Three-dimensional reconstruction using an adaptive simultaneous algebraic reconstruction technique in electron tomography

Wan, Xiaohua; Zhang, Fa; Chu, Qi; Zhang, Kai; Sun, Fei; Yuan, Bo; Li, Yong

doi:10.1016/j.jsb.2011.06.002

Cited by 46 publications

(37 citation statements)

References 24 publications

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“…There is no evidence that other choices lead to any significant improvement in the reconstructions or savings in computing time; see, e.g., [30], [50].…”

Section: The Basic Methodsmentioning

confidence: 99%

Multicore Performance of Block Algebraic Iterative Reconstruction Methods

Sørensen¹,

Hansen²

2014

SIAM J. Sci. Comput.

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Abstract. Algebraic iterative methods are routinely used for solving the ill-posed sparse linear systems arising in tomographic image reconstruction. Here we consider the Algebraic Reconstruction Techniques (ART) and the Simultaneous Iterative Reconstruction Techniques (SIRT), both of which rely on semi-convergence. Block versions of these methods, based on a partitioning of the linear system, are able to combine the fast semi-convergence of ART with the better multi-core properties of SIRT. These block methods separate into two classes: those that, in each iteration, access the blocks in a sequential manner, and those that compute a result for each block in parallel and then combine these results before the next iteration. The goal of this work is to demonstrate which block methods are best suited for implementation on modern multi-core computers. To compare the performance of the different block methods we use a fixed relaxation parameter in each method, namely, the one that leads to the fastest semi-convergence. Computational results show that for multi-core computers, the sequential approach is preferable.

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“…There is no evidence that other choices lead to any significant improvement in the reconstructions or savings in computing time; see, e.g., [30], [50].…”

Section: The Basic Methodsmentioning

confidence: 99%

Multicore Performance of Block Algebraic Iterative Reconstruction Methods

Sørensen¹,

Hansen²

2014

SIAM J. Sci. Comput.

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“…In our work, we proposed a new iterative reconstruction algorithm with inter-iteration adaptive NAD filter called FASART based on adaptive simultaneous algebraic reconstruction method [4]. An adaptive NAD filter is adopted to restrain the noise of the reconstructed data in the iterative process.…”

Section: Iterative Reconstruction With Inter-iteration Nad Filtermentioning

confidence: 99%

“…We adopted a projection error criterion [3,4] to evaluate the quality of reconstructed results. It is often used as a standard to quantify the quality of reconstruction results, and a lower projection error value indicates a higher image quality.…”

Section: Adaptive Parametersmentioning

confidence: 99%

“…Various reconstruction methods have been proposed to solve these reconstruction problems, such as weighted back-projection (WBP) [2] and some iterative reconstruction methods [3][4][5][6]. We further explored an iterative method named adaptive simultaneous algebraic reconstruction (ASART) [4], and found that it could sharpen the edge and generate high-contrast reconstructed results, but the results are always noisy so that it is necessary to consider extra methods for noise reduction.…”

Section: Introductionmentioning

confidence: 99%

“…We further explored an iterative method named adaptive simultaneous algebraic reconstruction (ASART) [4], and found that it could sharpen the edge and generate high-contrast reconstructed results, but the results are always noisy so that it is necessary to consider extra methods for noise reduction.…”

Section: Introductionmentioning

confidence: 99%

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FASART: An iterative reconstruction algorithm with inter-iteration adaptive NAD filter

Zhao

Zhang

et al. 2015

BME

Self Cite

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Abstract. Electron tomography (ET) is an essential imaging technique for studying structures of large biological specimens. These structures are reconstructed from a set of projections obtained at different sample orientations by tilting the specimen. However, most of existing reconstruction methods are not appropriate when the data are extremely noisy and incomplete. A new iterative method has been proposed: adaptive simultaneous algebraic reconstruction with inter-iteration adaptive non-linear anisotropic diffusion (NAD) filter (FASART). We also adopted an adaptive parameter and discussed the step for the filter in this reconstruction method. Experimental results show that FASART can restrain the noise generated in the process of iterative reconstruction and still preserve the more details of the structure edges.

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Reconstruction of a high‐quality volumetric image and a respiratory motion model from patient CBCT projections

et al. 2019

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Purpose To develop and evaluate a method of reconstructing a patient‐ and treatment day‐ specific volumetric image and motion model from free‐breathing cone‐beam projections and respiratory surrogate measurements. This Motion‐Compensated Simultaneous Algebraic Reconstruction Technique (MC‐SART) generates and uses a motion model derived directly from the cone‐beam projections, without requiring prior motion measurements from 4DCT, and can compensate for both inter‐ and intrabin deformations. The motion model can be used to generate images at arbitrary breathing points, which can be used for estimating volumetric images during treatment delivery. Methods The MC‐SART was formulated using simultaneous image reconstruction and motion model estimation. For image reconstruction, projections were first binned according to external surrogate measurements. Projections in each bin were used to reconstruct a set of volumetric images using MC‐SART. The motion model was estimated based on deformable image registration between the reconstructed bins, and least squares fitting to model parameters. The model was used to compensate for motion in both projection and backprojection operations in the subsequent image reconstruction iterations. These updated images were then used to update the motion model, and the two steps were alternated between. The final output is a volumetric reference image and a motion model that can be used to generate images at any other time point from surrogate measurements. Results A retrospective patient dataset consisting of eight lung cancer patients was used to evaluate the method. The absolute intensity differences in the lung regions compared to ground truth were 50.8 ± 43.9 HU in peak exhale phases (reference) and 80.8 ± 74.0 in peak inhale phases (generated). The 50th percentile of voxel registration error of all voxels in the lung regions with >5 mm amplitude was 1.3 mm. The MC‐SART was also applied to measured patient cone‐beam projections acquired with a linac‐mounted CBCT system. Results from this patient data demonstrate the feasibility of MC‐SART and showed qualitative image quality improvements compared to other state‐of‐the‐art algorithms. Conclusion We have developed a simultaneous image reconstruction and motion model estimation method that uses Cone‐beam computed tomography (CBCT) projections and respiratory surrogate measurements to reconstruct a high‐quality reference image and motion model of a patient in treatment position. The method provided superior performance in both HU accuracy and positional accuracy compared to other existing methods. The resultant reference image and motion model can be combined with respiratory surrogate measurements to generate volumetric images representing patient anatomy at arbitrary time points.

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Three-dimensional reconstruction using an adaptive simultaneous algebraic reconstruction technique in electron tomography

Cited by 46 publications

References 24 publications

Multicore Performance of Block Algebraic Iterative Reconstruction Methods

Multicore Performance of Block Algebraic Iterative Reconstruction Methods

FASART: An iterative reconstruction algorithm with inter-iteration adaptive NAD filter

Reconstruction of a high‐quality volumetric image and a respiratory motion model from patient CBCT projections

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