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| 光学相干断层扫描血管成像× | 影像组学× | |
|---|---|---|
| 领域 | 医学影像 | 医学影像 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份 | 2012 | 2012 |
| 提出者≠ | Yali Jia | Philippe Lambin |
| 类型≠ | Optical imaging technique for vasculature visualization | Machine learning-based texture and morphology analysis |
| 开创性文献≠ | Jia, Y., Tan, O., Tokayer, J., et al. (2012). Split-spectrum amplitude-decorrelation angiography with optical coherence tomography. Optics Express, 20(4), 4710-4725. DOI ↗ | Lambin, P., Rios-Velazquez, E., Leijenaar, R., et al. (2012). Radiomics: extracting more information from medical images using advanced feature analysis. Nature Reviews Clinical Oncology, 9(12), 676-684. DOI ↗ |
| 别名≠ | OCTA, OCT-A | texture analysis, radiomics analysis, quantitative imaging biomarkers |
| 相关 | 5 | 5 |
| 摘要≠ | Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that visualizes the microvasculature in the retina and choroid by detecting motion contrast from flowing blood. Developed by Jia and colleagues in 2012, OCTA uses repeated OCT scans of the same tissue location to identify blood flow based on the decorrelation signal. It has become a critical diagnostic tool in ophthalmology for detecting retinal and macular diseases without requiring fluorescein injection. | Radiomics is a computational methodology that extracts large numbers of quantitative features from medical images (CT, MRI, PET) using automated image analysis and machine learning to discover imaging biomarkers associated with disease phenotype, prognosis, and treatment response. Developed by Lambin, Gillies, and colleagues in 2012, radiomics aims to decode the biology underlying visible imaging patterns, enabling personalized medicine through image-based phenotyping. It has emerged as a powerful tool in oncology for tumor characterization, prognosis prediction, and therapy response assessment. |
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