Application of machine learning classifiers to X‐ray diffraction imaging with medically relevant phantoms-Reference-Cited by-同舟云学术

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

Published:2021-12 Issue:1 Volume:49 Page:532-546
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Medical Physics

Author:

Stryker Stefan¹,Kapadia Anuj J.¹²,Greenberg Joel A.¹³

Affiliation:

1. Medical Physics Graduate Program Duke University Durham North Carolina USA

2. Carl E. Ravin Advanced Imaging Laboratories Department of Radiology Duke University Durham North Carolina USA

3. Department of Electrical and Computer Engineering Duke University Durham North Carolina USA

Funder

North Carolina Biotechnology Center

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.15366

Reference63 articles.

1. X-ray scatter signatures for normal and neoplastic breast tissues

2. Bone-composition imaging using coherent-scatter computed tomography: Assessing bone health beyond bone mineral density

3. Coherent and incoherent scattering of 17.44 and 6.93 keV x-ray photons scattered from biological and biological-equivalent samples: characterization of tissues

4. X-ray diffraction method to identify epithelial to mesenchymal transition in breast cancer tissue

5. Breast cancer diagnosis using scattered X-rays

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1. Spectrum optimization for x-ray dual-mode imager comprising radiography and coherent scatter;Anomaly Detection and Imaging with X-Rays (ADIX) IX;2024-06-07

2. Towards a high-resolution volumetric x-ray diffraction imaging system for biospecimen;Medical Imaging 2024: Physics of Medical Imaging;2024-04-01

3. Symmetric Color Image Encryption Using a Novel Cross–Plane Joint Scrambling–Diffusion Method;Symmetry;2023-07-28

4. Implementation and validation of X-ray diffraction imaging systems in MC-GPU;Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms;2022-09

5. Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods;Scientific Reports;2022-06-15

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