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Next-generation imaging of the skeletal system and its blood supply

Nature Reviews Rheumatology volume 15, pages 533–549 (2019)Cite this article

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Abstract

Bone is organized in a hierarchical 3D architecture. Traditionally, analysis of the skeletal system was based on bone mass assessment by radiographic methods or on the examination of bone structure by 2D histological sections. Advanced imaging technologies and big data analysis now enable the unprecedented examination of bone and provide new insights into its 3D macrostructure and microstructure. These technologies comprise ex vivo and in vivo methods including high-resolution computed tomography (CT), synchrotron-based imaging, X-ray microscopy, ultra-high-field magnetic resonance imaging (MRI), light-sheet fluorescence microscopy, confocal and intravital two-photon imaging. In concert, these techniques have been used to detect and quantify a novel vascular system of trans-cortical vessels in bone. Furthermore, structures such as the lacunar network, which harbours and connects osteocytes, become accessible for 3D imaging and quantification using these methods. Next-generation imaging of the skeletal system and its blood supply are anticipated to contribute to an entirely new understanding of bone tissue composition and function, from macroscale to nanoscale, in health and disease. These insights could provide the basis for early detection and precision-type intervention of bone disorders in the future.

Key points

  • Bone is a complex tissue that has functional elements at size scales spanning five orders of magnitude.

  • Studying all the different structures of bone is necessary to comprehensively understand bone function in health and disease in experimental and clinical settings.

  • Comprehensive analysis of bone requires an array of different imaging approaches, including X-ray, magnetic resonance, and optical and electron microscopy imaging modalities.

  • Fundamental improvements of methodology in all these imaging approaches now enable a completely new view of bone, resulting in novel insights into its function and blood supply.

  • Massive amounts of imaging data emerging from such analyses require innovative image reconstruction algorithms, such as machine vision and deep learning, to extract meaningful information.

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Fig. 1: Imaging the multi-scale architecture of bone.
Fig. 2: Technical principles of bone imaging modalities.
Fig. 3: QCT in the clinic.
Fig. 4: HR-pQCT in the clinic.
Fig. 5: 3D whole-bone vascular analysis with LSFM.

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References

  1. Sommerfeldt, D. W. & Rubin, C. T. Biology of bone and how it orchestrates the form and function of the skeleton. Eur. Spine J. 10 (Suppl. 2), S86–S95 (2001).

    PubMed  PubMed Central  Google Scholar 

  2. Clarke, B. Normal bone anatomy and physiology. Clin. J. Am. Soc. Nephrol. 3 (Suppl. 3), S131–S139 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Agas, D., Marchetti, L., Douni, E. & Sabbieti, M. G. The unbearable lightness of bone marrow homeostasis. Cytokine Growth Factor Rev. 26, 347–359 (2015).

    CAS  PubMed  Google Scholar 

  4. McKee, M. D. & Cole, W. G. (ed.) Bone Matrix and Mineralization. 2 edn (Elsevier, 2012).

  5. Sims, N. A. & Martin, T. J. Coupling the activities of bone formation and resorption: a multitude of signals within the basic multicellular unit. Bonekey Rep. 3, 481 (2014).

    PubMed  PubMed Central  Google Scholar 

  6. Bozec, A. & Zaiss, M. M. T. Regulatory cells in bone remodelling. Curr. Osteoporos. Rep. 15, 121–125 (2017).

    PubMed  Google Scholar 

  7. Parra-Torres, A. Y., Valdés-Flores, M., Orozco, L. & Velázquez-Cruz, R. In Topics in Osteoporosis. https://doi.org/10.5772/54905 (InTech Open, 2013).

    Google Scholar 

  8. Shetty, S., Kapoor, N., Bondu, J. D., Thomas, N. & Paul, T. V. Bone turnover markers: emerging tool in the management of osteoporosis. Indian J. Endocrinol. Metab. 20, 846–852 (2016).

    PubMed  PubMed Central  Google Scholar 

  9. Hernlund, E. et al. Osteoporosis in the European Union: medical management, epidemiology and economic burden. Arch. Osteoporos. 8, 136 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Suen, P. K. & Qin, L. Sclerostin, an emerging therapeutic target for treating osteoporosis and osteoporotic fracture: a general review. J. Orthop. Translat. 4, 1–13 (2016).

    PubMed  Google Scholar 

  11. Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nat. Metab. 1, 236–250 (2019).

    PubMed  PubMed Central  Google Scholar 

  12. Andrews, J. C. et al. A high resolution, hard x-ray bio-imaging facility at SSRL. Synchrotron Radiat. News 21, 17–26 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Arboleya, L. & Castañedab, S. Osteoimmunology: the study of the relationship between the immune system and bone tissue. Reumatol. Clin. 9, 303–315 (2014).

    Google Scholar 

  14. Bach-Gansmo, F. L. et al. Osteocyte lacunar properties and cortical microstructure in human iliac crest as a function of age and sex. Bone 91, 11–19 (2016).

    PubMed  Google Scholar 

  15. Florencio-Silva, R., Sasso, G. R., Sasso-Cerri, E., Simoes, M. J. & Cerri, P. S. Biology of bone tissue: structure, function, and factors that influence bone cells. Biomed. Res. Int. 2015, 421746 (2015).

    PubMed  PubMed Central  Google Scholar 

  16. Dallas, S. L. & Bonewald, L. F. Dynamics of the transition from osteoblast to osteocyte. Ann. N. Y. Acad. Sci. 1192, 437–443 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Cardoso, L., Fritton, S. P., Gailani, G., Benalla, M. & Cowin, S. C. Advances in assessment of bone porosity, permeability and interstitial fluid flow. J. Biomech. 46, 253–265 (2013).

    PubMed  Google Scholar 

  18. Bonewald, L. F. & Johnson, M. L. Osteocytes, mechanosensing and Wnt signaling. Bone 42, 606–615 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Röntgen, W. C. Ueber eine neue Art von Strahlen. Vorläufige Mitteilung. Sitzungsberichte der Würzburger Physik.-medic. Gesellschaft, 137–147 (1895).

  20. Zhu, Y., Zhang, J., Li, A., Zhang, Y. & Fan, C. Synchrotron-based X-ray microscopy for sub-100nm resolution cell imaging. Curr. Opin. Chem. Biol. 39, 11–16 (2017).

    CAS  PubMed  Google Scholar 

  21. Varga, P. et al. Synchrotron X-ray phase nano-tomography-based analysis of the lacunar-canalicular network morphology and its relation to the strains experienced by osteocytes in situ as predicted by case-specific finite element analysis. Biomech. Model Mechanobiol. 14, 267–282 (2015).

    PubMed  Google Scholar 

  22. Sanderson, M. J., Smith, I., Parker, I. & Bootman, M. D. Fluorescence microscopy. Cold Spring Harb. Protoc. 2014, https://doi.org/10.1101/pdb.top071795 (2014).

