Simultaneous algebraic reconstruction technique based on guided image filtering

Ji, Dongjiang; Qu, Gangrong; Liu, Baodong

doi:10.1364/oe.24.015897

Cited by 25 publications

(21 citation statements)

References 23 publications

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“…When there is no statistical noise, the image (f) that was reconstructed using this algorithm converges to the weighted least-squares solution of min f Af À g k k, where A and g denote the system matrix and projection data, respectively. 17 The SART algorithm updates an image at pixel j, and each iteration n (f n j ), as…”

Section: A Image Reconstructionmentioning

confidence: 99%

“…0. 17 For TV minimization, the gradient descent method 22 was applied on f nþ1 j , as shown in Eq. (3):…”

Section: A Image Reconstructionmentioning

confidence: 99%

“…Appropriate use of prior information facilitates improvement to the reconstructed image quality. 17 When these regularization methods are not used, it results in undesirable noise amplification and artifacts in the reconstructed image.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous correction of sensitivity and spatial resolution in projection‐based magnetic particle imaging

Murase¹

2020

Medical Physics

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Purpose: The purpose of this study was to develop a method to simultaneously correct the spatial resolution and inhomogeneous sensitivity of a receiving coil in projection-based magnetic particle imagingand to investigate its efficacy through simulation and experimental studies. Methods: Magnetic particle imaging (MPI) images were reconstructed using the simultaneous algebraic reconstruction technique (SART), and simultaneous corrections to sensitivity and spatial resolution were performed by incorporating the sensitivity map of the receiving coil and the system function into the SART algorithm. After each SART update, the regularization methodwith total variation (TV) minimizationwas used to suppress noise amplification and artifact generation. For comparison, MPI images were also reconstructed using the filtered backprojection (FBP) method and the FBP-truncated singular value decomposition (TSVD) method, in which the system function was deconvolved from the projection data using TSVD. In simulation studies, the sensitivity map of a second-order, gradiometer-type receiving coil was generated using the Biot-Savart law, while the system function was obtained by calculating the MPI signals induced by magnetic nanoparticles at various distances from a field-free line (FFL), using a lock-in-amplifier model. The effects of a regularization parameter for TV minimization (a), number of iterations (N), and signal-to-noise ratio (SNR) of the MPI signals on the reconstructed MPI images of a numerical phantom were evaluated, using the image profiles and percent root mean square error (PRMSE). Experimental studies involved the calculation of the system function using a tube phantom. Projection data for an A-shaped phantom were acquired using our MPI scanner, and their MPI images were reconstructed from the projection data, as described above. Results: When both the sensitivity and spatial resolution were corrected (SART-SR), the quality of the reconstructed images was seen to have improved, compared to when the spatial resolution was not correctedor when the FBP and FBP-TSVD methods were used. When SNR was low (20), a larger a value yielded a better image. The minimum PRMSE occurred at N % 200-400 and increased with increasing N thereafter. When SNR was high (100), the image quality was generally not dependent on the a value within its studied range. The PRMSE decreased slowly with increasing N, and tended to converge to a constant value. The full width at half maximum (FWHM) of the profile was obtained from the A-shaped phantom, reconstructed using the SART-SR algorithm with a = 0.05 and N = 1000. The FWHM value of the tube (2 mm diameter) in the A-shaped phantom image was found to be 2.2 mm on average, whereas those calculated from the images obtained by the FBP and FBP-TSVD methods were 4.4 and 3.0 mm on average, respectively. Spatial resolution improved when using the FBP-TSVD method as compared to the FBP method but image distortion and artifacts were observed. Conclusions: Although further studies are necessary to opti...

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Section: A Image Reconstructionmentioning

confidence: 99%

“…0. 17 For TV minimization, the gradient descent method 22 was applied on f nþ1 j , as shown in Eq. (3):…”

Section: A Image Reconstructionmentioning

confidence: 99%

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Simultaneous correction of sensitivity and spatial resolution in projection‐based magnetic particle imaging

