Hessian-free ray-born inversion for quantitative ultrasound tomography
Furkejuvvon:
| Publikašuvnnas: | arXiv.org (May 17, 2024), p. n/a |
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| Váldodahkki: | |
| Almmustuhtton: |
Cornell University Library, arXiv.org
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| Fáttát: | |
| Liŋkkat: | Citation/Abstract Full text outside of ProQuest |
| Fáddágilkorat: |
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MARC
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| 001 | 2731611885 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2731611885 | ||
| 045 | 0 | |b d20240517 | |
| 100 | 1 | |a Javaherian, Ashkan | |
| 245 | 1 | |a Hessian-free ray-born inversion for quantitative ultrasound tomography | |
| 260 | |b Cornell University Library, arXiv.org |c May 17, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a This study proposes a Hessian-free ray-born inversion approach for biomedical ultrasound tomography. The proposed approach is a more efficient version of the ray-born inversion approach proposed in [3]. Using these approaches, the propagation of acoustic waves are modelled using a ray approximation to heterogeneous Green's function, and the inverse problem is solved in the frequency domain via iteratively linearisation and minimisation of the objective function from low to high frequencies. In [3], the linear subproblem associated with each frequency set is solved via an implicit and iterative inversion of the Hessian matrix (inner iterations). Instead, in this study, each linear subproblem is weighted in a way in which the Hessian matrix be diagonalised, and can thus be inverted in a single step. Using this Hessian-free approach, the computational cost for solving each linear subproblem becomes almost the same as solving one linear subproblem associated with a radon-type time-of-flight-based approach using bent rays. This computational cost is about an order of magnitude less than the equivalent Hessian-based approach proposed in [3]. More importantly, the assumptions made for diagonalising the Hessian matrix make the image reconstruction more robust than the inversion approach in [3] to noise or initial guess. | |
| 653 | |a Acoustic propagation | ||
| 653 | |a Tomography | ||
| 653 | |a Smoothness | ||
| 653 | |a Mathematical analysis | ||
| 653 | |a Image reconstruction | ||
| 653 | |a Iterative methods | ||
| 653 | |a Inverse problems | ||
| 653 | |a Hessian matrices | ||
| 653 | |a Wave propagation | ||
| 653 | |a Green's functions | ||
| 653 | |a Ultrasonic imaging | ||
| 653 | |a Preconditioning | ||
| 653 | |a Acoustic waves | ||
| 773 | 0 | |t arXiv.org |g (May 17, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2731611885/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2211.00316 |