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Abstract

Photoacoustic techniques have shown promise in identifying molecular changes in bone tissue and visualizing tissue microstructure. This capability represents significant advantages over gold standards (i.e., dual-energy X-ray absorptiometry) for bone evaluation without requiring ionizing radiation. Instead, photoacoustic imaging uses light to penetrate through bone, followed by acoustic pressure generation, resulting in highly sensitive optical absorption contrast in deep biological tissues. This review covers multiple bone-related photoacoustic imaging contributions to clinical applications, spanning bone cancer, joint pathologies, spinal disorders, osteoporosis, bone-related surgical guidance, consolidation monitoring, and transsphenoidal and transcranial imaging. We also present a summary of photoacoustic-based techniques for characterizing biomechanical properties of bone, including temperature, guided waves, spectral parameters, and spectroscopy. We conclude with a future outlook based on the current state of technological developments, recent achievements, and possible new directions.

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2023-06-08
2024-04-25
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Literature Cited

  1. 1.
    Beard P. 2011. Biomedical photoacoustic imaging. Interface Focus 1:4602–31
    [Google Scholar]
  2. 2.
    Xu M, Wang LV. 2006. Photoacoustic imaging in biomedicine. Rev. Sci. Instrum. 77:041101
    [Google Scholar]
  3. 3.
    Wiacek A, Bell MAL. 2021. Photoacoustic-guided surgery from head to toe. Biomed. Opt. Express 12:42079–117
    [Google Scholar]
  4. 4.
    Bell MAL. 2020. Photoacoustic imaging for surgical guidance: principles, applications, and outlook. J. Appl. Phys. 128:060904
    [Google Scholar]
  5. 5.
    Hernandez C, Beaupre G, Carter D. 2003. A theoretical analysis of the relative influences of peak BMD, age-related bone loss and menopause on the development of osteoporosis. Osteoporos. Int. 14:10843–47
    [Google Scholar]
  6. 6.
    Marcus R, Majumder S 2001. The nature of osteoporosis. Marcus and Feldman's Osteoporosis R Marcus, D Feldman, J Kelsey 3–17. San Diego, CA: Academic. , 2nd ed..
    [Google Scholar]
  7. 7.
    Obermayer-Pietsch BM, Marin F, McCloskey EV, Hadji P, Farrerons J et al. 2008. Effects of two years of daily teriparatide treatment on BMD in postmenopausal women with severe osteoporosis with and without prior antiresorptive treatment. J. Bone Miner. Res. 23:101591–600
    [Google Scholar]
  8. 8.
    Kaste S, Shidler T, Tong X, Srivastava D, Rochester R et al. 2004. Bone mineral density and osteonecrosis in survivors of childhood allogeneic bone marrow transplantation. Bone Marrow Transplant. 33:4435–41
    [Google Scholar]
  9. 9.
    Akamatsu Y, Mitsugi N, Hayashi T, Kobayashi H, Saito T. 2012. Low bone mineral density is associated with the onset of spontaneous osteonecrosis of the knee. Acta Orthop. 83:3249–55
    [Google Scholar]
  10. 10.
    Bjarnason NH, Hitz M, Jorgensen NR, Vestergaard P, Board Dan. Bone Soc 2008. Adverse bone effects during pharmacological breast cancer therapy. Acta Oncol. 47:4747–54
    [Google Scholar]
  11. 11.
    Aisenberg J, Hsieh K, Kalaitzoglou G, Whittam E, Heller G et al. 1998. Bone mineral density in young adult survivors of childhood cancer. J. Pediatr. Hematol./Oncol. 20:3241–45
    [Google Scholar]
  12. 12.
    Zionts L, Nash J, Rude R, Ross T, Stott N 1995. Bone mineral density in children with mild osteogenesis imperfecta. J. Bone Jt. Surg. 77:1143–47
    [Google Scholar]
  13. 13.
    Moore M, Minch C, Kruse R, Harcke H, Jacobson L, Taylor A. 1998. The role of dual energy X-ray absorptiometry in aiding the diagnosis of pediatric osteogenesis imperfecta. Am. J. Orthop. 27:12797–801
    [Google Scholar]
  14. 14.
    Brown TT, Qaqish RB. 2006. Antiretroviral therapy and the prevalence of osteopenia and osteoporosis: a meta-analytic review. AIDS 20:172165–74
    [Google Scholar]
  15. 15.
    Conway S, Morton A, Oldroyd B, Truscott J, White H et al. 2000. Osteoporosis and osteopenia in adults and adolescents with cystic fibrosis: prevalence and associated factors. Thorax 55:9798–804
    [Google Scholar]
  16. 16.
    Cerroni AM, Tomlinson GA, Turnquist JE, Grynpas MD. 2000. Bone mineral density, osteopenia, and osteoporosis in the rhesus macaques of Cayo Santiago. Am. J. Phys. Anthropol. 113:3389–410
    [Google Scholar]
  17. 17.
    Dudley-Javoroski S, Shields RK 2008. Muscle and bone plasticity after spinal cord injury: review of adaptations to disuse and to electrical muscle stimulation. J. Rehabil. Res. Dev. 45:2283–96
    [Google Scholar]
  18. 18.
