Weighted Filtered Back Projection Research Articles

MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data.

The advancement of x-ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new challenges and opportunities for clinicians and researchers. To address challenges such as dose constraints and scanning times while capitalizing on opportunities such as multi-contrast imaging and low-dose coronary angiography, these multi-channel imaging applications require a new generation of CT reconstruction tools. These new tools should exploit the relationships between imaging channels during reconstruction to set new image quality standards while serving as a platform for direct translation between the preclinical and clinical domains. We outline and demonstrate a new Multi-Channel Reconstruction (MCR) Toolkit for GPU-based analytical and iterative reconstruction of preclinical and clinical multi-energy and dynamic x-ray CT data. To promote open science, open-source distribution of the Toolkit will coincide with the release of this publication (GPL v3; gitlab.oit.duke.edu/dpc18/mcr-toolkit-public). The MCR Toolkit source code is implemented in C/C++ and NVIDIA's CUDA GPU programming interface, with scripting support from MATLAB and Python. The Toolkit implements matched, separable footprint CT reconstruction operators for projection and backprojection in two geometries: planar, cone-beam CT (CBCT) and 3rd generation, cylindrical multi-detector row CT (MDCT). Analytical reconstruction is performed using filtered backprojection (FBP) for circular CBCT, weighted FBP (WFBP) for helical CBCT, and cone-parallel projection rebinning followed by WFBP for MDCT. Arbitrary combinations of energy and temporal channels are iteratively reconstructed under a generalized multi-channel signal model for joint reconstruction. We solve this generalized model algebraically using the split Bregman optimization method and the BiCGSTAB(l) linear solver interchangeably for both CBCT and MDCT data. Rank-sparse kernel regression (RSKR) and patch-based singular value thresholding (pSVT) are used to regularize the energy and time dimensions, respectively. Under a Gaussian noise model, regularization parameters are estimated automatically from the input data, dramatically reducing algorithm complexity for end users. Multi-GPU parallelization of the reconstruction operators is supported to manage reconstruction times. Denoising with RSKR and pSVT and post-reconstruction material decomposition are illustrated with preclinical and clinical cardiac photon-counting (PC)CT data. A digital MOBY mouse phantom with cardiac motion is used to illustrate single energy (SE), multi-energy (ME), time resolved (TR), and combined multi-energy and time-resolved (METR) helical, CBCT reconstruction. A fixed set of projection data is used across all reconstruction cases to demonstrate the Toolkit's robustness to increasing data dimensionality. Identical reconstruction code is applied to in vivo cardiac PCCT data acquired in a mouse model of atherosclerosis (METR). Clinical cardiac CT reconstruction is illustrated using the XCAT phantom and the DukeSim CT simulator, while dual-source, dual-energy CT reconstruction is illustrated for data acquired with a Siemens Flash scanner. Benchmarking results with NVIDIA RTX 8000 GPU hardware demonstrate 61%-99% efficiency in scaling computation from one to four GPUs for these reconstruction problems. The MCR Toolkit provides a robust solution for temporal and spectral x-ray CT reconstruction problems and was built from the ground up to facilitate translation of CT research and development between preclinical and clinical applications.

Impact of Different Metal Artifact Reduction Techniques on Attenuation Correction of Normal Organs in 18F-FDG-PET/CT.

Purpose: To evaluate the impact of different metal artifact reduction algorithms on Hounsfield units (HU) and the standardized uptake value (SUV) in normal organs in patients with different metal implants. Methods: This study prospectively included 66 patients (mean age of 66.02 ± 13.1 years) with 87 different metal implants. CT image reconstructions were performed using weighted filtered back projection (WFBP) as the standard method, metal artifact reduction in image space (MARIS), and an iterative metal artifacts reduction (iMAR) algorithm for large implants. These datasets were used for PET attenuation correction. HU and SUV measurements were performed in nine predefined anatomical locations: liver, lower lung lobes, descending aorta, thoracic vertebral body, autochthonous back muscles, pectoral muscles, and internal jugular vein. Differences between HU and SUV measurements were compared using paired t-tests. The significance level was determined as p = 0.017 using Bonferroni correction. Results: No significant differences were observed between reconstructed images using iMAR and WFBP concerning HU and SUV measurements in liver (HU: p = 0.055; SUVmax: p = 0.586), lung (HU: p = 0.276; SUVmax: p = 1.0 for the right side and HU: p = 0.630; SUVmax: p = 0.109 for the left side), descending aorta (HU: p = 0.333; SUVmax: p = 0.083), thoracic vertebral body (HU: p = 0.725; SUVmax: p = 0.392), autochthonous back muscles (HU: p = 0.281; SUVmax: p = 0.839), pectoral muscles (HU: p = 0.481; SUVmax: p = 0.277 for the right side and HU: p = 0.313; SUVmax: p = 0.859 for the left side), or the internal jugular vein (HU: p = 0.343; SUVmax: p = 0.194). Conclusion: Metal artifact reduction algorithms such as iMAR do not alter the data information of normal organs not affected by artifacts.

