Phantomless Computed Tomography-Based Quantitative Bone Mineral Density Assessment: A Literature Review
Source abstract
Abstract
The global surge in aging populations has intensified osteoporosis challenges, necessitating improved diagnostic methods. While dual X-ray absorptiometry (DXA) is the conventional standard in assessing bone mineral density (BMD), the exploration of quantitative computed tomography (QCT) has been proposed, particularly phantomless techniques. Challenges in single-energy CT, emphasizing internal calibration standards like subcutaneous fat, are discussed. Advances in PL-QCT, notably with automatic region of interest (ROI) selection, show improved accuracy. Dual-energy CT (DECT) introduces new dimensions for musculoskeletal analysis. Despite advancements, challenges persist, including interindividual variability and patient-specific factors. Evolving single-energy CT and DECT techniques show promise in refining BMD assessment and osteoporosis diagnosis, enhancing patient care. Continued research and integration into clinical practice are vital for realizing these advancements’ full benefits. In this review, we evaluate and summarize current evidence on the feasibility and different approaches to achieve analysis of BMD with phantomless QCT.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced X-ray and CT Imaging · Dental Radiography and Imaging · Bone health and osteoporosis research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, 00100 Rome, Italy
Department of Radiology, Cittadella della Salute Azienda Sanitaria Locale di Lecce, 73100 Lecce, Italy
Department of Radiology, Antwerp University Hospital, 2650 Edegem, Belgium
Royal Perth Hospital (RPH), Perth, WA 6001, Australia
Medical School, University of Western Australia (UWA), Perth, WA 6009, Australia
Centre for Medical Sciences-CISMed, University of Trento, 38100 Trento, Italy
References (51)
Show source-provided references
1. Osteoporosis: Current Concepts10.1055/s-0038-1660790 ↗
2. An Overview and Management of Osteoporosis10.5152/eurjrheum.2016.048 ↗
3. Hernlund, E., Svedbom, A., Ivergård, M., Compston, J., Cooper, C., Stenmark, J., McCloskey, E.V., Jönsson, B., and Kanis, J.A. (2013). Osteoporosis in the European Union: Medical Management, Epidemiology and Economic Burden. Arch. Osteoporos., 8.10.1007/s11657-013-0136-1 ↗
4. Mallio, C.A., Beomonte Zobel, B., and Quattrocchi, C.C. (2015). Evaluating Rehabilitation Interventions in Parkinson′s Disease with Functional MRI: A Promising Neuroprotective Strategy. Neural Regen. Res., 10.10.4103/1673-5374.156957 ↗
5. V The Osteoporosis Treatment Gap10.1002/jbmr.2301 ↗
6. Dual-Energy X-ray Absorptiometry10.1016/j.jocd.2017.06.014 ↗
7. A Cross-Calibration Study Using a Novel Dual X-Ray Absorptiometry System for Bone Mineral Density Measurements with the European Spine Phantom10.21037/qims-22-619 ↗
8. Fracture Incidence and Association with Bone Mineral Density in Elderly Men and Women: The Rotterdam Study10.1016/j.bone.2003.10.001 ↗
9. DXA in Vivo BMD Methodology: An Erroneous and Misleading Research and Clinical Gauge of Bone Mineral Status, Bone Fragility, and Bone Remodelling10.1016/j.bone.2007.02.022 ↗
10. Simulated Increases in Body Fat and Errors in Bone Mineral Density Measurements by DXA and QCT10.1002/jbmr.506 ↗
11. Fracture Prediction, Imaging and Screening in Osteoporosis10.1038/s41574-019-0220-8 ↗
