Data-specific mask-guided image reconstruction for diffuse optical tomography
| Autor: | Rizza Pua, Seungryong Cho, Sohail Sabir, Kee Hyun Kim, Sanghoon Cho, Duchang Heo |
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| Rok vydání: | 2020 |
| Předmět: |
business.industry
Image quality Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image registration Iterative reconstruction Inverse problem 01 natural sciences Regularization (mathematics) Atomic and Molecular Physics and Optics Diffuse optical imaging 010309 optics Optics 0103 physical sciences Electrical and Electronic Engineering business Engineering (miscellaneous) Algorithm Image resolution |
| Zdroj: | Applied optics. 59(30) |
| ISSN: | 1539-4522 |
| Popis: | Conventional approaches in diffuse optical tomography (DOT) image reconstruction often address the ill-posed inverse problem via regularization with a constant penalty parameter, which uniformly smooths out the solution. In this study, we present a data-specific mask-guided scheme that incorporates a prior mask constraint into the image reconstruction framework. The prior mask was created from the DOT data itself by exploiting the multi-measurement vector formulation. We accordingly propose two methods to integrate the prior mask into the reconstruction process. First, as a soft prior by exploiting a spatially varying regularization. Second, as a hard prior by imposing a region-of-interest-limited reconstruction. Furthermore, the latter method iterates between discrete and continuous steps to update the mask and optical parameters, respectively. The proposed methods showed enhanced optical contrast accuracy, improved spatial resolution, and reduced noise level in DOT reconstructed images compared with the conventional approaches such as the modified Levenberg–Marquardt approach and the l 1 -regularization based sparse recovery approach. |
| Databáze: | OpenAIRE |
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