    Jiang SD, Dai LY, Jiang LS. 2006. Osteoporosis after spinal cord injury. Osteoporos. Int. 17:2180–92
    [Google Scholar]
  19. 19.
    Lazo M, Shirazi P, Sam M, Giobbie-Hurder A, Blacconiere M, Muppidi M. 2001. Osteoporosis and risk of fracture in men with spinal cord injury. Spinal Cord 39:4208–14
    [Google Scholar]
  20. 20.
    Morgan SL, Prater GL. 2017. Quality in dual-energy X-ray absorptiometry scans. Bone 104:13–28
    [Google Scholar]
  21. 21.
    Cohen B, Rushton N. 1995. Accuracy of DEXA measurement of bone mineral density after total hip arthroplasty. J. Bone Jt. Surg. 77:3479–83
    [Google Scholar]
  22. 22.
    Fathima SMN, Tamilselvi R, Beham MP 2019. Role of X-rays in assessment of bone mineral density—a review. Innovations in Electronics and Communication Engineering S Saini, RK Singh, G Kumar, GM Rather, K Santhi 51–59. Berlin: Springer
    [Google Scholar]
  23. 23.
    Donnelly E. 2011. Methods for assessing bone quality: a review. Clin. Orthop. Relat. Res. 469:82128–38
    [Google Scholar]
  24. 24.
    Sandino C, McErlain DD, Schipilow J, Boyd SK. 2015. The poro-viscoelastic properties of trabecular bone: a micro computed tomography–based finite element study. J. Mech. Behav. Biomed. Mater. 44:1–9
    [Google Scholar]
  25. 25.
    Nobakhti S, Shefelbine SJ. 2018. On the relation of bone mineral density and the elastic modulus in healthy and pathologic bone. Curr. Osteoporos. Rep. 16:4404–10
    [Google Scholar]
  26. 26.
    Nicolella DP, Ni Q, Chan KS. 2011. Non-destructive characterization of microdamage in cortical bone using low field pulsed NMR. J. Mech. Behav. Biomed. Mater. 4:3383–91
    [Google Scholar]
  27. 27.
    Vasilic B, Wehrli FW. 2005. A novel local thresholding algorithm for trabecular bone volume fraction mapping in the limited spatial resolution regime of in vivo MRI. IEEE Trans. Med. Imaging 24:121574–85
    [Google Scholar]
  28. 28.
    Kose K, Matsuda Y, Kurimoto T, Hashimoto S, Yamazaki Y et al. 2004. Development of a compact MRI system for trabecular bone volume fraction measurements. Magn. Reson. Med. 52:2440–44
    [Google Scholar]
  29. 29.
    Fernandez-Seara M, Song H, Wehrli F. 2001. Trabecular bone volume fraction mapping by low-resolution MRI. Magn. Reson. Med. 46:1103–13
    [Google Scholar]
  30. 30.
    Wu Y, Adeeb S, Doschak MR. 2015. Using micro-CT derived bone microarchitecture to analyze bone stiffness—a case study on osteoporosis rat bone. Front. Endocrinol. 6:80
    [Google Scholar]
  31. 31.
    Hsu JT, Huang HL, Tsai MT, Wu AJ, Tu MG, Fuh LJ. 2013. Effects of the 3D bone-to-implant contact and bone stiffness on the initial stability of a dental implant: micro-CT and resonance frequency analyses. Int. J. Oral Maxillofac. Surg. 42:2276–80
    [Google Scholar]
  32. 32.
    Hong AL, Ispiryan M, Padalkar MV, Jones BC, Batzdorf AS et al. 2019. MRI-derived bone porosity index correlates to bone composition and mechanical stiffness. Bone Rep. 11:100213
    [Google Scholar]
  33. 33.
    Jones BC, Jia S, Lee H, Feng A, Shetye SS et al. 2021. MRI-derived porosity index is associated with whole-bone stiffness and mineral density in human cadaveric femora. Bone 143:115774
    [Google Scholar]
  34. 34.
    Mroue KH, MacKinnon N, Xu J, Zhu P, McNerny E et al. 2012. High-resolution structural insights into bone: a solid-state NMR relaxation study utilizing paramagnetic doping. J. Phys. Chem. B 116:3811656–61
    [Google Scholar]
  35. 35.
    Chung H, Wehrli F, Williams J, Kugelmass S. 1993. Relationship between NMR transverse relaxation, trabecular bone architecture, and strength. PNAS 90:2110250–54
    [Google Scholar]
  36. 36.
    Laugier P, Haïat G. 2011. Bone Quantitative Ultrasound Berlin: Springer
  37. 37.
    Wear KA. 2001. Fundamental precision limitations for measurements of frequency dependence of backscatter: applications in tissue-mimicking phantoms and trabecular bone. J. Acoust. Soc. Am. 110:63275–82
    [Google Scholar]
  38. 38.
    Steinberg I, Turko N, Levi O, Gannot I, Eyal A. 2016. Quantitative study of optical and mechanical bone status using multispectral photoacoustics. J. Biophoton. 9:9924–33
    [Google Scholar]
  39. 39.
    Moilanen P, Kilappa V, Nicholson PH, Timonen J, Cheng S. 2004. Thickness sensitivity of ultrasound velocity in long bone phantoms. Ultrasound Med. Biol. 30:111517–21
    [Google Scholar]
  40. 40.
