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Computer-Assisted Quantification

  • Philipp Peloschek
  • Georg Langs
  • Reinhard Windhager
  • Franz KainbergerEmail author
Chapter
Part of the Medical Radiology book series (MEDRAD)

Abstract

Computer-aided image analysis and decision support has become an indispensable part of treatment planning in orthopaedic surgery and in osteology. The first use of computers for image interpretation probably was for a musculoskeletal application and was published by Lodwick who developed a computer diagnostic model for the pattern analysis of osteolytic bone tumours in 1963 (Lodwick et al. 1963). The next important step forward was, after quantitative computed tomography, the development of dual X-ray absorptiometry (DXA, the abbreviation DEXA should not be used) in 1987 (Wahner et al. 1994). With this technique, the image is not in the centre of the radiology report but is used as a guidance tool for generating measurable quantification parameters of the bone density, the trabecular architecture, and the body composition with the impact of expressing trends. A more detailed analysis of bone structure in the form of a “virtual biopsy” is possible with micro-CT systems and, for in vivo investigations, with high-resolution peripheral quantitative computed tomography (HRpqCT) (Rügsegger et al. 1996). Semiautomated and later fully automated measurements of the skeleton is a further application which bases on contour finding techniques and was developed for orthopaedic treatment planning and skeletal age assessment (Niethard 1999). Parallel to these applications for radiography and computed tomography, quantification schemes of MR data were established for the biochemical imaging of cartilage, for the perfusion of the synovial tissue in arthritis, and for the chemical shift imaging to measure the fat fraction in the bone marrow of patients with skeletal storage diseases (Kubassova 2007; Maas et al. 2011; Reiser et al. 1989; Trattnig et al. 2012). Many of these skeletal and soft tissue applications have been designed towards better patient’s and social outcomes, with important research in the field of maxillofacial surgery for designing individual prosthetic devices with and without 3D printing and for automated steering of implant positioning (Ploder et al. 1995; Windhager et al. 1996).

Notes

Acknowledgement

We want to thank Prof. Horst Bischof for his mentorship during the recent years.

This contribution was supported by a grant from the Austrian Funds for Scientific Research (No. ~P17083-N04).

References

  1. Aeberli D, Eser P, Bonel H, Widmer J, Caliezi G, Varisco PA, Möller B, Villiger PM (2010) Reduced trabecular bone mineral density and cortical thickness accompanied by increased outer bone circumference in metacarpal bone of rheumatoid arthritis patients: a cross-sectional study. Arthritis Res Ther 12:R119PubMedPubMedCentralCrossRefGoogle Scholar
  2. Aizenberg E, Roex EAH, Nieuwenhuis WP, Mangnus L, van der Helm-van Mil AHM, Reijnierse M, Bloem JL, Lelieveldt BPF, Stoel BC (2018) Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: a feasibility study. Magn Reson Med 79:1127–1134PubMedPubMedCentralCrossRefGoogle Scholar
  3. Allin S, Bleakney R, Zhang J, Munce S, Cheung AM, Jaglal S (2016) Evaluation of automated fracture risk assessment based on the Canadian Association of Radiologists and Osteoporosis Canada Assessment Tool. J Clin Densitom 19:332–339PubMedCrossRefGoogle Scholar
  4. Angwin J, Lloyd A, Heald G, Nepom G, Binks M, James MF (2004) Radiographic hand joint space width assessed by computer is a sensitive measure of change in early rheumatoid arthritis. J Rheumatol 31:1050–1061PubMedPubMedCentralGoogle Scholar
  5. Arbabshirani MR, Fornwalt BR, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, Moore GJ (2018) Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. npj Digit Med 1:9PubMedPubMedCentralCrossRefGoogle Scholar
  6. Axelsen MB, Ejbjerg BJ, Hetland ML, Skjødt H, Majgaard O, Lauridsen UB, Hørslev-Petersen K, Boesen M, Kubassova O, Bliddal H, Østergaard M (2014) Differentiation between early rheumatoid arthritis patients and healthy persons by conventional and dynamic contrast-enhanced magnetic resonance imaging. Scand J Rheumatol 43:109–118PubMedCrossRefGoogle Scholar
  7. Ballard DH, Trace AP, Ali S, Hodgdon T, Zygmont ME, DeBenedectis CM, Smith SE, Richardson ML, Patel MJ, Decker SJ, Lenchik L (2018) Clinical applications of 3D printing: primer for radiologists. Acad Radiol 25:52–65PubMedCrossRefGoogle Scholar
