Download PDF
Semin Musculoskelet Radiol 2024; 28(05): 557-559
DOI: 10.1055/s-0044-1788694
Review Article

Update on Quantitative Computed Tomography

Xiaoguang Cheng 
1   Department of Radiology, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
,
Ling Wang
1   Department of Radiology, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
,
Glen M. Blake
2   School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom
,
Giuseppe Guglielmi
3   Department of Clinical and Experimental Medicine Foggia University School of Medicine, Foggia, Italy
› Author Affiliations Funding Source Beijing Municipal Health Commission, BJRITO-RDP-2024 (http://dx.doi.org/10.13039/501100005088).
› Further Information
Also available at
    Permissions and Reprints

Abstract

Quantitative computed tomography (QCT) has important technical advantages for the measurement of bone mineral density, and the technique is well suited for both the diagnosis of osteoporosis and the monitoring of treatment. Its use deserves a wider application than at present. The use of QCT in both research and in the clinic has recently garnered increasing attention. In this review, we update the advances and application of QCT in the study of osteoporosis.

Keywords

osteoporosis - diagnosis - fracture risk - quantitative computed tomography


Publication History

Article published online:
15 October 2024

© 2024. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 

  • References

  • 1 Cann CE, Genant HK. Precise measurement of vertebral mineral content using computed tomography. J Comput Assist Tomogr 1980; 4 (04) 493-500
    CrossrefPubMedSearch in Google Scholar
  • 2 Faulkner KG, Glüer CC, Grampp S, Genant HK. Cross-calibration of liquid and solid QCT calibration standards: corrections to the UCSF normative data. Osteoporos Int 1993; 3 (01) 36-42
    CrossrefPubMedSearch in Google Scholar
  • 3 Cann CE, Genant HK, Ettinger B, Gordan GS. Spinal mineral loss in oophorectomized women. Determination by quantitative computed tomography. JAMA 1980; 244 (18) 2056-2059
    CrossrefPubMedSearch in Google Scholar
  • 4 Mazess RB, Barden HS. Measurement of bone by dual-photon absorptiometry (DPA) and dual-energy X-ray absorptiometry (DEXA). Ann Chir Gynaecol 1988; 77 (5–6): 197-203
    PubMedSearch in Google Scholar
  • 5 Cheng X, Yuan H, Cheng J. et al; Bone and Joint Group of Chinese Society of Radiology, Chinese Medical Association (CMA), Musculoskeletal Radiology Society of Chinese Medical Doctors Association, Osteoporosis Group of Chinese Orthopedic Association, Bone Density Group of Chinese Society of Imaging Technology, CMA*. Chinese expert consensus on the diagnosis of osteoporosis by imaging and bone mineral density. Quant Imaging Med Surg 2020; 10 (10) 2066-2077
    CrossrefPubMedSearch in Google Scholar
  • 6 Booz C. State-of-the-art research: current developments in CT imaging. Diagnostics (Basel) 2023; 13 (13) 2305
    CrossrefPubMedSearch in Google Scholar
  • 7 Zhang M, Qi W, Sun Y, Jiang Y, Liu X, Hong N. Screening for lung cancer using sub-millisievert chest CT with iterative reconstruction algorithm: image quality and nodule detectability. Br J Radiol 2018; 91 (1090): 20170658
    CrossrefPubMedSearch in Google Scholar
  • 8 Jiang B, Li N, Shi X. et al. Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT. Radiology 2022; 303 (01) 202-212
    CrossrefPubMedSearch in Google Scholar
  • 9 Bousse A, Kandarpa VSS, Rit S. et al. Systematic review on learning-based spectral CT. IEEE Trans Radiat Plasma Med Sci 2024; 8 (02) 113-137
    CrossrefPubMedSearch in Google Scholar
  • 10 Bhattarai A, Tanaka R, Yeung AWK, Vardhanabhuti V, Photon-Counting CT. Photon-counting CT material decomposition in bone imaging. J Imaging 2023; 9 (10) 209
