of 1s. In the simulation using a numerical phantom and in the experiment using a physical phantom, the proposed algorithm provides well-restored 3D motion-compensated images in both cases of small and large motion. In particular, in the case of large motion of the physical phantom, using a fiducial marker, we obtain remarkable improvement of image quality in cerebral arteries and a lesion as well as the skull. Quantitative evaluations using the image sharpness and root-mean-square error also show noticeable improvement of image quality in both simulations and experiments. We propose a framework for head motion correction in an axial CT scan, which consists of motion estimation and compensation steps. Two image-based ME algorithms for rigid motion tracking are developed according to the degree of head motion. The estimated motion information is then used for MC image reconstruction. Both motion estimation and compensation algorithms are based on computationally efficient filtered backprojection. Excellent performance of the proposed framework is illustrated by means of simulations using a numerical phantom and experiments using a physical phantom.

Optimization of data acquisition in axial CT under the framework of sampling on lattice for suppression of aliasing artifacts with algorithmic detector interlacing.

We present the methodology for analyzing and optimizing the sampling structure of projection data acquisition in axial multidetector CT (MDCT) and cone beam CT (CBCT) under the framework of sampling on lattice. Specifically, we propose and evaluate the scheme of interlaced detector cell binning for suppression of longitudinal aliasing artifacts. In addition, we investigate the proposed scheme's capability of mitigating shift variation in spatial resolution and possibility of improving CB image reconstruction accuracy. Under the framework of sampling on lattice, the proposed scheme is evaluated using an axial MDCT with its architecture similar to that of state-of-the-art CT scanners for diagnostic imaging in the clinic. The widely used FDK algorithm is adopted for image reconstruction, in which either horizontal/latitudinal or vertical/longitudinal interpolation is used for lining-up of projection data between interlaced detector cells. Using a spiral clock phantom, the capability of suppressing aliasing artifacts and possibility of improving reconstruction accuracy is quantitatively investigated. The in-plane spatial resolution, as assessed by the modulation transfer function (MTF), and its shift-variant property are quantitatively assessed using wire phantoms, while the through-plane spatial resolution and its shift-variant behavior are assessed by the slice sensitivity profile (SSP) using thin foil phantoms. The preliminary results show that the interlaced detector cell binning can suppress longitudinal aliasing artifacts effectively, while the shift variation in spatial resolution and reconstruction inaccuracy can be mitigated moderately. In addition, the direction, along which the interpolation is carried out to line up projection data between the interlaced detector cells for image reconstruction, plays a significant role in determining the in-plane and through-plane spatial resolution. The scheme of interlaced detector cell binning with longitudinal interpolation for data lining-up is an effective solution for suppression of longitudinal aliasing artifacts in axial MDCT and CBCT.

Inverse filtering approach to measure directional in-plane modulation transfer function using a sphere phantom for a digital tomosynthesis system.

We propose a method to measure the directional in-plane modulation transfer function (MTF) of a digital tomosynthesis system using a sphere phantom. To assess the spatial resolution of an in-plane image of the tomosynthesis system, projection data of a sphere phantom were generated within a limited data acquisition range of 40°, and reconstructed by the FDK algorithm. To measure the in-plane MTF, we divided the Fourier transform of the reconstructed sphere phantom by that of the ideal sphere phantom, and then performed plane integral along the fz-direction. When dividing, small values in the denominator can introduce estimation errors, and these errors were reduced by the proposed method. To evaluate the performance of the proposed method, the in-plane MTF estimated by simulation and experimental data was compared to the ideal in-plane MTF generated by computer simulations using a point object. For quantitative evaluation, we measured frequency values at half-maximum and full-maximum of the directional in-plane MTF along the three different directions (i.e., f0° -, f30° -, and f60° -directions) and compared them with those of the ideal in-plane MTF. Although the sphere phantom has been regarded as an inappropriate object due to the anisotropic characteristics of tomosynthesis image, our results show that the proposed method has a reliable estimation performance, demonstrating the sphere phantom is still suitable for measuring the directional in-plane MTF for a digital tomosynthesis system.

