Identification Of Molecular Structures Of Normal And Pathological Human Breast Tissue Using Synchrotron Radiation

Conceição, A.L.C.; Poletti, M.E.; Siu, Karen Kit Wan

doi:10.1063/1.3478202

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…1,2 X-ray diffraction (XRD) is a technology capable of detecting these changes through measuring the interference patterns of X-rays scattering off the molecular-scale structures. [3][4][5][6] While prior XRD measurement and analysis methods have proven useful for studying diseases, [7][8][9][10] they have typically been limited to analyzing only a few discrete locations at the surface of a prepared sample or specimen.…”

Section: Introductionmentioning

confidence: 99%

“…[11][12][13][14][15] These imaging methods, including energy-dispersive and angular-dispersive raster scanning measurements, coherent scattering computed tomography, and coded aperture systems, seek to measure the XRD signatures of materials throughout a large field of view within reasonable scan times while maintaining adequate spatial resolutions for imaging applications. While simulation studies are often conducted for the design and optimization of these novel imaging architectures, [16][17][18][19] experimental validations on biologically relevant phantoms [20][21][22][23] and real biospecimens 3,6,8,13,15,24 have also occurred. These results have shown that tissues can be differentiated by spatially resolved XRD measurements; however, the resulting rich spatio-spectral datasets necessitate automatic data processing and classification techniques to parse the large data volume.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…The carcinoma scatter peak is at 13 deg. 2,5,[7][8][9] The contrast between the tissue types is nearly doubled when using a system which utilizes the characteristic scatter rather than using attenuation imaging. 10 Differences are also seen in collagen alignment, which causes a diminishment of peaks at smaller momentum transfer values.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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Abstract. A system using a wide-slot beam and simple antiscatter grids or slots has been designed to provide a localized map of tissue type that could be overlaid on the simultaneous conventional transmission image to provide an inexpensive, low dose adjunct to conventional screening mammography. Depth information is obtainable from the stereoscopic viewing angles. The system was demonstrated to produce observable contrast between adipose tissue and a phantom chosen to mimic carcinoma at an exposure comparable with screening mammography. Imaging data was collected over a range of system parameters to optimize contrast and to allow verification of simulation modeling.

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“…Coherent scatter imaging is sensitive to disease at very early stages compared to conventional imaging. 2 Diffraction, which is the constructive interference of coherent scattered radiation, is highly dependent on a material's molecular structure. 3 Coherent scatter signals for tissues typically have been studied using a conventional diffraction measurement geometry.…”

Section: Introductionmentioning

confidence: 99%

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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Identification Of Molecular Structures Of Normal And Pathological Human Breast Tissue Using Synchrotron Radiation

Cited by 8 publications

References 17 publications

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

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

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

Coherent scatter imaging Monte Carlo simulation

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