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3D reconstruction from multiple images is the creation of three-dimensional models from a set of images. It is the reverse process of obtaining 2D images from 3D scenes. The essence of an image is a projection from a 3D scene onto a 2D plane, during which process the depth is lost.
In scientific visualization, a maximum intensity projection (MIP) is a method for 3D data that projects in the visualization plane the voxels with maximum intensity that fall in the way of parallel rays traced from the viewpoint to the plane of projection.
The 3D reconstruction of objects is a generally scientific problem and core technology of a wide variety of fields, such as Computer Aided Geometric Design , computer graphics, computer animation, computer vision, medical imaging, computational science, virtual reality, digital media, etc. [3] For instance, the lesion information of the ...
Example showing differences between filtered backprojection (right half) and iterative reconstruction method (left half) Iterative reconstruction refers to iterative algorithms used to reconstruct 2D and 3D images in certain imaging techniques. For example, in computed tomography an image must be reconstructed from projections of an object.
Modern 3 Tesla clinical MRI scanner.. Magnetic resonance imaging (MRI) is a medical imaging technique mostly used in radiology and nuclear medicine in order to investigate the anatomy and physiology of the body, and to detect pathologies including tumors, inflammation, neurological conditions such as stroke, disorders of muscles and joints, and abnormalities in the heart and blood vessels ...
The problem of reconstruction from projections has arisen independently in a large number of scientific fields, since it is widely applied in areas such as medical imaging, geophysical tomography, industrial radiography and so on.
Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that enables the measurement of the restricted diffusion of water in tissue in order to produce neural tract images instead of using this data solely for the purpose of assigning contrast or colors to pixels in a cross-sectional image.
Image reconstruction in RT-MRI benefits from machine learning (ML) or deep learning (DL). A nonlinear kernel, or mapping function, can be developed from the ACS to fill in k-space data and generate the final image. [37] This process as a whole significantly accelerates the MRI process.