    Lashkari B, Mandelis A. 2014. Coregistered photoacoustic and ultrasonic signatures of early bone density variations. J. Biomed. Opt. 19:036015
    [Google Scholar]
  41. 41.
    Xie W, Feng T, Zhang M, Li J, Ta D et al. 2021. Wavelet transform-based photoacoustic time-frequency spectral analysis for bone assessment. Photoacoustics 22:100259
    [Google Scholar]
  42. 42.
    He W, Zhu Y, Feng T, Yuan J, Cheng Q et al. 2017. Comparison study of photoacoustic and ultrasound spectrum analysis in osteoporosis detection. Chin. Opt. Lett. 15:111101
    [Google Scholar]
  43. 43.
    Feng T, Kozloff KM, Tian C, Perosky JE, Hsiao YS et al. 2015. Bone assessment via thermal photo-acoustic measurements. Opt. Lett. 40:81721–24
    [Google Scholar]
  44. 44.
    Feng T, Perosky JE, Kozloff KM, Xu G, Cheng Q et al. 2015. Characterization of bone microstructure using photoacoustic spectrum analysis. Opt. Express 23:1925217–24
    [Google Scholar]
  45. 45.
    Xia J, Bell MAL, Laufer J, Yao J. 2021. Translational photoacoustic imaging for disease diagnosis, monitoring, and surgical guidance: introduction to the feature issue. Biomed. Opt. Express 12:74115–18
    [Google Scholar]
  46. 46.
    Na S, Wang LV 2021. Photoacoustic computed tomography for functional human brain imaging. Biomed. Opt. Express 12:74056–83
    [Google Scholar]
  47. 47.
    Yao J, Wang LV. 2014. Photoacoustic brain imaging: from microscopic to macroscopic scales. Neurophotonics 1:011003
    [Google Scholar]
  48. 48.
    Liu Y, Liu H, Yan H, Liu Y, Zhang J et al. 2019. Aggregation-induced absorption enhancement for deep near-infrared II photoacoustic imaging of brain gliomas in vivo. Adv. Sci. 6:81801615
    [Google Scholar]
  49. 49.
    Poudel J, Lou Y, Anastasio MA. 2019. A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography. Phys. Med. Biol. 64:14TR01
    [Google Scholar]
  50. 50.
    Kostli K, Frauchiger D, Niederhauser JJ, Paltauf G, Weber HP, Frenz M. 2001. Optoacoustic imaging using a three-dimensional reconstruction algorithm. IEEE J. Sel. Top. Quantum Electron. 7:6918–23
    [Google Scholar]
  51. 51.
    Paltauf G, Viator J, Prahl S, Jacques S 2002. Iterative reconstruction algorithm for optoacoustic imaging. J. Acoust. Soc. Am. 112:41536–44
    [Google Scholar]
  52. 52.
    Wang X, Xu Y, Xu M, Yokoo S, Fry ES, Wang LV. 2002. Photoacoustic tomography of biological tissues with high cross-section resolution: reconstruction and experiment. Med. Phys. 29:122799–805
    [Google Scholar]
  53. 53.
    Norton SJ, Vo-Dinh T. 2003. Optoacoustic diffraction tomography: analysis of algorithms. J. Opt. Soc. Am. A 20:101859–66
    [Google Scholar]
  54. 54.
    Yang X, Wang LV. 2008. Monkey brain cortex imaging by photoacoustic tomography. J. Biomed. Opt. 13:044009
    [Google Scholar]
  55. 55.
    Schoonover RW, Wang LV, Anastasio MA. 2012. Numerical investigation of the effects of shear waves in transcranial photoacoustic tomography with a planar geometry. J. Biomed. Opt. 17:061215
    [Google Scholar]
  56. 56.
    Bell MAL, Ostrowski AK, Li K, Kaanzides P, Boctor E. 2015. Quantifying bone thickness, light transmission, and contrast interrelationships in transcranial photoacoustic imaging. Proc. SPIE 9323:67–73
    [Google Scholar]
  57. 57.
    Manwar R, Kratkiewicz K, Avanaki K. 2020. Investigation of the effect of the skull in transcranial photoacoustic imaging: a preliminary ex vivo study. Sensors 20:154189
    [Google Scholar]
  58. 58.
    Liang B, Wang S, Shen F, Liu QH, Gong Y, Yao J. 2021. Acoustic impact of the human skull on transcranial photoacoustic imaging. Biomed. Opt. Express 12:31512–28
    [Google Scholar]
  59. 59.
    Graham MT, Dunne RA, Bell MAL. 2021. Comparison of compressional and elastic wave simulations for patient-specific planning prior to transcranial photoacoustic-guided neurosurgery. J. Biomed. Opt. 26:076006
    [Google Scholar]
  60. 60.
    Yuan Z, Zhao H, Wu C, Zhang Q, Jiang H. 2006. Finite-element-based photoacoustic tomography: phantom and chicken bone experiments. Appl. Opt. 45:133177–83
    [Google Scholar]
  61. 61.
    Yuan Z, Wang Q, Jiang H. 2007. Reconstruction of optical absorption coefficient maps of heterogeneous media by photoacoustic tomography coupled with diffusion equation based regularized Newton method. Opt. Express 15:2618076–81
    [Google Scholar]
  62. 62.
