Iterative Image Reconstruction Algorithm Research Articles

Performance of Landweber iteration algorithm in tomographic image reconstruction

The rapid growth of computed tomography has been accompanied by equally advancing in image reconstruction algorithm also. The fundamental inverse problem is the reconstruction of a function from finitely many measurements, so pertaining to that function. The measured data are limited, and it cannot serve to determine one single correct solution. The iterative reconstruction image reconstruction algorithms have stable solution to the limited projection. In this paper, Landweber-based iteration image reconstruction is simulated and its performance is compared with different algorithm. Then, the quality of the reconstructed image is expressed in terms of mean absolute error and correlation coefficient as compared to the original image. The entire simulations are performed in Matlab tool.

Performance of Landweber iteration algorithm in tomographic image reconstruction

The rapid growth of computed tomography has been accompanied by equally advancing in image reconstruction algorithm also. The fundamental inverse problem is the reconstruction of a function from finitely many measurements, so pertaining to that function. The measured data are limited, and it cannot serve to determine one single correct solution. The iterative reconstruction image reconstruction algorithms have stable solution to the limited projection. In this paper, Landweber-based iteration image reconstruction is simulated and its performance is compared with different algorithm. Then, the quality of the reconstructed image is expressed in terms of mean absolute error and correlation coefficient as compared to the original image. The entire simulations are performed in Matlab tool.

A fast Total Variation-based iterative algorithm for digital breast tomosynthesis image reconstruction

In this work, we propose a fast iterative algorithm for the reconstruction of digital breast tomosynthesis images. The algorithm solves a regularization problem, expressed as the minimization of the sum of a least-squares term and a weighted smoothed version of the Total Variation regularization function. We use a Fixed Point method for the solution of the minimization problem, requiring the solution of a linear system at each iteration, whose coefficient matrix is a positive definite approximation of the Hessian of the objective function. We propose an efficient implementation of the algorithm, where the linear system is solved by a truncated Conjugate Gradient method. We compare the Fixed Point implementation with a fast first order method such as the Scaled Gradient Projection method, that does not require any linear system solution. Numerical experiments on a breast phantom widely used in tomographic simulations show that both the methods recover microcalcifications very fast while the Fixed Point is more efficient in detecting masses, when more time is available for the algorithm execution.

Evaluation of hybrid SART + OS + TV iterative reconstruction algorithm for optical-CT gel dosimeter imaging

Optical computed tomography (optical-CT) is a high-resolution, fast, and easily accessible readout modality for gel dosimeters. This paper evaluates a hybrid iterative image reconstruction algorithm for optical-CT gel dosimeter imaging, namely, the simultaneous algebraic reconstruction technique (SART) integrated with ordered subsets (OS) iteration and total variation (TV) minimization regularization. The mathematical theory and implementation workflow of the algorithm are detailed. Experiments on two different optical-CT scanners were performed for cross-platform validation. For algorithm evaluation, the iterative convergence is first shown, and peak-to-noise-ratio (PNR) and contrast-to-noise ratio (CNR) results are given with the cone-beam filtered backprojection (FDK) algorithm and the FDK results followed by median filtering (mFDK) as reference. The effect on spatial gradients and reconstruction artefacts is also investigated. The PNR curve illustrates that the results of SART + OS + TV finally converges to that of FDK but with less noise, which implies that the dose-OD calibration method for FDK is also applicable to the proposed algorithm. The CNR in selected regions-of-interest (ROIs) of SART + OS + TV results is almost double that of FDK and 50% higher than that of mFDK. The artefacts in SART + OS + TV results are still visible, but have been much suppressed with little spatial gradient loss. Based on the assessment, we can conclude that this hybrid SART + OS + TV algorithm outperforms both FDK and mFDK in denoising, preserving spatial dose gradients and reducing artefacts, and its effectiveness and efficiency are platform independent.