    Google Scholar 

  23. Wolf, D. E. Fundamentals of fluorescence and fluorescence microscopy. Methods Cell Biol. 81, 63–91 (2007).

    PubMed  Google Scholar 

  24. Engelke, K. et al. Clinical use of quantitative computed tomography and peripheral quantitative computed tomography in the management of osteoporosis in adults: The 2007 ISCD official positions. J. Clin. Densitom. 11, 123–162 (2008).

    PubMed  Google Scholar 

  25. Museyko, O., Bousson, V., Adams, J., Laredo, J. D. & Engelke, K. QCT of the proximal femur – which parameters should be measured to discriminate hip fracture? Osteoporos. Int. 27, 1137–1147 (2016).

    CAS  PubMed  Google Scholar 

  26. Wang, X. et al. Prediction of new clinical vertebral fractures in elderly men using finite element analysis of CT scans. J. Bone Miner. Res. 27, 808–816 (2012).

    PubMed  Google Scholar 

  27. Imai, K., Ohnishi, I., Matsumoto, T., Yamamoto, S. & Nakamura, K. Assessment of vertebral fracture risk and therapeutic effects of alendronate in postmenopausal women using a quantitative computed tomography-based nonlinear finite element method. Osteoporos. Int. 20, 801–810 (2009).

    CAS  PubMed  Google Scholar 

  28. Figueiredo, C. P. et al. Methods for segmentation of rheumatoid arthritis bone erosions in high-resolution peripheral quantitative computed tomography (HR-pQCT). Semin. Arthritis Rheum. 47, 611–618 (2017).

    PubMed  Google Scholar 

  29. Engelke, K. et al. Clinical use of quantitative computed tomography-based advanced techniques in the management of osteoporosis in adults: the 2015 ISCD official positions-part III. J. Clin. Densitom. 18, 393–407 (2015).

    PubMed  Google Scholar 

  30. Gausden, E. B., Nwachukwu, B. U., Schreiber, J. J., Lorich, D. G. & Lane, J. M. Opportunistic use of CT imaging for osteoporosis screening and bone density assessment: A qualitative systematic review. J. Bone Joint Surg. Am. 99, 1580–1590 (2017).

    PubMed  Google Scholar 

  31. Muhlberg, A. et al. Three-dimensional distribution of muscle and adipose tissue of the thigh at CT: association with acute hip fracture. Radiology 290, 426–434 (2018).

    PubMed  Google Scholar 

  32. Engelke, K. et al. Reanalysis precision of 3D quantitative computed tomography (QCT) of the spine. Bone 44, 566–572 (2009).

    PubMed  Google Scholar 

  33. Lang, T. F. et al. Volumetric quantitative computed tomography of the proximal femur: precision and relation to bone strength. Bone 21, 101–108 (1997).

    CAS  PubMed  Google Scholar 

  34. Gluer, C. C. 30 years of DXA technology innovations. Bone 104, 7–12 (2017).

    PubMed  Google Scholar 

  35. Engelke, K. et al. Quantitative computed tomography (QCT) of the forearm using general purpose spiral whole-body CT scanners: accuracy, precision and comparison with dual-energy X-ray absorptiometry (DXA). Bone 45, 110–118 (2009).

    PubMed  Google Scholar 

  36. Engelke, K. Quantitative computed tomography – current status and new developments. J. Clin. Densitom. 20, 309–321 (2017).

    PubMed  Google Scholar 

  37. Lin, E. & Alessio, A. What are the basic concepts of temporal, contrast, and spatial resolution in cardiac CT? J. Cardiovasc. Comput. Tomogr. 3, 403–408 (2009).

    PubMed  PubMed Central  Google Scholar 

  38. Li, Y. et al. CT slice thickness and convolution kernel affect performance of a radiomic model for predicting EGFR status in non-small cell lung cancer: a preliminary study. Sci. Rep. 8, 17913 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. Dougherty, G. & Newman, D. Measurement of thickness and density of thin structures by computed tomography: a simulation study. Med. Phys. 26, 1341–1348 (1999).

    CAS  PubMed  Google Scholar 

  40. Newman, D. L., Dougherty, G., al Obaid, A. & al Hajrasy, H. Limitations of clinical CT in assessing cortical thickness and density. Phys. Med. Biol. 43, 619–626 (1998).

    CAS  PubMed  Google Scholar 

  41. Zebaze, R. M. et al. Intracortical remodelling and porosity in the distal radius and post-mortem femurs of women: a cross-sectional study. Lancet 375, 1729–1736 (2010).

    PubMed  Google Scholar 

  42. Museyko, O., Gerner, B. & Engelke, K. A new method to determine cortical bone thickness in CT images using a hybrid approach of parametric profile representation and local adaptive thresholds: accuracy results. PLOS ONE 12, e0187097 (2017).

    PubMed  PubMed Central  Google Scholar 

  43. Treece, G. M. & Gee, A. H. Independent measurement of femoral cortical thickness and cortical bone density using clinical CT. Med. Image Anal. 20, 249–264 (2015).

    CAS  PubMed  Google Scholar 

  44. Treece, G. M., Poole, K. E. & Gee, A. H. Imaging the femoral cortex: thickness, density and mass from clinical CT. Med. Image Anal. 16, 952–965 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Damm, T. et al. Improved accuracy in the assessment of vertebral cortical thickness by quantitative computed tomography using the Iterative Convolution OptimizatioN (ICON) method. Bone 120, 194–203 (2018).

    PubMed  Google Scholar 

  46. Zysset, P. et al. Clinical use of quantitative computed tomography-based finite element analysis of the hip and spine in the management of osteoporosis in adults: the 2015 ISCD official positions – part II. J. Clin. Densitom. 18, 359–392 (2015).

    PubMed  Google Scholar 

  47. Bousson, V. D. et al. In vivo discrimination of hip fracture with quantitative computed tomography: results from the prospective European Femur Fracture Study (EFFECT). JBMR 26, 881–893 (2011).

    Google Scholar 

  48. Chappard, C. et al. Prediction of femoral fracture load: cross-sectional study of texture analysis and geometric measurements on plain radiographs versus bone mineral density. Radiology 255, 536–543 (2010).

    PubMed  Google Scholar 

  49. Cheng, X., Li, J., Lu, Y., Keyak, J. & Lang, T. Proximal femoral density and geometry measurements by quantitative computed tomography: association with hip fracture. Bone 40, 169–174 (2007).

    CAS  PubMed  Google Scholar 

  50. Yang, L. et al. Association of incident hip fracture with the estimated femoral strength by finite element analysis of DXA scans in the Osteoporotic Fractures in Men (MrOS) study. Osteoporos. Int. 29, 643–651 (2017).

    PubMed  PubMed Central  Google Scholar 

  51. Yang, L., Udall, W. J., McCloskey, E. V. & Eastell, R. Distribution of bone density and cortical thickness in the proximal femur and their association with hip fracture in postmenopausal women: a quantitative computed tomography study. Osteoporos. Int. 25, 251–263 (2014).

