Reconstruction with In-Line Digital Holography Quantitative Phase Imaging for Tissue-Mimicking Phantom Samples

Year 2021, Volume: 9 Issue: 2, 213 - 220, 30.04.2021

Gülhan Ustabas Kaya , Tuğba Özge Onur

https://doi.org/10.17694/bajece.829857

Abstract

Optical imaging has attracted recent attention as a non-invasive medical imaging method in biomedical and clinical applications. In optical imaging, a light beam is transmitted through an under-test tissue by using an optical source. The beams which are gone through the tissue and/or reflected from the tissue surfaces are received by an array sensor. Based on the light intensity of these received beams on the sensor, sub-tissue maps are generated to scan large tissue areas so that any further biopsy is not required. Although the large tissue areas in pathological images can be scanned by using various methods, nonlinear deformations occur. To overcome this problem, the reconstruction process is frequently used.
In this study, we propose an application of biomedical imaging based on performing the reconstruction of a phantom image via an in-line digital holography technique. Hence, many different sub-tissues can be imaged at the same time without the storage problem of the reconstructed image. To neglect the biopsy process required in medical imaging, the phantom image is obtained by using a linear array transducer for this study. We present the performance evaluation of the simulation results for the proposed technique by calculating the error metrics such as mean squared error (MSE), mean absolute error (MAE), and peak signal-to-noise ratio (PSNR). The obtained results reveal that the reconstructed images are well-matched to the original images, which are desired to be displayed by the holography technique.