    Yuan Z, Jiang H. 2007. Three-dimensional finite-element-based photoacoustic tomography: reconstruction algorithm and simulations. Med. Phys. 34:2538–46
    [Google Scholar]
  63. 63.
    Huang C, Nie L, Schoonover RW, Guo Z, Schirra CO et al. 2012. Aberration correction for transcranial photoacoustic tomography of primates employing adjunct image data. J. Biomed. Opt. 17:066016
    [Google Scholar]
  64. 64.
    Huang C, Nie L, Schoonover RW, Wang LV, Anastasio MA. 2012. Photoacoustic computed tomography correcting for heterogeneity and attenuation. J. Biomed. Opt. 17:061211
    [Google Scholar]
  65. 65.
    Mitsuhashi K, Poudel J, Matthews TP, Garcia-Uribe A, Wang LV, Anastasio MA. 2017. A forward-adjoint operator pair based on the elastic wave equation for use in transcranial photoacoustic computed tomography. SIAM J. Imaging Sci. 10:42022–48
    [Google Scholar]
  66. 66.
    Poudel J, Na S, Wang LV, Anastasio MA. 2020. Iterative image reconstruction in transcranial photoacoustic tomography based on the elastic wave equation. Phys. Med. Biol. 65:055009
    [Google Scholar]
  67. 67.
    Poudel J, Anastasio MA. 2020. Joint reconstruction of initial pressure distribution and spatial distribution of acoustic properties of elastic media with application to transcranial photoacoustic tomography. Inverse Probl. 36:124007
    [Google Scholar]
  68. 68.
    Shepherd J, Renaud G, Clouzet P, van Wijk K. 2020. Photoacoustic imaging through a cortical bone replica with anisotropic elasticity. Appl. Phys. Lett. 116:243704
    [Google Scholar]
  69. 69.
    Renaud G, Kruizinga P, Cassereau D, Laugier P. 2018. In vivo ultrasound imaging of the bone cortex. Phys. Med. Biol. 63:125010
    [Google Scholar]
  70. 70.
    Thomsen L. 1986. Weak elastic anisotropy. Geophysics 51:101954–66
    [Google Scholar]
  71. 71.
    Na S, Yuan X, Lin L, Isla J, Garrett D, Wang LV 2020. Transcranial photoacoustic computed tomography based on a layered back-projection method. Photoacoustics 20:100213
    [Google Scholar]
  72. 72.
    Chen P, Liu C, Feng T, Li Y, Ta D. 2020. Improved photoacoustic imaging of numerical bone model based on attention block U-net deep learning network. Appl. Sci. 10:228089
    [Google Scholar]
  73. 73.
    Eddins B, Bell MAL. 2017. Design of a multifiber light delivery system for photoacoustic-guided surgery. J. Biomed. Opt. 22:041011
    [Google Scholar]
  74. 74.
    Bell MAL, Ostrowski AK, Li K, Kazanzides P, Boctor EM. 2015. Localization of transcranial targets for photoacoustic-guided endonasal surgeries. Photoacoustics 3:278–87
    [Google Scholar]
  75. 75.
    Goss SA, Johnston RL, Dunn F. 1978. Comprehensive compilation of empirical ultrasonic properties of mammalian tissues. J. Acoust. Soc. Am. 64:2423–57
    [Google Scholar]
  76. 76.
    Graham MT, Huang J, Creighton FX, Bell MAL. 2020. Simulations and human cadaver head studies to identify optimal acoustic receiver locations for minimally invasive photoacoustic-guided neurosurgery. Photoacoustics 19:100183
    [Google Scholar]
  77. 77.
    Firouzi K, Cox B, Treeby B, Saffari N. 2012. A first-order k-space model for elastic wave propagation in heterogeneous media. J. Acoust. Soc. Am. 132:31271–83
    [Google Scholar]
  78. 78.
    Kempski KM, Graham MT, Gubbi MR, Palmer T, Bell MAL 2020. Application of the generalized contrast-to-noise ratio to assess photoacoustic image quality. Biomed. Opt. Express 11:73684–98
    [Google Scholar]
  79. 79.
    Manbachi A, Cobbold RS, Ginsberg HJ. 2014. Guided pedicle screw insertion: techniques and training. Spine J. 14:1165–79
    [Google Scholar]
  80. 80.
    Mason A, Paulsen R, Babuska JM, Rajpal S, Burneikiene S et al. 2014. The accuracy of pedicle screw placement using intraoperative image guidance systems: a systematic review. J. Neurosurg. Spine 20:2196–203
    [Google Scholar]
  81. 81.
    Abul-Kasim K, Ohlin A 2011. The rate of screw misplacement in segmental pedicle screw fixation in adolescent idiopathic scoliosis: the effect of learning and cumulative experience. Acta Orthop. 82:150–55
    [Google Scholar]
  82. 82.
    Shubert J, Bell MAL. 2018. Photoacoustic imaging of a human vertebra: implications for guiding spinal fusion surgeries. Phys. Med. Biol. 63:144001
    [Google Scholar]
  83. 83.
