Сравнение методов

	Морфометрия микро-КТ	Реология гидрогелей	Анализ пористости каркаса
Область	Биомеханика	Биомеханика	Биомеханика
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1989	1994	2000
Автор метода	Feldkamp	Christopher Macosko	Dietmar Hutmacher
Тип	3D image acquisition and quantitative analysis	Mechanical material characterization	Quantitative morphological analysis
Основополагающий источник	Feldkamp, L. A., Davis, L. C., & Kress, J. W. (1984). Practical cone-beam algorithm. Journal of the Optical Society of America A, 1(6), 612-619.	Almquist, B. D., & Lu, T. W. (2002). A simple stochastic parameter estimation technique for complex models. IEEE Transactions on Biomedical Engineering, 49(10), 1188-1193.	Hutmacher, D. W. (2000). Scaffolds in tissue engineering bone and cartilage. Biomaterials, 21(24), 2529-2543.
Другие названия	microCT, Micro-CT analysis, 3D bone morphometry	Viscoelastic analysis, Storage modulus, Gel characterization	Pore size distribution, Porosity measurement, Scaffold characterization
Связанные	3	3	3
Сводка	Micro-computed tomography (microCT) morphometry quantifies 3D bone and tissue architecture at micrometer resolution, enabling detailed assessment of bone density, trabecular structure, and porosity. Developed by Feldkamp and colleagues and standardized by the American Society for Bone and Mineral Research, microCT is the gold standard for preclinical bone analysis and has expanded to tissue engineering and material characterization.	Hydrogel rheology characterizes the mechanical viscoelastic properties of hydrogels used in tissue engineering, drug delivery, and biomedical devices. By measuring storage modulus (elastic component), loss modulus (viscous component), and their frequency dependence, practitioners assess gel stiffness, degradation, and suitability for specific applications.	Scaffold porosity analysis characterizes the pore structure of tissue engineering scaffolds, including total porosity, pore size distribution, pore shape, and pore interconnectivity. Essential for predicting cell seeding, nutrient diffusion, and mechanical properties, this quantitative approach bridges scaffold design and biological performance.