    CAS  PubMed  Google Scholar 

  52. Borggrefe, J. et al. Association of 3D geometric measures derived from quantitative computed tomography with hip fracture risk in older men. J. Bone Miner. Res. 31, 1550–1558 (2016).

    PubMed  Google Scholar 

  53. Bouxsein, M. L. et al. Age- and sex-specific differences in the factor of risk for vertebral fracture: a population-based study using QCT. J. Bone Miner. Res. 21, 1475–1482 (2006).

    PubMed  Google Scholar 

  54. Bruno, A. G. et al. Vertebral size, bone density, and strength in men and women matched for age and areal spine BMD. J. Bone Miner. Res. 29, 562–569 (2014).

    PubMed  Google Scholar 

  55. Hahn, M. H. & Won, Y. Y. Bone mineral density and fatty degeneration of thigh muscles measured by computed tomography in hip fracture patients. J. Bone. Metab. 23, 215–221 (2016).

    PubMed  PubMed Central  Google Scholar 

  56. Lang, T. et al. Computed tomographic measurements of thigh muscle cross-sectional area and attenuation coefficient predict hip fracture: the health, aging, and body composition study. J. Bone Miner. Res. 25, 513–519 (2010).

    PubMed  Google Scholar 

  57. Lang, T. et al. Pelvic body composition measurements by quantitative computed tomography: association with recent hip fracture. Bone 42, 798–805 (2008).

    CAS  PubMed  Google Scholar 

  58. Wong, A. K. et al. Bone-muscle indices as risk factors for fractures in men: the Osteoporotic Fractures in Men (MrOS) Study. J. Musculoskelet. Neuronal Interact. 14, 246–254 (2014).

    CAS  PubMed  Google Scholar 

  59. Lee, D. C., Hoffmann, P. F., Kopperdahl, D. L. & Keaveny, T. M. Phantomless calibration of CT scans for measurement of BMD and bone strength-Inter-operator reanalysis precision. Bone 103, 325–333 (2017).

    PubMed  PubMed Central  Google Scholar 

  60. Boutroy, S., Bouxsein, M. L., Munoz, F. & Delmas, P. D. In vivo assessment of trabecular bone microarchitecture by high-resolution peripheral quantitative computed tomography. J. Clin. Endocrinol. Metab. 90, 6508–6515 (2005).

    CAS  PubMed  Google Scholar 

  61. Burghardt, A. J., Link, T. M. & Majumdar, S. High-resolution computed tomography for clinical imaging of bone microarchitecture. Clin. Orthop. Relat. Res. 469, 2179–2193 (2011).

    PubMed  PubMed Central  Google Scholar 

  62. Nishiyama, K. K. & Shane, E. Clinical imaging of bone microarchitecture with HR-pQCT. Curr. Osteoporos. Rep. 11, 147–155 (2013).

    PubMed  PubMed Central  Google Scholar 

  63. Burghardt, A. J. et al. Multicenter precision of cortical and trabecular bone quality measures assessed by high-resolution peripheral quantitative computed tomography. J. Bone Miner. Res. 28, 524–536 (2013).

    PubMed  Google Scholar 

  64. Manske, S. L., Davison, E. M., Burt, L. A., Raymond, D. A. & Boyd, S. K. The estimation of second-generation HR-pQCT from first-generation HR-pQCT using in vivo cross-calibration. J. Bone Miner. Res. 32, 1514–1524 (2017).

    PubMed  Google Scholar 

  65. Laib, A. & Ruegsegger, P. Calibration of trabecular bone structure measurements of in vivo three-dimensional peripheral quantitative computed tomography with 28-microm-resolution microcomputed tomography. Bone 24, 35–39 (1999).

    CAS  PubMed  Google Scholar 

  66. Manske, S. L., Zhu, Y., Sandino, C. & Boyd, S. K. Human trabecular bone microarchitecture can be assessed independently of density with second generation HR-pQCT. Bone 79, 213–221 (2015).

    CAS  PubMed  Google Scholar 

  67. Tjong, W., Nirody, J., Burghardt, A. J., Carballido-Gamio, J. & Kazakia, G. J. Structural analysis of cortical porosity applied to HR-pQCT data. Med. Phys. 41, 013701 (2014).

    PubMed  Google Scholar 

  68. Bousson, V. et al. Distribution of intracortical porosity in human midfemoral cortex by age and gender. J. Bone Miner. Res. 16, 1308–1317 (2001).

    CAS  PubMed  Google Scholar 

  69. Paccou, J. et al. Bone microarchitecture in men and women with diabetes: the importance of cortical porosity. Calcif. Tissue Int. 98, 465–473 (2016).

    CAS  PubMed  Google Scholar 

  70. Patsch, J. M. et al. Increased cortical porosity in type 2 diabetic postmenopausal women with fragility fractures. J. Bone Miner. Res. 28, 313–324 (2013).

    PubMed  Google Scholar 

  71. Cheung, A. M. et al. High-resolution peripheral quantitative computed tomography for the assessment of bone strength and structure: a review by the Canadian Bone Strength Working Group. Curr. Osteoporos. Rep. 11, 136–146 (2013).

    PubMed  PubMed Central  Google Scholar 

  72. Nishiyama, K., Dal’Ara, E. & Engelke, K. In Advanced Techniques of Bone Mass Measurements in Adults (Bilezikian, J. P., ed.) (Wiley, 2018).

  73. Schett, G. & Gravallese, E. Bone erosion in rheumatoid arthritis: mechanisms, diagnosis and treatment. Nat. Rev. Rheumatol. 8, 656–664 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Regensburger, A. et al. A comparative analysis of magnetic resonance imaging and high-resolution peripheral quantitative computed tomography of the hand for the detection of erosion repair in rheumatoid arthritis. Rheumatology 54, 1573–1581 (2015).

    CAS  PubMed  Google Scholar 

  75. Bhatnagar, S., Krishnamurthy, V. & Pagare, S. S. Diagnostic efficacy of panoramic radiography in detection of osteoporosis in post-menopausal women with low bone mineral density. J. Clin. Imaging Sci. 3, 23 (2013).

    PubMed  PubMed Central  Google Scholar 

  76. de Charry, C. et al. Clinical cone beam computed tomography compared to high-resolution peripheral computed tomography in the assessment of distal radius bone. Osteoporos. Int. 27, 3073–3082 (2016).

    PubMed  Google Scholar 

  77. Posadzy, M., Desimpel, J. & Vanhoenacker, F. Cone beam CT of the musculoskeletal system: clinical applications. Insights Imaging 9, 35–45 (2018).

    PubMed  PubMed Central  Google Scholar 

  78. Nardi, C. et al. The role of cone beam CT in the study of symptomatic total knee arthroplasty (TKA): a 20 cases report. Br. J. Radiol. 90, 20160925 (2017).

    PubMed  PubMed Central  Google Scholar 

  79. Mys, K., Stockmans, F., Vereecke, E. & van Lenthe, G. H. Quantification of bone microstructure in the wrist using cone-beam computed tomography. Bone 114, 206–214 (2018).