Three-dimensional weighting reconstruction algorithm for circular cone-beam CT under large scan angles

Improving imaging quality of cone-beam CT under large cone angle scan has been an important area of CT imaging research. Considering the idea of conjugate rays and making up missing data, we propose a three-dimensional (3D) weighting reconstruction algorithm for cone-beam CT. The 3D weighting function is added in the back-projection process to reduce the axial density drop and improve the accuracy of FDK algorithm. Having a simple structure, the algorithm can be implemented easily without rebinning the native cone-beam data into cone-parallel beam data. Performance of the algorithm is evaluated using two computer simulations and a real industrial component, and the results show that the algorithm achieves better performance in reduction of axial intensity drop artifacts and has a wide range of application.

An efficient iterative CBCT reconstruction approach using gradient projection sparse reconstruction algorithm.

The purpose of this study is to develop a fast and convergence proofed CBCT reconstruction framework based on the compressed sensing theory which not only lowers the imaging dose but also is computationally practicable in the busy clinic. We simplified the original mathematical formulation of gradient projection for sparse reconstruction (GPSR) to minimize the number of forward and backward projections for line search processes at each iteration. GPSR based algorithms generally showed improved image quality over the FDK algorithm especially when only a small number of projection data were available. When there were only 40 projections from 360 degree fan beam geometry, the quality of GPSR based algorithms surpassed FDK algorithm within 10 iterations in terms of the mean squared relative error. Our proposed GPSR algorithm converged as fast as the conventional GPSR with a reasonably low computational complexity. The outcomes demonstrate that the proposed GPSR algorithm is attractive for use in real time applications such as on-line IGRT.

SU-G-IeP2-08: Investigation On Signal Detectability in Volumetric Cone Beam CT Images with Anatomical Background

Purpose: To investigate the slice direction dependent detectability in cone beam CT images with anatomical background. Methods: We generated 3D anatomical background images using breast anatomy model. To generate 3D breast anatomy, we filtered 3D Gaussian noise with a square root of 1/f3, and then assigned the attenuation coefficient of glandular (0.8cm−1) and adipose (0.46 cm−1) tissues based on voxel values. Projections were acquired by forward projection, and quantum noise was added to the projection data. The projection data were reconstructed by FDK algorithm. We compared the detectability of a 3 mm spherical signal in the image reconstructed from four different backprojection Methods: Hanning weighted ramp filter with linear interpolation (RECON1), Hanning weighted ramp filter with Fourier interpolation (RECON2), ramp filter with linear interpolation (RECON3), and ramp filter with Fourier interpolation (RECON4), respectively. We computed task SNR of the spherical signal in transverse and longitudinal planes using channelized Hotelling observer with Laguerre-Gauss channels. Results: Transverse plane has similar task SNR values for different backprojection methods, while longitudinal plane has a maximum task SNR value in RECON1. For all backprojection methods, longitudinal plane has higher task SNR than transverse plane. Conclusion: In this work, we investigated detectability for different slice direction in cone beam CT images with anatomical background. Longitudinal plane has a higher task SNR than transverse plane, and backprojection with hanning weighted ramp filter with linear interpolation method (i.e., RECON1) produced the highest task SNR among four different backprojection methods. This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the IT Consilience Creative Programs(IITP-2015-R0346-15-1008) supervised by the IITP (Institute for Information & Communications Technology Promotion), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the MSIP (2015R1C1A1A01052268) and framework of international cooperation program managed by NRF (NRF-2015K2A1A2067635).