  8. Baumbach SF, Binder J, Synek A, Mück FG, Chevalier Y, Euler E, Langs G, Fischer L (2017) Analysis of the three-dimensional anatomical variance of the distal radius using 3D shape models. BMC Med Imaging 17:23PubMedPubMedCentralCrossRefGoogle Scholar
  9. Beard DV, Eberly D, Hemminger B, Pizer SM (1994) Interacting with image hierarchies for fast and accurate object segmentation. In: SPIE Conf. on Medical Imaging, Bellingham, WA, pp 10–17Google Scholar
  10. Bredbenner TL, Mason RL, Havill LM, Orwoll ES, Nicolella DP, The Osteoporotic Fractures in Men (MrOS) Study (2014) Fracture risk predictions based on statistical shape and density modeling of the proximal femur. J Bone Miner Res 29:2090–2100PubMedPubMedCentralCrossRefGoogle Scholar
  11. Beichel R, Bischof H, Leberl F, Sonka M (2005) Robust active appearance models and their application to medical image analysis. IEEE Trans Med Imaging 24:1151–1169PubMedCrossRefGoogle Scholar
  12. Bilezikian JP, Brandi ML, Eastell R, Silverberg SJ, Udelsman R, Marcocci C, Potts JT (2014) Guidelines for the management of asymptomatic primary hyperparathyroidism: summary statement from the Fourth International Workshop. J Clin Endocrinol Metab 99:3561–3569PubMedPubMedCentralCrossRefGoogle Scholar
  13. Binkley N, Krueger D, Gangnon R, Genant HK, Drezner MK (2005) Lateral vertebral assessment: a valuable technique to detect clinically significant vertebral fractures. Osteoporos Int 16:1513–1518PubMedCrossRefGoogle Scholar
  14. Binkley N, Krueger D, Siglinsky E, Shives E, Buehring B, Hansen KE (2018) An interpretation template reduces DXA reporting errors. J Clin Densitom 21:28CrossRefGoogle Scholar
  15. Bird P, Lassere M, Shnier R, Edmonds J (2003a) Computerized measurement of magnetic resonance imaging erosion volumes in patients with rheumatoid arthritis: a comparison with existing magnetic resonance imaging scoring systems and standard clinical outcome measures. Arthritis Rheum 48:614–624PubMedCrossRefGoogle Scholar
  16. Bird P, Ejbjerg B, McQueen F, Ostergaard M, Lassere M, Edmonds J (2003b) OMERACT Rheumatoid Arthritis Magnetic Resonance Imaging Studies. Exercise 5: an international multicenter reliability study using computerized MRI erosion volume measurements. J Rheumatol 30:1380–1384PubMedGoogle Scholar
  17. Bird P, Lassere M, Shnier R, Edmonds J (2004) Magnetic resonance imaging computerized assessment in rheumatoid arthritis: comment on the article by Goldbach-Mansky et al. Arthritis Rheum 50:1011–1012PubMedCrossRefGoogle Scholar
  18. Bird P, Joshua F, Lassere M, Shnier R, Edmonds J (2005) Training and calibration improve inter-reader reli- ability of joint damage assessment using magnetic resonance image scoring and computerized erosion volume measurement. J Rheumatol 32:1452–1458PubMedGoogle Scholar
  19. Burns JE, Yao J, Muñoz H, Summers RM (2016) Automated detection, localization, and classification of traumatic vertebral body fractures in the thoracic and lumbar spine at CT. Radiology 278:64–73PubMedCrossRefGoogle Scholar
  20. Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26:1124–1137PubMedCrossRefGoogle Scholar
  21. Bresnihan B, Newmark R, Robbins S, Genant HK (2004) Effects of anakinra monotherapy on joint damage in patients with rheumatoid arthritis. Extension of a 24-week randomized, placebo-controlled trial. J Rheumatol 31:1103–1111PubMedGoogle Scholar
  22. Bretschi M, Franzle A, Merz M, Hillengass J, Semmler W, Bendl R, Bauerle T (2014) Acad Radiol 21:1177–1118PubMedCrossRefGoogle Scholar
  23. Brinkley JF (1993) A flexible, generic model for anatomic shape: application to interactive two-dimensional medical image segmentation and matching. Comput Biomed Res 26:121–142PubMedCrossRefGoogle Scholar
  24. Brunet M (2005) Female athlete triad. Clin Sports Med 24:623–636PubMedCrossRefGoogle Scholar
  25. Bruynesteyn K, van der Heijde D, Boers M, van der Linden S, Lassere M, van der Vleuten C (2004) The Sharp/van der Heijde method out-performed the Larsen/Scott method on the individual patient level in assessing radiographs in early rheumatoid arthritis. J Clin Epidemiol 57:502–512PubMedCrossRefGoogle Scholar
  26. Buck TA, Ehricke HH, Strasser W, Thurfjel L (1995) 3D segmentation of medical structures by integration of ray-casting with anatomic knowledge. Comput Graph 19:441–449CrossRefGoogle Scholar
  27. Cann CE, Adams JE, Brown JK, Brett AD (2014) CTXA hip--an extension of classical DXA measurements using quantitative CT. PLoS One 17:e91904CrossRefGoogle Scholar
