Design for a coherent-scatter imaging system compatible with screening mammography

Kern, Katie; Peerzada, Lubna; Hassan, Laila; MacDonald, Carolyn A.

doi:10.1117/1.jmi.3.3.030501

Cited by 7 publications

(7 citation statements)

References 18 publications

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“…9 Normalized lab measured spectrum 14 (black) and normalized simulated spectrum (red, dashed) of Mo source at 47.5 kV. The simulation and data coincide.…”

Section: Simulation Verificationmentioning

confidence: 99%

“…Graphite diffracts at 12 deg for the photon energy of 17.4 keV. 14,16,33 The experimental system compared the scatter from graphite and fat, to verify the simulation.…”

Section: Simulation Verificationmentioning

confidence: 99%

“…However, early experimental measurements [13][14][15] had been performed with the imaging system using graphite, and those measurements included comparisons with fatty tissue. Equation (19) was used to compute the diffraction probability for graphite.…”

Section: Coherent Scattermentioning

confidence: 99%

“…In this study, an imaging technique [13][14][15] compatible with screening mammography was investigated using a Monte Carlo simulation. The coherent scatter technique employed a wide-slot system such as the one used in slot scan mammography.…”

Section: Introductionmentioning

confidence: 99%

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Coherent scatter imaging Monte Carlo simulation

Hassan

MacDonald

2016

J. Med. Imag

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Abstract. Conventional mammography can suffer from poor contrast between healthy and cancerous tissues due to the small difference in attenuation properties. Coherent scatter slot scan imaging is an imaging technique which provides additional information and is compatible with conventional mammography. A Monte Carlo simulation of coherent scatter slot scan imaging was performed to assess its performance and provide system optimization. Coherent scatter could be exploited using a system similar to conventional slot scan mammography system with antiscatter grids tilted at the characteristic angle of cancerous tissues. System optimization was performed across several parameters, including source voltage, tilt angle, grid distances, grid ratio, and shielding geometry. The simulated carcinomas were detectable for tumors as small as 5 mm in diameter, so coherent scatter analysis using a wide-slot setup could be promising as an enhancement for screening mammography. Employing coherent scatter information simultaneously with conventional mammography could yield a conventional high spatial resolution image with additional coherent scatter information.

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“…9 Normalized lab measured spectrum 14 (black) and normalized simulated spectrum (red, dashed) of Mo source at 47.5 kV. The simulation and data coincide.…”

Section: Simulation Verificationmentioning

confidence: 99%

“…Graphite diffracts at 12 deg for the photon energy of 17.4 keV. 14,16,33 The experimental system compared the scatter from graphite and fat, to verify the simulation.…”

Section: Simulation Verificationmentioning

confidence: 99%

Section: Coherent Scattermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coherent scatter imaging Monte Carlo simulation

Hassan

MacDonald

2016

J. Med. Imag

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“…Direct coherent scattering systems [18], [19], [20] have great potential to distinguish subtleties between different tissues due to angular sensitivity. Current scattering-based imaging techniques perform slot scanning aided by a collimated detector.…”

Section: Introductionmentioning

confidence: 99%

Novel Detection Scheme for X-Ray Small-Angle Scattering

Cong

Michaelson

et al. 2018

IEEE Trans. Radiat. Plasma Med. Sci.

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X-ray imaging techniques, including x-ray radiography and computed tomography, have been in use for decades and proven effective and indispensable in diagnosis and therapy due to their fine resolution and fast acquisition speed. However, the innate disadvantage of x-ray is the poor soft tissue contrast. Small-angle scattering signals were shown to provide unique information about the abnormality of soft tissues that is complementary to the traditional attenuation image. Currently, there is no effective small-angle scattering detection system. In this paper, we propose a new “collimation” design dedicated to capture a small-angle scattering radiographic image directly, which carries critical pathological information for differentiation between normal and abnormal tissues. Our design consists of two interlaced gratings so that both the primary flux and Compton scattering photons are effectively blocked to leave the apertures mainly open to small-angle scattering photons. Theoretical analysis and Monte Carlo simulations demonstrate that small-angle scattering radiography is feasible with our proposed technology.

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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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Design for a coherent-scatter imaging system compatible with screening mammography

Cited by 7 publications

References 18 publications

Coherent scatter imaging Monte Carlo simulation

Coherent scatter imaging Monte Carlo simulation

Novel Detection Scheme for X-Ray Small-Angle Scattering

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

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