SU‐D‐206‐04: Iterative CBCT Scatter Shading Correction Without Prior Information

Purpose:To estimate and remove the scatter contamination in the acquired projection of cone‐beam CT (CBCT), to suppress the shading artifacts and improve the image quality without prior information.Methods:The uncorrected CBCT images containing shading artifacts are reconstructed by applying the standard FDK algorithm on CBCT raw projections. The uncorrected image is then segmented to generate an initial template image. To estimate scatter signal, the differences are calculated by subtracting the simulated projections of the template image from the raw projections. Since scatter signals are dominantly continuous and low‐frequency in the projection domain, they are estimated by low‐pass filtering the difference signals and subtracted from the raw CBCT projections to achieve the scatter correction. Finally, the corrected CBCT image is reconstructed from the corrected projection data. Since an accurate template image is not readily segmented from the uncorrected CBCT image, the proposed scheme is iterated until the produced template is not altered.Results:The proposed scheme is evaluated on the Catphan©600 phantom data and CBCT images acquired from a pelvis patient. The result shows that shading artifacts have been effectively suppressed by the proposed method. Using multi‐detector CT (MDCT) images as reference, quantitative analysis is operated to measure the quality of corrected images. Compared to images without correction, the method proposed reduces the overall CT number error from over 200 HU to be less than 50 HU and can increase the spatial uniformity.Conclusion:An iterative strategy without relying on the prior information is proposed in this work to remove the shading artifacts due to scatter contamination in the projection domain. The method is evaluated in phantom and patient studies and the result shows that the image quality is remarkably improved. The proposed method is efficient and practical to address the poor image quality issue of CBCT images.This work is supported by the Zhejiang Provincial Natural Science Foundation of China (Grant No. LR16F010001), National High‐tech R&D Program for Young Scientists by the Ministry of Science and Technology of China (Grant No. 2015AA020917).

Reducing streak artifacts in computed tomography via sparse representation in coupled dictionaries.

Reducing the number of acquired projections is a simple and efficient way to reduce the radiation dose in computed tomography (CT). Unfortunately, this results in streak artifacts in the reconstructed images that can significantly reduce their diagnostic value. This paper presents a novel algorithm for suppressing these artifacts in 3D CT. The proposed algorithm is based on the sparse representation of small blocks of 3D CT images in learned overcomplete dictionaries. It learns two dictionaries, the first dictionary (D(a)) is for artifact-full images that have been reconstructed from a small number (approximately 100) of projections. The other dictionary (D(c)) is for clean artifact-free images. The core idea behind the proposed algorithm is to relate the representation coefficients of an artifact-full block in D(a) to the representation coefficients of the corresponding artifact-free block in D(c). The relation between these coefficients is modeled with a linear mapping. The two dictionaries and the linear relation between the coefficients are learned simultaneously from the training data. To remove the artifacts from a test image, small blocks are extracted from this image and their sparse representation is computed in D(a). The linear map is then used to compute the corresponding coefficients in D(c), which are then used to produce the artifact-suppressed blocks. The authors apply the proposed algorithm on real cone-beam CT images. Their results show that the proposed algorithm can effectively suppress the artifacts and substantially improve the quality of the reconstructed images. The images produced by the proposed algorithm have a higher quality than the images reconstructed by the FDK algorithm from twice as many projections. The proposed sparsity-based algorithm can be a valuable tool for postprocessing of CT images reconstructed from a small number of projections. Therefore, it has the potential to be an effective tool for low-dose CT.

Investigation on slice direction dependent detectability of volumetric cone beam CT images.

We investigate the detection performance of transverse and longitudinal planes for various signal sizes (i.e., 1 mm to 8 mm diameter spheres) in cone beam computed tomography (CBCT) images. CBCT images are generated by computer simulation and images are reconstructed using an FDK algorithm. For each slice direction and signal size, a human observer study is conducted with a signal-known-exactly/background-known-exactly (SKE/BKE) binary detection task. The detection performance of human observers is compared with that of a channelized Hotelling observer (CHO). The detection performance of an ideal linear observer is also calculated using a CHO with Laguerre-Gauss (LG) channels. The detectability of high contrast small signals (i.e., up to 4-mm-diameter spheres) is higher in the longitudinal plane than the transverse plane. It is also shown that CHO performance correlates well with human observer performance in both transverse and longitudinal plane images.