Impact of different iterative metal artifact reduction (iMAR) algorithms on PET/CT attenuation correction after port implementation

PurposeTo evaluate the effect of various interactive metal artifact reduction (iMAR) algorithms on attenuation correction in the vicinity of port chambers in PET/CT. Material and methodsIn this prospective study, 30 oncological patients (12 female, 18 male, mean age 59.6 ± 10.5y) with implanted port chambers undergoing 18F-FDG PET/CT were included. CT images were reconstructed with standard weighted filtered back projection (WFBP) and three different iMAR algorithms (hip, dental filling (DF) and pacemaker (PM)). PET attenuation correction was performed with all four CT datasets. SUVmean, SUVmax and HU measurements were performed in fat and muscle tissue in the vicinity of the port chamber at the location of the strongest bright and dark band artifacts. Differences between HU and SUV values across all CT- and PET-images were investigated using a paired t-test. Bonferroni correction was used to prevent alpha-error accumulation (p < 0.008). ResultsIn comparison to WFBP (fat: 94.2 ± 53.9 HU, muscle: 197.6 ± 49.2 HU) all three iMAR algorithms led to a decrease of HU in bright band artifacts. iMAR-DF led to a decrease of 159.2 % (fat: -51.9 ± 58.5 HU, muscle: 94.5 ± 55.3 HU), iMAR-hip of 138.3 % (fat: -30.3 ± 58.5, muscle: 70.4 ± 28.8) and iMAR-PM of 122.3 % (fat: -21.2 ± 47.2 HU, muscle: 72.5 ± 25.1 HU; for all p < 0.008). There was no significant effect of iMAR on SUV measurements in comparison to WFBP. ConclusioniMAR leads to a significant change of HU values in artifacts caused by port catheter chambers in comparison to WFBP. However, no significant differences in attenuation correction and consecutive changes in SUV measurements can be observed.

Impact of different metal artifact reduction techniques on attenuation correction in 18F-FDG PET/CT examinations.

To evaluate the impact of different metal artifact reduction (MAR) algorithms on Hounsfield unit (HU) and standardized uptake values (SUV) in a phantom setting and verify these results in patients with metallic implants undergoing oncological PET/CT examinations. In this prospective study, PET-CT examinations of 28 oncological patients (14 female, 14 male, mean age 69.5 ± 15.2y) with 38 different metal implants were included. CT datasets were reconstructed using standard weighted filtered back projection (WFBP) without MAR, MAR in image space (MARIS) and iterative MAR (iMAR, hip algorithm). The three datasets were used for PET attenuation correction. SUV and HU measurements were performed at the site of the most prominent bright and dark band artifacts. Differences between HU and SUV values across the different reconstructions were compared using paired t-tests. Bonferroni correction was used to prevent alpha-error accumulation (p < 0.017). For bright band artifacts, MARIS led to a non-significant mean decrease of 12.0% (345 ± 315 HU) in comparison with WFBP (391 ± 293 HU), whereas iMAR led to a significant decrease of 68.3% (125 ± 185 HU, p < 0.017). For SUVmean, MARIS showed no significant effect in comparison with WFBP (WFBP: 0.99 ± 0.40, MARIS: 0.96 ± 0.39), while iMAR led to a significant decrease of 11.1% (0.88 ± 0.35, p < 0.017). Similar results were observed for dark band artifacts. iMAR significantly reduces artifacts caused by metal implants in CT and thus leads to a significant change of SUV measurements in bright and dark band artifacts compared with WFBP and MARIS, thus probably improving PET quantification. The present work indicates that MAR algorithms such as iMAR algorithm in integrated PET/CT scanners are useful to improve CT image quality as well as PET quantification in the evaluation of tracer uptake adjacent to large metal implants. A detailed analysis of oncological patients with various large metal implants using different MAR algorithms in PET/CT has not been conducted yet.

