Application of Binary Genetic Algorithm for Holographic Vascular Mimicking Phantom Reconstruction

Abstract

Since medical imaging is one of the essential methods for the diagnosis and treatment of several diseases, the characterization and calibration of medical imaging systems with low-cost equipment is the most crucial issue. In this context, tissue-mimicking phantoms have long been used for this purpose. The advantage of phantoms is that, in addition to the desired size and internal properties, they can be produced in a way that best carries the characteristic properties of tissue models and can be standardized so that they can be used in imaging environments. For this reason, it is important to make low-cost phantom designs produced from materials that are easy to shape and available and to ensure that they can be imaged with high quality. In this study, digital holography and binary genetic algorithm (BGA) were used to reconstruct the images of phantoms that mimic the human vascular system produced at a low cost. The obtained results showed that BGA can be used as an alternative to the reconstruction methods commonly used in digital holography. Since BGA provides an alternative solution to obtain the image with high resolution in the reconstruction process without any image processing algorithm, it enables the diagnosis of diseases related to thin vascular structures in real-time with a reliable and non-contact method.

Keywords

• binary genetic algorithm
• lateral shearing
• reconstruction
• thin vessel structures
• tissue-mimicking phantom

References

1. M. S. Ahmad, N. Suardi, A. Shukri, H. Mohammad, A. A. Oglat, A. Alarab, O. Makhamrak, ''Chemical characteristics, motivation and strategies in choice of materials used as liver phantom: A literature review.'' Journal of Medical Ultrasound, vol. 28, 2020, pp 7-16, 2020.
2. R. A. O. Jaime, R. H. Q. Basto, B. Lamien, H. R. B., Orlande, S., Eibner, and O. Fudym, ''Fabrication methods of phantoms simulating optical and thermal properties.'' Procedia Engineering, vol. 59, 2013, pp 30 – 36.
3. K. Qiu, G. Haghiashtiani, and M. C. McAlpine, ''3D printed organ models for surgical applications.'' AnnualReviewofAnalyticalChemistry, vol. 11, 2018, pp 287‑306.
4. S. Singh, and R. Repaka, ''Numerical study to establish relationship between coagulation volume and target tip temperature during temperature‑controlled radiofrequency ablation.'' ElectromagneticBiologyandMedicine, vol. 37, 2018, pp 13‑22.
5. T. Kondo, M. Kitatuji, Y. Shikinami, K. Tuta, and H. Kanda, ''New tissue mimicking materials for ultrasound phantoms.'' In Proceedings of IEEE Ultrasonic Symposium, vol. 3, 2005, pp 1664‑177.
6. E. In, H. Naguib, and M. Haider, ''Mechanical stability analysis of carrageenan-based polymer gel for magnetic resonance imaging liver phantom with lesion particles.'' Journal of Medical Imaging,vol. 1, no. 3, 2014, pp 035502.
7. M. Arif, A. Moelker, and T. van Walsum, ''Needle tip visibility in 3D ultrasound images.'' CardioVascular and Interventional Radiology, vol. 41, 2018, pp 145‑152.
8. M. Kavitha, M. Ramasubba Reddy, and S. Suresh, "Modelling, design and development of tissue mimicking phantoms for ultrasound elastography." 2012IEEE-EMBSConferenceonBiomedicalEngineeringandSciences, 2012, pp 564-567.