  28. Caselles V, Catté F, Coll B, Dibos F (1993) A geometric model for active contours in image processing. Numer Math 66:1–1CrossRefGoogle Scholar
  29. Chang G, Xia D, Sherman O, Strauss E, Jazrawi L, Recht MP, Regatte RR (2013) High resolution morphologic imaging and T2 mapping of cartilage at 7 Tesla: comparison of cartilage repair patients and healthy controls. MAGMA 26:539–548PubMedPubMedCentralCrossRefGoogle Scholar
  30. Conn KS, Clarke MT, Hallett JP (2002) A simple guide to determine the magnification of radiographs and to improve the accuracy of preoperative templating. J Bone Joint Surg Br 84:269–272PubMedCrossRefGoogle Scholar
  31. Conrozier T, Favret H, Mathieu P et al (2004) Influence of the quality of tibial plateau lignment on the reproducibility of computer joint space measurement from Lyon schuss radiographic views of the knee in patients with knee osteoarthritis. Osteoarthritis Cartilage 12:765–770PubMedCrossRefPubMedCentralGoogle Scholar
  32. Cody DD, Gross GJ, Hou FJ, Spencer HJ, Goldstein SA, Fyhrie DP (1999) Femoral strength is better predicted by finite element models than QCT and DXA. J Biomech 32:1013–1020PubMedCrossRefGoogle Scholar
  33. Cootes T, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Image Process 23:681–685Google Scholar
  34. Dacre JE, Huskisson EC (1989) The automatic assessment of knee radiographs in osteoarthritis using digital image analysis. Br J Rheumatol 28:506–510PubMedCrossRefGoogle Scholar
  35. Dall’Ara E, Pahr D, Varga P, Kainberger F, Zysset P (2012) QCT-based finite element models predict human vertebral strength in vitro significantly better than simulated DEXA. Osteoporos Int 23:563–572PubMedCrossRefPubMedCentralGoogle Scholar
  36. Donner R, Reiter M, Langs G, Peloschek P, Bischof H (2006) Fast active appearance model search using canonical correlation analysis. IEEE Trans Pattern Anal Mach Intell 28:1690–1694PubMedCrossRefGoogle Scholar
  37. Duryea J, Zaim S, Wolfe F (2002) Neural network based automated algorithm to identify joint locations on hand/wrist radiographs for arthritis assessment. Med Phys 29:403–411PubMedCrossRefPubMedCentralGoogle Scholar
  38. Duryea J, Neumann G, Brem MH et al (2007) Novel fast semi-automated software to segment cartilage for knee MR acquisitions. Osteoarthritis Cartilage 15:487–492PubMedCrossRefGoogle Scholar
  39. Elmasria K, Hicksa Y, Yanga X, Sunb X, Pettitc R, Evans W (2016) Automatic detection and quantification of abdominal aortic calcification in dual energy X-ray absorptiometry. Procedia Comput Sci 96:1011–1021CrossRefGoogle Scholar
  40. Engelke K, Stampa B, Steiger P, Fuerst T, Genant HK (2010) quantitative morphometry on spinal x-rays: initial evaluation of a new workflow tool for measuring vertebral body height in fractured vertebrae. J Bone Miner Res 25(Suppl 1)Google Scholar
  41. England JR, Colletti PM (2018) Automated reporting of DXA studies using a custom-built computer program. Clin Nucl Med 43:474–475PubMedPubMedCentralGoogle Scholar
  42. Falcao A, Udupa J, Samarasekra S, Sharma S, Hirsch B, Lotufo R (1998) User-steered image segmentation paradigms—live wire and live lane. Graph Models Image Process 60:233–260CrossRefGoogle Scholar
  43. Falcao AX, Udupa JK, Miyazawa FK (2000) An ultra-fast user-steered image segmentation paradigm: live wire on the fly. IEEE Trans Med Imaging 19:55–62PubMedCrossRefPubMedCentralGoogle Scholar
  44. Feldkamp LA, Goldstein SA, Parfitt AM, Jesion G, Kleerekoper M (1989) The direct examination of three-dimensional bone architecture in vitro by computed tomography. J Bone Miner Res 4:3–11PubMedCrossRefPubMedCentralGoogle Scholar
  45. Fenerty KE, Patronas NJ, Heery CR, Gulley JL, Folio LR (2016) Resources required for semi-automatic volumetric measurements in metastatic chordoma: is potentially improved tumor burden assessment worth the time burden? J Digit Imaging 29:357–364PubMedCrossRefPubMedCentralGoogle Scholar
  46. Ferrar L, Jiang G, Adams J, Eastell R (2005) Identification of vertebral fractures: an update. Osteoporos Int 16:717–728PubMedCrossRefPubMedCentralGoogle Scholar
  47. Figueiredo CP, Kleyer A, Simon D, Stemmler F, d’Oliveira I, Weissenfels A, Museyko O, Friedberger A, Hueber AJ, Haschka J, Englbrecht M, Pereira RMR, Rech J, Schett G, Engelke K (2018) Methods for segmentation of rheumatoid arthritis bone erosions in high-resolution peripheral quantitative computed tomography (HR-pQCT). Semin Arthritis Rheum 47:611–618PubMedCrossRefPubMedCentralGoogle Scholar
