Normalization of Optical Fluence Distribution for Three-dimensional Functional Optoacoustic Tomography of the Breast

Overview

Journal J Biomed Opt

Specialties Biomedical Engineering
Ophthalmology

Date 2022 Mar 16

PMID 35293163

Authors

Seonyeong Park

Frank J Brooks

Umberto Villa

Richard Su

Mark A Anastasio

Alexander A Oraevsky

Affiliations

Soon will be listed here.

Abstract

Significance: In three-dimensional (3D) functional optoacoustic tomography (OAT), wavelength-dependent optical attenuation and nonuniform incident optical fluence limit imaging depth and field of view and can hinder accurate estimation of functional quantities, such as the vascular blood oxygenation. These limitations hinder OAT of large objects, such as a human female breast.

Aim: We aim to develop a measurement-data-driven method for normalization of the optical fluence distribution and to investigate blood vasculature detectability and accuracy for estimating vascular blood oxygenation.

Approach: The proposed method is based on reasonable assumptions regarding breast anatomy and optical properties. The nonuniform incident optical fluence is estimated based on the illumination geometry in the OAT system, and the depth-dependent optical attenuation is approximated using Beer-Lambert law.

Results: Numerical studies demonstrated that the proposed method significantly enhanced blood vessel detectability and improved estimation accuracy of the vascular blood oxygenation from multiwavelength OAT measurements, compared with direct application of spectral linear unmixing without optical fluence compensation. Experimental results showed that the proposed method revealed previously invisible structures in regions deeper than 15 mm and/or near the chest wall.

Conclusions: The proposed method provides a straightforward and computationally inexpensive approximation of wavelength-dependent effective optical attenuation and, thus, enables mitigation of the spectral coloring effect in functional 3D OAT imaging.

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References

Sivaramakrishnan M, Maslov K, Zhang H, Stoica G, Wang L . Limitations of quantitative photoacoustic measurements of blood oxygenation in small vessels. Phys Med Biol. 2007; 52(5):1349-61. DOI: 10.1088/0031-9155/52/5/010. View

Lansford R, Bearman G, Fraser S . Resolution of multiple green fluorescent protein color variants and dyes using two-photon microscopy and imaging spectroscopy. J Biomed Opt. 2001; 6(3):311-8. DOI: 10.1117/1.1383780. View

Xu H, Rice B . In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique. J Biomed Opt. 2010; 14(6):064011. DOI: 10.1117/1.3258838. View

Glatz J, Deliolanis N, Buehler A, Razansky D, Ntziachristos V . Blind source unmixing in multi-spectral optoacoustic tomography. Opt Express. 2011; 19(4):3175-84. DOI: 10.1364/OE.19.003175. View

Alshahrani S, Yan Y, Alijabbari N, Pattyn A, Avrutsky I, Malyarenko E . All-reflective ring illumination system for photoacoustic tomography. J Biomed Opt. 2019; 24(4):1-7. PMC: 6906953. DOI: 10.1117/1.JBO.24.4.046004. View

Gammon S, Leevy W, Gross S, Gokel G, Piwnica-Worms D . Spectral unmixing of multicolored bioluminescence emitted from heterogeneous biological sources. Anal Chem. 2006; 78(5):1520-7. PMC: 2615584. DOI: 10.1021/ac051999h. View

Kruger R, Lam R, Reinecke D, Del Rio S, Doyle R . Photoacoustic angiography of the breast. Med Phys. 2010; 37(11):6096-100. PMC: 2994935. DOI: 10.1118/1.3497677. View

Hochuli R, An L, Beard P, Cox B . Estimating blood oxygenation from photoacoustic images: can a simple linear spectroscopic inversion ever work?. J Biomed Opt. 2019; 24(12):1-13. PMC: 7005536. DOI: 10.1117/1.JBO.24.12.121914. View

Schoustra S, Piras D, Huijink R, Op t Root T, Alink L, Kobold W . Twente Photoacoustic Mammoscope 2: system overview and three-dimensional vascular network images in healthy breasts. J Biomed Opt. 2019; 24(12):1-12. PMC: 7005569. DOI: 10.1117/1.JBO.24.12.121909. View

10.

Chorvat Jr D, Kirchnerova J, Cagalinec M, Smolka J, Mateasik A, Chorvatova A . Spectral unmixing of flavin autofluorescence components in cardiac myocytes. Biophys J. 2005; 89(6):L55-7. PMC: 1367005. DOI: 10.1529/biophysj.105.073866. View

11.

Wang Z, Bovik A, Sheikh H, Simoncelli E . Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004; 13(4):600-12. DOI: 10.1109/tip.2003.819861. View

12.

Bashkatov A, Berezin K, Dvoretskiy K, Chernavina M, Genina E, Genin V . Measurement of tissue optical properties in the context of tissue optical clearing. J Biomed Opt. 2018; 23(9):1-31. DOI: 10.1117/1.JBO.23.9.091416. View

13.

Sandell J, Zhu T . A review of in-vivo optical properties of human tissues and its impact on PDT. J Biophotonics. 2011; 4(11-12):773-87. PMC: 3321368. DOI: 10.1002/jbio.201100062. View

14.

Badano A, Graff C, Badal A, Sharma D, Zeng R, Samuelson F . Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial. JAMA Netw Open. 2019; 1(7):e185474. PMC: 6324392. DOI: 10.1001/jamanetworkopen.2018.5474. View

15.

Yu L, Nina-Paravecino F, Kaeli D, Fang Q . Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms. J Biomed Opt. 2018; 23(1):1-4. PMC: 5785911. DOI: 10.1117/1.JBO.23.1.010504. View

16.

Lin L, Hu P, Shi J, Appleton C, Maslov K, Li L . Single-breath-hold photoacoustic computed tomography of the breast. Nat Commun. 2018; 9(1):2352. PMC: 6003984. DOI: 10.1038/s41467-018-04576-z. View

17.

Oraevsky A, Clingman B, Zalev J, Stavros A, Yang W, Parikh J . Clinical optoacoustic imaging combined with ultrasound for coregistered functional and anatomical mapping of breast tumors. Photoacoustics. 2018; 12:30-45. PMC: 6172480. DOI: 10.1016/j.pacs.2018.08.003. View

18.

Dean-Ben X, Fehm T, Gostic M, Razansky D . Volumetric hand-held optoacoustic angiography as a tool for real-time screening of dense breast. J Biophotonics. 2015; 9(3):253-9. DOI: 10.1002/jbio.201500008. View

19.

Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J, Pujol S . 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012; 30(9):1323-41. PMC: 3466397. DOI: 10.1016/j.mri.2012.05.001. View

20.

Poudel J, Lou Y, Anastasio M . A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography. Phys Med Biol. 2019; 64(14):14TR01. DOI: 10.1088/1361-6560/ab2017. View