    Gonzalez EA, Jain A, Bell MAL. 2020. Combined ultrasound and photoacoustic image guidance of spinal pedicle cannulation demonstrated with intact ex vivo specimens. IEEE Trans. Biomed. Eng. 68:82479–89
    [Google Scholar]
  84. 84.
    Graham MT, Bell MAL. 2020. Photoacoustic spatial coherence theory and applications to coherence-based image contrast and resolution. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 67:102069–84
    [Google Scholar]
  85. 85.
    Donnelly EM, Kubelick KP, Dumani DS, Emelianov SY. 2018. Photoacoustic image-guided delivery of plasmonic-nanoparticle-labeled mesenchymal stem cells to the spinal cord. Nano Lett. 18:106625–32
    [Google Scholar]
  86. 86.
    Kubelick KP, Emelianov SY 2020. Prussian blue nanocubes as a multimodal contrast agent for image-guided stem cell therapy of the spinal cord. Photoacoustics 18:100166
    [Google Scholar]
  87. 87.
    Kubelick KP, Emelianov SY. 2020. In vivo photoacoustic guidance of stem cell injection and delivery for regenerative spinal cord therapies. Neurophotonics 7:030501
    [Google Scholar]
  88. 88.
    Cui A, Li H, Wang D, Zhong J, Chen Y, Lu H 2020. Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies. eClinicalMedicine 29:100587
    [Google Scholar]
  89. 89.
    Alamanos Y, Drosos AA. 2005. Epidemiology of adult rheumatoid arthritis. Autoimmun. Rev. 4:3130–36
    [Google Scholar]
  90. 90.
    Nishiyama M, Namita T, Kondo K, Yamakawa M, Shiina T. 2019. Ring-array photoacoustic tomography for imaging human finger vasculature. J. Biomed. Opt. 24:096005
    [Google Scholar]
  91. 91.
    van Es P, Biswas SK, Moens HJB, Steenbergen W, Manohar S. 2014. Initial results of finger imaging using photoacoustic computed tomography. J. Biomed. Opt. 19:060501
    [Google Scholar]
  92. 92.
    Jo J, Xu G, Cao M, Marquardt A, Francis S et al. 2017. A functional study of human inflammatory arthritis using photoacoustic imaging. Sci. Rep. 7:15026
    [Google Scholar]
  93. 93.
    Jo J, Tian C, Xu G, Sarazin J, Schiopu E et al. 2018. Photoacoustic tomography for human musculoskeletal imaging and inflammatory arthritis detection. Photoacoustics 12:82–89
    [Google Scholar]
  94. 94.
    Rajian JR, Girish G, Wang X. 2012. Photoacoustic tomography to identify inflammatory arthritis. J. Biomed. Opt. 17:096013
    [Google Scholar]
  95. 95.
    Wu M, van Teeffelen BC, Ito K, van de Vosse FN, Janssen RP et al. 2021. Spectroscopic photoacoustic imaging of cartilage damage. Osteoarthr. Cartil. 29:71071–80
    [Google Scholar]
  96. 96.
    Xu G, Rajian JR, Girish G, Kaplan MJ, Fowlkes JB et al. 2012. Photoacoustic and ultrasound dual-modality imaging of human peripheral joints. J. Biomed. Opt. 18:010502
    [Google Scholar]
  97. 97.
    Liu Z, Au M, Wang X, Chan PMB, Lai P et al. 2019. Photoacoustic imaging of synovial tissue hypoxia in experimental post-traumatic osteoarthritis. Prog. Biophys. Mol. Biol. 148:12–20
    [Google Scholar]
  98. 98.
    Guo H, Wang Q, Qi W, Sun X, Ke B, Xi L. 2019. Assessing the development and treatment of rheumatoid arthritis using multiparametric photoacoustic and ultrasound imaging. J. Biophoton. 12:11e201900127
    [Google Scholar]
  99. 99.
    Jo J, Xu G, Zhu Y, Burton M, Sarazin J et al. 2018. Detecting joint inflammation by an LED-based photoacoustic imaging system: a feasibility study. J. Biomed. Opt. 23:110501
    [Google Scholar]
  100. 100.
    Sun Y, Jiang H. 2009. Quantitative three-dimensional photoacoustic tomography of the finger joints: phantom studies in a spherical scanning geometry. Phys. Med. Biol. 54:185457–67
    [Google Scholar]
  101. 101.
    Sun Y, Sobel ES, Jiang H. 2009. Quantitative three-dimensional photoacoustic tomography of the finger joints: an in vivo study. J. Biomed. Opt. 14:064002
    [Google Scholar]
  102. 102.
    Sun Y, Sobel ES, Jiang H. 2011. First assessment of three-dimensional quantitative photoacoustic tomography for in vivo detection of osteoarthritis in the finger joints. Med. Phys. 38:74009–17
    [Google Scholar]
  103. 103.
    Hagiwara Y, Izumi T, Yabe Y, Sato M, Sonofuchi K et al. 2015. Simultaneous evaluation of articular cartilage and subchondral bone from immobilized knee in rats by photoacoustic imaging system. J. Orthop. Sci. 20:2397–402
    [Google Scholar]
  104. 104.
    Wang X, Chamberland DL, Carson PL, Fowlkes JB, Bude RO et al. 2006. Imaging of joints with laser-based photoacoustic tomography: an animal study. Med. Phys. 33:82691–97
    [Google Scholar]
  105. 105.
