Compara mètodes

	Radiòmica	Reconstrucció Iterativa de TC	DEXA	Modelització cinètica per PET
Camp	Imatge mèdica	Imatge mèdica	Imatge mèdica	Imatge mèdica
Família	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	2012	1974	1987	1983
Autor original	Philippe Lambin	Richard Gordon	Harold Wahner	Christoph Patlak
Tipus	Machine learning-based texture and morphology analysis	Algorithm for tomographic image reconstruction	X-ray-based bone density measurement	Mathematical framework for tracer kinetics in PET imaging
Font seminal	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.	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.	Kanis, J. A. (1994). Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. World Health Organization Technical Report Series, 843, 1-129.	Patlak, C. S., Blasberg, R. G., Fenstermacher, J. D. (1983). Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Journal of Cerebral Blood Flow & Metabolism, 3(1), 1-7.
Àlies	texture analysis, radiomics analysis, quantitative imaging biomarkers	MBIR, ASIR, IR-CT, statistical reconstruction	Dual X-ray absorptiometry, DXA, bone densitometry	PET pharmacokinetics, Dynamic PET, PET compartmental modeling
Relacionats	5	5	5	5
Resum	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.	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.	Dual-Energy X-ray Absorptiometry (DEXA or DXA) is a non-invasive imaging technique that quantifies bone mineral density (BMD) by measuring the attenuation of X-rays at two different energies as they pass through bone and soft tissue. First developed by Wahner and colleagues in 1987, DEXA has become the gold standard for osteoporosis screening and fracture risk assessment. It is recommended by the World Health Organization for diagnosing osteoporosis and monitoring treatment response.	PET kinetic modeling is a quantitative analysis technique that tracks the temporal behavior of radioactive tracers in tissue to extract physiological parameters such as blood flow, metabolic rate, and receptor density. Established by Patlak, Logan, and Gunn in the 1980s and 1990s, kinetic modeling transforms raw PET time-activity curves into interpretable biological measures. It is widely used in neurology, oncology, and cardiology to assess disease severity, treatment response, and regional tissue function.