  48. Fischer L, Valentinitsch A, DiFranco MD, Schueller-Weidekamm C, Kienzl D, Resch H, Gross T, Weber M, Jaksch P, Klepetko W, Zweytick B, Pietschmann P, Kainberger F, Langs G, Patsch JM (2015) High-resolution peripheral quantitative CT imaging: cortical porosity, poor trabecular bone microarchitecture, and low bone strength in lung transplant recipients. Radiology 274:473–481PubMedCrossRefPubMedCentralGoogle Scholar
  49. Fouque-Aubert A, Boutroy S, Marotte H, Vilayphiou N, Bacchetta J, Miossec P, Delmas PD, Chapurlat RD (2010) Assessment of hand bone loss in rheumatoid arthritis by high-resolution peripheral quantitative CT. Ann Rheum Dis 69:1671–1676PubMedCrossRefPubMedCentralGoogle Scholar
  50. Gallagher D, Thornton JC, He Q, Wang J, Yu W, Bradstreet TE, Burke J, Heymsfield SB, Rivas VM, Kaufman R (2010) Quantitative magnetic resonance fat measurements in humans correlate with established methods but are biased. Obesity (Silver Spring) 18(10):2047–2054CrossRefGoogle Scholar
  51. Gefen S, Tretiak OJ, Piccoli CW et al (2003) ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis. IEEE Trans Med Imaging 22:170–177PubMedCrossRefPubMedCentralGoogle Scholar
  52. Genant HK (1997) Assessment of vertebral fractures in osteoporosis research. J Rheumatol 24:1212–1214PubMedPubMedCentralGoogle Scholar
  53. Gent YY et al (2015) Subclinical synovitis detected by macrophage PET, but not MRI, is related to short-term flare of clinical disease activity in early RA patients: an exploratory study. Arthritis Res Ther 17:266PubMedPubMedCentralCrossRefGoogle Scholar
  54. Glinkowski WM, Narloch J (2017) CT-scout based, semi-automated vertebral morphometry after digital image enhancement. Eur J Radiol 94:195–200PubMedCrossRefPubMedCentralGoogle Scholar
  55. Gnudi S, Malavolta N, Testi D, Viceconti M (2004) Differences in proximal femur geometry distinguish vertebral from femoral neck fractures in osteoporotic women. Br J Radiol 77:219–223PubMedCrossRefPubMedCentralGoogle Scholar
  56. Guglielmi G, Bazzocchi A (2017) Bone mineral densitometry pitfalls. In: Peh W (ed) Pitfalls in musculoskeletal radiology. Springer, New York, pp 893–924CrossRefGoogle Scholar
  57. Haddad FS, Garbuz DS, Duncan CP (2001) Osteotomies around the hip: radiographic planning and postoperative evaluation. Instr Course Lect 50:253–261PubMedPubMedCentralGoogle Scholar
  58. Hadler NM, Gillings DB, Imbus HR et al (1978) Hand structure and function in an industrial setting. Arthritis Rheum 21:210–220PubMedCrossRefPubMedCentralGoogle Scholar
  59. Hefti F (1995) Spherical assessment of the hip on standard AP radiographs: a simple method for the measurement of the contact area between acetabulum and femoral head and of acetabular orientation. J Pediatr Orthop 15:797–805PubMedCrossRefPubMedCentralGoogle Scholar
  60. Herold CJ, Lewin JS, Wibmer AG, Thrall JH, Krestin GP, Dixon AK, Schoenberg SO, Geckle RJ, Muellner A, Hricak H (2016) Imaging in the Age of Precision Medicine: Summary of the Proceedings of the 10th Biannual Symposium of the International Society for Strategic Studies in Radiology. Radiology 279:226–238PubMedCrossRefPubMedCentralGoogle Scholar
  61. Herring JL, Dawant BM (2001) Automatic lumbar vertebral identification using surface-based registration. J Biomed Inform 34:74–84PubMedCrossRefPubMedCentralGoogle Scholar
  62. Hinshaw KP, Altman RB, Brinkley JF (1995) Shape-based models for interactive segmentation of medical images. In: SPIE Conf. on Medical Imaging: Bellingham, WA, pp 771–780Google Scholar
  63. Hong W, Georgescu B, Zhou XS, Krishnan S, Ma Y, Comaniciu D (2006) Database-guided simultaneous multi-slice 3D segmentation for volumetric data. In: ECCV 2006. Springer, Berlin, pp 397–409Google Scholar
  64. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510PubMedPubMedCentralCrossRefGoogle Scholar
  65. International Society of Clinical Densitometry (2015) Official Positions 2015 Adult & Pediatric. ISCD, MiddletownGoogle Scholar
  66. Iv M, Patel MR, Santos A, Kang YS (2011) Informatics in radiology: use of a macro scripting editor to facilitate transfer of dual-energy X-ray absorptiometry reports into an existing departmental voice recognition dictation system. Radiographics 31:1181–1189PubMedCrossRefPubMedCentralGoogle Scholar
  67. Jiang G, Eastell R, Barrington NA, Ferrar L (2004) Comparison of methods for the visual identification of prevalent vertebral fracture in osteoporosis. Osteoporos Int 15:887–896PubMedCrossRefPubMedCentralGoogle Scholar