    Chamberland DL, Wang X, Roessler BJ. 2008. Photoacoustic tomography of carrageenan-induced arthritis in a rat model. J. Biomed. Opt. 13:011005
    [Google Scholar]
  106. 106.
    Deng Z, Li C. 2016. Noninvasively measuring oxygen saturation of human finger-joint vessels by multi-transducer functional photoacoustic tomography. J. Biomed. Opt. 21:061009
    [Google Scholar]
  107. 107.
    Wang X, Chamberland DL, Jamadar DA. 2007. Noninvasive photoacoustic tomography of human peripheral joints toward diagnosis of inflammatory arthritis. Opt. Lett. 32:203002–4
    [Google Scholar]
  108. 108.
    Biswas SK, van Es P, Steenbergen W, Manohar S. 2016. A method for delineation of bone surfaces in photoacoustic computed tomography of the finger. Ultrason. Imaging 38:163–76
    [Google Scholar]
  109. 109.
    Park EY, Lee D, Lee C, Kim C. 2020. Non-ionizing label-free photoacoustic imaging of bones. IEEE Access 8:160915–20
    [Google Scholar]
  110. 110.
    Amin S, Achenbach SJ, Atkinson EJ, Khosla S, Melton LJ III 2014. Trends in fracture incidence: a population-based study over 20 years. J. Bone Miner. Res. 29:3581–89
    [Google Scholar]
  111. 111.
    Schlickewei CW, Kleinertz H, Thiesen DM, Mader K, Priemel M et al. 2019. Current and future concepts for the treatment of impaired fracture healing. Int. J. Mol. Sci. 20:225805
    [Google Scholar]
  112. 112.
    Protopappas VC, Vavva MG, Fotiadis DI, Malizos KN. 2008. Ultrasonic monitoring of bone fracture healing. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 55:61243–55
    [Google Scholar]
  113. 113.
    Sakas G, Grimm M, Savopoulos A. 1995. Optimized maximum intensity projection (MIP). EUROGRAPHICS Workshop on Rendering Techniques51–63. Berlin: Springer
    [Google Scholar]
  114. 114.
    Lomelí-Mejía P, Jiménez Pérez JL, Orea AC, Castrejón HV, Butron HL, Lira MM 2005. Photoacoustic analysis of bone osteogenesis to different doses of laser irradiation. J. Vac. Sci. Technol. 23:4761–63
    [Google Scholar]
  115. 115.
    Mirabello L, Troisi RJ, Savage SA. 2009. International osteosarcoma incidence patterns in children and adolescents, middle ages and elderly persons. Int. J. Cancer 125:1229–34
    [Google Scholar]
  116. 116.
    Broadhead ML, Clark J, Myers DE, Dass CR, Choong PF. 2011. The molecular pathogenesis of osteosarcoma: a review. Sarcoma 2011:959248
    [Google Scholar]
  117. 117.
    Abiri B, Vafa M. 2021. Antioxidant vitamins in acute lymphoblastic leukemia. Cancer: Oxidative Stress and Dietary Antioxidants VR Preedy, VB Patel 539–44. Amsterdam: Elsevier. , 2nd ed..
    [Google Scholar]
  118. 118.
    Baran N, Konopleva M. 2017. Molecular pathways: hypoxia-activated prodrugs in cancer therapy. Clin. Cancer Res. 23:102382–90
    [Google Scholar]
  119. 119.
    Hu J, Yu M, Ye F, Xing D. 2011. In vivo photoacoustic imaging of osteosarcoma in a rat model. J. Biomed. Opt. 16:020503
    [Google Scholar]
  120. 120.
    Wood C, Harutyunyan K, Sampaio DR, Konopleva M, Bouchard R. 2019. Photoacoustic-based oxygen saturation assessment of murine femoral bone marrow in a preclinical model of leukemia. Photoacoustics 14:31–36
    [Google Scholar]
  121. 121.
    Humbert J, Will O, Peñate-Medina T, Peñate-Medina O, Jansen O et al. 2020. Comparison of photoacoustic and fluorescence tomography for the in vivo imaging of ICG-labelled liposomes in the medullary cavity in mice. Photoacoustics 20:100210
    [Google Scholar]
  122. 122.
    Rachner TD, Khosla S, Hofbauer LC. 2011. Osteoporosis: now and the future. Lancet 377:97731276–87
    [Google Scholar]
  123. 123.
    Steinberg I, Shiloh L, Gannot I, Eyal A. 2018. First-in-human study of bone pathologies using low-cost and compact dual-wavelength photoacoustic system. IEEE J. Sel. Top. Quantum Electron. 25:17201908
    [Google Scholar]
  124. 124.
    Weiss M, Ben-Shlomo A, Hagag P, Rapoport M. 2000. Reference database for bone speed of sound measurement by a novel quantitative multi-site ultrasound device. Osteoporos. Int. 11:8688–96
    [Google Scholar]
  125. 125.
    Feng T, Zhu Y, Morris R, Kozloff KM, Wang X. 2020. Functional photoacoustic and ultrasonic assessment of osteoporosis: a clinical feasibility study. BME Front. 2020:1081540
    [Google Scholar]
  126. 126.