Improved iterative image reconstruction algorithm for the exterior problem of computed tomography

In industrial applications that are limited by the angle of a fan-beam and the length of a detector, the exterior problem of computed tomography (CT) uses only the projection data that correspond to the external annulus of the objects to reconstruct an image. Because the reconstructions are not affected by the projection data that correspond to the interior of the objects, the exterior problem is widely applied to detect cracks in the outer wall of large-sized objects, such as in-service pipelines. However, image reconstruction in the exterior problem is still a challenging problem due to truncated projection data and beam-hardening, both of which can lead to distortions and artifacts. Thus, developing an effective algorithm and adopting a scanning trajectory suited for the exterior problem may be valuable. In this study, an improved iterative algorithm that combines total variation minimization (TVM) with a region scalable fitting (RSF) model was developed for a unilateral off-centered scanning trajectory and can be utilized to inspect large-sized objects for defects. Experiments involving simulated phantoms and real projection data were conducted to validate the practicality of our algorithm. Furthermore, comparative experiments show that our algorithm outperforms others in suppressing the artifacts caused by truncated projection data and beam-hardening.

Noise weighting with an exponent for transmission CT

It is widely believed that the correct weighting function is the reciprocal of the noise variance of the associated measurement. Many researchers are making great efforts to find the accurate variance for the measurements for imaging systems so that they can hopefully achieve an optimal reconstruction. An ‘optimal’ solution in the context of this paper is referred to as the image that reaches optimum according to a criterion or criteria among a group of candidates, regardless how the images in the group are obtained. This ‘optimal’ solution is not a theoretical concept, but is simply the ‘best of the bunch’. The goal of the paper is to investigate how the weighting function affects the image noise when the image contrast is pre-specified in an iterative algorithm for x-ray CT. This paper makes some interesting observations: there is no universal optimal weighting function. The noise weighting function can introduce artifacts. The optimal noise weighting varies with the object to be reconstructed and targeted image contrast in an iterative image reconstruction algorithm and in a filtered backprojection algorithm that incorporates the projection noise. It is suggested that an exponent be used in the weighting function so that the artifacts caused by the weighting function can be reduced.

TU‐D‐207A‐00: Quantitative Assessment of CT Systems with Iterative Image Reconstruction Algorithms

In recent several years, motivated by the need to reduce radiation doses in CT exams, all of the major CT manufacturers have commercialized different iterative image reconstruction techniques and these innovative techniques were used in clinical routines with increasing popularity. However, due to the intrinsic nonlinearity of these new techniques, the well accepted quantitative image quality assessment metrics such as spatial resolution and contrast to noise ratio are not sufficient to provide the needed quantitative metrics for assessing image quality and for guiding the CT scan protocol optimization. This symposium aims at providing a thorough update to AAPM community on what we have understood in the past for linear CT imaging system, what are the new challenges and opportunities offered by the nonlinear iterative image reconstruction, and what would be the future directions in quantitative image quality assessment that the AAPM community can work together to address the challenges and to adapt the nonlinear image reconstruction methods to routine clinical practice to improve patient care. Three invited speakers will lead the discussions in this symposium.Dr. Ke Li from the University of Wisconsin‐Madison will be presenting the new challenges introduced in model‐based iterative reconstruction (MBIR) method with a focus on how the nonlinear nature of the MBIR reconstruction poses challenges in quantitative image quality metrics such as spatial resolution, noise power spectrum, and the limitations of CNR in protocol optimization which eventually leads to the need of task‐based detectability index as the metric.Dr. Shuai Leng from Mayo Clinic will then present how the CNR metric is insufficient for nonlinear reconstruction algorithms, and how task based model observers can be used to more accurately quantify image quality, and to optimize imaging system and scanning protocols for nonlinear iterative image reconstruction algorithms based on specific imaging task. Practical considerations regarding the application of task based image quality metrics will also be discussed.Dr. Ehsan Samei from Duke University will start the symposium by summarizing a to‐be‐released AAPM Task Group report (TG 233: Performance Evaluation of Computed Tomography Systems). He will cover what has been understood and accepted for linear CT imaging system, what are the new challenges in CT image quality assessment introduced by the nonlinear iterative reconstruction (IR) techniques, and summarize both taskneutral and task‐based metrologies developed in TG 233 to partially address these challenges, and foresee additional challenges in the future. Learning Objectives: Understand the challenges in quantitative image quality assessment introduced by nonlinear iterative reconstruction techniques Understand the challenges and methods in noise performance assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in spatial resolution assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in task‐based observer performance assessment for nonlinear iterative reconstruction techniquesFunding support received from NIH and DOD; Funding support received from GE Healthcare; Funding support received from Siemens AX; Patent royalties received from GE Healthcare; S. Leng, R01 EB071095; U01 EB017185; E. Samei, Research grant, Siemens; Research grant, GE; K. Li, Funding from NIH, DOD and AAPM