Fully 3D refraction correction dosimetry system

The irradiation of selective regions in a polymer gel dosimeter results in an increase in optical density and refractive index (RI) at those regions. An optical tomography-based dosimeter depends on rayline path through the dosimeter to estimate and reconstruct the dose distribution. The refraction of light passing through a dose region results in artefacts in the reconstructed images. These refraction errors are dependant on the scanning geometry and collection optics.We developed a fully 3D image reconstruction algorithm, algebraic reconstruction technique-refraction correction (ART-rc) that corrects for the refractive index mismatches present in a gel dosimeter scanner not only at the boundary, but also for any rayline refraction due to multiple dose regions inside the dosimeter. In this study, simulation and experimental studies have been carried out to reconstruct a 3D dose volume using 2D CCD measurements taken for various views. The study also focuses on the effectiveness of using different refractive-index matching media surrounding the gel dosimeter. Since the optical density is assumed to be low for a dosimeter, the filtered backprojection is routinely used for reconstruction. We carry out the reconstructions using conventional algebraic reconstruction (ART) and refractive index corrected ART (ART-rc) algorithms. The reconstructions based on FDK algorithm for cone-beam tomography has also been carried out for comparison.Line scanners and point detectors, are used to obtain reconstructions plane by plane. The rays passing through dose region with a RI mismatch does not reach the detector in the same plane depending on the angle of incidence and RI. In the fully 3D scanning setup using 2D array detectors, light rays that undergo refraction are still collected and hence can still be accounted for in the reconstruction algorithm. It is found that, for the central region of the dosimeter, the usable radius using ART-rc algorithm with water as RI matched medium is 71.8%, an increase of 6.4% compared to that achieved using conventional ART algorithm. Smaller diameter dosimeters are scanned with dry air scanning by using a wide-angle lens that collects refracted light. The images reconstructed using cone beam geometry is seen to deteriorate in some planes as those regions are not scanned. Refraction correction is important and needs to be taken in to consideration to achieve quantitatively accurate dose reconstructions. Refraction modeling is crucial in array based scanners as it is not possible to identify refracted rays in the sinogram space.

Iterative CT shading correction with no prior information

Shading artifacts in CT images are caused by scatter contamination, beam-hardening effect and other non-ideal imaging conditions. The purpose of this study is to propose a novel and general correction framework to eliminate low-frequency shading artifacts in CT images (e.g. cone-beam CT, low-kVp CT) without relying on prior information. The method is based on the general knowledge of the relatively uniform CT number distribution in one tissue component. The CT image is first segmented to construct a template image where each structure is filled with the same CT number of a specific tissue type. Then, by subtracting the ideal template from the CT image, the residual image from various error sources are generated. Since forward projection is an integration process, non-continuous shading artifacts in the image become continuous signals in a line integral. Thus, the residual image is forward projected and its line integral is low-pass filtered in order to estimate the error that causes shading artifacts. A compensation map is reconstructed from the filtered line integral error using a standard FDK algorithm and added back to the original image for shading correction. As the segmented image does not accurately depict a shaded CT image, the proposed scheme is iterated until the variation of the residual image is minimized. The proposed method is evaluated using cone-beam CT images of a Catphan©600 phantom and a pelvis patient, and low-kVp CT angiography images for carotid artery assessment. Compared with the CT image without correction, the proposed method reduces the overall CT number error from over 200 HU to be less than 30 HU and increases the spatial uniformity by a factor of 1.5. Low-contrast object is faithfully retained after the proposed correction. An effective iterative algorithm for shading correction in CT imaging is proposed that is only assisted by general anatomical information without relying on prior knowledge. The proposed method is thus practical and attractive as a general solution to CT shading correction.

TU‐F‐CAMPUS‐I‐03: Enhancement of 4D CBCT Image Quality Using An Adaptive Prior Image Constrained Compressed Sensing

Purpose:To develop an iterative reconstruction algorithm using a compressed sensing with adaptive prior image constraints to solve 4D CBCT reconstruction problem.Methods:The images reconstructed by the FDK algorithm with a full set of unsorted projections are served as prior images for partial projections in each phase group and are utilized as an initial guess. Additionally, the prior images are clustered into several regions by applying intensity‐based thresholding, which is referred to as the segmented prior images. The segmented prior images are employed to detect any possible mismatched areas compared with the target images generated by partial projection data. With these two prior images, our algorithm alternately performs the simultaneous algebraic reconstruction technique and anisotropic total variation regularization while adjusting a weighted relaxation map during the iterative reconstruction process. The weighted relaxation map depends on binary images created by the voxel‐dependent comparison between the segmented prior and segmented target images. For the segmented target images, the k‐means clustering with a geometric weighting is applied on the reconstruction images generated in each iteration step. The inverse values of the distance map converted from binary images are assigned to be the values of the relaxation map. Evaluations using Catphan504 phantom with a motion platform were carried out.Results:Qualitative and quantitative analyses showed that the method provides high‐quality CBCT reconstruction images when compared with those generated by the FDK, CS, and PICCS algorithms, with higher contrast‐to‐noise ratio and faster convergence caused by minimizing data fidelity. Especially, the proposed method was superior to PICCS in the aspect of updating locally‐mismatched region.Conclusion:The proposed method not only improves the image quality of 4D CBCT by adaptive updates during the reconstruction process, but also leads to a lower imaging dose and faster acquisition time by using a regular 3D CBCT scan.

