Compressive single-pixel snapshot x-ray diffraction imaging

Greenberg, Joel A.; Krishnamurthy, Kalyani; Brady, David J.

doi:10.1364/ol.39.000111

Cited by 99 publications

(40 citation statements)

References 20 publications

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“…However, using the Laplace density function as a sparsity prior results in a Bayesian inference that cannot be calculated in closed form because the Laplace prior is not conjugate to the Gaussian likelihood shown in Eq. (9). Previous authors [23,24] have provided a solution using the relevance vector machine (RVM) framework that imposes a hierarchical prior with similar properties but are conjugates to the Gaussian likelihood in Eq.…”

Section: Methodsmentioning

confidence: 99%

“…Previous authors [23,24] have provided a solution using the relevance vector machine (RVM) framework that imposes a hierarchical prior with similar properties but are conjugates to the Gaussian likelihood in Eq. (9). A zero-mean Gaussian prior is first defined on ω ,…”

Section: Methodsmentioning

confidence: 99%

“…They also significantly improve single detector imaging methods by increasing the sampling speed and efficiency over raster-scanning [20], as demonstrated in applications as diverse as optical coherence tomography (OCT) [4], single photon imaging [5,6], infrared imaging [7], millimeter wave imaging [8], and x-ray imaging [9,10]. CS achieves this by overcoming the Nyquist sampling limit, requiring only a few samples to reconstruct the original signal if it is sparse on some basis, such as the wavelet basis [26].…”

Section: Introductionmentioning

confidence: 99%

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Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging

et al. 2017

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Abstract:In order to improve speed and efficiency over traditional scanning methods, a Bayesian compressive sensing algorithm using adaptive spatial sampling is developed for single detector millimeter wave synthetic aperture imaging. The application of this algorithm is compared to random sampling to demonstrate that the adaptive algorithm converges faster for simple targets and generates more reliable reconstructions for complex targets.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging

et al. 2017

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“…Coded aperture coherent scatter spectral imaging, which has been used for material-identification in nonmedical 15,16 and medical [17][18][19] applications, uses a coded aperture placed in front of the detector to make multiplexed measurements of the scatter. When the coded aperture is placed in front of the x-ray detector, it spatially modulates the scattered x-rays by a frequency dependent on the location of the scatter origin.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of coded aperture coherent scatter spectral imaging of cancerous and healthy breast tissue samples

et al. 2016

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Abstract. A scatter imaging technique for the differentiation of cancerous and healthy breast tissue in a heterogeneous sample is introduced in this work. Such a technique has potential utility in intraoperative margin assessment during lumpectomy procedures. In this work, we investigate the feasibility of the imaging method for tumor classification using Monte Carlo simulations and physical experiments. The coded aperture coherent scatter spectral imaging technique was used to reconstruct three-dimensional (3-D) images of breast tissue samples acquired through a single-position snapshot acquisition, without rotation as is required in coherent scatter computed tomography. We perform a quantitative assessment of the accuracy of the cancerous voxel classification using Monte Carlo simulations of the imaging system; describe our experimental implementation of coded aperture scatter imaging; show the reconstructed images of the breast tissue samples; and present segmentations of the 3-D images in order to identify the cancerous and healthy tissue in the samples. From the Monte Carlo simulations, we find that coded aperture scatter imaging is able to reconstruct images of the samples and identify the distribution of cancerous and healthy tissues (i.e., fibroglandular, adipose, or a mix of the two) inside them with a cancerous voxel identification sensitivity, specificity, and accuracy of 92.4%, 91.9%, and 92.0%, respectively. From the experimental results, we find that the technique is able to identify cancerous and healthy tissue samples and reconstruct differential coherent scatter cross sections that are highly correlated with those measured by other groups using x-ray diffraction. Coded aperture scatter imaging has the potential to provide scatter images that automatically differentiate cancerous and healthy tissue inside samples within a time on the order of a minute per slice.

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“…For example, we have shown that 3D hyperspectral [18], diffraction [19], and x-ray scatter [20,21] images may be reconstructed from 2D data. We have also analyzed compressive sampling for reconstruction of 3D objects with conventional optics [22].…”

Section: Introductionmentioning

confidence: 99%

Compressed sampling strategies for tomography

Kaganovsky

Holmgren

et al. 2014

J. Opt. Soc. Am. A

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We investigate new sampling strategies for projection tomography, enabling one to employ fewer measurements than expected from classical sampling theory without significant loss of information. Inspired by compressed sensing, our approach is based on the understanding that many real objects are compressible in some known representation, implying that the number of degrees of freedom defining an object is often much smaller than the number of pixels/voxels. We propose a new approach based on quasi-random detector subsampling, whereas previous approaches only addressed subsampling with respect to source location (view angle). The performance of different sampling strategies is considered using object-independent figures of merit, and also based on reconstructions for specific objects, with synthetic and real data. The proposed approach can be implemented using a structured illumination of the interrogated object or the detector array by placing a coded aperture/mask at the source or detector side, respectively. Advantages of the proposed approach include (i) for structured illumination of the detector array, it leads to fewer detector pixels and allows one to integrate detectors for scattered radiation in the unused space; (ii) for structured illumination of the object, it leads to a reduced radiation dose for patients in medical scans; (iii) in the latter case, the blocking of rays reduces scattered radiation while keeping the same energy in the transmitted rays, resulting in a higher signal-to-noise ratio than that achieved by lowering exposure times or the energy of the source; (iv) compared to view-angle subsampling, it allows one to use fewer measurements for the same image quality, or leads to better image quality for the same number of measurements. The proposed approach can also be combined with view-angle subsampling.

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Compressive single-pixel snapshot x-ray diffraction imaging

Cited by 99 publications

References 20 publications

Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging

Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging

Design and implementation of coded aperture coherent scatter spectral imaging of cancerous and healthy breast tissue samples

Compressed sampling strategies for tomography

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