Keywords

In-line digital holography, image reconstruction, optical imaging, phantom image

References

• [1] D. Welch, A.D. Harken, G. Randers‐Pehrson, D.J. Brenner. "Construction of mouse phantoms from segmented CT scan data for radiation dosimetry studies." Phys Med Biol. vol. 60, 1, 2015, pp 3589-3598.
• [2] F. Zito, E. De Bernardi, C. Soffientini, C. Canzi, R. Casati, P. Gerundini, G. Baselli. "The use of zeolites to generate PET phantoms for the validation of quantification strategies in oncology." Med Phys. Vol. 39, 9, 2012, pp 5353-5361.
• [3] A. Hellerbach, V. Schuster, A. Jansen, J. Sommer. "MRI phantoms – are there alternatives to Agar?" PLoS ONE, vol. 8, 8, 2013, pp e70343.
• [4] A.P. Gibson, J.C. Hebden, S.R. Arridge. "Recent advances in diffuse optical imaging." Phys. Med. Biol. Vol 50, 4, 2005, pp R1-R43
• [5] G.D. Lieo, R.M. Trimboli, T. Sella, F. Sardanelli. "Optical imaging of the breast: basic principles and clinical applications." American Journal of Roentgenology, vol. 290, 1, 2017, pp 230-238.
• [6] A. Anand, I. Moon, B. Javidi. "Automated disease identification with 3-D optical imaging: a medical diagnostic tool." Proc. IEEE, vol. 105, 5, 2017, pp 924-946.
• [7] F. Maes, D. Loeckx, D. Vandermeulen, P. Suetens, Image registration using mutual information. In: Handbook of biomedical imaging, Springer US, 2015, p. 295–308.
• [8] U. Schnars, W. Jueptner, Digital Holography: Digital Hologram Recording, Numerical Reconstruction, and Related Techniques, Springer-Verlag Berlin Heidelberg, 2005, p. 164.
• [9] A. Doblas, E. Roche, F. Ampudia-Blasco, M. Martinez-Corral, G. Saavedra, and J. Garcia-Sucerquia. "Diabetes screening by telecentric digital holographic microscopy." J. Microsc. Vol. 261, 3, 2016, pp 285–290.
• [10] C. D. Depeursinge, E. Cuche, T. Colomb, P. Dahlgren, A. M. Marian, F. Monfort, P. Marquet, P. J. Magistretti, "Cells and tissue imaging with digital holographic microscopy." Novel Optical Instrumentation for Biomedical Applications, 14 October 2003, SPIE vol. 5143.
• [11] N.F. Boyd, H. Guo, L.J. Martin. "Mammographic density and the risk and detection of breast cancer." N. Engl. J. Med. vol. 356, 3, 2007 pp 227–236.
• [12] C.K. Kuhl. "Current status of breast MR imaging. Part 2: clinical applications." Radiology, vol. 244, 3, 2007, pp 672–691.
• [13] N.H. Peters, R. Borel, N.P. Zuithoff, W.P. Mali, K.G. Moons, P.H. Peeters. "Meta-analysis of MR imaging in the diagnosis of breast lesions." Radiology, vol. 246,1, 2008, pp 116–124.
• [14] C. C. Chen, Y.L. Wan, Y.Y., Wai, H.L. Liu. "Quality assurance of clinical MRI scanners using ACR MRI phantom: preliminary results." J Digit Imaging vol. 17, 2004, pp 279–284.
• [15] M. Freed, J. A. de Zwart, J. T. Loud, R. H. El Khouli, K. J. Myers, M. H. Greene, J. H. Duyn, A. Badano. "An anthropomorphic phantom for quantitative evaluation of breast MRI." Med Phys. Vol. 38, 2, 2011, pp 743–753.
• [16] K. Hattori, Y. Ikemoto, W. Takao, T. Harimoto, S. Kanazawa, M. Oita, K. Shibuya, M. Kuroda, H. Kato. "Development of MRI phantom equivalent to human tissues for 3.0‐T MRI." Med Phys. Vol. 40, 3, 2013, pp 032303‐1–032303‐11.
• [17] M. Kugler, Y. Goto, Y. Tamura, N. Kawamura, H. Kobayashi, T. Yokota, C. Iwamoto, K. Ohuchida, M. Hashizume, A. Shimizu, H. Hontani. "Robust 3D image reconstruction of pancreatic cancer tumors from histopathological images with different stains and its quantitative performance evaluation." International Journal of Computer Assisted Radiology and Surgery, vol. 14, 1, 2019, pp 2047–2055.
• [18] J,.Pichat, J.E. Iglesias, T. Yousry, S. Ourselin, M. Modat. "A survey of methods for 3D histology reconstruction." Medical Image Analysis, vol. 46, 1, 2018, pp 73-105.
• [19] Y. Song, D. Treanor, A. J. Bulpitt, D. R. Magee. "3D reconstruction of multiple stained histology images." J Pathol Inform. Vol. 4, 2, 2013, pp S7
• [20]M. Kugler, Y. Goto, N. Kawamura, H. Kobayashi, T. Yokota, C. Iwamoto, K. Ohuchida,, M. Hashizume, H. Hontani, Accurate 3D reconstruction of a whole pancreatic cancer tumor from pathology images with different stains. In: Computational pathology and ophthalmic medical image analysis, Springer International Publishing, 2018, p. 35–43.
