Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
153 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Design, Implementation and Analysis of a Compressed Sensing Photoacoustic Projection Imaging System (2402.15750v1)

Published 24 Feb 2024 in cs.CV, cs.NA, math.NA, and physics.med-ph

Abstract: Significance: Compressed sensing (CS) uses special measurement designs combined with powerful mathematical algorithms to reduce the amount of data to be collected while maintaining image quality. This is relevant to almost any imaging modality, and in this paper we focus on CS in photoacoustic projection imaging (PAPI) with integrating line detectors (ILDs). Aim: Our previous research involved rather general CS measurements, where each ILD can contribute to any measurement. In the real world, however, the design of CS measurements is subject to practical constraints. In this research, we aim at a CS-PAPI system where each measurement involves only a subset of ILDs, and which can be implemented in a cost-effective manner. Approach: We extend the existing PAPI with a self-developed CS unit. The system provides structured CS matrices for which the existing recovery theory cannot be applied directly. A random search strategy is applied to select the CS measurement matrix within this class for which we obtain exact sparse recovery. Results: We implement a CS PAPI system for a compression factor of $4:3$, where specific measurements are made on separate groups of 16 ILDs. We algorithmically design optimal CS measurements that have proven sparse CS capabilities. Numerical experiments are used to support our results. Conclusions: CS with proven sparse recovery capabilities can be integrated into PAPI, and numerical results support this setup. Future work will focus on applying it to experimental data and utilizing data-driven approaches to enhance the compression factor and generalize the signal class.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (41)
  1. Compressed sensing photoacoustic tomography reduces to compressed sensing for undersampled fourier measurements. SIAM Journal on Imaging Sciences, 14(3):1039–1077, 2021.
  2. Mark A Anastasio. Deep learning and photoacoustic image formation: promises and challenges. In Photons Plus Ultrasound: Imaging and Sensing 2023, page PC1237901. SPIE, 2023.
  3. Deep learning for photoacoustic tomography from sparse data. Inverse problems in science and engineering, 27(7):987–1005, 2019.
  4. Nett regularization for compressed sensing photoacoustic tomography. In Photons Plus Ultrasound: Imaging and Sensing 2019, volume 10878, pages 272–282. SPIE, 2019.
  5. Accelerated high-resolution photoacoustic tomography via compressed sensing. Phys. Med. Biol., 61(24):8908, 2016.
  6. All-optical photoacoustic projection imaging. Biomedical optics express, 8(9):3938–3951, 2017.
  7. Photoacoustic projection imaging using a 64-channel fiber optic detector array. In Photons Plus Ultrasound: Imaging and Sensing 2015, volume 9323, pages 585–590. SPIE, 2015.
  8. Combining geometry and combinatorics: A unified approach to sparse signal recovery. In 46th Annual Allerton Conference on Communication, Control, and Computing, 2008, pages 798–805, 2008.
  9. Acoustic wave field reconstruction from compressed measurements with application in photoacoustic tomography. IEEE Trans. Comput. Imaging, 3:710–721, 2017.
  10. Temporal back-projection algorithms for photoacoustic tomography with integrating line detectors. Inverse Probl., 23(6):S65–S80, 2007.
  11. Thermoacoustic tomography using a fiber-based fabry-perot interferometer as an integrating line detector. In Photons Plus Ultrasound: Imaging and Sensing 2006: The Seventh Conference on Biomedical Thermoacoustics, Optoacoustics, and Acousto-optics, volume 6086, pages 434–442. SPIE, 2006.
  12. Thermoacoustic tomography with integrating area and line detectors. IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 52(9):1577–1583, 2005.
  13. T. T. Cai and A. Zhang. Sharp RIP bound for sparse signal and low-rank matrix recovery. Appl. Comput. Harmon. Anal., 35(1):74–93, 2013.
  14. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on information theory, 52(2):489–509, 2006.
  15. Deep learning optoacoustic tomography with sparse data. Nature Machine Intelligence, 1(10):453–460, 2019.
  16. David L Donoho. Compressed sensing. IEEE Transactions on information theory, 52(4):1289–1306, 2006.
  17. Convergence rates for the joint solution of inverse problems with compressed sensing data. Inverse Problems, 39(1):015011, 2022.
  18. Inversion of spherical means and the wave equation in even dimensions. SIAM J. Appl. Math., 68(2):392–412, 2007.
  19. S. Foucart and H. Rauhut. A mathematical introduction to compressive sensing. Springer, 2013.
  20. Deep learning for biomedical photoacoustic imaging: A review. Photoacoustics, 22:100241, 2021.
  21. Compressed sensing in photoacoustic tomography in vivo. J. Biomed. Opt., 15(2):021311, 2010.
  22. M. Haltmeier. Sampling conditions for the circular radon transform. IEEE Trans. Image Process., 25(6):2910–2919, 2016.
  23. Compressed sensing and sparsity in photoacoustic tomography. J. Opt., 18(11):114004–12pp, 2016.
  24. A sparsification and reconstruction strategy for compressed sensing photoacoustic tomography. The Journal of the Acoustical Society of America, 143(6):3838–3848, 2018.
  25. Model-based learning for accelerated, limited-view 3-d photoacoustic tomography. IEEE Trans. Med. Imaging, 37(6):1382–1393, 2018.
  26. Deep learning in photoacoustic tomography: current approaches and future directions. Journal of Biomedical Optics, 25(11):112903–112903, 2020.
  27. Single-pixel camera photoacoustic tomography. Journal of biomedical optics, 24(12):121907–121907, 2019.
  28. Comparison of piezoelectric and optical projection imaging for three-dimensional in vivo photoacoustic tomography. Journal of Imaging, 5(1):15, 2019.
  29. Full field detection in photoacoustic tomography. Optics express, 18(6):6288–6299, 2010.
  30. Photoacoustic tomography using a Mach-Zehnder interferometer as an acoustic line detector. Appl. Opt., 46(16):3352–3358, 2007.
  31. Piezoelectric line detector array for photoacoustic tomography. Photoacoustics, 8:28–36, 2017.
  32. Photoacoustic tomography using a mach-zehnder interferometer as an acoustic line detector. Applied optics, 46(16):3352–3358, 2007.
  33. Photoacoustic tomography with integrating area and line detectors. In Photoacoustic imaging and spectroscopy, pages 251–264. CRC Press, 2017.
  34. A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography. Physics in Medicine & Biology, 64(14):14TR01, 2019.
  35. J. Provost and F. Lesage. The application of compressed sensing for photo-acoustic tomography. IEEE Trans. Med. Imag., 28(4):585–594, 2009.
  36. Acoustic inversion in optoacoustic tomography: A review. Current Medical Imaging, 9(4):318–336, 2013.
  37. A novel compressed sensing scheme for photoacoustic tomography. SIAM J. Appl. Math., 75(6):2475–2494, 2015.
  38. Photoacoustic and thermoacoustic tomography: image formation principles. In Handbook of Mathematical Methods in Imaging.
  39. Photoacoustic tomography: in vivo imaging from organelles to organs. science, 335(6075):1458–1462, 2012.
  40. M. Xu and L. V. Wang. Photoacoustic imaging in biomedicine. Rev. Sci. Instruments, 77(4):041101 (22pp), 2006.
  41. Multiscale factorization of the wave equation with application to compressed sensing photoacoustic tomography. SIAM Journal on Imaging Sciences, 14(2):558–579, 2021.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now