    Feng T, Zhu Y, Morris R, Kozloff KM, Wang X. 2021. The feasibility study of the transmission mode photoacoustic measurement of human calcaneus bone in vivo. Photoacoustics 23:100273
    [Google Scholar]
  127. 127.
    Larina IV, Larin KV, Esenaliev RO. 2005. Real-time optoacoustic monitoring of temperature in tissues. J. Phys. D 38:152633–39
    [Google Scholar]
  128. 128.
    Esenaliev RO, Oraevsky AA, Larin KV, Larina IV, Motamedi M. 1999. Real-time optoacoustic monitoring of temperature in tissues. Proc. SPIE 3601:268–75
    [Google Scholar]
  129. 129.
    Petrova EV, Ermilov SA, Su R, Nadvoretskiy V, Conjusteau A, Oraevsky AA. 2014. Temperature dependence of Grüneisen parameter in optically absorbing solutions measured by 2D optoacoustic imaging. Proc. SPIE 8943:89430S
    [Google Scholar]
  130. 130.
    Petrova E, Ermilov SA, Su R, Nadvoretskiy V, Conjusteau A, Oraevsky AA. 2013. Using optoacoustic imaging for measuring the temperature dependence of Grüneisen parameter in optically absorbing solutions. Opt. Express 21:2125077–90
    [Google Scholar]
  131. 131.
    Pramanik M, Erpelding TN, Jankovic L, Wang LV. 2010. Tissue temperature monitoring using thermoacoustic and photoacoustic techniques. Proc. SPIE 7564:75641Y
    [Google Scholar]
  132. 132.
    Pramanik M, Wang LV. 2009. Thermoacoustic and photoacoustic sensing of temperature. J. Biomed. Opt. 14:054024
    [Google Scholar]
  133. 133.
    Zhou Y, Li M, Liu W, Sankin G, Luo J et al. 2019. Thermal memory based photoacoustic imaging of temperature. Optica 6:2198–205
    [Google Scholar]
  134. 134.
    Thella AK, Rizkalla J, Helmy A, Suryadevara VK, Salama P, Rizkalla ME. 2016. Non-invasive photo acoustic approach for human bone diagnosis. J. Orthopaed. 13:4394–400
    [Google Scholar]
  135. 135.
    Thella AK, Rizkalla J, Rathi N, Kakani M, Helmy A et al. 2017. Dynamic thermal/acoustic response for human bone materials at different energy levels: a diagnosis approach. J. Orthopaed. 14:185–90
    [Google Scholar]
  136. 136.
    Rizkalla J, Suryadevara VK, Thella AK, Helmy A, Salama P, Rizkalla ME. 2016. Photo acoustic thermal for human bone characterization: a feasibility study. J. Biomed. Sci. Eng. 9:9445–49
    [Google Scholar]
  137. 137.
    Kakani M, Rathi N, Helmy A, Thella AK, Rizkalla ME et al. 2017. Photo acoustic energy applications for the detection of human arterial blockages via multiple skin/bone layers, a non-invasive approach. World J. Cardiovasc. Dis. 7:8251–70
    [Google Scholar]
  138. 138.
    Chimenti D. 1997. Guided waves in plates and their use in materials characterization. Appl. Mech. Rev. 50:5247–84
    [Google Scholar]
  139. 139.
    Rogers WP. 1995. Elastic property measurement using Rayleigh-Lamb waves. Res. Nondestruct. Eval. 6:4185–208
    [Google Scholar]
  140. 140.
    Jansons E, Tatarinov A, Dzenis V, Kregers A. 1984. Constructional peculiarities of the human tibia defined by reference to ultrasound measurement data. Biomaterials 5:4221–26
    [Google Scholar]
  141. 141.
    Lefebvre F, Deblock Y, Campistron P, Ahite D, Fabre JJ. 2002. Development of a new ultrasonic technique for bone and biomaterials in vitro characterization. J. Biomed. Mater. Res. 63:4441–46
    [Google Scholar]
  142. 142.
    Prins S, Jørgensen H, Jørgensen L, Hassager C. 1998. The role of quantitative ultrasound in the assessment of bone: a review. Clin. Physiol. 18:13–17
    [Google Scholar]
  143. 143.
    Oelze ML, Mamou J. 2016. Review of quantitative ultrasound: envelope statistics and backscatter coefficient imaging and contributions to diagnostic ultrasound. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 63:2336–51
    [Google Scholar]
  144. 144.
    Muller M, Moilanen P, Bossy E, Nicholson P, Kilappa V et al. 2005. Comparison of three ultrasonic axial transmission methods for bone assessment. Ultrasound Med. Biol. 31:5633–42
    [Google Scholar]
  145. 145.
    Njeh C, Saeed I, Grigorian M, Kendler D, Fan B et al. 2001. Assessment of bone status using speed of sound at multiple anatomical sites. Ultrasound Med. Biol. 27:101337–45
    [Google Scholar]
  146. 146.
    Kilappa V, Xu K, Moilanen P, Heikkola E, Ta D, Timonen J. 2013. Assessment of the fundamental flexural guided wave in cortical bone by an ultrasonic axial-transmission array transducer. Ultrasound Med. Biol. 39:71223–32
    [Google Scholar]
  147. 147.