TU‐D‐207A‐03: Quantitative Assessment of CT Systems with Iterative Image Reconstruction Algorithms

In recent several years, motivated by the need to reduce radiation doses in CT exams, all of the major CT manufacturers have commercialized different iterative image reconstruction techniques and these innovative techniques were used in clinical routines with increasing popularity. However, due to the intrinsic nonlinearity of these new techniques, the well accepted quantitative image quality assessment metrics such as spatial resolution and contrast to noise ratio are not sufficient to provide the needed quantitative metrics for assessing image quality and for guiding the CT scan protocol optimization. This symposium aims at providing a thorough update to AAPM community on what we have understood in the past for linear CT imaging system, what are the new challenges and opportunities offered by the nonlinear iterative image reconstruction, and what would be the future directions in quantitative image quality assessment that the AAPM community can work together to address the challenges and to adapt the nonlinear image reconstruction methods to routine clinical practice to improve patient care. Three invited speakers will lead the discussions in this symposium.Dr. Ke Li from the University of Wisconsin‐Madison will be presenting the new challenges introduced in model‐based iterative reconstruction (MBIR) method with a focus on how the nonlinear nature of the MBIR reconstruction poses challenges in quantitative image quality metrics such as spatial resolution, noise power spectrum, and the limitations of CNR in protocol optimization which eventually leads to the need of task‐based detectability index as the metric.Dr. Shuai Leng from Mayo Clinic will then present how the CNR metric is insufficient for nonlinear reconstruction algorithms, and how task based model observers can be used to more accurately quantify image quality, and to optimize imaging system and scanning protocols for nonlinear iterative image reconstruction algorithms based on specific imaging task. Practical considerations regarding the application of task based image quality metrics will also be discussed.Dr. Ehsan Samei from Duke University will start the symposium by summarizing a to‐be‐released AAPM Task Group report (TG 233: Performance Evaluation of Computed Tomography Systems). He will cover what has been understood and accepted for linear CT imaging system, what are the new challenges in CT image quality assessment introduced by the nonlinear iterative reconstruction (IR) techniques, and summarize both taskneutral and task‐based metrologies developed in TG 233 to partially address these challenges, and foresee additional challenges in the future. Learning Objectives: Understand the challenges in quantitative image quality assessment introduced by nonlinear iterative reconstruction techniques Understand the challenges and methods in noise performance assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in spatial resolution assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in task‐based observer performance assessment for nonlinear iterative reconstruction techniquesFunding support received from NIH and DOD; Funding support received from GE Healthcare; Funding support received from Siemens AX; Patent royalties received from GE Healthcare; S. Leng, R01 EB071095; U01 EB017185; E. Samei, Research grant, Siemens; Research grant, GE; K. Li, Funding from NIH, DOD and AAPM

TU‐D‐207A‐02: Quantitative Assessment of CT Systems with Iterative Image Reconstruction Algorithms