    PubMed  Google Scholar 

  80. Paulus, M. J., Gleason, S. S., Kennel, S. J., Hunsicker, P. R. & Johnson, D. K. High resolution X-ray computed tomography: an emerging tool for small animal cancer research. Neoplasia 2, 62–70 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Schneider, P., Meier, M., Wepf, R. & Müller, R. Serial FIB/SEM imaging for quantitative 3D assessment of the osteocyte lacuno-canalicular network. Bone 49, 304–311 (2011).

    PubMed  Google Scholar 

  82. Langer, M. et al. X-Ray phase nanotomography resolves the 3D human bone ultrastructure. PLOS ONE 7, e35691 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Hasegawa, T. et al. Three-dimensional ultrastructure of osteocytes assessed by focused ion beam-scanning electron microscopy (FIB-SEM). Histochem. Cell Biol. 149, 423–432 (2018).

    CAS  PubMed  Google Scholar 

  84. Carl Zeiss Microscopy GmbH. Multi-length Scale Imaging Bridging the 3D Resolution Gap. Zeiss.de https://www.zeiss.de/content/dam/Microscopy/us/download/pdf/technical-notes/x-ray-microscopy/multi-length-scale-imaging.pdf (2013).

  85. Bonse, U. (ed.) Developments in X-ray Tomography III. Vol. 4503, 1–386 (SPIE Press, 2002).

  86. Langer, M. & Peyrin, F. 3D X-ray ultra-microscopy of bone tissue. Osteoporos. Int. 27, 441–455 (2016).

    CAS  PubMed  Google Scholar 

  87. Muller, R. Hierarchical microimaging of bone structure and function. Nat. Rev. Rheumatol. 5, 373–381 (2009).

    PubMed  Google Scholar 

  88. Schneider, P. et al. Simultaneous 3D visualization and quantification of murine bone and bone vasculature using micro-computed tomography and vascular replica. Microsc. Res. Tech. 72, 690–701 (2009).

    PubMed  Google Scholar 

  89. Schropp, A. et al. Hard x-ray scanning microscopy with coherent radiation: beyond the resolution of conventional x-ray microscopes. Appl. Phys. Lett. 100, 253112–253112 (2012).

    Google Scholar 

  90. Ma, S. et al. Synchrotron imaging assessment of bone quality. Clin. Rev. Bone Miner. Metab. 14, 150–160 (2016).

    PubMed  PubMed Central  Google Scholar 

  91. Paul, G. R., Malhotra, A. & Muller, R. Mechanical stimuli in the local in vivo environment in bone: computational approaches linking organ-scale loads to cellular signals. Curr. Osteoporos. Rep. 16, 395–403 (2018).

    PubMed  PubMed Central  Google Scholar 

  92. Zizak, I. et al. Characteristics of mineral particles in the human bone/cartilage interface. J. Struct. Biol. 141, 208–217 (2003).

    CAS  PubMed  Google Scholar 

  93. Gupta, H. S. et al. Nanoscale deformation mechanisms in bone. Nano Lett. 5, 2108–2111 (2005).

    CAS  PubMed  Google Scholar 

  94. Hammond, M. A., Gallant, M. A., Burr, D. B. & Wallace, J. M. Nanoscale changes in collagen are reflected in physical and mechanical properties of bone at the microscale in diabetic rats. Bone 60, 26–32 (2013).

    PubMed  PubMed Central  Google Scholar 

  95. Gelb, J. Functionality to failure: materials engineering in the 4th dimension. Adv. Mater. Process. 170, 14–18 (2012).

    Google Scholar 

  96. Tkachuk, A. et al. X-ray computed tomography in Zernike phase contrast mode at 8 keV with 50-nm resolution using Cu rotating anode X-ray source. Z. Kristallogr. 222, 650–655 (2007).

    CAS  Google Scholar 

  97. Gelb, J. et al. Non-destructive local X-ray tomography for multi-length scale analysis of reservoir rocks: validations and observations. in Int. Symposium of the Society of Core Analysts. 1–6 (2012).

  98. Wang, J. et al. Automated markerless full field hard x-ray microscopic tomography at sub-50 nm 3-dimension spatial resolution. Appl. Phys. Lett. 100, 143107 (2012).

    Google Scholar 

  99. Tian, Y. et al. High resolution hard x-ray microscope on a second generation synchrotron source. Rev. Sci. Instrum. 79, 103708/103701 (2008).

    Google Scholar 

  100. Chu, Y. S. et al. Hard-x-ray microscopy with Fresnel zone plates reaches 40 nm Rayleigh resolution. Appl. Phys. Lett. 92, 103119 (2008).

    Google Scholar 

  101. Ladd, M. E. et al. Pros and cons of ultra-high-field MRI/MRS for human application. Progr. Nucl. Magn. Reson. Spectrosc. 109, 1–50 (2018).

    CAS  Google Scholar 

  102. van Wijk, D. F. et al. Increasing spatial resolution of 3T MRI scanning improves reproducibility of carotid arterial wall dimension measurements. MAGMA 27, 219–226 (2014).

    PubMed  Google Scholar 

  103. Nowogrodzki, A. The world’s strongest MRI machines are pushing human imaging to new limits. Nature 563, 24–26 (2018).

    CAS  PubMed  Google Scholar 

  104. Kraff, O. & Quick, H. H. 7T: Physics, safety, and potential clinical applications. J. Magn. Reson. Imaging 46, 1573–1589 (2017).

    PubMed  Google Scholar 

  105. Krug, R., Stehling, C., Kelley, D. A. C., Majumdar, S. & Link, T. M. Imaging of the musculoskeletal system in vivo using ultra-high field magnetic resonance at 7 T. Invest. Radiol. 44, 613–618 (2009).

    PubMed  Google Scholar 

  106. Chang, G. et al. MRI of the hip at 7T: feasibility of bone microarchitecture, high-resolution cartilage, and clinical imaging. J. Magn. Reson. Imaging 39, 1384–1393 (2014).

    PubMed  Google Scholar 

  107. Chang, G. et al. 7T MRI detects deterioration in subchondral bone microarchitecture in subjects with mild knee osteoarthritis as compared with healthy controls. J. Magn. Reson. Imaging 41, 1311–1317 (2015).

    PubMed  Google Scholar 

  108. Lazik, A. et al. 7 Tesla quantitative hip MRI: T1, T2 and T2* mapping of hip cartilage in healthy volunteers. Eur. Radiol. 26, 1245–1253 (2015).

    PubMed  Google Scholar 

  109. Lazik-Palm, A. et al. Morphological and quantitative 7 T MRI of hip cartilage transplants in comparison to 3 T – initial experiences. Invest. Radiol. 51, 552–559 (2016).

    PubMed  Google Scholar 

  110. Juras, V. et al. The compositional difference between ankle and knee cartilage demonstrated by T2 mapping at 7 Tesla MR. Eur. J. Radiol. 85, 771–777 (2016).