  68. Johannesdottir F, Allaire B, Bouxsein ML (2018) Fracture prediction by computed tomography and finite element analysis: current and future perspectives. Curr Osteoporos Rep 16:411–422PubMedCrossRefPubMedCentralGoogle Scholar
  69. Joseph GB, Baum T, Carballido-Gamio J, Nardo L, Virayavanich W, Alizai H, Lynch JA, McCulloch CE, Majumdar S, Link TM (2011) Texture analysis of cartilage T2 maps: individuals with risk factors for OA have higher and more heterogeneous knee cartilage MR T2 compared to normal controls—data from the osteoarthritis initiative. Arthritis Res Ther 13:R153PubMedPubMedCentralCrossRefGoogle Scholar
  70. Kass M, Witkin A, Terzopoulos D (1987) Snakes: active contour models. Int J Comput Vision 1:321–331CrossRefGoogle Scholar
  71. Keyak JH, Rossi SA, Jones KA, Les CM, Skinner HB (2001) Prediction of fracture location in the proximal femur using finite element models. Med Eng Phys 23:657–664PubMedCrossRefPubMedCentralGoogle Scholar
  72. Keystone EC, Kavanaugh AF, Sharp JT et al (2004) Radiographic, clinical, and functional outcomes of treatment with adalimumab (a human anti-tumor necrosis factor monoclonal antibody) in patients with active rheumatoid arthritis receiving concomitant methotrexate therapy: a randomized, placebo-controlled, 52-week trial. Arthritis Rheum 50:1400–1411PubMedCrossRefPubMedCentralGoogle Scholar
  73. Khoo BCC, Brown JK, Cann CE, Zhu K, Henzell S et al (2009) Comparison of QCT-derived and DXA-derived areal bone mineral density and T scores. Osteoporos Int 20:1539–1545PubMedCrossRefPubMedCentralGoogle Scholar
  74. Kubassova O (2007) Automatic segmentation of blood vessels from dynamic MRI datasets. Med Image Comput Comput Assist Interv 10:593–600PubMedPubMedCentralGoogle Scholar
  75. Krackow KA, Pepe CL, Galloway EJ (1990) A mathematical analysis of the effect of flexion and rotation on apparent varus/valgus alignment at the knee. Orthopedics 13:861–868PubMedCrossRefPubMedCentralGoogle Scholar
  76. Krug KB, Weber C, Schwabe H, Sinzig NM, Wein B, Müller D, Wegmann K, Peters S, Sendler V, Ewen K, Hellmich M, Maintz D (2014) Comparison of image quality using a X-ray stereotactical whole-body system and a direct flat-panel X-ray device in examinations of the pelvis and knee. Rofo 186:67–76PubMedPubMedCentralGoogle Scholar
  77. Langs G, Peloschek P, Bischof H (2005) Optimal sub-shape models by minimum description length. CVPR 2005: 310–315Google Scholar
  78. Langs G, Peloschek P, Bischof H, Kainberger F (2006) Automatic detection of erosions in rheumatoid arthritis assessment. In: MICCAI Joint Disease Workshop, pp 33–40Google Scholar
  79. Langs G, Röhrich S, Hofmanninger J, Prayer F, Pan J, Herold C, Prosch H (2018) Machine learning: from radiomics to discovery and routine. Radiologe Epub ahead of printGoogle Scholar
  80. Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP (2018) Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 287:313–322PubMedCrossRefPubMedCentralGoogle Scholar
  81. Leslie WD, Lix LM, Morin SN, Johansson H, Odén A, McCloskey EV, Kanis JA (2016) Adjusting hip fracture probability in men and women using hip axis length: the Manitoba Bone Density Database. Clin Densitom 19:326–331CrossRefGoogle Scholar
  82. Leventon M, Grimson W, Faugeras O (2000) Statistical shape influence in Geodesic active contours. In: Proceedings of IEEE CVPR00, pp 316–322Google Scholar
  83. Lewiecki EM, Binkley N, Morgan SL, Shuhart CR, Camargos BM, Carey JJ, Gordon CM, Jankowski LG, Lee JK, Leslie WD, International Society for Clinical Densitometry (2016) Best practices for dual-energy X-ray absorptiometry measurement and reporting: International Society for Clinical Densitometry Guidance. J Clin Densitom 19(2):127–140PubMedCrossRefGoogle Scholar
  84. Li W, Kezele I, Collins DL, Zijdenbos A, Keyak J, Kornak J, Koyama A, Saeed I, Leblanc A, Harris T, Lu Y, Lang T (2007) Voxel-based modeling and quantification of the proximal femur using inter-subject registration of quantitative CT images. Bone 41:888–895PubMedPubMedCentralCrossRefGoogle Scholar
  85. Li X, Pai A, Blumenkrantz G, Carballido-Gamio J, Link T, Ma B, Ries M, Majumdar S (2009) Spatial distribution and relationship of T1rho and T2 relaxation times in knee cartilage with osteoarthritis. Magn Reson Med 61:1310–1318PubMedPubMedCentralCrossRefGoogle Scholar
  86. Liacouras PC, Sahajwalla D, Beachler MD, Sleeman T, Ho VB, Lichtenberger JP (2017) Using computed tomography and 3D printing to construct custom prosthetics attachments and devices. 3D Print Med 3:3–7CrossRefGoogle Scholar
