Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization
We propose a model-based image reconstruction method for photoacoustic tomography (PAT) involving a novel form of regularization and demonstrate its ability to recover good quality images from significantly reduced size datasets. The regularization is constructed to suit the physical structure of ty...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/146416 |
| _version_ | 1824456163598532608 |
|---|---|
| author | Rejesh, Nadaparambil Aravindakshan Kalva, Sandeep Kumar Pramanik, Manojit Arigovindan, Muthuvel |
| author2 | School of Chemical and Biomedical Engineering |
| author_facet | School of Chemical and Biomedical Engineering Rejesh, Nadaparambil Aravindakshan Kalva, Sandeep Kumar Pramanik, Manojit Arigovindan, Muthuvel |
| author_sort | Rejesh, Nadaparambil Aravindakshan |
| collection | NTU |
| description | We propose a model-based image reconstruction method for photoacoustic tomography (PAT) involving a novel form of regularization and demonstrate its ability to recover good quality images from significantly reduced size datasets. The regularization is constructed to suit the physical structure of typical PAT images. We construct it by combining second-order derivatives and intensity into a non-convex form to exploit a structural property of PAT images that we observe: in PAT images, high intensities and high second-order derivatives are jointly sparse. The specific form of regularization constructed here is a modification of the form proposed for fluorescence image restoration. This regularization is combined with a data fidelity cost, and the required image is obtained as the minimizer of this cost. As this regularization is non-convex, the efficiency of the minimization method is crucial in obtaining artifact-free reconstructions. We develop a custom minimization method for efficiently handling this non-convex minimization problem. Further, as non-convex minimization requires a large number of iterations and the PAT forward model in the data-fidelity term has to be applied in the iterations, we propose a computational structure for efficient implementation of the forward model with reduced memory requirements. We evaluate the proposed method on both simulated and real measured data sets and compare them with a recent reconstruction method that is based on a well-known fast iterative shrinkage threshold algorithm (FISTA). |
| first_indexed | 2025-02-19T03:49:44Z |
| format | Journal Article |
| id | ntu-10356/146416 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:49:44Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1464162023-12-29T06:54:23Z Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization Rejesh, Nadaparambil Aravindakshan Kalva, Sandeep Kumar Pramanik, Manojit Arigovindan, Muthuvel School of Chemical and Biomedical Engineering Engineering::Bioengineering Photoacoustic Tomography (PAT) Image Reconstruction We propose a model-based image reconstruction method for photoacoustic tomography (PAT) involving a novel form of regularization and demonstrate its ability to recover good quality images from significantly reduced size datasets. The regularization is constructed to suit the physical structure of typical PAT images. We construct it by combining second-order derivatives and intensity into a non-convex form to exploit a structural property of PAT images that we observe: in PAT images, high intensities and high second-order derivatives are jointly sparse. The specific form of regularization constructed here is a modification of the form proposed for fluorescence image restoration. This regularization is combined with a data fidelity cost, and the required image is obtained as the minimizer of this cost. As this regularization is non-convex, the efficiency of the minimization method is crucial in obtaining artifact-free reconstructions. We develop a custom minimization method for efficiently handling this non-convex minimization problem. Further, as non-convex minimization requires a large number of iterations and the PAT forward model in the data-fidelity term has to be applied in the iterations, we propose a computational structure for efficient implementation of the forward model with reduced memory requirements. We evaluate the proposed method on both simulated and real measured data sets and compare them with a recent reconstruction method that is based on a well-known fast iterative shrinkage threshold algorithm (FISTA). Accepted version 2021-02-16T07:55:20Z 2021-02-16T07:55:20Z 2020 Journal Article Rejesh, N. A., Kalva, S. K., Pramanik, M., & Arigovindan, M. (2020). Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization. Journal of Instrumentation, 15(12), P12028-. doi:10.1088/1748-0221/15/12/P12028 1748-0221 https://hdl.handle.net/10356/146416 10.1088/1748-0221/15/12/P12028 2-s2.0-85098275722 12 15 P12028 en Journal of Instrumentation © 2020 IOP Publishing Ltd. All rights reserved. This is an author-created, un-copyedited version of an article accepted for publication in Journal of Instrumentation. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher authenticated version is available online at https://doi.org/10.1088/1748-0221/15/12/P12028 application/pdf |
| spellingShingle | Engineering::Bioengineering Photoacoustic Tomography (PAT) Image Reconstruction Rejesh, Nadaparambil Aravindakshan Kalva, Sandeep Kumar Pramanik, Manojit Arigovindan, Muthuvel Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization |
| title | Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization |
| title_full | Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization |
| title_fullStr | Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization |
| title_full_unstemmed | Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization |
| title_short | Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization |
| title_sort | photo acoustic tomographic image reconstruction from reduced data using physically inspired regularization |
| topic | Engineering::Bioengineering Photoacoustic Tomography (PAT) Image Reconstruction |
| url | https://hdl.handle.net/10356/146416 |
| work_keys_str_mv | AT rejeshnadaparambilaravindakshan photoacoustictomographicimagereconstructionfromreduceddatausingphysicallyinspiredregularization AT kalvasandeepkumar photoacoustictomographicimagereconstructionfromreduceddatausingphysicallyinspiredregularization AT pramanikmanojit photoacoustictomographicimagereconstructionfromreduceddatausingphysicallyinspiredregularization AT arigovindanmuthuvel photoacoustictomographicimagereconstructionfromreduceddatausingphysicallyinspiredregularization |