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- W3198203472 abstract "With technological advances and increasing demand for finer resolution images, tomographic medical imaging can be a huge computational problem, consequently, the processing time for image construction can be prohibitively large and impractical for real-time applications. The main bottlenecks are retrieving data and solving the inverse problem to attain medical images. Proper Orthogonal Decomposition (POD) is a model-reduction technique that can be used to compress a large set of images into an orthonormal basis whose elements can accurately generate any images in the original collection with the fewest possible modes. Applicability of POD on tomographic images is not possible without the linearity of the inverse problem. Due to its structure, the first few POD elements contain all the dominant features of the entire image collection. Thus, instead of performing the inverse Radon transform on all medical tomographic images, one needs to process only the primary POD modes once and reuse them to construct all tomographic images, rendering its computational savings. In this article, we improve the POD method further by implementing its hybrid version. Namely, the computation of the covariance matrix and associated eigenvalues and eigenvectors can be expensive for large-size images. This process can be sped by working with the down-sampled data and use the resulting coefficients to reconstruct the full-resolution images. Image reconstruction of fish eggs in a test tube will be presented. Errors and computational savings will also be discussed." @default.
- W3198203472 created "2021-09-13" @default.
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- W3198203472 date "2021-01-01" @default.
- W3198203472 modified "2023-09-23" @default.
- W3198203472 title "Reduced-Order Methods in Medical Imaging" @default.
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- W3198203472 doi "https://doi.org/10.1007/978-3-030-72983-7_11" @default.
- W3198203472 hasPublicationYear "2021" @default.
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