In recent several years, motivated by the need to reduce radiation doses in CT exams, all of the major CT manufacturers have commercialized different iterative image reconstruction techniques and these innovative techniques were used in clinical routines with increasing popularity. However, due to the intrinsic nonlinearity of these new techniques, the well accepted quantitative image quality assessment metrics such as spatial resolution and contrast to noise ratio are not sufficient to provide the needed quantitative metrics for assessing image quality and for guiding the CT scan protocol optimization. This symposium aims at providing a thorough update to AAPM community on what we have understood in the past for linear CT imaging system, what are the new challenges and opportunities offered by the nonlinear iterative image reconstruction, and what would be the future directions in quantitative image quality assessment that the AAPM community can work together to address the challenges and to adapt the nonlinear image reconstruction methods to routine clinical practice to improve patient care. Three invited speakers will lead the discussions in this symposium.Dr. Ke Li from the University of Wisconsin‐Madison will be presenting the new challenges introduced in model‐based iterative reconstruction (MBIR) method with a focus on how the nonlinear nature of the MBIR reconstruction poses challenges in quantitative image quality metrics such as spatial resolution, noise power spectrum, and the limitations of CNR in protocol optimization which eventually leads to the need of task‐based detectability index as the metric.Dr. Shuai Leng from Mayo Clinic will then present how the CNR metric is insufficient for nonlinear reconstruction algorithms, and how task based model observers can be used to more accurately quantify image quality, and to optimize imaging system and scanning protocols for nonlinear iterative image reconstruction algorithms based on specific imaging task. Practical considerations regarding the application of task based image quality metrics will also be discussed.Dr. Ehsan Samei from Duke University will start the symposium by summarizing a to‐be‐released AAPM Task Group report (TG 233: Performance Evaluation of Computed Tomography Systems). He will cover what has been understood and accepted for linear CT imaging system, what are the new challenges in CT image quality assessment introduced by the nonlinear iterative reconstruction (IR) techniques, and summarize both taskneutral and task‐based metrologies developed in TG 233 to partially address these challenges, and foresee additional challenges in the future. Learning Objectives: Understand the challenges in quantitative image quality assessment introduced by nonlinear iterative reconstruction techniques Understand the challenges and methods in noise performance assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in spatial resolution assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in task‐based observer performance assessment for nonlinear iterative reconstruction techniquesFunding support received from NIH and DOD; Funding support received from GE Healthcare; Funding support received from Siemens AX; Patent royalties received from GE Healthcare; S. Leng, R01 EB071095; U01 EB017185; E. Samei, Research grant, Siemens; Research grant, GE; K. Li, Funding from NIH, DOD and AAPM

TU‐D‐207A‐01: Quantitative Assessment of CT Systems with Iterative Image Reconstruction Algorithms

In recent several years, motivated by the need to reduce radiation doses in CT exams, all of the major CT manufacturers have commercialized different iterative image reconstruction techniques and these innovative techniques were used in clinical routines with increasing popularity. However, due to the intrinsic nonlinearity of these new techniques, the well accepted quantitative image quality assessment metrics such as spatial resolution and contrast to noise ratio are not sufficient to provide the needed quantitative metrics for assessing image quality and for guiding the CT scan protocol optimization. This symposium aims at providing a thorough update to AAPM community on what we have understood in the past for linear CT imaging system, what are the new challenges and opportunities offered by the nonlinear iterative image reconstruction, and what would be the future directions in quantitative image quality assessment that the AAPM community can work together to address the challenges and to adapt the nonlinear image reconstruction methods to routine clinical practice to improve patient care. Three invited speakers will lead the discussions in this symposium.Dr. Ke Li from the University of Wisconsin‐Madison will be presenting the new challenges introduced in model‐based iterative reconstruction (MBIR) method with a focus on how the nonlinear nature of the MBIR reconstruction poses challenges in quantitative image quality metrics such as spatial resolution, noise power spectrum, and the limitations of CNR in protocol optimization which eventually leads to the need of task‐based detectability index as the metric.Dr. Shuai Leng from Mayo Clinic will then present how the CNR metric is insufficient for nonlinear reconstruction algorithms, and how task based model observers can be used to more accurately quantify image quality, and to optimize imaging system and scanning protocols for nonlinear iterative image reconstruction algorithms based on specific imaging task. Practical considerations regarding the application of task based image quality metrics will also be discussed.Dr. Ehsan Samei from Duke University will start the symposium by summarizing a to‐be‐released AAPM Task Group report (TG 233: Performance Evaluation of Computed Tomography Systems). He will cover what has been understood and accepted for linear CT imaging system, what are the new challenges in CT image quality assessment introduced by the nonlinear iterative reconstruction (IR) techniques, and summarize both taskneutral and task‐based metrologies developed in TG 233 to partially address these challenges, and foresee additional challenges in the future. Learning Objectives: Understand the challenges in quantitative image quality assessment introduced by nonlinear iterative reconstruction techniques Understand the challenges and methods in noise performance assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in spatial resolution assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in task‐based observer performance assessment for nonlinear iterative reconstruction techniquesFunding support received from NIH and DOD; Funding support received from GE Healthcare; Funding support received from Siemens AX; Patent royalties received from GE Healthcare; S. Leng, R01 EB071095; U01 EB017185; E. Samei, Research grant, Siemens; Research grant, GE; K. Li, Funding from NIH, DOD and AAPM

Combination of voxel-based and projection-based methods in terms of convergence for CT reconstruction.

