Enhancing photoacoustic imaging reconstruction using modified fuzzy-based CNN-BiLSTM with PSO

Selvan C; Farooque Azam; Sasikala.S; S Iwin Thanakumar Joseph; Senthil Kumar R

doi:10.31893/multiscience.2025366

Keywords:

Photoacoustic tomography

Deep learning

Convolutional neural network

fuzzy

Abstract

Photoacoustic Tomography (PAT) is a recently discovered medical imaging modality that combines the benefits of pure optical and ultrasonic images with strong optical contrasts and great depth of penetration. Deep Learning (DL), a novel paradigm, has attracted much interest recently due to its capability to enhance medical imaging. Similarly, DL is commonly employed in Photo Acoustic Imaging (PAI) to address some of the constraints of PAI. In photoacoustic imaging, traditional reconstruction algorithms such as delay-and-sum were extensively utilized. Recent experiments have shown that deep neural networks can reconstruct PAI from unprocessed photoacoustic data. This paper provides a modified fuzzy-based CNN-BiLSTM architecture as a foundation for a neural network to tackle PAI image reconstruction with Modified Particle swarm optimization (MFCB-PS). The numerical simulation results reveal that the suggested frameworks outperform standard reconstruction approaches significantly. Furthermore, the suggested reconstruction technique surpasses existing standard reconstruction algorithms regarding time consumption, indicating its potential for real-time image analysis.
References
1. Attia, Amalina B. E., Ghayathri B., Mohesh M., Dinish U.C, Renzhe B., Vasilis N., & Malini O. (2019). A review of 535 clinical photoacoustic imaging: Current and future trends, Photoacoustics 16: 100144. 536
2. Moudgil, V., Hewage, K., Hussain, S.A. & Sadiq, R. (2023). Integration of IoT in building energy infrastructure: A 537 critical review on challenges and solutions. Renewable and Sustainable Energy Reviews, 174, 113-121. 538
3. Sabapathy, Sundaresan, Surendar M., Suresh K K., Ananth K. T., & Nishanth R. (2022). Competent and Affordable 539 Rehabilitation Robots for Nervous System Disorders Powered with Dynamic CNN and HMM. Intelligent Systems for 540 Rehabilitation Engineering, 57-93. 541
4. Singh, Mithun K. A., & Wiendelt S. (2015). Photoacoustic-guided focused ultrasound (PAFUSion) for identifying 542 reflection artifacts in photoacoustic imaging. Photoacoustics 3(4), 123-131. 543
5. Prasad, Alisha, Ardalan C., Parker D. K., Joseph F., & Manas R. G. (2019). Current and future functional imaging 544 techniques for post-traumatic stress disorder. RSC advances, 9( 42), 24568-24594. 545
6. Kheradmandi, Narges, and Vida Mehranfar. "A critical review and comparative study on image segmentation-based 546 techniques for pavement crack detection." Construction and Building Materials 321 (2022): 126162. 547
7. Suresh K., & Helen C. S. (2022). Local search five‐element cycle optimized reLU‐BiLSTM for multilingual aspect‐based 548 text classification. Concurrency and Computation: Practice and Experience, 34(28), 7374. 549
8. Wang., &Yiping. (2010). Review of long period fiber gratings written by CO2 laser. Journal of Applied Physics, 108(8). 550
9. Bydlon., Torre M., Rami N., Nimmi R., Henricus J. S., & Benno H. H. (2015). Chromophore based analyses of steady‐551 state diffuse reflectance spectroscopy: current status and perspectives for clinical adoption. Journal of 552 biophotonics, 8(1), 9-24. 553
10. Chan, Ming-Hsien, Wen-Tse H., Kuan-Chun C., Ting-Yi S., Yung-Chieh C., Michael H., & Ru-Shi L. (2022). The optical 554 research progress of nano phosphors composed of transition elements in the fourth period of near-infrared 555 windows I and II for deep-tissue theranostics. Nanoscale, 14(19), 7123-7136.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Selvan C, Azam, F., Sasikala.S, S Iwin Thanakumar Joseph, & Senthil Kumar R. (2024). Enhancing photoacoustic imaging reconstruction using modified fuzzy-based CNN-BiLSTM with PSO. Multidisciplinary Science Journal, (| Accepted Articles). https://doi.org/10.31893/multiscience.2025366

[1] Attia, Amalina B. E., Ghayathri B., Mohesh M., Dinish U.C, Renzhe B., Vasilis N., & Malini O. (2019). A review of 535 clinical photoacoustic imaging: Current and future trends, Photoacoustics 16: 100144. 536

[2] Moudgil, V., Hewage, K., Hussain, S.A. & Sadiq, R. (2023). Integration of IoT in building energy infrastructure: A 537 critical review on challenges and solutions. Renewable and Sustainable Energy Reviews, 174, 113-121. 538

[3] Sabapathy, Sundaresan, Surendar M., Suresh K K., Ananth K. T., & Nishanth R. (2022). Competent and Affordable 539 Rehabilitation Robots for Nervous System Disorders Powered with Dynamic CNN and HMM. Intelligent Systems for 540 Rehabilitation Engineering, 57-93. 541

[4] Singh, Mithun K. A., & Wiendelt S. (2015). Photoacoustic-guided focused ultrasound (PAFUSion) for identifying 542 reflection artifacts in photoacoustic imaging. Photoacoustics 3(4), 123-131. 543

[5] Prasad, Alisha, Ardalan C., Parker D. K., Joseph F., & Manas R. G. (2019). Current and future functional imaging 544 techniques for post-traumatic stress disorder. RSC advances, 9( 42), 24568-24594. 545

[6] Kheradmandi, Narges, and Vida Mehranfar. "A critical review and comparative study on image segmentation-based 546 techniques for pavement crack detection." Construction and Building Materials 321 (2022): 126162. 547

[7] Suresh K., & Helen C. S. (2022). Local search five‐element cycle optimized reLU‐BiLSTM for multilingual aspect‐based 548 text classification. Concurrency and Computation: Practice and Experience, 34(28), 7374. 549

[8] Wang., &Yiping. (2010). Review of long period fiber gratings written by CO2 laser. Journal of Applied Physics, 108(8). 550

[9] Bydlon., Torre M., Rami N., Nimmi R., Henricus J. S., & Benno H. H. (2015). Chromophore based analyses of steady‐551 state diffuse reflectance spectroscopy: current status and perspectives for clinical adoption. Journal of 552 biophotonics, 8(1), 9-24. 553

[10] Chan, Ming-Hsien, Wen-Tse H., Kuan-Chun C., Ting-Yi S., Yung-Chieh C., Michael H., & Ru-Shi L. (2022). The optical 554 research progress of nano phosphors composed of transition elements in the fourth period of near-infrared 555 windows I and II for deep-tissue theranostics. Nanoscale, 14(19), 7123-7136.

Abstract

References

How to cite