    PubMed  Google Scholar 

  111. Krug, R. et al. In vivo ultra-high-field magnetic resonance imaging of trabecular bone microarchitecture at 7 T. J. Magn. Reson. Imaging 27, 854–859 (2008).

    PubMed  Google Scholar 

  112. Wright, A. C. et al. Helmholtz-pair transmit coil with integrated receive array for high-resolution MRI of trabecular bone in the distal tibia at 7T. J. Magn. Reson. 210, 113–122 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Griffin, L. M. et al. 7T MRI of distal radius trabecular bone microarchitecture: how trabecular bone quality varies depending on distance from end-of-bone. J. Magn. Reson. Imaging 45, 872–878 (2016).

    PubMed  PubMed Central  Google Scholar 

  114. Bhagat, Y. A. et al. Performance of μMRI-based virtual bone biopsy for structural and mechanical analysis at the distal tibia at 7T field strength. J. Magn. Reson. Imaging 33, 372–381 (2011).

    PubMed  PubMed Central  Google Scholar 

  115. Chang, G. et al. In vivo estimation of bone stiffness at the distal femur and proximal tibia using ultra-high-field 7-Tesla magnetic resonance imaging and micro-finite element analysis. J. Bone Miner. Metab. 30, 243–251 (2011).

    PubMed  PubMed Central  Google Scholar 

  116. Weiger, M., Stampanoni, M. & Pruessmann, K. P. Direct depiction of bone microstructure using MRI with zero echo time. Bone 54, 44–47 (2013).

    PubMed  Google Scholar 

  117. Wehrli, F. W. & Fernandez-Seara, M. A. Nuclear magnetic resonance studies of bone water. Ann. Biomed. Eng. 33, 79–86 (2005).

    PubMed  Google Scholar 

  118. Jara, H., Wehrli, F. W., Chung, H. & Ford, J. C. High-resolution variable flip angle 3D MR imaging of trabecular microstructure in vivo. Magn. Reson. Med. 29, 528–539 (1993).

    CAS  PubMed  Google Scholar 

  119. Liu, X. S. et al. Accuracy of high-resolution in vivo micro magnetic resonance imaging for measurements of microstructural and mechanical properties of human distal tibial bone. J. Bone Miner. Res. 25, 2039–2050 (2010).

    PubMed  PubMed Central  Google Scholar 

  120. Anumula, S., Wehrli, S. L., Magland, J., Wright, A. C. & Wehrli, F. W. Ultra-short echo-time MRI detects changes in bone mineralization and water content in OVX rat bone in response to alendronate treatment. Bone 46, 1391–1399 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Krug, R. et al. Ultrashort echo time MRI of cortical bone at 7 Tesla field strength: a feasibility study. J. Magn. Reson. Imaging 34, 691–695 (2011).

    PubMed  PubMed Central  Google Scholar 

  122. Lazik-Palm, A. et al. Morphological imaging and T2 and T2* mapping of hip cartilage at 7 Tesla MRI under the influence of intravenous gadolinium. Eur. Radiol. 26, 3923–3931 (2016).

    PubMed  Google Scholar 

  123. Rietsch, S. H. G. et al. An 8-channel transceiver 7-channel receive RF coil setup for high SNR ultrahigh-field MRI of the shoulder at 7T. Med. Phys. 44, 6195–6208 (2017).

    PubMed  Google Scholar 

  124. Johst, S., Wrede, K. H., Ladd, M. E. & Maderwald, S. Time-of-flight magnetic resonance angiography at 7 T using venous saturation pulses with reduced flip angles. Invest. Radiol. 47, 445–450 (2012).

    PubMed  Google Scholar 

  125. Mattern, H. et al. Prospective motion correction enables highest resolution time-of-flight angiography at 7T. Magn. Reson. Med. 80, 248–258 (2017).

    PubMed  PubMed Central  Google Scholar 

  126. Fu, R. et al. Ultra-wide bore 900MHz high-resolution NMR at the national high magnetic field laboratory. J. Magn. Reson. 177, 1–8 (2005).

    CAS  PubMed  Google Scholar 

  127. Schepkin, V. D., Brey, W. W., Gor’kov, P. L. & Grant, S. C. Initial in vivo rodent sodium and proton MR imaging at 21.1 T. Magn. Reson. Imaging 28, 400–407 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. Budinger, T. F. & Bird, M. D. MRI and MRS of the human brain at magnetic fields of 14 T to 20 T: technical feasibility, safety, and neuroscience horizons. NeuroImage 168, 509–531 (2018).

    PubMed  Google Scholar 

  129. Budinger, T. F. et al. Toward 20 T magnetic resonance for human brain studies: opportunities for discovery and neuroscience rationale. Magn. Reson. Mater. Phys. Biol. Med. 29, 617–639 (2016).

    CAS  Google Scholar 

  130. Schulz, R. B. & Semmler, W. Fundamentals of optical imaging Handb. Exp. Pharmacol. 185, 3–22 (2008).

    CAS  Google Scholar 

  131. Hell, S. W. Far-field optical nanoscopy. Science 316, 1153–1158 (2007).

    CAS  PubMed  Google Scholar 

  132. Sigal, Y. M., Zhou, R. & Zhuang, X. Visualizing and discovering cellular structures with super-resolution microscopy. Science 361, 880–887 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Adams, M. W., Loftus, A. F., Dunn, S. E., Joens, M. S. & Fitzpatrick, J. A. Light sheet fluorescence microscopy (LSFM). Curr. Protoc. Cytom. 71, 12 37 11–12 37 15 (2015).

    Google Scholar 

  134. Santi, P. A. Light sheet fluorescence microscopy: a review. J. Histochem. Cytochem. 59, 129–138 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. Keller, P. J. & Stelzer, E. H. Quantitative in vivo imaging of entire embryos with digital scanned laser light sheet fluorescence microscopy. Curr. Opin. Neurobiol. 18, 624–632 (2008).

    CAS  PubMed  Google Scholar 

  136. Richardson, D. S. & Lichtman, J. W. Clarifying tissue clearing. Cell 162, 246–257 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Klingberg, A. et al. Fully automated evaluation of total glomerular number and capillary tuft size in nephritic kidneys using lightsheet microscopy. J. Am. Soc. Nephrol. 28, 452–459 (2017).

    CAS  PubMed  Google Scholar 

  138. Chatterjee, K., Pratiwi, F. W., Wu, F. C. M., Chen, P. & Chen, B. C. Recent progress in light sheet microscopy for biological applications. Appl. Spectrosc. 72, 1137–1169 (2018).

    CAS  PubMed  Google Scholar 

  139. Keller, P. J., Schmidt, A. D., Wittbrodt, J. & Stelzer, E. H. Digital scanned laser light-sheet fluorescence microscopy (DSLM) of zebrafish and Drosophila embryonic development. Cold Spring Harb. Protoc. 2011, 1235–1243 (2011).