Recent applications of iterative image reconstruction algorithms to multislice helical CT have shown that iterative reconstruction can significantly improve image quality and reduce artifacts. In this paper, the authors introduce a combination of two different algorithms with different convergence properties: ordered subsets separable paraboloidal surrogates (OS-SPS) and iterative coordinate descent (ICD). The first one updates image voxels simultaneously, slightly changing attenuation values iteration by iteration. The second algorithm updates image voxel by voxel, each time performing full forward and backward projections of the voxel. It has been shown that ICD converges better at high-frequency areas and requires more iterations to reconstruct low-frequency components of the image. In contrast to ICD, SPS requires multiple iterations to reconstruct high-frequency areas. In this paper, the authors introduce an algorithm which leverages the benefits of both ICD and SPS. The idea is to update the entire image with SPS, determine high-frequency components, and focus ICD computations on it using nonhomogeneous ICD update. The authors have successfully implemented OS-SPS, ICD, their hybrid approach, and few variations of ICD based on spatially nonuniform updates. The authors have examined the convergence of different algorithms and found that proposed algorithm converges better than OS-SPS, ICD, as well as various improved variants of ICD.

FIRT: Filtered iterative reconstruction technique with information restoration.

Electron tomography (ET) combining subsequent sub-volume averaging has been becoming a unique way to study the in situ 3D structures of macromolecular complexes. However, information missing in electron tomography due to limited angular sampling is still the bottleneck in high-resolution electron tomography application. Here, based on the understanding of smooth nature of biological specimen, we present a new iterative image reconstruction algorithm, FIRT (filtered iterative reconstruction technique) for electron tomography by combining the algebra reconstruction technique (ART) and the nonlinear diffusion (ND) filter technique. Using both simulated and experimental data, in comparison to ART and weight back projection method, we proved that FIRT could generate a better reconstruction with reduced ray artifacts and significant improved correlation with the ground truth and partially restore the information at the non-sampled angular region, which was proved by investigating the 90° re-projection and by the cross-validation method. This new algorithm will be subsequently useful in the future for both cellular and molecular ET with better quality and improved structural details.

Image Reconstruction for ECT Based on Extended Sensitivity Matrix

Image reconstruction for electrical capacitance tomography (ECT) is usually based on a sensitivity matrix. To reduce the effect of soft-filed property of ECT and enhance the quality of reconstructed images, a new image reconstruction method for ECT based on an extended sensitivity matrix is proposed. The elements used in constructing the sensitivity matrix are a set of block bases instead of each individual pixel. Using the new block basis approach, an iterative algorithm for image reconstruction in ECT is implemented with a 12-electrode square ECT sensor. The performance of the new approach is evaluated according to the simulation and experimental results. Preliminary results show that the image reconstruction based on an extended sensitivity matrix with a suitably designed block basis can improve the image quality and stability compared with the Landweber iteration algorithm based on the conventional one-pixel basis.

A new linearized split Bregman iterative algorithm for image reconstruction in sparse-view X-ray computed tomography

In this paper, a new linearized split Bregman iterative algorithm is proposed for sparse-view X-ray computed tomography, which can avoid solving a large-scale and unstructured linear system in each iteration. Remarkably, our method can be generalized to efficiently resolve some other image processing and analysis models, for instance, the robust compressed sensing, the total variation-ℓ1, and the ℓ1–ℓ1. We also give rigorous proofs for the convergence of the proposed method under appropriate conditions for the aforementioned problems. Experimental results demonstrate that our algorithm has better performance in terms of reconstruction quality, effectiveness and robustness, compared with some other methods (e.g. gradient-flow-based semi-implicit finite element method, split Bregman, etc.) for the robust image reconstruction in sparse-view X-ray computed tomography.

Comparative Studies on Various Iterative Image Reconstruction Algorithms for Emission Tomography

Comparative Studies on Various Iterative Image Reconstruction Algorithms for Emission Tomography

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction.

The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two nonsmooth penalty functions that will lead to further reductions in image reconstruction times while preserving image quality. Moreover, with the help of a mixed sparsity-regularization, better preservation of soft-tissue structures can be potentially obtained. The algorithms were systematically evaluated by use of computer-simulated and clinical data sets.

Convergence studies on block iterative algorithms for image reconstruction

The sequential block-iterative scheme based on Landweber’s method is a general iterative one for image reconstruction. In this paper, we give the finite termination convergence for this scheme, which provides an approach to choose relaxation coefficients. Furthermore, sufficient and necessary convergence conditions are also established for the sequential block-iterative scheme case.