• [21] D. H. Adler, J. Pluta, S. Kadivar, C. Craige, J. C Gee, B. B. Avants, P. A. Yushkevich. "Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI." Neuroimage, vol. 84, 1, 2014, pp 505–523.
• [22] P. Hariharan, Optical holography: principles, techniques and applications, Cambridge University Press, 2nd Edition, September 1996, p. 428.
• [23]C.B. Burckhardt, R.J. Collier, L. H. Lin, Optical holography, Academic Press, New York, NY, USA, 1971, p. 605
• [24]J. Di, Y. Li, M. Xie, J. Zhang, C. Ma, T. Xi, E. Li, J. Zhao. "Dual-wavelength common-path digital holographic microscopy for quantitative phase imaging based on lateral shearing interferometry." Applied Optics, vol. 55, 26, 2016, pp 7287-7293.
• [25]P. Ledwig, F. E. Robles "Epi-mode tomographic quantitative phase imaging in thick scattering samples." Biomedical Optics Express, vol. 10, 7, 2019, pp 3605-362.
• [26]K. Lee, K. Kim, J. Jung, J. Heo, S. Cho, S. Lee, G. Chang, Y. J. Jo, H. Park, Y. K. Park. "Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications." Sensors (Basel), vol. 13, 4, 2013, pp 4170‐4191.
• [27] Y. K. Park, C. Depeursinge, G. Popescu, "Quantitative phase imaging in biomedicine." Nature Photonics, vol. 12, 1, 2018, pp 578–589.
• [28]Y. S. Kim, S. Lee, J. Jung, S. Shin, H. G. Choi, G. H. Cha, W. Park, S. Lee, Y. Park. "Combining three-dimensional quantitative phase imaging and fluorescence microscopy for the study of Cell pathophysiology." Yale Journal of Biology and Medicine, vol. 91, 3, 2018, pp 267-277.
• [29]M. K. Kim. "Applications of digital holography in biomedical microscopy." Journal of the Optical Society of Korea, vol. 14, 2, 2010, pp 77-89.
• [30]F. Yi, I. Moon, B. Javidi. "Automated red blood cells extraction from holographic images using fully convolutional neural networks." Biomedical Optics Express, vol. 8, 10, 2017, pp 4466‐4479.
• [31] A. Calabuig, M. Mugnano, L. Miccio, S. Grilli, P. Ferraro. "Investigating fibroblast cells under "safe" and "injurious" blue-light exposure by holographic microscopy." Journal of Biophotonics, vol. 10, 6-7, 2017, pp 919-927.
• [32]E. M. Zetsche, A. El Mallahi, F. J. R. Meysman. "Digital holographic microscopy: a novel tool to study themorphology, physiology and ecology of diatoms." Diatom Research, vol. 31, 1, 2016, pp 1-16.
• [33]Z. El-Schich, A. L. Mölder, A. G. Wingren. "Quantitative phase imagingfor label-free analysis of cancer cells-focus on digital holographic microscopy." Applied Sciences, vol. 8, 7, 2018, pp 1027.
• [34]T. Ö. Onur, R. Hacıoğlu, "Image enhancement in ultrasound imaging." SIU 2016-Signal Processing and Communication Application Conference, Zonguldak, Turkey, 16-19 May 2016.
• [35]T. Ö. Onur, "Reducing Speckle in Ultrasound Images with Image Compounding." SIU 2020-Signal Processing and Communication Application Conference, Gaziantep, Turkey, 05-07 October 2020.
• [36]T. Latychevskaia, H. W. Fink. "Practical algorithms for simulation and reconstruction of digital in-line holograms." Applied Optics, vol. 54, 9, 2015, pp 2424-2434.
• [37] T. Kreis, Handbook of holographic interferometry, optical and digital methods, Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, 1st edition, 2005, p. 554.
• [38]E. Cuche, P. Marquet, C. Depeursinge. "Spatial filtering for zero-order and twin-image elimination in digital off-axis holography." Applied Optics, vol. 39, 23, 2000, pp 4070-4075.
• [39]G. Ustabaş Kaya, Z. Saraç, D. Önal Tayyar, "Image reconstruction from phase hologram obtained by using single phase information." SIU 2014-Signal Processing and Communication Application Conference, Trabzon, Turkey, 23-25 April 2014.
• [40]C. Sasi Varnan, A. Jagan, J. Kaur, D. Jyoti, Dr. D. S. Rao. "Image quality assessment techniques pn spatial domain." International Journal of Computer Science and Technology IJCST, vol. 2, 3, 2011, pp 177-184.
• [41] U. Sara, M. Akter, M. S. Uddin. "Image quality assessment through FSIM, SSIM, MSE and PSNR—A comparative study." Journal of Computer and Communications, vol. 7, 3, 2019, pp 8-18.
• [42]D. Asamoah, E. O. Oppong, S. O. Oppong, J. Danso. "Measuring the performance of image contrast enhancement technique." Int. J. Comput. Appl. vol.181, 22, 2018, pp. 6-13.