4D cone beam CT via spatiotemporal tensor framelet

Gao, Hao; Li, Ruijiang; Lin, Yuting; Li, Xing

doi:10.1118/1.4762288

Cited by 72 publications

(101 citation statements)

References 24 publications

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“…In this section, we conduct experiments on the data in Fig. 2 (b) based on other three tight frames: contourlet [4,45], patch based directional wavelets (PBDW) [5] and framelet [46][47][48]. Both contourlet and PBDW explore the geometric information to further sparsify MR images thus are good at preserving the edge of MR images [4,5,45].…”

Section: ) Experiments On Other Tight Framesmentioning

confidence: 99%

Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

Liu

Zhan

Cai

et al. 2016

IEEE Trans. Med. Imaging

156

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Abstract-Compressed sensing has shown great potentials in accelerating magnetic resonance imaging. Fast image reconstruction and high image quality are two main issues faced by this new technology. It has been shown that, redundant image representations, e.g. tight frames, can significantly improve the image quality. But how to efficiently solve the reconstruction problem with these redundant representation systems is still challenging. This paper attempts to address the problem of applying iterative soft-thresholding algorithm (ISTA) to tight frames based magnetic resonance image reconstruction. By introducing the canonical dual frame to construct the orthogonal projection operator on the range of the analysis sparsity operator, we propose a projected iterative soft-thresholding algorithm (pISTA) and further accelerate it by incorporating the strategy proposed by Beck and Teboulle in 2009. We theoretically prove that pISTA converges to the minimum of a function with a balanced tight frame sparsity. Experimental results demonstrate that the proposed algorithm achieves better reconstruction than the widely used synthesis sparse model and the accelerated pISTA converges faster or comparable to the state-of-art smoothing FISTA. One major advantage of pISTA is that only one extra parameter, the step size, is introduced and the numerical solution is stable to it in terms of image reconstruction errors, thus allowing easily setting in many fast magnetic resonance imaging applications.

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Section: ) Experiments On Other Tight Framesmentioning

confidence: 99%

Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

Liu

Zhan

Cai

et al. 2016

IEEE Trans. Med. Imaging

156

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“…In the result section, we compare TICMR with FBP and the following TV based material reconstruction

Z^{*} = arg min_{Z} \frac{1}{2} {false‖ A Z B - P false‖}_{W}^{2} + λ {false| \nabla Z false|}_{1} .

normals . normalt . Z C = D, L \leq Z \leq U .

Note that in terms of the regularization in (17), the alternative strategies can be used, such as tensor framelet transform (as a natural high-order generalization of isotropic TV) [3], [11], [29]–[31], and low-rank models [7], [10], [15], [32], [33]. …”

Section: Methodsmentioning

confidence: 99%

TICMR: Total Image Constrained Material Reconstruction via Nonlocal Total Variation Regularization for Spectral CT

Liu

Ding

Molloi

et al. 2016

IEEE Trans. Med. Imaging

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This work develops a material reconstruction method for spectral CT, namely Total Image Constrained Material Reconstruction (TICMR), to maximize the utility of projection data in terms of both spectral information and high signal-to-noise ratio (SNR). This is motivated by the following fact: when viewed as a spectrally-integrated measurement, the projection data can be used to reconstruct a total image without spectral information, which however has a relatively high SNR; when viewed as a spectrally-resolved measurement, the projection data can be utilized to reconstruct the material composition, which however has a relatively low SNR. The material reconstruction synergizes material decomposition and image reconstruction, i.e., the direct reconstruction of material compositions instead of a two-step procedure that first reconstructs images and then decomposes images. For material reconstruction with high SNR, we propose TICMR with nonlocal total variation (NLTV) regularization. That is, first we reconstruct a total image using spectrally-integrated measurement without spectral binning, and build the NLTV weights from this image that characterize nonlocal image features; then the NLTV weights are incorporated into a NLTV-based iterative material reconstruction scheme using spectrally-binned projection data, so that these weights serve as a high-SNR reference to regularize material reconstruction. Note that the nonlocal property of NLTV is essential for material reconstruction, since material compositions may have significant local intensity variations although their structural information is often similar. In terms of solution algorithm, TICMR is formulated as an iterative reconstruction method with the NLTV regularization, in which the nonlocal divergence is utilized based on the adjoint relationship. The alternating direction method of multipliers is developed to solve this sparsity optimization problem. The proposed TICMR method was validated using both simulated and experimental data. In comparison with FBP and total-variation-based iterative method, TICMR had improved image quality, e.g., contrast-to-noise ratio and spatial resolution.

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“…It has the disadvantage of acquiring accurate sample images to build dictionaries, and is not effective for a totally unknown object. Tensor framelet based iterative image reconstruction (TFIR) has been previously proposed (Gao et al , 2012); (Gao et al , 2013) to address the piecewise-constant artifacts by TV, through a multi-level and multi-filtered tensor version of tight frame. TFIR offers a general sparse representation and total variation with high-order differencing, and it also generalizes the wavelet with redundant representation (Gao et al , 2012).…”

Section: Introductionmentioning

confidence: 99%

A high-resolution photon-counting breast CT system with tensor-framelet based iterative image reconstruction for radiation dose reduction

et al. 2014

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Both computer simulations and experimental phantom studies were carried out to investigate the radiation dose reduction with tensor framelet based iterative image reconstruction (TFIR) for a dedicated high-resolution spectral breast computed tomography (CT) based on a silicon strip photon-counting detector. The simulation was performed with a 10 cm-diameter water phantom including three contrast materials (polyethylene, 8 mg/ml iodine and B-100 bone-equivalent plastic). In the experimental study, the data were acquired with a 1.3 cm-diameter polymethylmethacrylate (PMMA) phantom containing iodine in three concentrations (8, 16 and 32 mg/ml) at various radiation doses (1.2, 2.4 and 3.6 mGy) and then CT images were reconstructed using filtered-back-projection (FBP) technique and TFIR technique, respectively. The image quality between these two techniques was evaluated by the quantitative analysis on contrast-to-noise ratio (CNR) and spatial resolution that was evaluated using the tasked-based modulation transfer function (MTF). Both simulation and experimental results indicated that the task-based MTF obtained from TFIR reconstruction with one-third of the radiation dose was comparable to that from FBP reconstruction for low contrast target. For high contrast target, TFIR was substantially superior to FBP reconstruction in term of spatial resolution. In addition, TFIR was able to achieve a factor of 1.6 to 1.8 increase in CNR depending on the target contrast level. This study demonstrates that TFIR can reduce the required radiation dose by a factor of two-third for a CT image reconstruction compared to FBP technique. It achieves much better CNR and spatial resolution for high contrast target in addition to retaining similar spatial resolution for low contrast target. This TFIR technique has been implemented with a graphic processing unit (GPU) system and it takes approximately 10 seconds to reconstruct a single-slice CT image, which can be potentially used in a future multi-slit multi-slice (MSMS) spiral CT system.

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4D cone beam CT via spatiotemporal tensor framelet

Abstract: By effectively utilizing the spatiotemporal coherence of the patient anatomy among different respiratory phases in a multilevel fashion with multibasis sparsifying transform, the proposed STF method potentially enables fast and low-dose 4DCBCT with improved image quality.

Cited by 72 publications

References 24 publications

Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

TICMR: Total Image Constrained Material Reconstruction via Nonlocal Total Variation Regularization for Spectral CT

A high-resolution photon-counting breast CT system with tensor-framelet based iterative image reconstruction for radiation dose reduction

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