A new projection-based iterative image reconstruction algorithm for dual-energy computed tomography

Dual-energy CT can be represented as the dual-energy equations by decomposing the linear attenuation coefficient of the X-ray scanned object into two material basis functions of photoelectric absorption and Compton scatter. To solve the dual-energy equations, in this paper, we apply the mean-value theorem for integrals and propose a new projection-based iterative algorithm. We discuss the convergence of the proposed algorithm and carry out various numerical simulations for demonstrating its feasibility.

On the computational implementation of forward and back-projection operations for cone-beam computed tomography.

Forward- and back-projection operations are the main computational burden in iterative image reconstruction in computed tomography. In addition, their implementation has to be accurate to ensure stable convergence to a high-quality image. This paper reviews and compares some of the variations in the implementation of these operations in cone-beam computed tomography. We compare four algorithms for computing the system matrix, including a distance-driven algorithm, an algorithm based on cubic basis functions, another based on spherically symmetric basis functions, and a voxel-driven algorithm. The focus of our study is on understanding how the choice of the implementation of the system matrix will influence the performance of iterative image reconstruction algorithms, including such factors as the noise strength and spatial resolution in the reconstructed image. Our experiments with simulated and real cone-beam data reveal the significance of the speed-accuracy trade-off in the implementation of the system matrix. Our results suggest that fast convergence of iterative image reconstruction methods requires accurate implementation of forward- and back-projection operations, involving a direct estimation of the convolution of the footprint of the voxel basis function with the surface of the detectors. The required accuracy decreases by increasing the resolution of the projection measurements beyond the resolution of the reconstructed image. Moreover, reconstruction of low-contrast objects needs more accurate implementation of these operations. Our results also show that, compared with regularized reconstruction methods, the behavior of iterative reconstruction algorithms that do not use a proper regularization is influenced more significantly by the implementation of the forward- and back-projection operations.

Improved dose calculation accuracy for low energy brachytherapy by optimizing dual energy CT imaging protocols for noise reduction using sinogram affirmed iterative reconstruction

The goal of this study was to evaluate the noise reduction achievable from dual energy computed tomography (CT) imaging (DECT) using filtered backprojection (FBP) and iterative image reconstruction algorithms combined with increased imaging exposure. We evaluated the data in the context of imaging for brachytherapy dose calculation, where accurate quantification of electron density ρe and effective atomic number Zeff is beneficial.A dual source CT scanner was used to scan a phantom containing tissue mimicking inserts. DECT scans were acquired at 80 kVp/140Sn kVp (where Sn stands for tin filtration) and 100 kVp/140Sn kVp, using the same values of the CT dose index CTDIvol for both settings as a measure for the radiation imaging exposure. Four CTDIvol levels were investigated. Images were reconstructed using FBP and sinogram affirmed iterative reconstruction (SAFIRE) with strength 1,3 and 5. From DECT scans two material quantities were derived, Zeff and ρe. DECT images were used to assign material types and the amount of improperly assigned voxels was quantified for each protocol. The dosimetric impact of improperly assigned voxels was evaluated with Geant4 Monte Carlo (MC) dose calculations for an 125I source in numerical phantoms.Standard deviations for Zeff and ρe were reduced up to a factor ∼2 when using SAFIRE with strength 5 compared to FBP. Standard deviations on Zeff and ρe as low as 0.15 and 0.006 were achieved for the muscle insert representing typical soft tissue using a CTDIvol of 40 mGy and 3mm slice thickness. Dose calculation accuracy was generally improved when using SAFIRE. Mean (maximum absolute) dose errors of up to 1.3% (21%) with FBP were reduced to less than 1% (6%) with SAFIRE at a CTDIvol of 10 mGy. Using a CTDIvol of 40mGy and SAFIRE yielded mean dose calculation errors of the order of 0.6% which was the MC dose calculation precision in this study and no error was larger than ±2.5% as opposed to errors of up to -4% with FPB.This phantom study showed that the SAFIRE image reconstruction algorithm provided reduced standard deviations of Zeff and ρe in uniform regions of interest while preserving mean Zeff and ρe values. This resulted in improved material type assignment. The use of SAFIRE improved brachytherapy dose calculations for the materials from the phantom investigated in this study using 125I.