Compressed‐sensing‐based content‐driven hierarchical reconstruction: Theory and application to C‐arm cone‐beam tomography

Langet, Hélène; Riddell, Cyril; Reshef, Aymeric; Trousset, Yves; Tenenhaus, Arthur; Lahalle, Elisabeth; Fleury, Gilles; Paragios, Nikos

doi:10.1118/1.4928144

Cited by 9 publications

(11 citation statements)

References 52 publications

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“…Before, obesity has been considered to be an important cause of coronary heart disease. Studies of Langet et al [14] showed that the inflammatory cells produced by various adipose tissues in the human body changed the function of vascular wall endothelial cells, smoothed muscle tissue, and other cells, resulting in the production of atherosclerotic plaque. Based on the theory of compressed sensing, an optimized reconstruction algorithm was constructed firstly, and the algorithm in the diagnosis of the CT image was used.…”

Section: Discussionmentioning

confidence: 99%

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Optimized Reconstruction Algorithm-Processed CT Image in the Diagnosis of Correlation between Epicardial Fat Volume and Coronary Heart Disease

Xue

Jing

2022

Scientific Programming

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This study was to analyze the application value of a reconstruction algorithm in CT images of patients with coronary heart disease and analyze the correlation between epicardial fat volume and coronary heart disease. An optimized reconstruction algorithm was constructed based on compressed sensing theory in this study. Then, the optimized algorithm was applied to the image reconstruction of multislice spiral CT image data after testing its sensitivity, accuracy, and specificity. 60 patients with suspected angina pectoris were divided into lesion group (40 cases) and normal group (20 cases) according to whether there were coronary atherosclerotic plaques in cardiac vessels. The results showed that the sensitivity, specificity, and accuracy of the optimized reconstruction algorithm were 91.78%, 84.27%, and 95.32%, and the running time was (12.18 ± 2.49) s. The CT value of the liver and the CT ratio of the liver and spleen in the lesion group were (53.81 ± 5.91) and (3.88 ± 0.67), respectively. There was no significant difference between the two groups ( P > 0.05 ). The body mass index and epicardial fat volume in the lesion group were (31.93 ± 4.54) kg/m2 and (120.09 ± 22.01) cm3, respectively. The body mass index and fat volume in the lesion group were significantly higher than those in the normal group ( P < 0.05 ). The epicardial fat constitution increased with the increase of the number of coronary arteries involved, and there was a positive correlation between them. Among patients with different coronary atherosclerotic plaques, the epicardial fat volume in patients with mixed plaques was the largest ( P < 0.05 ). In summary, optimizing CT images under compressed a sensing reconstruction algorithm could effectively improve the diagnostic accuracy of doctors. Epicardial fat volume was positively correlated with coronary heart disease. Epicardial fat volume could be used as one of the important indexes to predict coronary heart disease.

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Section: Discussionmentioning

confidence: 99%

“…Compressed sensing (CS) is a new theory different from traditional data processing technologies. With the major theoretical breakthrough made by Donoho in 2006 as a hallmark, compressed sensing theory is marching forward continuously [14]. Its basic theoretical system equation is as follows:…”

Section: E Optimized Compressed Sensing Ct Reconstructionmentioning

confidence: 99%

Optimized Reconstruction Algorithm-Processed CT Image in the Diagnosis of Correlation between Epicardial Fat Volume and Coronary Heart Disease

Xue

Jing

2022

Scientific Programming

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“…Quadratic forms like

{scriptQ}_{n} false(f false)

have already been used with sparsity‐enforcing regularizers to correct for angular subsampling and cone‐beam artifacts. Dual‐rotation, however, is aimed at avoiding the need for a strong a priori like sparsity.…”

Section: Methodsmentioning

confidence: 99%

Dual‐rotation C‐arm cone‐beam computed tomography to increase low‐contrast detection