9. Y. H. Kao, O. S. Luddington, S. R. Culleton, R. J. Francis, and J. A. Boucek,''A gelatin liver phantom of suspended 90Y resin microspheres to simulate the physiologic microsphere biodistribution of a postradioembolization liver.'' JournalofNuclearMedicineTechnology, vol. 42, no. 4, 2014, pp 265‑273.
10. W. Dabrowski, J. Dunmore-Buyze, R.N. Rankin, D.W. Holdsworth, and A.Fenster, ''A real vessel phantom for imaging experimentation. ''Medical Physics, vol. 24, no. 5, 1997, pp. 687-693.
11. W.C. Vogt, C. Jia, K.A. Wear, B.S. Garra, and T.J. Pfefer, ''Phantom-based image quality test methods for photoacoustic imaging systems. ''Journal of Biomedical Optics, vol. 22, no. 9, 2017, pp. 095002-1-14.
12. Y. He, Y. Liu, B.A. Dyer, J.M. Boone, S. Liu, T. Chen, F. Zheng, Y. Zhu, Y. Sun, Y. Rong, and J. Qui, ''3D-printed breast phantom for multi-purpose and multi-modality imaging. '' Quantative Imaging In Medicine And Surgery, vol. 9, no. 1, 2019.
13. H. Nisar, J. Moore, R. Piazza, E. Maneas, E.C.S. Chen, and T.M. Peters, ''A simple, realistic walled phantom for intravascular and intracardiac applications. ''International Journal of Computer Assisted Radiology and Surgery, vol. 15, 2020, pp. 1513-1523.
14. T.O., Onur, G. Ustabas Kaya, and C. Kaya, ''Phase shifted‑lateral shearing digital holographic microscopy imaging for early diagnosis of cysts in soft tissue‑mimicking phantom.'' AppliedPhysicsB, vol. 127, no. 61, 2021.
15. A. Anand, P. Vora, S. Mahajan, V. Trivedi, V. Chhaniwal, A. Singh, L. Leitgeb, and B. Javidi, ''Compact, common path quantitative phase microscopic techniques for imaging cell dynamics.'' Pramana, vol.82, no. 1, 2014, pp. 71–78.
16. A. S. G. Singh, A. Anand, R. A. Leitgeb, and B. Javidi, ''Lateral shearing digital holographic imaging of small biological specimens.''Optics Express,vol. 20, 2012, pp. 23617-23622.
17. Y. Park, C. Depeursinge, and G. Popescu, ''Quantitative phase imaging in biomedicine.''NaturePhotonics, vol. 12, no. 10, 2018, pp 578–589.
18. S. Devinder, A. Lal, T. R. Dastidar, and S. K. Dubey, ''Quantitative analysis of numerically focused red blood cells using subdivided two-beam interference (STBI) based lateral-shearing digital holographic.''arXiv:1909.03454 [eess.IV], 2019.
19. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading Massachusetts, 1989.
20. M. Mitchell, An Introduction to Genetic Algorithms, MIT Press, Cambridge, Massachusetts, 1999.
21. Y. Y. Song, F. L. Wang, and X. X. Chen, ''An improved genetic algorithm for numerical function optimization,'' Applied Intelligence, vol. 49, no. 5, 2019, pp 1880–1902.
22. S. Katoch, S. S. Chauhan, and V. Kumar, ''A review on genetic algorithm: past, present, and future.'' MultimediaToolsand Applications, vol. 80, 2021, pp8091–8126.
23. G. Ustabas Kaya, ''Imaging of transparent objects with phase shifting- lateral shearing digital holographic microscopy.'' IEEE, 2020 28th Signal Processing and Communications Applications Conference (SIU), 2020.
24. J.E. Greivenkamp, FieldGuidetoGeometricalOptics, SPIE Press, Bellingham, WA, 2004.
25. T. Günel, and S. Kent, ''Genetic approach for the determination of object parameters from X Ray projections.'' Turkish Journal of Electrical Engineering and Computer Science, vol.6, no.3, 1998, pp. 277-286.
26. J. H. Uliana, D. R. T. Sampaio, A. A. O. Carneiro, andT. Z. Pavan,''Photoacoustic-based thermal image formation and optimization using an evolutionary genetic algorithm.''Biomed Research International,vol. 34, no. 2, 2018, pp. 147-156.
27. M. Ziemczonok, A. Kuś, P. Wasylczyk, and M. Kujawińska, ''3D-printed biological cell phantom for testing 3D quantitative phase imaging systems.'' Scientific Reports, vol. 9, no. 1, 2019, pp. 1–9.
28. F. Magliani, L. Sani, S. Cagnoni, and A. Prati,''Genetic algorithms for the optimization of diffusion parameters in content-based image retrieval.'' arXiv:1908.06896v1 [cs.CV] 19 Aug 2019.
29. M. Devi, S. Singh,S. Tiwari, S. C. Patel, and M. T. A. Ayana, ''Survey of soft computing approaches in biomedical imaging.''Journal of Healthcare Engineering, vol. 2021, 2021, pp. 1-15.
30. A. M. T. Gouicem, K. Benmahammed, R. Drai, M. Yahi, and A. Taleb-ahmed, ''Multi-objective GA optimization of fuzzy penalty for image reconstruction from projections in X-ray tomography,'' Digital Signal Processing, vol. 22, no. 3, 2012, pp. 486–496.