  87. Liu F, Zhou Z, Samsonov A, Blankenbaker D, Larison W, Kanarek A, Lian K, Kambhampati S, Kijowski R (2018) Deep learning approach for evaluating knee mr images: achieving high diagnostic performance for cartilage lesion detection. Radiology Epub ahead of printGoogle Scholar
  88. Lodwick GS, Haun CL, Smith WE, Keller RF, Robertson ED (1963) Computer diagnosis of primary bone tumors: a preliminary report. Radiology 80:273–275CrossRefGoogle Scholar
  89. Maas M, Kuijper M, Akkerman EM (2011) From Gaucher’s disease to metabolic radiology: translational radiological research and clinical practice. Semin Musculoskelet Radiol 15:301–316CrossRefGoogle Scholar
  90. Müller-Gerbl M (1998) The subchondral bone plate. Adv Anat Embryol Cell Biol, 141:III–XI, 1–134Google Scholar
  91. Mayerhoefer ME, Breitenseher M, Hofmann S et al (2004) Computer-assisted quantitative analysis of bone marrow edema of the knee: initial experience with a new method. AJR Am J Roentgenol 182:1399–1403PubMedCrossRefGoogle Scholar
  92. Mayerhoefer ME, Breitenseher MJ, Kramer J, Aigner N, Norden C, Hofmann S (2005) STIR vs. T1-weighted fat-suppressed gadolinium-enhanced MRI of bone marrow edema of the knee: computer-assisted quantitative comparison and influence of injected contrast media volume and acquisition parameters. J Magn Reson Imaging 22:788–793PubMedCrossRefGoogle Scholar
  93. Mazzuca SA, Brandt KD, Buckwalter KA (2003) Detection of radiographic joint space narrowing in subjects with knee osteoarthritis: longitudinal comparison of the metatarsophalangeal and semiflexed anteroposterior views. Arthritis Rheum 48:385–390CrossRefGoogle Scholar
  94. Mazzuca SA, Brandt KD, Buckwalter KA, Lequesne M (2004) Pitfalls in the accurate measurement of joint space narrowing in semiflexed, anteroposterior radiographic imaging of the knee. Arthritis Rheum 50:2508–2515PubMedCrossRefGoogle Scholar
  95. Millington SA, Li B, Tang J et al (2007) Quantitative and topographical evaluation of ankle articular cartilage using high resolution MRI. J Orthop Res 25:143–151PubMedCrossRefGoogle Scholar
  96. Nakashima D, Kanchiku T, Nishida N, Ito S, Ohgi J, Suzuki H, Imajo Y, Funaba M, Chen X, Taguchi T (2018) Finite element analysis of compression fractures at the thoracolumbar junction using models constructed from medical images. Exp Ther Med 15:3225–3230PubMedPubMedCentralGoogle Scholar
  97. Narayan N, Owen DR, Taylor PC (2017) Advances in positron emission tomography for the imaging of rheumatoid arthritis. Rheumatology (Oxford) 56:1837–1846CrossRefGoogle Scholar
  98. Neumann A, Lorenz C (1999) Statistical shape model based segmentation of medical images. Comput Med Imaging Graph 22:133–143CrossRefGoogle Scholar
  99. Niethard FU (1999) Computer-assisted orthopedic surgery (CAOS) in hip joint prosthetics. Z Orthop Ihre Grenzgeb 137:99–100PubMedCrossRefGoogle Scholar
  100. Norman B, Pedoia V, Majumdar S (2018) Use of 2D U-net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry. Radiology 288:177–185PubMedPubMedCentralCrossRefGoogle Scholar
  101. Odenbring S, Berggren AM, Peil L (1993) Roentgenographic assessment of the hip-knee-ankle axis in medial gonarthrosis. A study of reproducibility. Clin Orthop Relat Res:195–196Google Scholar
  102. Okino T, Kamishima T, Lee Sutherland K, Fukae J, Narita A, Ichikawa S, Tanimura K (2018) Radiographic temporal subtraction analysis can detect finger joint space narrowing progression in rheumatoid arthritis with clinical low disease activity. Acta Radiol 59:460–467PubMedCrossRefGoogle Scholar
  103. Pache G et al (2010) Dual-energy CT virtual noncalcium technique: detecting posttraumatic bone marrow lesions–feasibility study. Radiology 256:617–624PubMedCrossRefGoogle Scholar
  104. Patsch JM, Burghardt AJ, Kazakia G, Majumdar S (2011) Noninvasive imaging of bone microarchitecture. Ann N Y Acad Sci 1240:77–87PubMedPubMedCentralCrossRefGoogle Scholar
  105. Patsch JM, Zulliger MA, Vilayphou N, Samelson EJ, Cejka D, Diarra D, Berzaczy G, Burghardt AJ, Link TM, Weber M, Loewe C (2014) Quantification of lower leg arterial calcifications by high-resolution peripheral quantitative computed tomography. Bone 58:42–47PubMedCrossRefGoogle Scholar
  106. Paul R et al (2016) Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography 2:388–395PubMedPubMedCentralCrossRefGoogle Scholar
  107. Peloschek P, Langs G, Weber M, Sailer J, Reisegger M, Imhof H, Bischof H, Kainberger F (2007) An automatic model-based system for joint space measurements on hand radiographs: initial experience. Radiology 245:855–862PubMedCrossRefGoogle Scholar