Image Noise Covariance Can be Adjusted by a Noise Weighted Filtered Backprojection Algorithm.

It was believed that the noise covariance of an image reconstructed with the filtered backprojection (FBP) algorithm is anisotropic. This paper shows that the noise-weighted FBP algorithm is able to alter the noise covariance and make it approximately isotropic. For the noise-weighted FBP algorithm, this paper develops a closed-form expression for the noise variance image and a closed-form expression for the noise covariance image. Computer simulations are carried out to evaluate the noise covariance with and without the noise weighting in the FBP algorithm. Transmission and emission noise models are used in computer simulations. The noise weighted FBP algorithm has a parameter that emulates the iteration number in the iterative Landweber algorithm. It is observed that the noise covariance can be altered by this emulated iteration number when noise weighting is used. The noise weighting in the noise-weighted FBP algorithm is able to change the noise covariance and a proper selection of the emulated iteration number may be able to give an approximately isotropic image noise covariance function.

Metal Artifact Reduction Computed Tomography of Arthroplasty Implants: Effects of Combined Modeled Iterative Reconstruction and Dual-Energy Virtual Monoenergetic Extrapolation at Higher Photon Energies.

The aim of this study was to compare the effects of combined virtual monoenergetic extrapolation (VME) of dual-energy computed tomography data and iterative metal artifact reduction (iMAR) at higher photon energies on low- and high-density metal artifacts and overall image quality of the ankle arthroplasty implants with iMAR, weighted filtered back projection (WFBP), and WFBP-based VME. Total ankle arthroplasty implants in 6 human cadaver ankles served as surrogates for arthroplasty implants. All specimens underwent computed tomography with a 2 × 192-slice dual-source computed tomography scanner at tube voltages of 80 and tin-filtered 150 kVp to produce mixed 120 kVp equivalent polychromatic and virtual monoenergetic extrapolated images at 150 and 190 keV (VME 150 and VME 190, respectively). By implementing the WFBP and iMAR reconstruction algorithms on polychromatic, VME 150 and VME 190 data, 6 image datasets were created: WFBP-Polychromatic, iMAR-Polychromatic, WFBP-VME 150, WFBP-VME 190, iMAR-VME 150, and iMAR-VME 190. High-density and low-density artifacts were separately quantified with a threshold-based computer algorithm. After anonymization and randomization, 2 observers independently ranked the datasets for overall image quality. Repeated measures analysis of variance, Friedman, and Cohen weighted κ tests were applied for statistical analysis. A conservative P value of less than 0.001 was considered statistically significant. iMAR-VME 190 keV and iMAR-VME 150 keV created the least amount of high-density artifacts (all P < 0.001), whereas iMAR-Polychromatic was the most effective method to mitigate low-density streaks (P < 0.001). For low- and high-density artifacts, polychromatic iMAR acquisition was superior to WFBP-VME 150 keV and WFBP-VME 190 keV (all P < 0.001). On sharp kernel reconstructions, readers ranked the overall image quality of iMAR-Polychromatic images highest (all P < 0.001). Similarly, on soft tissue kernel reconstructions, readers ranked iMAR-Polychromatic images highest with a statistically significant difference over other techniques (all P < 0.001), except for iMAR-VME 150 keV (P = 0.356). In computed tomography imaging of ankle arthroplasty implants, iMAR reconstruction results in fewer metal artifacts and better image quality than WFBP reconstruction for both polychromatic and virtual monoenergetic data. The combination of iMAR and VME at higher photon energies results in mixed effects on implant-induced metal artifacts, including decreased high-density and increased low-density artifacts, which in combination does not improve image quality over iMAR reconstruction of the polychromatic data. Our results suggest that, for ankle arthroplasty implants, the highest image quality is obtained by iMAR reconstruction of the polychromatic data without the need to implement VME at high-energy levels.

Iterative metal artefact reduction in CT: can dedicated algorithms improve image quality after spinal instrumentation?