SU‐E‐I‐10: Investigation On Detectability of a Small Target for Different Slice Direction of a Volumetric Cone Beam CT Image

Purpose:To investigate the detectability of a small target for different slice direction of a volumetric cone beam CT image and its impact on dose reduction.Methods:Analytic projection data of a sphere object (1 mm diameter, 0.2/cm attenuation coefficient) were generated and reconstructed by FDK algorithm. In this work, we compared the detectability of the small target from four different backprojectionMethods:hanning weighted ramp filter with linear interpolation (RECON 1), hanning weighted ramp filter with Fourier interpolation (RECON2), ramp filter with linear interpolation (RECON 3), and ramp filter with Fourier interpolation (RECON4), respectively. For noise simulation, 200 photons per measurement were used, and the noise only data were reconstructed using FDK algorithm. For each reconstructed volume, axial and coronal slice were extracted and detection‐SNR was calculated using channelized Hotelling observer (CHO) with dense difference‐of‐Gaussian (D‐DOG) channels.Results:Detection‐SNR of coronal images varies for different backprojection methods, while axial images have a similar detection‐SNR. Detection‐SNR2 ratios of coronal and axial images in RECON1 and RECON2 are 1.33 and 1.15, implying that the coronal image has a better detectability than axial image. In other words, using coronal slices for the small target detection can reduce the patient dose about 33% and 15% compared to using axial slices in RECON 1 and RECON 2.Conclusion:In this work, we investigated slice direction dependent detectability of a volumetric cone beam CT image. RECON 1 and RECON 2 produced the highest detection‐SNR, with better detectability in coronal slices. These results indicate that it is more beneficial to use coronal slice to improve detectability of a small target in a volumetric cone beam CT image.This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the IT Consilience Creative Program (NIPA‐2014‐H0201‐14‐1002) supervised by the NIPA (National IT Industry Promotion Agency). Authors declares that s/he has no conflict of Interest in relation to the work in this abstract.

SU‐C‐207‐04: Reconstruction Artifact Reduction in X‐Ray Cone Beam CT Using a Treatment Couch Model

Purpose:to mitigate artifacts induced by the presence of the RT treatment couch in on‐board CBCT and improve image qualityMethods:a model of a Varian IGRT couch is constructed using a CBCT scan of the couch in air. The model is used to generate a set of forward projections (FP) of the treatment couch at specified gantry angles. The model couch forward projections are then used to process CBCT scan projections which contain the couch in addition to the scan object (Catphan phantom), in order to remove the attenuation component of the couch at any given gantry angle. Prior to pre‐processing with the model FP, the Catphan projection data is normalized to an air scan with bowtie filter. The filtered Catphan projections are used to reconstruct the CBCT with an in‐house FDK algorithm. The artifact reduction in the processed CBCT scan is assessed visually, and the image quality improvement is measured with the CNR over a few selected ROIs of the Catphan modules.Results:Sufficient match between the forward projected data and the x‐ray projections is achieved to allow filtering in attenuation space. Visual improvement of the couch induced artifacts is achieved, with a moderate expense of CNR.Conclusion:Couch model‐based correction of CBCT projection data has a potential for qualitative improvement of clinical CBCT scans, without requiring position specific correction data. The technique could be used to produce models of other artifact inducing devices, such as immobilization boards, and reduce their impact on patient CBCT images.

