Compact energy dispersive X-ray microdiffractometer for diagnosis of neoplastic tissues

Sosa, Carlos Hernán; Malezan, A.; Poletti, M.E.; Pérez, Roberto D.

doi:10.1016/j.radphyschem.2016.11.005

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…XRD imaging approaches have been developed that seek to circumvent sample preparation, which is often destructive to samples and their spatial correlations, while enabling faster measurements with larger fields of view. [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.…”

Section: Introductionmentioning

confidence: 99%

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 accuracy of the classification decreases considerably when the tissue is inhomogeneous because of the overlap caused by the diffraction spectra belonging to different molecular structures 5 . X-Ray Microdiffraction (micro-XRD) solves this problem allowing to select the analysis area with micrometric spatial resolution 5,6 . Using either reflection or transmission geometries, Micro-XRD technique can be implemented.…”

Section: Introductionmentioning

confidence: 99%

“…Using either reflection or transmission geometries, Micro-XRD technique can be implemented. However, transmission geometry is more suitable, since it has smaller beam path into the sample minimizing heterogeneity effects on the scattering intensity 6 . In this geometry, the absorption of the radiation significantly affects the shape of the scattering profiles.…”

Section: Introductionmentioning

confidence: 99%

Breast cancer analysis by confocal energy dispersive micro-XRD

et al. 2020

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“…The imaging techniques currently used for breast cancer detection mainly include ultrasound [4]- [6], X-ray [7]- [9],computed tomography (CT) [10], magnetic resonance image (MRI) [11], [12] and near infrared transmission imaging [13]- [15]. Due to the non-standardization of detection accuracy, it is difficult to assess the detection effect of ultrasound on different people.…”

Section: Introductionmentioning

confidence: 99%

A Fusion Method in Frequency Domain for Multi-Wavelength Transmission Image

Liu

Yan

et al. 2019

IEEE Access

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Multi-wavelength transmission imaging provides a possibility for early breast cancer detection. However, due to the strong scattering effect of light source in the transmission process of biological tissue, there exist many difficulties in the heterogeneous identification of multi-wavelength images. In this paper, a multi-wavelength images frequency fusion method based on regional variance significance and regional space energy weighting is proposed to improve the accuracy of heterogeneous detection. Firstly, the acquisition experiment of phantom multi-wavelength transmission images is designed. And R\G\B threechannel wavelength images are extracted from multi-wavelength images as an example to realize subsequent image fusion. Secondly, frame accumulation technique and second-order Butterworth filter are used to improve the signal-to-noise ratio of images and suppress the noise of images. Then, after homomorphic filtering, it is found that the same frequency domain of images is connected, which is not conducive to the recognition of heterogeneity in image. In order to break the connectivity of images in the same frequency domain and improve the accuracy of heterogeneous detection, the regional variance significance and the regional space energy weighting fusion strategies are used to reconstruct the low-frequency and high-frequency fusion images respectively. Finally, the heterogeneous detection results of the proposed image fusion method and the state-of-the-art methods in the multi-wavelength image are compared. The experimental results show that compared with the state-of-the-art image fusion methods, the fusion method proposed in this paper can identify the location of heterogeneous region more accurately and significantly improve the contrast between heterogeneous region and normal region in the fusion image. In conclusion, the fusion algorithm not only effectively realizes the recognition of heterogeneity in multi-wavelength images, but also promotes the potential application of multi-wavelength transmission image in early breast cancer detection. INDEX TERMS Multi-wavelength transmission image, fusion method, regional variance significance (RVS), regional space energy weighting (RSEW), heterogeneous region.

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Compact energy dispersive X-ray microdiffractometer for diagnosis of neoplastic tissues

Cited by 10 publications

References 24 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

Breast cancer analysis by confocal energy dispersive micro-XRD

A Fusion Method in Frequency Domain for Multi-Wavelength Transmission Image

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