    PubMed  Google Scholar 

  140. Kromm, D., Thumberger, T. & Wittbrodt, J. An eye on light-sheet microscopy. Methods Cell Biol. 133, 105–123 (2016).

    CAS  PubMed  Google Scholar 

  141. Dean, K. M. & Fiolka, R. Uniform and scalable light-sheets generated by extended focusing. Opt. Express 22, 26141–26152 (2014).

    PubMed  Google Scholar 

  142. Dean, K. M., Roudot, P., Welf, E. S., Danuser, G. & Fiolka, R. Deconvolution-free subcellular imaging with axially swept light sheet microscopy. Biophys. J. 108, 2807–2815 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Genina, E. A., Bashkatov, A. N. & Tuchin, V. V. Optical clearing of cranial bone. Adv. Opt. Technol. 2008, 1–8 (2008).

    Google Scholar 

  144. Vigouroux, R. J., Belle, M. & Chedotal, A. Neuroscience in the third dimension: shedding new light on the brain with tissue clearing. Mol. Brain 10, 33 (2017).

    PubMed  PubMed Central  Google Scholar 

  145. Zundler, S. et al. Three-dimensional cross-sectional light-sheet microscopy imaging of the inflamed mouse gut. Gastroenterology 153, 898–900 (2017).

    PubMed  Google Scholar 

  146. Männ, L. et al. CD11c.DTR mice develop a fatal fulminant myocarditis after local or systemic treatment with diphtheria toxin. Eur. J. Immunol. 46, 2028–2042 (2016).

    PubMed  Google Scholar 

  147. Tuchin, V. V. Optical clearing of tissues and blood using the immersion method. J. Phys. D: Appl. Phys. 38, 2497–2518 (2005).

    CAS  Google Scholar 

  148. Ariel, P. A beginner’s guide to tissue clearing. Int. J. Biochem. Cell Biol. 84, 35–39 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Renier, N. et al. iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159, 896–910 (2014).

    CAS  PubMed  Google Scholar 

  150. Becker, K., Jahrling, N., Saghafi, S., Weiler, R. & Dodt, H. U. Chemical clearing and dehydration of GFP expressing mouse brains. PLOS ONE 7, e33916 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  151. Becker, K., Jahrling, N., Kramer, E. R., Schnorrer, F. & Dodt, H. U. Ultramicroscopy: 3D reconstruction of large microscopical specimens. J. Biophotonics 1, 36–42 (2008).

    CAS  PubMed  Google Scholar 

  152. Schwarz, M. K. et al. Fluorescent-protein stabilization and high-resolution imaging of cleared, intact mouse brains. PLOS ONE 10, e0124650 (2015).

    PubMed  PubMed Central  Google Scholar 

  153. Zukor, K. A., Kent, D. T. & Odelberg, S. J. Fluorescent whole-mount method for visualizing three-dimensional relationships in intact and regenerating adult newt spinal cords. Dev. Dyn. 239, 3048–3057 (2010).

    PubMed  PubMed Central  Google Scholar 

  154. Acar, M. et al. Deep imaging of bone marrow shows non-dividing stem cells are mainly perisinusoidal. Nature 526, 126–130 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Chen, J. Y. et al. Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche. Nature 530, 223–227 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. de Saint-Georges, L. & Miller, S. C. The microcirculation of bone and marrow in the diaphysis of the rat hemopoietic long bones. Anat. Rec. 233, 169–177 (1992).

    PubMed  Google Scholar 

  157. Herisson, F. et al. Direct vascular channels connect skull bone marrow and the brain surface enabling myeloid cell migration. Nat. Neurosci. 21, 1209–1217 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. Augustin, H. G. & Koh, G. Y. Organotypic vasculature: from descriptive heterogeneity to functional pathophysiology. Science 357, eaal2379 (2017).

    PubMed  Google Scholar 

  159. Ramasamy, S. K. et al. Blood flow controls bone vascular function and osteogenesis. Nat. Commun. 7, 13601 (2016).

    PubMed  PubMed Central  Google Scholar 

  160. Ramasamy, S. K. Structure and functions of blood vessels and vascular niches in bone. Stem Cells Int. 2017, 5046953 (2017).

    PubMed  PubMed Central  Google Scholar 

  161. Jonkman, J. & Brown, C. M. Any way you slice it – a comparison of confocal microscopy techniques. J. Biomol. Tech. 26, 54–65 (2015).

    PubMed  PubMed Central  Google Scholar 

  162. Takaku, T. et al. Hematopoiesis in 3 dimensions: human and murine bone marrow architecture visualized by confocal microscopy. Blood 116, e41–e55 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  163. Morrison, S. J. & Scadden, D. T. The bone marrow niche for haematopoietic stem cells. Nature 505, 327–334 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. Kusumbe, A. P., Ramasamy, S. K. & Adams, R. H. Coupling of angiogenesis and osteogenesis by a specific vessel subtype in bone. Nature 507, 323–328 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  165. Ramasamy, S. K., Kusumbe, A. P., Wang, L. & Adams, R. H. Endothelial Notch activity promotes angiogenesis and osteogenesis in bone. Nature 507, 376–380 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  166. Kunisaki, Y. et al. Arteriolar niches maintain haematopoietic stem cell quiescence. Nature 502, 637–643 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  167. Mizuhashi, K. et al. Resting zone of the growth plate houses a unique class of skeletal stem cells. Nature 563, 254–258 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  168. Cahalan, M. D., Parker, I., Wei, S. H. & Miller, M. J. Two-photon tissue imaging: seeing the immune system in a fresh light. Nat. Rev. Immunol. 2, 872–880 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  169. Erturk, A. et al. Three-dimensional imaging of solvent-cleared organs using 3DISCO. Nat. Protoc. 7, 1983–1995 (2012).

    CAS  PubMed  Google Scholar 

  170. Tanaka, K. et al. Intravital imaging of gastrointestinal diseases in preclinical models using two-photon laser scanning microscopy. Surg. Today 43, 123–129 (2013).

    PubMed  Google Scholar 

  171. Phan, T. G. & Bullen, A. Practical intravital two-photon microscopy for immunological research: faster, brighter, deeper. Immunol. Cell Biol. 88, 438–444 (2010).

    PubMed  Google Scholar 

  172. Perry, S. W., Burke, R. M. & Brown, E. B. Two-photon and second harmonic microscopy in clinical and translational cancer research. Ann. Biomed. Eng. 40, 277–291 (2012).

    PubMed  PubMed Central  Google Scholar 

  173. Bousso, P. & Moreau, H. D. Functional immunoimaging: the revolution continues. Nat. Rev. Immunol. 12, 858–864 (2012).

    CAS  PubMed  Google Scholar 

  174. Germain, R. N., Robey, E. A. & Cahalan, M. D. A decade of imaging cellular motility and interaction dynamics in the immune system. Science 336, 1676–1681 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  175. Niesner, R. A., Andresen, V. & Gunzer, M. Intravital 2-photon microscopy – focus on speed and time resolved imaging modalities. Immunol. Rev. 221, 7–25 (2008).