12. Mallio, C.A., Vadalà, G., Russo, F., Bernetti, C., Ambrosio, L., Zobel, B.B., Quattrocchi, C.C., Papalia, R., and Denaro, V. (2022). Novel Magnetic Resonance Imaging Tools for the Diagnosis of Degenerative Disc Disease: A Narrative Review. Diagnostics, 12.10.3390/diagnostics12020420 ↗
13. Computed Tomography Density Changes of Bone Metastases after Concomitant Denosumab10.1007/s00256-023-04326-3 ↗
14. Quantitative computed tomography and opportunistic bone density screening by dual use of computed tomography scans10.1016/j.jot.2015.08.006 ↗
15. Quantitative Computed Tomography Discriminates between Postmenopausal Women with Low Spine Bone Mineral Density with Vertebral Fractures and Those with Low Spine Bone Mineral Density Only: The SHATTER Study10.1007/s00198-020-05317-z ↗
16. Clinical Use of Quantitative Computed Tomography and Peripheral Quantitative Computed Tomography in the Management of Osteoporosis in Adults: The 2007 ISCD Official Positions10.1016/j.jocd.2007.12.010 ↗
17. Li, N., Li, X., Xu, L., Sun, W., Cheng, X., and Tian, W. (2013). Comparison of QCT and DXA: Osteoporosis Detection Rates in Postmenopausal Women. Int. J. Endocrinol., 2013.10.1155/2013/895474 ↗
18. Improved Prediction of Incident Vertebral Fractures Using Opportunistic QCT Compared to DXA10.1007/s00330-019-06018-w ↗
19. A Phantom Study Comparing Low-Dose CT Physical Image Quality from Five Different CT Scanners10.21037/qims-21-245 ↗
20. Lyu, T., Zhao, W., Zhu, Y., Wu, Z., Zhang, Y., Chen, Y., Luo, L., Li, S., and Xing, L. (2021). Estimating Dual-Energy CT Imaging from Single-Energy CT Data with Material Decomposition Convolutional Neural Network. Med. Image Anal., 70.10.1016/j.media.2021.102001 ↗
21. Ghasemi Shayan, R., Oladghaffari, M., Sajjadian, F., and Fazel Ghaziyani, M. (2020). Image Quality and Dose Comparison of Single-Energy CT (SECT) and Dual-Energy CT (DECT). Radiol. Res. Pract., 2020.10.1155/2020/1403957 ↗
22. Vertebral Bone Mineral Density Measured by Quantitative Computed Tomography With and Without a Calibration Phantom: A Comparison Between 2 Different Software Solutions10.1016/j.jocd.2017.12.003 ↗
23. Automatic Phantom-Less QCT System with High Precision of BMD Measurement for Osteoporosis Screening: Technique Optimisation and Clinical Validation
24. Phantomless Calibration of CT Scans for Measurement of BMD and Bone Strength—Inter-Operator Reanalysis Precision10.1016/j.bone.2017.07.029 ↗
25. Xiongfeng, T., Cheng, Z., Meng, H., Chi, M., Deming, G., Huan, Q., Bo, C., Kedi, Y., Xianyue, S., and Tak-Man, W. (2022). One Novel Phantom-Less Quantitative Computed Tomography System for Auto-Diagnosis of Osteoporosis Utilizes Low-Dose Chest Computed Tomography Obtained for COVID-19 Screening. Front. Bioeng. Biotechnol., 10.10.3389/fbioe.2022.856753 ↗
26. Lee, Y.H., Kim, J.J., and Jang, I.G. (2019). Patient-Specific Phantomless Estimation of Bone Mineral Density and Its Effects on Finite Element Analysis Results: A Feasibility Study. Comput. Math. Methods Med., 2019.10.1155/2019/4102410 ↗
27. Phantom-Less QCT BMD System as Screening Tool for Osteoporosis without Additional Radiation10.1016/j.ejrad.2010.02.008 ↗
28. Simultaneous Screening for Osteoporosis at CT Colonography: Bone Mineral Density Assessment Using MDCT Attenuation Techniques Compared with the DXA Reference Standard10.1002/jbmr.428 ↗
29. Lumbar Bone Mineral Density Phantomless Computed Tomography Measurements and Correlation with Age and Fracture Incidence10.1080/15389588.2015.1054029 ↗