    CrossrefPubMedSearch in Google Scholar
  • 11 Brown JK, Timm W, Bodeen G. et al. Asynchronously calibrated quantitative bone densitometry. J Clin Densitom 2017; 20 (02) 216-225
    CrossrefPubMedSearch in Google Scholar
  • 12 Wang L, Su Y, Wang Q. et al. Validation of asynchronous quantitative bone densitometry of the spine: accuracy, short-term reproducibility, and a comparison with conventional quantitative computed tomography. Sci Rep 2017; 7 (01) 6284
    CrossrefPubMedSearch in Google Scholar
  • 13 Ziemlewicz TJ, Maciejewski A, Binkley N. et al Direct comparison of unenhanced and contrast-enhanced CT for opportunistic proximal femur bone mineral density measurement: implications for osteoporosis screening. AJR Am J Roentgenol 2016; 206 (04) 694-698
    CrossrefPubMedSearch in Google Scholar
  • 14 Xu L, Duanmu Y, Blake GM. et al. Validation of goose liver fat measurement by QCT and CSE-MRI with biochemical extraction and pathology as reference. Eur Radiol 2018; 28 (05) 2003-2012
    CrossrefPubMedSearch in Google Scholar
  • 15 Guo Z, Blake GM, Li K. et al. Liver fat content measurement with quantitative CT validated against MRI proton density fat fraction: a prospective study of 400 healthy volunteers. Radiology 2020; 294 (01) 89-97 Published correction appears in Radiology 2024;310(3):e249010
    CrossrefPubMedSearch in Google Scholar
  • 16 Guo Z, Blake GM, Graffy PM. et al. Hepatic steatosis: CT-based prevalence in adults in China and the United States and associations with age, sex, and body mass index. AJR Am J Roentgenol 2022; 218 (05) 846-857
    CrossrefPubMedSearch in Google Scholar
  • 17 Cheng X, Zhang Y, Wang C. et al. The optimal anatomic site for a single slice to estimate the total volume of visceral adipose tissue by using the quantitative computed tomography (QCT) in Chinese population. Eur J Clin Nutr 2018; 72 (11) 1567-1575
    CrossrefPubMedSearch in Google Scholar
  • 18 Zeng Q, Wang L, Dong S. et al; China Health Big Data (China Biobank) project investigators. CT-derived abdominal adiposity: distributions and better predictive ability than BMI in a nationwide study of 59,429 adults in China. Metabolism 2021; 115: 154456
    CrossrefPubMedSearch in Google Scholar
  • 19 Wu Y, Guo Z, Fu X. et al. The study protocol for the China Health Big Data (China Biobank) project. Quant Imaging Med Surg 2019; 9 (06) 1095-1102
    CrossrefPubMedSearch in Google Scholar
  • 20 Gregson CL, Armstrong DJ, Bowden J. et al. UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos 2022; 17 (01) 58 Published correction appears in Arch Osteoporos 2022;17(1):80
    CrossrefPubMedSearch in Google Scholar
  • 21 Kanis JA, Cooper C, Rizzoli R, Reginster JY. Scientific Advisory Board of the European Society for Clinical and Economic Aspects of Osteoporosis (ESCEO) and the Committees of Scientific Advisors and National Societies of the International Osteoporosis Foundation (IOF). European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int 2019; 30 (01) 3-44 Published corrections appear in Osteoporos Int 2020;31(1):209, and Osteoporos Int 2020;31(4):801
    CrossrefPubMedSearch in Google Scholar
  • 22 The Asia-Pacific Regional Audit. Epidemiology, costs & burden of osteoporosis in 2013. Available at: https://www.osteoporosis.foundation/sites/iofbonehealth/files/2019-06/2013_Asia_Pacific_Audit_English.pdf Accessed July 9, 2024
    PubMed
  • 23 Li N, Li XM, Xu L, Sun WJ, Cheng XG, Tian W. Comparison of QCT and DXA: osteoporosis detection rates in postmenopausal women. Int J Endocrinol 2013; 2013: 895474
    PubMedSearch in Google Scholar
  • 24 Xu XM, Li N, Li K. et al. Discordance in diagnosis of osteoporosis by quantitative computed tomography and dual-energy X-ray absorptiometry in Chinese elderly men. J Orthop Translat 2018; 18: 59-64