  108. Ploder O, Wagner A, Enislidis G, Ewers R (1995) Computer-assisted intraoperative visualization of dental implants. Augmented reality in medicine. Radiologe 35:569–572PubMedGoogle Scholar
  109. Rea JA, Chen MB, Li J et al (2001) Vertebral morphometry: a comparison of long-term precision of morphometric X-ray absorptiometry and morphometric radiography in normal and osteoporotic subjects. Osteoporos Int 12:158–166PubMedCrossRefGoogle Scholar
  110. Reiser MF, Bongartz GP, Erlemann R, Schneider M, Pauly T, Sittek H, Peters PE (1989) Gadolinium-DTPA in rheumatoid arthritis and related diseases: first results with dynamic magnetic resonance imaging. Skeletal Radiol 18:591–597PubMedCrossRefGoogle Scholar
  111. Ringl H, Schernthaner RE, Schueller G, Balassy C, Kienzl D, Botosaneanu A, Weber M, Czerny C, Hajdu S, Mang T, Herold CJ, Schima W (2010) The skull unfolded: a cranial CT visualization algorithm for fast and easy detection of skull fractures. Radiology 255:553–562PubMedCrossRefGoogle Scholar
  112. Ringl H, Stiassny F, Schima W, Toepker M, Czerny C, Schueller G, Asenbaum U, Furtner J, Hajdu S, Serles W, Weber M, Herold CJ (2013) Intracranial hematomas at a glance: Advanced Visualization for Fast and Easy Detection. Radiology 267:522–530PubMedCrossRefPubMedCentralGoogle Scholar
  113. Ringl H, Lazar M, Töpker M, Woitek R, Prosch H, Asenbaum U, Balassy C, Toth D, Weber M, Hajdu S, Soza G, Wimmer A, Mang T (2015) The ribs unfolded—a CT visualization algorithm for fast detection of rib fractures: effect on sensitivity and specificity in trauma patients. Eur Radiol 25:1865–1874PubMedCrossRefPubMedCentralGoogle Scholar
  114. Roberts M, Cootes TF, Adams JE (2006) Vertebral morphometry: semiautomatic determination of detailed shape from dual-energy X-ray absorptiometry images using active appearance models. Invest Radiol 41:849–859PubMedCrossRefPubMedCentralGoogle Scholar
  115. Rubin GD, Lyo JK, Paik DS et al (2005) Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection. Radiology 234:274–283PubMedCrossRefPubMedCentralGoogle Scholar
  116. Rügsegger P, Koller B, Müller R (1996) A microtomographic system for the nondestructive evaluation of bone architecture. Calcif Tissue Int 58:24–29CrossRefGoogle Scholar
  117. Sailer J, Scharitzer M, Peloschek P, Giurea A, Imhof H, Grampp S (2005) Quantification of axial alignment of the lower extremity on conventional and digital total leg radiographs. Eur Radiol 15:170–173PubMedCrossRefPubMedCentralGoogle Scholar
  118. Castro FJ, Pollo C, Meuli R et al (2006) A cross validation study of deep brain stimulation targeting: from experts to atlas-based, segmentation-based and automatic registration algorithms. IEEE Trans Med Imaging 25:1440–1450PubMedCrossRefPubMedCentralGoogle Scholar
  119. Sharp JT (2004) Measurement of structural abnormalities in arthritis using radiographic images. Radiol Clin North Am 42:109–119PubMedCrossRefPubMedCentralGoogle Scholar
  120. Sharp JT, Gardner JC, Bennett EM (2000) Computer-based methods for measuring joint space and estimating erosion volume in the fi and wrist joints of patients with rheumatoid arthritis. Arthritis Rheum 43:1378–1386PubMedCrossRefPubMedCentralGoogle Scholar
  121. Shearman CM, Brandser EA, Kathol MH, Clark WA, Callaghan JJ (1998) An easy linear estimation of the mechanical axis on long-leg radiographs. AJR Am J Roentgenol 170:1220–1222PubMedCrossRefPubMedCentralGoogle Scholar
  122. Shen W, Wang Z, Tang H, Heshka S, Punyanitya M, Zhu S, Lei J, Heymsfield SB (2003) Volume estimates by imaging methods: model comparisons with visible woman as the reference. Obes Res 11:217–225PubMedPubMedCentralCrossRefGoogle Scholar
  123. Shepherd JA, Ng BK, Sommer MJ, Heymsfield SB (2017) Body composition by DXA. Bone 104:101–105PubMedPubMedCentralCrossRefGoogle Scholar
  124. Siminoski K, Leslie WD, Frame H, Hodsman A, Josse RG, Khan A, Lentle BC, Lévesque J, Lyons DJ, Tarulli G, Brown JP, Canadian Association of Radiologists (2005) Recommendations for bone mineral density reporting in Canada. Can Assoc Radiol J 56:178–188PubMedGoogle Scholar
  125. Soo MS, Rosen EL, Xia JQ, Ghate S, Baker JA (2005) Computer-aided detection of amorphous calcifications. Am J Roentgenol 184:887–892CrossRefGoogle Scholar
  126. Stegmann MB, Ersboll BK, Larsen R (2003) FAME—a flexible appearance modeling environment. IEEE Trans Med Imaging 22:1319–1331PubMedCrossRefGoogle Scholar
