Compressive tomography

Brady, David J.; Mrozack, Alex; MacCabe, Ken; Llull, Patrick

doi:10.1364/aop.7.000756

Cited by 55 publications

(13 citation statements)

References 125 publications

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“…As such, the presented concept belongs to a larger class of methods by which tomographic images are reconstructed from undersampled data. The focus of such methods has initially been on angular undersampling [8,9] but, more recently, and to some extent inspired by compressed sensing, it has shifted to lateral undersampling, e.g., the acquisition of incomplete projections by using "randomized" absorbing patterns in the beam path [10][11][12]. Cycloidal computed tomography differs from such approaches, as a highly structured illumination pattern is used and data recovery is not based on compressed sensing.…”

Section: Methodsmentioning

confidence: 99%

Cycloidal Computed Tomography

et al. 2020

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A general concept is presented for increasing the in-slice spatial resolution in computed tomography in a flexible manner and with relatively large source focal spots and detector pixels. The flexibility is a result of probing the sample with an array of thin beamlets shaped by a mask. Provided that the mask apertures are smaller than the combined blur of the x-ray source and detector and that no significant overlap between beamlets occurs, this introduces higher spatial frequencies into the image-formation process. The application of a cycloidal acquisition scheme, by which the sample is simultaneously rotated and translated, facilitates an efficient exploitation of these frequencies and their reconstruction into high-resolution tomographic images. Additionally, a preliminary study of the signal-to-noise ratio versus the delivered dose reveals significant dose-saving potential.

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Section: Methodsmentioning

confidence: 99%

Cycloidal Computed Tomography

et al. 2020

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“…3D computed tomography using illumination masks (known as compressive tomography, e.g., [21], [22]) also has many similarities to the current work. Again, the illuminating masks are known a-priori, and here the bucket signal is actually a 2D image (i.e., with a position sensitive detector).…”

Section: Introductionmentioning

confidence: 66%

“…(iii) The third method uses conjugate-gradient [33] crosscorrelation (CG-XC) to iteratively improve the reconstruction [34]. (iv) Compressed-sensing (CS) improvement to IXC is also considered (CS-CX), utilising various forms of sparsity constraint [6], [19], [22], [35], [36]. Three typical types of sparsity, relevant in the present context, are (a) image-space sparsity, where T ϕj (x) is assumed to be negligible for most pixels, (b) gradient sparsity, for which |∇ ⊥ T ϕj (x)| is negligible for most pixels, ∇ ⊥ being the gradient operator in the x plane, and (c) frequency-space sparsity, where F[T ϕj (x)] is negligible for most spatial frequencies, typically those with…”

Section: Projection Imagesmentioning

confidence: 99%

X-Ray Ghost-Tomography: Artefacts, Dose Distribution, and Mask Considerations

Kingston

Myers

Pelliccia³

et al. 2019

IEEE Trans. Comput. Imaging

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Ghost imaging has recently been successfully achieved in the X-ray regime; due to the penetrating power of X-rays this immediately opens up the possibility of X-ray ghost tomography. No research into this topic currently exists in the literature. Here we present adaptations of conventional tomography techniques to this new ghost imaging scheme. Several numerical implementations for tomography through X-ray ghost imaging are considered. Specific attention is paid to schemes for denoising of the resulting tomographic reconstruction, issues related to dose fractionation, and considerations regarding the ensemble of illuminating masks used for ghost imaging. Each theme is explored through a series of numerical simulations, and several suggestions offered for practical realisations of Xray ghost tomography.

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“…The last decade has seen a number of SCI systems [3], [4], [5], [6], [8], [9], [10], [14], [32], [33], [34], [35], [36] with the development of compressive sensing [1], [37], [38]. The underlying principle is encoding the high-dimensional data on a 2D sensor with dispersion for spectral imaging [9], [10], [33], temporal-variant mask for high-speed imaging [3], [4], [5], [14], and angular variation for light-field imaging [32].…”

Section: Systemsmentioning

confidence: 99%

Rank Minimization for Snapshot Compressive Imaging

Liu

Yuan

Suo

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

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338

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Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications. This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.Index Terms-Compressive sensing, computational imaging, coded aperture, image processing, video processing, nuclear norm, rank minimization, low rank, hyperspectral images, coded aperture snapshot spectral imaging (CASSI), coded aperture compressive temporal imaging (CACTI).

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Compressive tomography

Cited by 55 publications

References 125 publications

Cycloidal Computed Tomography

Cycloidal Computed Tomography

X-Ray Ghost-Tomography: Artefacts, Dose Distribution, and Mask Considerations

Rank Minimization for Snapshot Compressive Imaging

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