    CAS  PubMed  Google Scholar 

  176. Wang, T. et al. Three-photon imaging of mouse brain structure and function through the intact skull. Nat. Methods 15, 789–792 (2018).

    PubMed  PubMed Central  Google Scholar 

  177. Carriles, R. et al. Invited review article: imaging techniques for harmonic and multiphoton absorption fluorescence microscopy. Rev. Sci. Instrum. 80, 081101 (2009).

    PubMed  PubMed Central  Google Scholar 

  178. Vielreicher, M. et al. Taking a deep look: modern microscopy technologies to optimize the design and functionality of biocompatible scaffolds for tissue engineering in regenerative medicine. J. R. Soc. Interface 10, 20130263 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  179. Georgiadis, M., Muller, R. & Schneider, P. Techniques to assess bone ultrastructure organization: orientation and arrangement of mineralized collagen fibrils. J. R. Soc. Interface 13, 20160088 (2016).

    PubMed  PubMed Central  Google Scholar 

  180. Genthial, R. et al. Label-free imaging of bone multiscale porosity and interfaces using third-harmonic generation microscopy. Sci. Rep. 7, 3419 (2017).

    PubMed  PubMed Central  Google Scholar 

  181. Saitou, T., Kiyomatsu, H. & Imamura, T. Quantitative morphometry for osteochondral tissues using second harmonic generation microscopy and image texture information. Sci. Rep. 8, 2826 (2018).

    PubMed  PubMed Central  Google Scholar 

  182. Okada, T., Takahashi, S., Ishida, A. & Ishigame, H. In vivo multiphoton imaging of immune cell dynamics. Pflugers Arch. 468, 1793–1801 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  183. Mazo, I. B. et al. Hematopoietic progenitor cell rolling in bone marrow microvessels: parallel contributions by endothelial selectins and vascular cell adhesion molecule 1. J. Exp. Med. 188, 465–474 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  184. Vandoorne, K. et al. Imaging the vascular bone marrow niche during inflammatory stress. Circ. Res. 123, 415–427 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  185. Bixel, M. G. et al. Flow dynamics and HSPC homing in bone marrow microvessels. Cell. Rep. 18, 1804–1816 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  186. Hasenberg, A. et al. Catchup: a mouse model for imaging-based tracking and modulation of neutrophil granulocytes. Nat. Methods 12, 445–452 (2015).

    CAS  PubMed  Google Scholar 

  187. Spencer, J. A. et al. Direct measurement of local oxygen concentration in the bone marrow of live animals. Nature 508, 269–273 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  188. Köhler, A. et al. G-CSF mediated thrombopoietin release triggers neutrophil motility and mobilization from bone marrow via induction of Cxcr2 ligands. Blood 117, 4349–4357 (2011).

    PubMed  PubMed Central  Google Scholar 

  189. Junt, T. et al. Dynamic visualization of thrombopoiesis within bone marrow. Science 317, 1767–1770 (2007).

    CAS  PubMed  Google Scholar 

  190. Massberg, S. et al. Immunosurveillance by hematopoietic progenitor cells trafficking through blood, lymph, and peripheral tissues. Cell 131, 994–1008 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  191. Devi, S. et al. Neutrophil mobilization via plerixafor-mediated CXCR4 inhibition arises from lung demargination and blockade of neutrophil homing to the bone marrow. J. Exp. Med. 210, 2321–2336 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  192. Lassailly, F., Foster, K., Lopez-Onieva, L., Currie, E. & Bonnet, D. Multimodal imaging reveals structural and functional heterogeneity in different bone marrow compartments: functional implications on hematopoietic stem cells. Blood 122, 1730–1740 (2013).

    CAS  PubMed  Google Scholar 

  193. Köhler, A. et al. Altered cellular dynamics and endosteal location of aged early hematopoietic progenitor cells revealed by time-lapse intravital imaging in long bones. Blood 114, 290–298 (2009).

    PubMed  PubMed Central  Google Scholar 

  194. Kiel, M. J., Iwashita, T., Yilmaz, O. H. & Morrison, S. J. Spatial differences in hematopoiesis but not in stem cells indicate a lack of regional patterning in definitive hematopoietic stem cells. Dev. Biol. 283, 29–39 (2005).

    CAS  PubMed  Google Scholar 

  195. Chan, C. K. et al. Endochondral ossification is required for haematopoietic stem-cell niche formation. Nature 457, 490–494 (2009).

    CAS  PubMed  Google Scholar 

  196. Nombela-Arrieta, C. & Manz, M. G. Quantification and three-dimensional microanatomical organization of the bone marrow. Blood Adv. 1, 407–416 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  197. Otto, L., Zelinskyy, G., Schuster, M., Dittmer, U. & Gunzer, M. Imaging of cytotoxic antiviral immunity while considering the 3R principle of animal research. J. Mol. Med. 96, 349–360 (2018).

    CAS  PubMed  Google Scholar 

  198. Mill, L. et al. In Bildverarbeitung für die Medizin 2018. Springer, 115–120 (2018).

  199. Aichert, A. et al. Epipolar consistency in transmission imaging. IEEE Trans Med Imaging 34, 2205–2219 (2015).

    PubMed  Google Scholar 

  200. Bier, B. et al. X-ray-transform invariant anatomical landmark detection for pelvic trauma surgery. arXiv e-prints https://arxiv.org/abs/1803.08608 (2018).

  201. Choi, J. H. et al. Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. II. Experiment. Med. Phys. 41, 061902 (2014).

    PubMed  PubMed Central  Google Scholar 

  202. Pan, X., Sidky, E. Y. & Vannier, M. Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? Inverse Probl. 25, 1230009 (2009).

    PubMed  PubMed Central  Google Scholar 

  203. Würfl, T., Ghesu, F. C., Christlein, V. & Maier, A. in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. 432–440 (2016).

  204. Zhu, B., Liu, J. Z., Cauley, S. F., Rosen, B. R. & Rosen, M. S. Image reconstruction by domain-transform manifold learning. Nature 555, 487–492 (2018).

    CAS  PubMed  Google Scholar 

  205. Ye, J. C., Han, Y. & Cha, E. Deep convolutional framelets: a general deep learning framework for inverse problems. arXiv e-prints https://arxiv.org/abs/1707.00372 (2017).

  206. Kobler, E., Klatzer, T., Hammernik, K. & Pock, T. Variational networks: connecting variational methods and deep learning, in German Conference on Pattern Recognition. Springer, 281–293 (2017).

  207. Halabi, S. S. et al. The RSNA pediatric bone age machine learning challenge. Radiology 290, 498–503 (2018).

    PubMed  Google Scholar 

  208. Ellmann, S. et al. Prediction of early metastatic disease in experimental breast cancer bone metastasis by combining PET/CT and MRI parameters to a model-averaged neural network. Bone 120, 254–261 (2018).

