Accelerating the over-relaxed iterative shrinkage-thresholding algorithms with fast and exact line search for high resolution tomographic image reconstruction

Zibetti, Marcelo V. W.; Helou, Elias S.; Migueles, Eduardo X.; Pierro, Alvaro R. De

doi:10.1109/icip.2015.7351213

Cited by 8 publications

(18 citation statements)

References 11 publications

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“…This is why some researchers have recently proposed specific

ℓ_{2}

–

ℓ_{1}

line search procedures, that have shown to be more efficient than more general methods. See for instance [10,52,53] and the references therein.…”

Section: Review Of the Algorithmsmentioning

confidence: 99%

“…Finally, we assess the Over-Relaxation of Monotone Fast Iterative Shrinkage-Thresholding Algorithm with Optimal Line Search (OMFISTA-OLS) as a variant of OMFISTA proposed in [10] for computed tomography. Here, the stepsize

α_{k}

is calculated by the line search procedure proposed in [53], as opposed to the fixed stepsize used in OMFISTA.…”

Section: Review Of the Algorithmsmentioning

confidence: 99%

“…The application of this concept ranges, for example, from Super-resolution [6,7,8], to Computed Tomography (CT) [9,10,11], Photoacoustic Tomography (PAT) [12,13,14], and Magnetic Resonance Imaging (MRI) [15,16], just to mention a few.…”

Section: Introductionmentioning

confidence: 99%

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An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging

Valente

Zibetti

Pipa

et al. 2017

Sensors

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Ultrasonic image reconstruction using inverse problems has recently appeared as an alternative to enhance ultrasound imaging over beamforming methods. This approach depends on the accuracy of the acquisition model used to represent transducers, reflectivity, and medium physics. Iterative methods, well known in general sparse signal reconstruction, are also suited for imaging. In this paper, a discrete acquisition model is assessed by solving a linear system of equations by an ℓ1-regularized least-squares minimization, where the solution sparsity may be adjusted as desired. The paper surveys 11 variants of four well-known algorithms for sparse reconstruction, and assesses their optimization parameters with the goal of finding the best approach for iterative ultrasound imaging. The strategy for the model evaluation consists of using two distinct datasets. We first generate data from a synthetic phantom that mimics real targets inside a professional ultrasound phantom device. This dataset is contaminated with Gaussian noise with an estimated SNR, and all methods are assessed by their resulting images and performances. The model and methods are then assessed with real data collected by a research ultrasound platform when scanning the same phantom device, and results are compared with beamforming. A distinct real dataset is finally used to further validate the proposed modeling. Although high computational effort is required by iterative methods, results show that the discrete model may lead to images closer to ground-truth than traditional beamforming. However, computing capabilities of current platforms need to evolve before frame rates currently delivered by ultrasound equipments are achievable.

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“…This is why some researchers have recently proposed specific

ℓ_{2}

–

ℓ_{1}

line search procedures, that have shown to be more efficient than more general methods. See for instance [10,52,53] and the references therein.…”

Section: Review Of the Algorithmsmentioning

confidence: 99%

α_{k}

is calculated by the line search procedure proposed in [53], as opposed to the fixed stepsize used in OMFISTA.…”

Section: Review Of the Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging

Valente

Zibetti

Pipa

et al. 2017

Sensors

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“…Also, [14] introduces a more general optimal line search, however with a high computational cost, so using it in all iterations is not worthwhile. In [15] it was proposed the use of the exact and fast line search from [13] as a method to compute the optimal step size of the OMFISTA. This new method is named OMFISTA with line search (OMFISTA-LS), and it is also revisited in detail in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we extend the ideas of line search for OMFISTA [15] and propose its use in MFISTA. The use of line search in OMFISTA is revisited, detailed, and the underlying theory extended accordingly.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions

Zibetti

Helou

Pipa

2017

IEEE Trans. on Image Process.

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Recently, specially crafted unidimensional optimization has been successfully used as line search to accelerate the overrelaxed and monotone fast iterative shrinkage-threshold algorithm (OMFISTA) for computed tomography. In this paper, we extend the use of fast line search to the monotone fast iterative shrinkage-threshold algorithm (MFISTA) and some of its variants. Line search can accelerate the FISTA family considering typical synthesis priors, such as the ℓ-norm of wavelet coefficients, as well as analysis priors, such as anisotropic total variation. This paper describes these new MFISTA and OMFISTA with line search, and also shows through numerical results that line search improves their performance for tomographic high-resolution image reconstruction.

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