Simulation based evaluation of a fan beam coded aperture x-ray diffraction imaging system for biospecimen analysis

Stryker, Stefan; Kapadia, Anuj J.; Greenberg, Joel A.

doi:10.1088/1361-6560/abe779

Cited by 10 publications

(11 citation statements)

References 36 publications

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“…Water is treated as the positive class since it is the simulant for cancer. For the CC algorithm, nonbinary labels were generated by using a CC metric 19 that ranged from 0 (PLA) to 1 (water) based on the material's match. For LS, the abundance was taken as the nonbinary label.…”

Section: Methodsmentioning

confidence: 99%

“…[38][39][40] This X-ray imaging system was used to record the co-registered X-ray transmission and diffraction images used for training and testing in this study. As shown in Figure 1 and discussed in depth in prior work, 15,19 the system consists of a spectrally filtered X-ray tube (XRB Monoblock, Spellman HV, NY) that is spatially collimated to produce a fan beam at the sample plane. The sample is illuminated by the primary fan beam, and both the transmitted and XRD signals are measured at the detector (Xineos-2329, Teledyne Dalsa, CA).…”

Section: X-ray Fan Beam-coded Aperture Imaging System Data Acquisitionmentioning

confidence: 99%

“…Our previously developed X-ray fan beam-coded aperture imaging system 15,19 allows for reduced measurement time by making multiplexed measurements of an object's XRD data. The fan beam measures a 150 × 1 mm 2 region through a single step-and-shoot exposure,…”

Section: X-ray Fan Beam-coded Aperture Imaging System Data Acquisitionmentioning

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%

“…These phantoms provide well-characterized ground-truths that allow us to investigate the performance of multiple classification algorithms operating on data-rich XRD images. Our previously developed X-ray fan beam-coded aperture imaging system 15,19 is used to obtain XRD images that are fed into the classification algorithms for analysis. For all phantoms, four classification algorithms are compared: cross-correlation (CC), least-squares (LS) unmixing, support vector machines (SVM), and shallow neural networks (SNN).…”

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: Methodsmentioning

confidence: 99%