TU-F-CAMPUS-I-05: Investigation of An EMCCD Detector with Variable Gain in a Micro-CT System

Purpose: To investigate the performance of a newly built Electron Multiplying Charged Coupled Device (EMCCD) based Micro-CT system, with variable detector gain, using a phantom containing contrast agent of different concentrations. Methods: We built a micro- CT system with an EMCCD having 8 microns pixels and on-chip variable gain. We tested the system using a phantom containing five tubes filled with different iodine contrast solutions (30% to 70%). First, we scanned the phantom using various x-ray exposures values at 40 kVp and constant detector gain. Next, for the same tube currents, the detector gain was increased to maintain the air value of the projection image constant. A standard FDK algorithm was used to reconstruct the data. Performance was analyzed by comparing the signal-to-noise ratio (SNR) measurements for increased gain with those for the low constant gain at each exposure. Results: The high detector gain reconstructed data SNR was always greater than the low gain data SNR for all x-ray settings and for all iodine features. The largest increases were observed for low contrast features, 30% iodine concentration, where the SNR improvement approached 2. Conclusion: One of the first implementations of an EMCCD based micro- CT system was presented and used to image a phantom with various iodine solution concentrations. The analysis of the reconstructed volumes showed a significant improvement of the SNR especially for low contrast features. The unique on-chip gain feature is a substantial benefit allowing the use of the system at very low x-ray exposures per frame.Partial support: NIH grant R01EB002873 and Toshiba Medical Systems Corp. Partial support: NIH grant R01EB002873 and Toshiba Medical Systems Corp.

WE‐G‐207‐07: Iterative CT Shading Correction Method with No Prior Information

Purpose:Shading artifacts are caused by scatter contamination, beam hardening effects and other non‐ideal imaging condition. Our Purpose is to propose a novel and general correction framework to eliminate low‐frequency shading artifacts in CT imaging (e.g., cone‐beam CT, low‐kVp CT) without relying on prior information.Methods:Our method applies general knowledge of the relatively uniform CT number distribution in one tissue component. Image segmentation is applied to construct template image where each structure is filled with the same CT number of that specific tissue. By subtracting the ideal template from CT image, the residual from various error sources are generated. Since the forward projection is an integration process, the non‐continuous low‐frequency shading artifacts in the image become continuous and low‐frequency signals in the line integral. Residual image is thus forward projected and its line integral is filtered using Savitzky‐Golay filter to estimate the error. A compensation map is reconstructed on the error using standard FDK algorithm and added to the original image to obtain the shading corrected one. Since the segmentation is not accurate on shaded CT image, the proposed scheme is iterated until the variation of residual image is minimized.Results:The proposed method is evaluated on a Catphan600 phantom, a pelvic patient and a CT angiography scan for carotid artery assessment. Compared to the one without correction, our method reduces the overall CT number error from >200 HU to be <35 HU and increases the spatial uniformity by a factor of 1.4.Conclusion:We propose an effective iterative algorithm for shading correction in CT imaging. Being different from existing algorithms, our method is only assisted by general anatomical and physical information in CT imaging without relying on prior knowledge. Our method is thus practical and attractive as a general solution to CT shading correction.This work is supported by the National Science Foundation of China (NSFC Grant No. 81201091), National High Technology Research and Development Program of China (863 program, Grant No. 2015AA020917), and Fund Project for Excellent Abroad Scholar Personnel in Science and Technology.

Algorithm-enabled exploration of image-quality potential of cone-beam CT in image-guided radiation therapy

Kilo-voltage (KV) cone-beam computed tomography (CBCT) unit mounted onto a linear accelerator treatment system, often referred to as on-board imager (OBI), plays an increasingly important role in image-guided radiation therapy. While the FDK algorithm is currently used for reconstructing images from clinical OBI data, optimization-based reconstruction has also been investigated for OBI CBCT. An optimization-based reconstruction involves numerous parameters, which can significantly impact reconstruction properties (or utility). The success of an optimization-based reconstruction for a particular class of practical applications thus relies strongly on appropriate selection of parameter values. In the work, we focus on tailoring the constrained-TV-minimization-based reconstruction, an optimization-based reconstruction previously shown of some potential for CBCT imaging conditions of practical interest, to OBI imaging through appropriate selection of parameter values. In particular, for given real data of phantoms and patient collected with OBI CBCT, we first devise utility metrics specific to OBI-quality-assurance tasks and then apply them to guiding the selection of parameter values in constrained-TV-minimization-based reconstruction. The study results show that the reconstructions are with improvement, relative to clinical FDK reconstruction, in both visualization and quantitative assessments in terms of the devised utility metrics.

Weighted FDK Algorithm from Spiral Cone-Beam Computed Tomography with Displaced Detector

Weighted FDK Algorithm from Spiral Cone-Beam Computed Tomography with Displaced Detector