    PubMed  Google Scholar 

  209. Yune, S. et al. Beyond human perception: sexual dimorphism in hand and wrist radiographs is discernible by a deep learning model. J. Digit. Imaging https://doi.org/10.1007/s10278-018-0148-x (2018).

    PubMed Central  Google Scholar 

  210. Maier, A., Syben, C., Lasser, T. & Riess, C. A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik 29, 86–101 (2018).

  211. Maier, A. et al. Precision learning: towards use of known operators in neural networks. arXiv e-prints https://arxiv.org/abs/1712.00374 (2017).

  212. Andrews, J. C. et al. Nanoscale X-ray microscopic imaging of mammalian mineralized tissue. Microsc. Microanal. 16, 327–336 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  213. Stelzer, E. H. Light-sheet fluorescence microscopy for quantitative biology. Nat. Methods 12, 23–26 (2014).

    Google Scholar 

  214. Pan, C. et al. Shrinkage-mediated imaging of entire organs and organisms using uDISCO. Nat. Methods 13, 859–867 (2016).

    CAS  PubMed  Google Scholar 

  215. Moser, E., Stadlbauer, A., Windischberger, C., Quick, H. H. & Ladd, M. E. Magnetic resonance imaging methodology. Eur. J. Nucl. Med. Mol. Imaging 36 (Suppl. 1), S30–S41 (2009).

    PubMed  Google Scholar 

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Acknowledgements

The authors thank the IMaging Centre ESsen, the Optical Imaging Centre Erlangen and the Erwin L. Hahn Institute for Magnetic Resonance Imaging at University Duisburg-Essen for support with imaging. The authors’ work is supported by funding from the German Research Foundation (SPP1480 Immunobone), to M.G. and G.S.; the Collaborative Research Centre (CRC) 1181, to G.S.; and the European Union (EU HEALTH-2013-INNOVATION-1, MATHIAS), to M.G.. The work of S.K., A.M. and G.S. is also supported by the European Research Council (ERC) Synergy grant NanoScope (grant no. 810316) and the work of G.S. is also supported by the Innovative Medicine Initiative (IMI)-funded project RTCure.

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Authors and Affiliations

  1. Department of Internal Medicine 3 – Rheumatology and Immunology, Friedrich-Alexander-University Erlangen-Nuremberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany

    Anika Grüneboom, Klaus Engelke & Georg Schett

  2. Max Planck Institute for the Science of Light, Christiansen Research Group, Erlangen, Germany

    Lasse Kling, Silke Christiansen & Leonid Mill

  3. Helmholtz-Zentrum Berlin für Materialien und Energie, Berlin, Germany

    Lasse Kling & Silke Christiansen

  4. Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany

    Leonid Mill & Andreas Maier

  5. Institute of Medical Physics, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Universitätsklinikum Erlangen, Erlangen, Germany

    Klaus Engelke

  6. Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany

    Harald H. Quick

  7. High-Field and Hybrid MR Imaging, University Hospital, University Duisburg-Essen, Essen, Germany

    Harald H. Quick

  8. Institute for Experimental Immunology and Imaging, University Hospital, University Duisburg-Essen, Essen, Germany

    Matthias Gunzer

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Supplementary Information

Glossary

Haversian system

Structural unit of bone consisting of a central Haversian canal, which surrounds blood vessels and nerves, surrounded by concentric rings called lamellae.

Lacuno-canalicular network

(LCN). A system of sub-micrometre-sized channels (canaliculi) inside cortical bone that form a network with osteocyte-containing lacunae, through which osteocyte membrane processes make contact with other osteocytes or endothelial cells of trans-cortical blood vessels.

Pixels

Elements (or ‘picture elements’) of a 2D digital image, typically numbering in the hundreds of thousands or millions and arranged in rows and columns. Each pixel has a level of brightness (grey level) and, for coloured images, a combination of several (typically three) colour components.

Voxels

Similar to a pixel, a voxel (or ‘volume pixel’) is an image element in a 3D image such as a tomographic scan.

Synchrotron-based imaging

Imaging modality comprising X-ray-based CT coupled to a powerful source of X-rays (synchrotron).

Absorption coefficient

Describes how easily a tissue can be penetrated by X-ray radiation, expressed as the fraction of an X-ray beam that is absorbed or scattered per unit of thickness.

Hounsfield units

(HU). Values representing linearly transformed absorption coefficients, used to standardize CT scanners; by definition, the radiodensity of water is zero HU and air negative 1,000 HU. Bone is between +500 and +1,500 HU and fat is negative ~100 HU.

Calibration phantom

An object containing artificial materials with known physical density and X-ray absorption behaviour that is imaged by CT together with a patient to enable precise assessment of these properties in the patient´s tissue.

Areal BMD

A measurement of bone mineral content, determined by dividing the amount of bone mineral (in grams) by the area of the 2D bone site scanned (in square centimetres).

Kernels

In the context of CT image reconstruction, a special function that can be varied within a certain range in order to tune the pixel noise and the geometrical resolution.

Partial volume artefacts

Occur when a CT voxel encompasses tissues with different absorptions, so that the beam attenuation represents the average value of these tissues.

Fresnel zone plate

A device to focus X-rays, consisting of a group of radially symmetrical, alternately opaque and transparent rings (zones); an X-ray wave hitting the zone plate diffracts around the opaque zones, and the zones can be engineered in such a way that the waves are focused.

Tesla

(T). An SI unit that describes the strength of a magnetic field. 1 Tesla is ~20,000 times larger than Earth’s natural magnetic field.

Signal-to-noise ratio

(SNR). Describes the ratio of the power of an anticipated signal to the power of background noise.

Ultra-short echo time

Technique used in MRI sequences that enables visualization of tissues with very short transverse relaxation times by starting spatial encoding and data acquisition as soon as possible after the radiofrequency pulse.

Zero echo time

Technique used in MRI sequences that enables visualization of tissues with very short transverse relaxation times by starting spatial encoding before the radiofrequency pulse and starting data acquisition as soon as possible after the radiofrequency pulse.

Time-of-flight magnetic resonance angiography

(TOF MRA). An MRI technique used for high-resolution imaging of blood vessels, based on the principle that, when using short echo times, unsaturated blood entering the imaging slice gives a much higher (brighter) signal than the surrounding static tissue, which is saturated and thus remains dark.

Refractive index

(RI). A descriptor of how fast light propagates through a material, expressed as the ratio of the velocity of light in a vacuum to its velocity in that material.

Excitation maximum

A fluorophore fluoresces when its electrons absorb incoming photons (excitation) and then return to their original energy level, releasing excess energy in the form of a red-shifted photon; the excitation maximum is the optimal energy (i.e. wavelength or colour) that incoming photons must have to make this process most effective.

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Grüneboom, A., Kling, L., Christiansen, S. et al. Next-generation imaging of the skeletal system and its blood supply. Nat Rev Rheumatol 15, 533–549 (2019). https://doi.org/10.1038/s41584-019-0274-y

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