  127. Tajmir SH, Lee H, Shailam R, Gale HI, Nguyen JC, Westra SJ, Lim R, Yune S, Gee MS, Do S (2018) Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiol Epub ahead of printGoogle Scholar
  128. Tan WK, Hassanpour S, Heagerty PJ, Rundell SD, Suri P, Huhdanpaa HT, James K, Carrell DS, Langlotz CP, Organ NL, Meier EN, Sherman KJ, Kallmes DF, Luetmer PH, Griffith B, Nerenz DR, Jarvik JG (2018) Comparison of natural language processing rules-based and machine-learning systems to identify lumbar spine imaging findings related to low back pain. Acad Radiol Epub ahead of printGoogle Scholar
  129. Tetsworth K, Paley D (1994) Malalignment and degenerative arthropathy. Orthop Clin North Am 25:367–377PubMedPubMedCentralGoogle Scholar
  130. Thompson PM, Woods RP, Mega MS, Toga AW (2000) Mathematical/computational challenges in creating deformable and probabilistic atlases of the human brain. Hum Brain Mapp 9:81–92PubMedCrossRefGoogle Scholar
  131. Trattnig S, Zbýň Š, Schmitt B, Friedrich K, Juras V, Szomolanyi P, Bogner W (2012) Advanced MR methods at ultra-high field (7 Tesla) for clinical musculoskeletal applications. Eur Radiol 22:2338–2346PubMedCrossRefGoogle Scholar
  132. Tsai IT, Tsai MY, Wu MT, Chen CK (2016) Development of an automated bone mineral density software application: facilitation radiologic reporting and improvement of accuracy. J Digit Imaging 29:380–387PubMedCrossRefGoogle Scholar
  133. Valentinitsch A, Patsch JM, Burghardt AJ, Link TM, Majumdar S, Fischer L, Schueller-Weidekamm C, Resch H, Kainberger F, Langs G (2013) Computational identification and quantification of trabecular microarchitecture classes by 3-D texture analysis-based clustering. Bone 54:133–140PubMedCrossRefPubMedCentralGoogle Scholar
  134. van de Giessen M, Foumani M, Vos FM, Strackee SD, Maas M, Van Vliet LJ, Grimbergen CA, Streekstra GJ (2012) A 4D statistical model of wrist bone motion patterns. IEEE Trans Med Imaging 31:613–625PubMedCrossRefPubMedCentralGoogle Scholar
  135. van der Heijde DM (2000) Radiographic imaging: the ‘gold standard’ for assessment of disease progression in rheumatoid arthritis. Rheumatology (Oxford) 39(Suppl 1):9–16CrossRefGoogle Scholar
  136. van Rietbergen B, Ito K (2015) A survey of micro-finite element analysis for clinical assessment of bone strength: the first decade. J Biomech 48:832–841PubMedCrossRefPubMedCentralGoogle Scholar
  137. Vehkomäki T, Gerig G, Székely G (1997) A user-guided tool for efficient segmentation of medical data. In: Troccaz J, Grimson E (eds) Virtual reality and robotics in medicine and medical robotics and computer-assisted surgery (CVRMed-MRCAS’97). Springer, Berlin, pp 685–694Google Scholar
  138. Wachsmann J, Blain K, Thompson M, Cherian S, Oz OK, Browning T (2018) Electronic medical record integration for streamlined DXA reporting. J Digit Imaging 31:159–166PubMedCrossRefPubMedCentralGoogle Scholar
  139. Wade R, Yang H, McKenna C, Faria R, Gummerson N, Woolacott N (2013) A systematic review of the clinical effectiveness of EOS 2D/3D X-ray imaging system. Eur Spine J. 22(2):296–304PubMedCrossRefPubMedCentralGoogle Scholar
  140. Wahner HW, Steiger P, von Stetten E (1994) Instruments and measurement techniques. In: Wahner HW, Fogelman I (eds) The evaluation of osteoporosis: dual energy X-ray absorptiometry in clinical practice. Martin Dunitz, London, pp 14–34Google Scholar
  141. Wagner DR (2013) Ultrasound as a tool to assess body fat. J Obes 280713Google Scholar
  142. Weber GW, Bookstein FL, Strait DS (2011) Virtual anthropology meets biomechanics. J Biomech 17:1429–1432CrossRefGoogle Scholar
  143. Welsch GH, Mamisch TC, Hughes T, Zilkens C, Quirbach S, Scheffler K, Kraff O, Schweitzer ME, Szomolanyi P, Trattnig S (2008) In vivo biochemical 7.0 Tesla magnetic resonance: preliminary results of dGEMRIC, zonal T2, and T2* mapping of articular cartilage. Invest Radiol 43(9):619–626PubMedCrossRefPubMedCentralGoogle Scholar
  144. Windhager R, Karner J, Kutschera HP, Polterauer P, Salzer-Kuntschik M, Kotz R (1996) Limb salvage in periacetabular sarcomas: review of 21 consecutive cases. Clin Orthop Relat Res 31:265–276CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Philipp Peloschek
    • 1
  • Georg Langs
    • 2
  • Reinhard Windhager
    • 3
  • Franz Kainberger
    • 2
    Email author
  1. 1.Radiology Center ViennaViennaAustria
  2. 2.CIR—Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided TherapyMedical University of ViennaViennaAustria
  3. 3.Department of Orthopaedic & Trauma SurgeryMedical University of ViennaViennaAustria

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