Snapshot molecular imaging using coded energy-sensitive detection

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

doi:10.1364/oe.21.025480

Cited by 56 publications

(59 citation statements)

References 19 publications

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“…A Gaussian measurement model is not appropriate for many important applications, including X-ray [9,20] and chemical imaging [29,30,31]. The observed data in these applications are characterized by counts, typically under Poisson statistics.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Further, we focus on the Poisson measurement model, for which there are no previous results on estimating the sensing matrix. The new theory developed the system, and c) X-ray transmission image of the coded aperture [9].…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Hardware Review. The basic structure of the coded aperture coherent scatter spectral imaging (CACSSI) [9] system considered in this paper is illustrated in Figure 1. The object is illuminated with an X-ray pencil beam (transmitted beam along a single linear direction, or "pencil"), and a coded aperture is placed in front of a linear array of energy-sensitive detectors; the system measures coherently scattered (energy-dependent) X-rays.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…If the X-ray beam is characterized by a range of energies E over a finite bandwidth, then each energy within that bandwidth scatters at an angle θ(E, d) defined by material properties d and energy E. The gray-scale gradient of scattered waves in Figure 1(a) reflects variation in θ(E, d) as a function of E over the bandwidth, for fixed d; the d is fixed by the properties of the local material, and E represents the energy, which varies across the bandwidth of the source. The reader is referred to [9] for further details.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…The coded aperture modulates the scattered photons in a manner that depends on the position of emission and on the angle of scatter, helping to disambiguate the source of observed photons when seeking to recover f k from y k . Further details on the X-ray system may be found in [9,20].…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

Wang

Huang

Yuan

et al. 2015

SIAM J. Imaging Sci.

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Abstract. The measurement matrix employed in compressive sensing typically cannot be known precisely a priori, and must be estimated via calibration. One may take multiple compressive measurements, from which the measurement matrix and underlying signals may be estimated jointly. This is of interest as well when the measurement matrix may change as a function of the details of what is measured. This problem has been considered recently for Gaussian measurement noise, and here we develop this idea with application to Poisson systems. A collaborative maximum likelihood algorithm and alternating proximal gradient algorithm are proposed, and associated theoretical performance guarantees are established based on newly derived concentration-of-measure results. A Bayesian model is then introduced, to improve flexibility and generality. Connections between the maximum likelihood methods and the Bayesian model are developed, and example results are presented for a real compressive X-ray imaging system.

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Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

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Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

Wang

Huang

Yuan

et al. 2015

SIAM J. Imaging Sci.

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Application of machine learning classifiers to X‐ray diffraction imaging with medically relevant phantoms

2021

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Recent studies have demonstrated the ability to rapidly produce large field of view X-ray diffraction (XRD) images, which provide rich new data relevant to the understanding and analysis of disease. However, work has only just begun on developing algorithms that maximize the performance toward decision-making and diagnostic tasks. In this study, we present the implementation of and comparison between rules-based and machine learning (ML) classifiers on XRD images of medically relevant phantoms to explore the potential for increased classification performance. Methods: Medically relevant phantoms were utilized to provide wellcharacterized ground-truths for comparing classifier performance. Water and polylactic acid (PLA) plastic were used as surrogates for cancerous and healthy tissue, respectively, and phantoms were created with varying levels of spatial complexity and biologically relevant features for quantitative testing of classifier performance. Our previously developed X-ray scanner was used to acquire co-registered X-ray transmission and diffraction images of the phantoms. For classification algorithms, we explored and compared two rules-based classifiers (cross-correlation, or matched-filter, and linear least-squares unmixing) and two ML classifiers (support vector machines and shallow neural networks). Reference XRD spectra (measured by a commercial diffractometer) were provided to the rules-based algorithms, while 60% of the measured XRD pixels were used for training of the ML algorithms. The area under the receiver operating characteristic curve (AUC) was used as a comparative metric between the classification algorithms, along with the accuracy performance at the midpoint threshold for each classifier. Results:The AUC values for material classification were 0.994 (crosscorrelation [CC]), 0.994 (least-squares [LS]), 0.995 (support vector machine [SVM]), and 0.999 (shallow neural network [SNN]). Setting the classification threshold to the midpoint for each classifier resulted in accuracy values of CC = 96.48%, LS = 96.48%, SVM = 97.36%, and SNN = 98.94%. If only considering pixels ±3 mm from water-PLA boundaries (where partial volume effects could occur due to imaging resolution limits), the classification accuracies were CC = 89.32%, LS = 89.32%, SVM = 92.03%, and SNN = 96.79%, demonstrating an even larger improvement produced by the machine-learned algorithms in spatial regions critical for imaging tasks. Classification by transmission data alone produced an AUC of 0.773 and accuracy of 85.45%, well below the performance levels of any of the classifiers applied to XRD image data. Conclusions: We demonstrated that ML-based classifiers outperformed rulesbased approaches in terms of overall classification accuracy and improved the spatially resolved classification performance on XRD images of medical phantoms. In particular, the ML algorithms demonstrated considerably improved 532

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Advances in explosives analysis—part II: photon and neutron methods

et al. 2015

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The number and capability of explosives detection and analysis methods have increased dramatically since publication of the Analytical and Bioanalytical Chemistry special issue devoted to Explosives Analysis [Moore DS, Goodpaster JV, Anal Bioanal Chem 395:245-246, 2009]. Here we review and critically evaluate the latest (the past five years) important advances in explosives detection, with details of the improvements over previous methods, and suggest possible avenues towards further advances in, e.g., stand-off distance, detection limit, selectivity, and penetration through camouflage or packaging. The review consists of two parts. Part I discussed methods based on animals, chemicals (including colorimetry, molecularly imprinted polymers, electrochemistry, and immunochemistry), ions (both ion-mobility spectrometry and mass spectrometry), and mechanical devices. This part, Part II, will review methods based on photons, from very energetic photons including X-rays and gamma rays down to the terahertz range, and neutrons.

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Snapshot molecular imaging using coded energy-sensitive detection

Cited by 56 publications

References 19 publications

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

Application of machine learning classifiers to X‐ray diffraction imaging with medically relevant phantoms

Advances in explosives analysis—part II: photon and neutron methods

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