Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| CT iteratiivne rekonstruktsioon× | Radiomics× | |
|---|---|---|
| Valdkond | Meditsiiniline piltdiagnostika | Meditsiiniline piltdiagnostika |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 1974 | 2012 |
| Looja≠ | Richard Gordon | Philippe Lambin |
| Tüüp≠ | Algorithm for tomographic image reconstruction | Machine learning-based texture and morphology analysis |
| Algallikas≠ | Gordon, R., Bender, R., Herman, G. T. (1974). Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography. Journal of Theoretical Biology, 29(3), 471-481. link ↗ | 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 ↗ |
| Rööpnimetused≠ | MBIR, ASIR, IR-CT, statistical reconstruction | texture analysis, radiomics analysis, quantitative imaging biomarkers |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | CT Iterative Reconstruction (IR) is a computational technique that reconstructs tomographic images from raw X-ray projection data by iteratively refining an estimate of tissue attenuation until it matches the measured projections. Developed from algebraic reconstruction techniques pioneered by Gordon in 1974, iterative reconstruction has revolutionized clinical CT by enabling high-quality images at reduced radiation dose. Variants such as Adaptive Statistical Iterative Reconstruction (ASIR) and Model-Based Iterative Reconstruction (MBIR) are now standard on modern CT scanners. | 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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