Sparsifying regularizations for stochastic sample average minimization in ultrasound computed tomography
| Autor: | Andrea Zunino, Christian Boehm, Andreas Fichtner, Ines Elisa Ulrich |
|---|---|
| Přispěvatelé: | Byram, Brett C., Ruiter, Nicole |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Sample average
medicine.diagnostic_test business.industry Computer science Ultrasound Stochastic optimization ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computed tomography Ultrasound computed tomography Regularization (mathematics) Imaging phantom Sparsity l1-minimization Curvelets Curvelet medicine Minification business Algorithm |
| Zdroj: | Proceedings of SPIE, 11602 Medical Imaging 2020: Ultrasonic Imaging and Tomography. Proceedings SPIE Medical Imaging | 15-20 February 2020 |
| ISSN: | 0277-786X |
| Popis: | Ultrasound computed tomography (USCT) is a promising imaging modality for breast cancer screening. Two challenges commonly arising in time-of-flight USCT are (1) to efficiently deal with large data sets and (2) to effectively mitigate the ill-posedness for an adequate reconstruction of the model. In this contribution, we develop an optimization strategy based on a stochastic descent method that adaptively subsamples the data, and analyze its performance in combination with different sparsity-enforcing regularization techniques. The algorithms are tested on numerical as well as real data obtained from synthetic phantom scans of the previous USCT Data Challenges. Proceedings of SPIE, 11602 ISSN:0277-786X Medical Imaging 2020: Ultrasonic Imaging and Tomography. Proceedings SPIE Medical Imaging | 15-20 February 2020 ISBN:978-1-5106-4033-7 ISBN:978-1-5106-4034-4 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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