To investigate the value of dedicated computed tomography (CT) iterative metal artefact reduction (iMAR) algorithms in patients after spinal instrumentation. Post-surgical spinal CT images of 24 patients performed between March 2015 and July 2016 were retrospectively included. Images were reconstructed with standard weighted filtered back projection (WFBP) and with two dedicated iMAR algorithms (iMAR-Algo1, adjusted to spinal instrumentations and iMAR-Algo2, adjusted to large metallic hip implants) using a medium smooth kernel (B30f) and a sharp kernel (B70f). Frequencies of density changes were quantified to assess objective image quality. Image quality was rated subjectively by evaluating the visibility of critical anatomical structures including the central canal, the spinal cord, neural foramina, and vertebral bone. Both iMAR algorithms significantly reduced artefacts from metal compared with WFBP (p<0.0001). Results of subjective image analysis showed that both iMAR algorithms led to an improvement in visualisation of soft-tissue structures (median iMAR-Algo1=3; interquartile range [IQR]:1.5-3; iMAR-Algo2=4; IQR: 3.5-4) and bone structures (iMAR-Algo1=3; IQR:3-4; iMAR-Algo2=4; IQR:4-5) compared to WFBP (soft tissue: median 2; IQR: 0.5-2 and bone structures: median 2; IQR: 1-3; p<0.0001). Compared with iMAR-Algo1, objective artefact reduction and subjective visualisation of soft-tissue and bone structures were improved with iMAR-Algo2 (p<0.0001). Both iMAR algorithms reduced artefacts compared with WFBP, however, the iMAR algorithm with dedicated settings for large metallic implants was superior to the algorithm specifically adjusted to spinal implants.

Metal Artifact Reduction in Computed Tomography After Deep Brain Stimulation Electrode Placement Using Iterative Reconstructions.

Diagnostic accuracy of intraoperative computed tomography (CT) after deep brain stimulation (DBS) electrode placement is limited due to artifacts induced by the metallic hardware, which can potentially mask intracranial postoperative complications. Different metal artifact reduction (MAR) techniques have been introduced to reduce artifacts from metal hardware in CT. The purpose of this study was to assess the impact of a novel iterative MAR technique on image quality and diagnostic performance in the follow-up of patients with DBS electrode implementation surgery. Seventeen patients who had received routine intraoperative CT of the head after implantation of DBS electrodes between March 2015 and June 2015 were retrospectively included. Raw data of all patients were reconstructed with standard weighted filtered back projection (WFBP) and additionally with a novel iterative MAR algorithm. We quantified frequencies of density changes to assess quantitative artifact reduction. For evaluation of qualitative image quality, the visibility of numerous cerebral anatomic landmarks and the detectability of intracranial electrodes were scored according to a 4-point scale. Furthermore, artifact strength overall and adjacent to the electrodes was rated. Our results of quantitative artifact reduction showed that images reconstructed with iterative MAR (iMAR) contained significantly lower metal artifacts (overall low frequency values, 1608.6 ± 545.5; range, 375.5-3417.2) compared with the WFBP (overall low frequency values, 4487.3 ± 875.4; range, 2218.3-5783.5) reconstructed images (P < 0.004). Qualitative image analysis showed a significantly improved image quality for iMAR (overall anatomical landmarks, 2.49 ± 0.15; median, 3; range, 0-3; overall electrode characteristics, 2.35 ± 0.16; median, 2; range, 0-3; artifact characteristics, 2.16 ± 0.08; median, 2.5; range, 0-3) compared with WFBP (overall anatomical landmarks, 1.21 ± 0.64; median, 1; range, 0-3; overall electrode characteristics, 0.74 ± 0.37; median, 1; range, 0-2; artifact characteristics, 0.51 ± 0.15; median, 0.5; range, 0-2; P < 0.002). Reconstructions of cranial CT images with the novel iMAR algorithm in patients after DBS implantation allows an efficient reduction of metal artifacts near DBS electrodes compared with WFBP reconstructions. We demonstrated an improvement of quantitative and qualitative image quality of iMAR compared with WFBP in patients with DBS electrodes.

Iterative metal artifact reduction: evaluation and optimization of technique.