Reshef

Riddell²,

Trousset³

et al. 2017

Medical Physics

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Purpose: This paper investigates the capabilities of a dual-rotation C-arm cone-beam computed tomography (CBCT) framework to improve non-contrast-enhanced low-contrast detection for full volume or volume-of-interest (VOI) brain imaging. Method: The idea is to associate two C-arm short-scan rotational acquisitions (spins): one over the full detector field of view (FOV) at low dose, and one collimated to deliver a higher dose to the central densest parts of the head. The angular sampling performed by each spin is allowed to vary in terms of number of views and angular positions. Collimated data is truncated and does not contain measurement of the incoming X-ray intensities in air (air calibration). When targeting full volume reconstruction, the method is intended to act as a virtual bow-tie. When targeting VOI imaging, the method is intended to provide the minimum full detector FOV data that sufficiently corrects for truncation artifacts. A single dedicated iterative algorithm is described that handles all proposed sampling configurations despite truncation and absence of air calibration. Results: Full volume reconstruction of dual-rotation simulations and phantom acquisitions are shown to have increased low-contrast detection for less dose, with respect to a single-rotation acquisition. High CNR values were obtained on 1% inserts of the Catphan r 515 module in 0.94 mm thick slices. Image quality for VOI imaging was preserved from truncation artifacts even with less than 10 non-truncated views, without using the sparsity a priori common to such context. Conclusion: A flexible dual-rotation acquisition and reconstruction framework is proposed that has the potential to improve low-contrast detection in clinical C-arm brain soft-tissue imaging.

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“…A high frequency Moire pattern is visible when using H * (right image) due to the redundancy introduced by oversampling the projection, while the backprojected view remains uniform with K (left image). An estimate of x is obtained by adopting a sparse inducing formulation, reminiscent from the literature on compressive sensing [9,25,26,30,36]. We solve the penalized least squares problem (10) with g = ρ W • 1 , W ∈ R N ×N being the orthogonal Symlet 2 wavelet transform on 2 resolution levels, and ρ > 0 the associated regularization parameter.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Convergence of proximal gradient algorithm in the presence of adjoint mismatch ^*

Chouzenoux

Pesquet

Riddell³

et al. 2021

Inverse Problems

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We consider the proximal gradient algorithm for solving penalized least-squares minimization problems arising in data science. This first-order algorithm is attractive due to its flexibility and minimal memory requirements allowing to tackle large-scale minimization problems involving non-smooth penalties. However, for problems such as X-ray computed tomography, the applicability of the algorithm is dominated by the cost of applying the forward linear operator and its adjoint at each iteration. In practice, the adjoint operator is thus often replaced by an alternative operator with the aim to reduce the overall computation burden and potentially improve conditioning issues. In this paper, we propose to analyze the effect of such an adjoint mismatch on the convergence of the proximal gradient algorithm in an infinite-dimensional setting, thus generalizing the existing results on PGA. We derive conditions on the stepsize and on the gradient of the smooth part of the objective function under which convergence of the algorithm to a fixed point is guaranteed. We also derive bounds on the error between this point and the solution to the original minimization problem. We illustrate our theoretical findings with two image reconstruction tasks in computed tomography.

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Compressed‐sensing‐based content‐driven hierarchical reconstruction: Theory and application to C‐arm cone‐beam tomography

Abstract: A flexible compressed-sensing-based algorithmic approach is proposed that is able to accommodate for a wide range of constraints. It is successfully applied to C-arm CBCT images that may not be so well approximated by piecewise constant functions.

Cited by 9 publications

References 52 publications

Optimized Reconstruction Algorithm-Processed CT Image in the Diagnosis of Correlation between Epicardial Fat Volume and Coronary Heart Disease

Optimized Reconstruction Algorithm-Processed CT Image in the Diagnosis of Correlation between Epicardial Fat Volume and Coronary Heart Disease

Dual‐rotation C‐arm cone‐beam computed tomography to increase low‐contrast detection

Convergence of proximal gradient algorithm in the presence of adjoint mismatch ^*

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Compressed‐sensing‐based content‐driven hierarchical reconstruction: Theory and application to C‐arm cone‐beam tomography

Abstract: A flexible compressed-sensing-based algorithmic approach is proposed that is able to accommodate for a wide range of constraints. It is successfully applied to C-arm CBCT images that may not be so well approximated by piecewise constant functions.

Cited by 9 publications

References 52 publications

Optimized Reconstruction Algorithm-Processed CT Image in the Diagnosis of Correlation between Epicardial Fat Volume and Coronary Heart Disease

Optimized Reconstruction Algorithm-Processed CT Image in the Diagnosis of Correlation between Epicardial Fat Volume and Coronary Heart Disease

Dual‐rotation C‐arm cone‐beam computed tomography to increase low‐contrast detection

Convergence of proximal gradient algorithm in the presence of adjoint mismatch *

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Convergence of proximal gradient algorithm in the presence of adjoint mismatch ^*