Magnetic resonance imaging (MRI), computed tomography scanning (CT scan), and ultrasound imaging techniques (UI) were used for data acquisition to construct/develop a 3D solid model of the human tibia, femur, and skull. CT scan was found to be an acceptable technique for cadavers. CT scans are harmfui to the human body in large doses, while MRIs and ultrasound are known to be safe. However, MRIs form a better tool in performing this image generation task for living beings because of its high resolution capacity when compared with images obtained using uitrasound techniques. High resolution poses to be a very important factor, as the consideration of various material properties of the bones was part of the emphasis of this research. MRIs have the capacity of displaying a distinct boundary between the muscles and the bone, in addition to the boundary between the cortical and the cancellous region within the bone. Ultrasound was found to be the cheapest technique and gave reasonably good results for just the outside boundaries of the bone. The modeis of the human bones were generated on a Computer Aided Design (CAD) system. The cross-sections obtained from (MRI, CT, or UI) were scanned into the computer. Image processing software was used to detect the boundaries of the bones. A C + + program was used to read the coordinates of the edges and construct a B-spline curve on the CAD system. The curves were converted to a B-rep solid using skinning. The solid models were meshed, constrained, and material properties were assigned to different regions of the models for Finite Element Analysis (FEM).

The recently proposed compressed sensing theory equips us with methods to recover exactly or approximately, high resolution images from very few encoded measurements of the scene. The traditional ill-posed problem of MRI image recovery from heavily under-sampled -space data can be thus solved using CS theory. Differing from the soft thresholding methods that have been used earlier in the case of CS MRI, we suggest a simple iterative hard thresholding algorithm which efficiently recovers diagnostic quality MRI images from highly incomplete -space measurements. The new multi-scale redundant systems, curvelets and contourlets having high directionality and anisotropy, and thus best suited for curved-edge representation are used in this iterative hard thresholding framework for CS MRI reconstruction and their performance is compared. The -space under-sampling schemes such as the variable density sampling and the more conventional radial sampling are experimented at the same sampling rate and the effect of encoding scheme on iterative hard thresholding compressed sensing reconstruction is studied.