Iterative metal artifact reduction (IMAR) is a sinogram inpainting technique that incorporates high-frequency data from standard weighted filtered back projection (WFBP) reconstructions to reduce metal artifact on computed tomography (CT). This study was designed to compare the image quality of IMAR and WFBP in total shoulder arthroplasties (TSA); determine the optimal amount of WFBP high-frequency data needed for IMAR; and compare image quality of the standard 3D technique with that of a faster 2D technique. Eight patients with nine TSA underwent CT with standardized parameters: 140 kVp, 300 mAs, 0.6mm collimation and slice thickness, and B30 kernel. WFBP, three 3D IMAR algorithms with different amounts of WFBP high-frequency data (IMARlo, lowest; IMARmod, moderate; IMARhi, highest), and one 2D IMAR algorithm were reconstructed. Differences in attenuation near hardware and away from hardware were measured and compared using repeated measures ANOVA. Five readers independently graded image quality; scores were compared using Friedman's test. Attenuation differences were smaller with all 3D IMAR techniques than with WFBP (p < 0.0063). With increasing high-frequency data, the attenuation difference increased slightly (differences not statistically significant). All readers ranked IMARmod and IMARhi more favorably than WFBP (p < 0.05), with IMARmod ranked highest for most structures. The attenuation difference was slightly higher with 2D than with 3D IMAR, with no significant reader preference for 3D over 2D. IMAR significantly decreases metal artifact compared to WFBP both objectively and subjectively in TSA. The incorporation of a moderate amount of WFBP high-frequency data and use of a 2D reconstruction technique optimize image quality and allow for relatively short reconstruction times.

Clinical evaluation of the normalized metal artefact reduction algorithm caused by dental fillings in CT

The aim of this study was to evaluate the performance of a normalized metal artefact reduction (NMAR) algorithm in patients with high-density dental fillings in CT images, and compare the results with weighted filtered back-projection (WFBP) and linear interpolation metal artefact reduction (MARli) algorithms. A total of 15 patients who had dental fillings were included in this study. The CT raw data sets were processed on an offline workstation. For each data set, one image series was reconstructed with WFBP, one with MARli and one with NMAR. Two observers qualitatively graded the severity of metal artefacts and their impacts on surrounding and distant soft tissue using a five-point scale. Six regions of interest were selected to measure the CT values and the standard deviation (SD) for quantitatively evaluating the effects of artefact reduction. A total of 217 slices with metal artefacts from 15 patients were included in the qualitative analysis. The average score (mean ± SD) with the WFBP and MARli algorithms was 2.24 ± 1.06 and 2.71 ± 0.73, respectively. Image artefacts were significantly reduced using the NMAR algorithm compared with the other two algorithms, with an average score of 1.70 ± 0.83. The mean CT value in the most hypodense streak artefacts around the metal fillings was significantly improved with both MARli and NMAR. The mean SDs of measured CT values from surrounding or distant soft tissues were lower in NMAR images than in WFBP and MARli images. The NMAR algorithm can significantly reduce the artefacts caused by dental fillings, compared with the WFBP and linear interpolation algorithms.

Weighted filtered backprojection for quantitative fluorescence optical projection tomography

Reconstructing images from a set of fluorescence optical projection tomography (OPT) projections is a relatively new problem. Several physical aspects of fluorescence OPT necessitate a different treatment of the inverse problem to that required for non-fluorescence tomography. Given a fluorophore within the depth of field of the imaging system, the power received by the optical system, and therefore the CCD detector, is related to the distance of the fluorophore from the objective entrance pupil. Additionally, due to the slight blurring of images of sources positioned off the focal plane, the CCD image of a fluorophore off the focal plane is lower in intensity than the CCD image of an identical fluorophore positioned on the focal plane. The filtered backprojection (FBP) algorithm does not take these effects into account and so cannot be expected to yield truly quantitative results. A full model of image formation is introduced which takes into account the effects of isotropic emission and defocus. The model is used to obtain a weighting function which is used in a variation of the FBP algorithm called weighted filtered backprojection (WFBP). This new algorithm is tested with simulated data and with experimental data from a phantom consisting of fluorescent microspheres embedded in an agarose gel.

Weighted FBP—a simple approximate 3D FBP algorithm for multislice spiral CT with good dose usage for arbitrary pitch

A new 3D reconstruction scheme, weighted filtered backprojection (WFBP) for multirow spiral CT based on an extension of the two-dimensional SMPR algorithm is described and results are presented. In contrast to other 3D algorithms available, the algorithm makes use of all available data for all pitch values. The algorithm is a FBP algorithm: linear convolution of the parallel data along the row direction followed by a 3D backprojection. Data usage for arbitrary pitch values is maintained through a weighting scheme which takes into account redundant data. If proper row weighting is applied, the image quality is superior to the image quality of the SMPR algorithm.