Murase¹

2020

Medical Physics

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“…The empirical-knowledge based methods can utilize the known information (e.g., the boundary, shape, density range and the sparsity) of an object to formulate the reconstruction model, such as the total variation (TV) based reconstruction algorithm [23], tensor based reconstruction algorithm [24], low-rank based reconstruction algorithm [25], and so on. The prior-image knowledge based methods explore both image sparsity and similarity by utilizing the high-quality prior images, e.g., the dictionary learning based reconstruction algorithm [26], the prior image constrained compressed sensing (PICCS) algorithm [27], and the guided image filtering (GIF) [28] based simultaneous algebraic reconstruction technique algorithm [29]. The IR methods have the excellent performance for the few-projection CT reconstruction, however, they often require large memory space for very large computational tasks, and they also take a long execution time (typically costing tens of minutes and even a few hours for one CT).…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional visualization of microvasculature from few-projection data using a novel CT reconstruction algorithm for propagation-based X-ray phase-contrast imaging

Zhao

et al. 2019

Biomed. Opt. Express

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Propagation-based X-ray phase-contrast imaging (PBI) is a powerful nondestructive imaging technique that can reveal the internal detailed structures in weakly absorbing samples. Extending PBI to CT (PBCT) enables high-resolution and high-contrast 3D visualization of microvasculature, which can be used for the understanding, diagnosis and therapy of diseases involving vasculopathy, such as cardiovascular disease, stroke and tumor. However, the long scan time for PBCT impedes its wider use in biomedical and preclinical microvascular studies. To address this issue, a novel CT reconstruction algorithm for PBCT is presented that aims at shortening the scan time for microvascular samples by reducing the number of projections while maintaining the high quality of reconstructed images. The proposed algorithm combines the filtered backprojection method into the iterative reconstruction framework, and a weighted guided image filtering approach (WGIF) is utilized to optimize the intermediate reconstructed images. Notably, the homogeneity assumption on the microvasculature sample is adopted as prior knowledge, and therefore, a prior image of microvasculature structures can be acquired by a k-means clustering approach. Then, the prior image is used as the guided image in the WGIF procedure to effectively suppress streaking artifacts and preserve microvasculature structures. To evaluate the effectiveness and capability of the proposed algorithm, simulation experiments on 3D microvasculature numerical phantom and real experiments with CT reconstruction on the microvasculature sample are performed. The results demonstrate that the proposed algorithm can, under noise-free and noisy conditions, significantly reduce the artifacts and effectively preserve the microvasculature structures on the reconstructed images and thus enables it to be used for clear and accurate 3D visualization of microvasculature from few-projection data. Therefore, for 3D visualization of microvasculature, the proposed algorithm can be considered an effective approach for reducing the scan time required by PBCT.

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“…Although SART algorithms can improve the signal-to-noise ratio of the output images, these methods usually have a heavy computing burden and always lead to oversmoothed output images. These limitations of analytic CT algorithms and iterative algorithms usually degrade the image resolution of clinical CT data, and noisy low-resolution (LR) images may result in misdiagnosis 3,4 . Therefore, the utilization of super resolution (SR) based on dictionary learning and sparse representation is necessary to obtain high-resolution (HR) images and avoid excessive smoothing.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution CT Image Reconstruction Based on Dictionary Learning and Sparse Representation

Jiang

Zhang

Fan

et al. 2018

Sci Rep

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In this paper, a single-computed tomography (CT) image super-resolution (SR) reconstruction scheme is proposed. This SR reconstruction scheme is based on sparse representation theory and dictionary learning of low- and high-resolution image patch pairs to improve the poor quality of low-resolution CT images obtained in clinical practice using low-dose CT technology. The proposed strategy is based on the idea that image patches can be well represented by sparse coding of elements from an overcomplete dictionary. To obtain similarity of the sparse representations, two dictionaries of low- and high-resolution image patches are jointly trained. Then, sparse representation coefficients extracted from the low-resolution input patches are used to reconstruct the high-resolution output. Sparse representation is used such that the trained dictionary pair can reduce computational costs. Combined with several appropriate iteration operations, the reconstructed high-resolution image can attain better image quality. The effectiveness of the proposed method is demonstrated using both clinical CT data and simulation image data. Image quality evaluation indexes (root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR)) indicate that the proposed method can effectively improve the resolution of a single CT image.

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Simultaneous algebraic reconstruction technique based on guided image filtering

Cited by 25 publications

References 23 publications

Simultaneous correction of sensitivity and spatial resolution in projection‐based magnetic particle imaging

Simultaneous correction of sensitivity and spatial resolution in projection‐based magnetic particle imaging

Three-dimensional visualization of microvasculature from few-projection data using a novel CT reconstruction algorithm for propagation-based X-ray phase-contrast imaging

Super-resolution CT Image Reconstruction Based on Dictionary Learning and Sparse Representation

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