    PubMedSearch in Google Scholar
  • 25 Wang L, Ran L, Zha X. et al. Adjustment of DXA BMD measurements for anthropometric factors and its impact on the diagnosis of osteoporosis. Arch Osteoporos 2020; 15 (01) 155
    CrossrefPubMedSearch in Google Scholar
  • 26 Li K, Chen J, Zhao LF. et al. The establishment of QCT spinal vBMD reference database and the validation of the diagnosis criteria of osteoporosis with QCT for Chinese [in Chinese]. Zhongguo Guzhi Shusong Zazhi 2019; 25: 1257-1272
    PubMedSearch in Google Scholar
  • 27 The Committee for the Chinese Guideline for the Diagnosis of Osteoporosis with Quantitative Computed Tomography. Chinese guideline for the diagnosis of osteoporosis with quantitative computed tomography. Zhonghua Jiankang Guanlixue Zazhi 2019; 13 (03) 195-200
    PubMedSearch in Google Scholar
  • 28 Expert consensus on the equipment quality control and clinical application of bone densitometry [in Chinese]. Zhonghua Fang She Xue Za Zhi 2024; 58 (04) 381-387
    PubMedSearch in Google Scholar
  • 29 McClung MR, San Martin J, Miller PD. et al. Opposite bone remodeling effects of teriparatide and alendronate in increasing bone mass. Arch Intern Med 2005; 165 (15) 1762-1768 Published correction appears in Arch Intern Med 2005;165(18):2120
    CrossrefPubMedSearch in Google Scholar
  • 30 Engelke K, Fuerst T, Dardzinski B. et al. Odanacatib treatment affects trabecular and cortical bone in the femur of postmenopausal women: results of a two-year placebo-controlled trial. J Bone Miner Res 2015; 30 (01) 30-38
    CrossrefPubMedSearch in Google Scholar
  • 31 Ito M, Sone T, Shiraki M. et al. The effect of once-yearly zoledronic acid on hip structural and biomechanical properties derived using computed tomography (CT) in Japanese women with osteoporosis. Bone 2018; 106: 179-186
    CrossrefPubMedSearch in Google Scholar
  • 32 Damilakis J, Adams JE, Guglielmi G, Link TM. Radiation exposure in X-ray-based imaging techniques used in osteoporosis. Eur Radiol 2010; 20 (11) 2707-2714
    CrossrefPubMedSearch in Google Scholar
  • 33 Zysset P, Qin L, Lang T. 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 2015; 18 (03) 359-392
    CrossrefPubMedSearch in Google Scholar
  • 34 Wei Y, Feng W, Li G. et al. Experimental testing and biomechanical CT analysis of Chinese cadaveric vertebrae with different modeling approaches. Med Eng Phys 2021; 93: 8-16
    CrossrefPubMedSearch in Google Scholar
  • 35 Huber FA, Singhal V, Tuli S. et al. Biomechanical CT to assess bone after sleeve gastrectomy in adolescents with obesity: a prospective longitudinal study. J Bone Miner Res 2023; 38 (07) 933-942
    CrossrefPubMedSearch in Google Scholar
  • 36 Stewart A, Kumar V, Reid DM. Long-term fracture prediction by DXA and QUS: a 10-year prospective study. J Bone Miner Res 2006; 21 (03) 413-418
    CrossrefPubMedSearch in Google Scholar
  • 37 Black DM, Bouxsein ML, Marshall LM. et al; Osteoporotic Fractures in Men (MrOS) Research Group. Proximal femoral structure and the prediction of hip fracture in men: a large prospective study using QCT. J Bone Miner Res 2008; 23 (08) 1326-1333
    CrossrefPubMedSearch in Google Scholar
  • 38 Johannesdottir F, Allaire B, Bouxsein ML. Fracture prediction by computed tomography and finite element analysis: current and future perspectives. Curr Osteoporos Rep 2018; 16 (04) 411-422 Published correction appears in Curr Osteoporos Rep 2022;20(5):364
    CrossrefPubMedSearch in Google Scholar
  • 39 Bouxsein ML, Eastell R, Lui LY. et al; FNIH Bone Quality Project. Change in bone density and reduction in fracture risk: a meta-regression of published trials. J Bone Miner Res 2019; 34 (04) 632-642
    CrossrefPubMedSearch in Google Scholar