    Moilanen P, Zhao Z, Karppinen P, Karppinen T, Kilappa V et al. 2014. Photo-acoustic excitation and optical detection of fundamental flexural guided wave in coated bone phantoms. Ultrasound Med. Biol. 40:3521–31
    [Google Scholar]
  148. 148.
    Moilanen P, Talmant M, Kilappa V, Nicholson P, Cheng S et al. 2008. Modeling the impact of soft tissue on axial transmission measurements of ultrasonic guided waves in human radius. J. Acoust. Soc. Am. 124:42364–73
    [Google Scholar]
  149. 149.
    Lashkari B, Yang L, Mandelis A 2015. The application of backscattered ultrasound and photoacoustic signals for assessment of bone collagen and mineral contents. Quant. Imaging Med. Surg. 5:146–56
    [Google Scholar]
  150. 150.
    Yang L, Lashkari B, Mandelis A, Tan JW. 2015. Bone composition diagnostics: photoacoustics versus ultrasound. Int. J. Thermophys. 36:5862–67
    [Google Scholar]
  151. 151.
    Yang L, Lashkari B, Tan JW, Mandelis A. 2015. Photoacoustic and ultrasound imaging of cancellous bone tissue. J. Biomed. Opt. 20:076016
    [Google Scholar]
  152. 152.
    Yang L, Chen C, Zhang Z, Wei X. 2021. Diagnosis of bone mineral density based on backscattering resonance phenomenon using coregistered functional laser photoacoustic and ultrasonic probes. Sensors 21:248243
    [Google Scholar]
  153. 153.
    Ding R, Zhang J, Koushki E, Tayebee R, Ding X. 2021. Nonlinear photoacoustic and optical properties of hydroxyapatite and calcium phosphate. Towards a new method for the densitometry of bones. Optik 226:165922
    [Google Scholar]
  154. 154.
    Glatz J, Deliolanis NC, Buehler A, Razansky D, Ntziachristos V. 2011. Blind source unmixing in multi-spectral optoacoustic tomography. Opt. Express 19:43175–84
    [Google Scholar]
  155. 155.
    Gröhl J, Kirchner T, Adler T, Maier-Hein L. 2019. Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-QPAI). arXiv:1902.05839 [physics.med-ph]
  156. 156.
    Gonzalez EA, Graham CA, Bell MAL. 2021. Acoustic frequency-based approach for identification of photoacoustic surgical biomarkers. Front. Photon. 2:716656
    [Google Scholar]
  157. 157.
    Arabul M, Rutten M, Bruneval P, van Sambeek M, van de Vosse F, Lopata R 2019. Unmixing multi-spectral photoacoustic sources in human carotid plaques using non-negative independent component analysis. Photoacoustics 15:100140
    [Google Scholar]
  158. 158.
    Weber J, Beard PC, Bohndiek SE. 2016. Contrast agents for molecular photoacoustic imaging. Nat. Methods 13:8639–50
    [Google Scholar]
  159. 159.
    Tzoumas S, Ntziachristos V. 2017. Spectral unmixing techniques for optoacoustic imaging of tissue pathophysiology. Philos. Trans. R. Soc. B 375:210720170262
    [Google Scholar]
  160. 160.
    Hosseinaee Z, Le M, Bell K, Reza PH. 2020. Towards non-contact photoacoustic imaging. Photoacoustics 20:100207
    [Google Scholar]
  161. 161.
    Feng T, Xie Y, Xie W, Chen Y, Wang P et al. 2022. Characterization of multi-biomarkers for bone health assessment based on photoacoustic physicochemical analysis method. Photoacoustics 25:100320
    [Google Scholar]
  162. 162.
    Feng T, Zhu Y, Kozloff KM, Khoury B, Xie Y et al. 2020. Bone chemical composition assessment with multi-wavelength photoacoustic analysis. Appl. Sci. 10:228214
    [Google Scholar]
  163. 163.
    Feng T, Ge Y, Xie Y, Xie W, Liu C et al. 2021. Detection of collagen by multi-wavelength photoacoustic analysis as a biomarker for bone health assessment. Photoacoustics 24:100296
    [Google Scholar]
  164. 164.
    Feng T, Xie Y, Xie W, Ta D, Cheng Q. 2020. Bone chemical composition analysis using photoacoustic technique. Front. Phys. 8:594
    [Google Scholar]
  165. 165.
    Li ML, Oh JT, Xie X, Ku G, Wang W et al. 2008. Simultaneous molecular and hypoxia imaging of brain tumors in vivo using spectroscopic photoacoustic tomography. Proc. IEEE 96:3481–89
    [Google Scholar]
  166. 166.
    Laufer J, Elwell C, Delpy D, Beard P. 2005. In vitro measurements of absolute blood oxygen saturation using pulsed near-infrared photoacoustic spectroscopy: accuracy and resolution. Phys. Med. Biol. 50:184409–28
    [Google Scholar]
  167. 167.
    Cox BT, Laufer JG, Beard PC, Arridge SR. 2012. Quantitative spectroscopic photoacoustic imaging: a review. J. Biomed. Opt. 17:061202
    [Google Scholar]
  168. 168.
    Welch BL. 1947. The generalization of ‘Student's’ problem when several different population variances are involved. Biometrika 34:1/228–35
    [Google Scholar]
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