30. Pan, Y., Zhao, F., Cheng, G., Wang, H., Lu, X., He, D., Wu, Y., Ma, H., Hu, L., and Yu, T. (2023). Automated Vertebral Bone Mineral Density Measurement with Phantomless Internal Calibration in Chest LDCT Scans Using Deep Learning. Br. J. Radiol., 96.10.1259/bjr.20230047 ↗
31. Cortical Endplate Bone Density Measured by Novel Phantomless Quantitative Computed Tomography May Predict Cage Subsidence More Conveniently and Accurately10.1111/os.13897 ↗
32. Dual-Energy CT: Radiation Dose Aspects10.2214/AJR.12.9210 ↗
33. Dual-Energy CT of the Lung10.2214/AJR.12.9112 ↗
34. Comparison of Virtual Monoenergetic Imaging between a Rapid Kilovoltage Switching Dual-Energy Computed Tomography with Deep-Learning and Four Dual-Energy CTs with Iterative Reconstruction10.21037/qims-21-708 ↗
35. Optimization of Dual Energy Computed Tomography Post-Processing to Reduce Lower Limb Artifacts in Gout10.21037/qims-21-321 ↗
36. Diagnostic Accuracy of Quantitative Dual-Energy CT-Based Volumetric Bone Mineral Density Assessment for the Prediction of Osteoporosis-Associated Fractures10.1007/s00330-021-08323-9 ↗
37. V Bone Mineral Assessment: New Dual-Energy CT Approach10.1148/radiology.168.1.3380964 ↗
38. Booz, C., Noeske, J., Albrecht, M.H., Lenga, L., Martin, S.S., Yel, I., Huizinga, N.A., Vogl, T.J., and Wichmann, J.L. (2020). Diagnostic Accuracy of Quantitative Dual-Energy CT-Based Bone Mineral Density Assessment in Comparison to Hounsfield Unit Measurements Using Dual X-ray Absorptiometry as Standard of Reference. Eur. J. Radiol., 132.10.1016/j.ejrad.2020.109321 ↗
39. Quantitative Dual-Energy CT for Phantomless Evaluation of Cancellous Bone Mineral Density of the Vertebral Pedicle: Correlation with Pedicle Screw Pull-out Strength10.1007/s00330-014-3529-7 ↗
40. Bias in Artificial Intelligence
41. New Horizons: Artificial Intelligence Tools for Managing Osteoporosis10.1210/clinem/dgac702 ↗
42. Alkaissi, H., and McFarlane, S.I. (2023). Artificial Hallucinations in ChatGPT: Implications in Scientific Writing. Cureus, 15.10.7759/cureus.35179 ↗
43. Artificial Intelligence to Reduce or Eliminate the Need for Gadolinium-Based Contrast Agents in Brain and Cardiac MRI10.1097/RLI.0000000000000983 ↗
44. Epicardial and Pericardial Fat Analysis on CT Images and Artificial Intelligence: A Literature Review10.21037/qims-21-945 ↗
45. Framing the Challenges of Artificial Intelligence in Medicine10.1136/bmjqs-2018-008551 ↗
46. Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning–Based Virtual Native Enhancement10.1161/CIRCULATIONAHA.122.060137 ↗
47. Application of Artificial Intelligence in Diagnosis of Osteoporosis Using Medical Images: A Systematic Review and Meta-Analysis10.1007/s00198-021-05887-6 ↗
48. Opportunistic Osteoporosis Screening Using Chest CT with Artificial Intelligence10.1007/s00198-022-06491-y ↗
49. Xue, Z., Huo, J., Sun, X., Sun, X., Ai, S., Zhang, L., and Liu, C. (2022). Using Radiomic Features of Lumbar Spine CT Images to Differentiate Osteoporosis from Normal Bone Density. BMC Musculoskelet. Disord., 23.10.1186/s12891-022-05309-6 ↗
50. Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures from MRI Data10.1002/jmri.26280 ↗
51. Lee, H., Park, S., Kwack, K.-S., and Yun, J.S. (2023). CT and MR for Bone Mineral Density and Trabecular Bone Score Assessment in Osteoporosis Evaluation. Sci. Rep., 13.10.1038/s41598-023-43850-z ↗