上海交通大学学报(医学版) ›› 2023, Vol. 43 ›› Issue (3): 385-390.doi: 10.3969/j.issn.1674-8115.2023.03.016
• 综述 • 上一篇
收稿日期:
2022-08-22
接受日期:
2023-02-17
出版日期:
2023-03-28
发布日期:
2023-03-28
通讯作者:
张伟
E-mail:chenjunliuorth@163.com;orthozhang@sjtu.edu.cn
作者简介:
刘辰骏(1998—),男,博士生;电子信箱:chenjunliuorth@163.com。
基金资助:
LIU Chenjun(), YIN Bohao, SUN Hui, ZHANG Wei()
Received:
2022-08-22
Accepted:
2023-02-17
Online:
2023-03-28
Published:
2023-03-28
Contact:
ZHANG Wei
E-mail:chenjunliuorth@163.com;orthozhang@sjtu.edu.cn
Supported by:
摘要:
早期筛查并及时治疗能够有效地降低骨质疏松性骨折的致残率和病死率,高效准确的影像学技术是其关键。相比于骨活检、灌注成像等侵入性手段,非侵入性影像学技术往往在临床活动推广应用中受到的阻力更小。尽管双能X射线吸收法已经被确定为骨质疏松症诊断的最主要方法,但是它的效能受到各种因素影响而相对有限,难以完全反映骨组织的真实状况。近年来影像学技术发展迅速,计算机断层扫描、磁共振成像、定量超声等影像学技术被广泛应用于骨质疏松症的研究和临床应用中,可提供更全面更详尽的骨密度和骨结构信息,为早期筛查诊断、治疗方案设计和疗效预后监测奠定基础。随着医学与计算机科学交织密切,人工智能已经能够协助处理骨质疏松症相关的图像,甚至可以独立进行影像学分析,使得对大样本影像数据库进行疾病筛查成为可能。合理应用上述影像学技术将会极大地减轻骨质疏松症造成的经济和社会负担。该文就非侵入性影像学技术在骨质疏松症方面的技术特点和最新研究进展进行综述。
中图分类号:
刘辰骏, 尹博浩, 孙辉, 张伟. 非侵入性影像学技术在骨质疏松症中的应用[J]. 上海交通大学学报(医学版), 2023, 43(3): 385-390.
LIU Chenjun, YIN Bohao, SUN Hui, ZHANG Wei. Application of non-invasive methods of radiology to the osteoporosis[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(3): 385-390.
1 | WRIGHT N C, SAAG K G, DAWSON-HUGHES B, et al. The impact of the new National Bone Health Alliance (NBHA) diagnostic criteria on the prevalence of osteoporosis in the United States: supplementary presentation[J]. Osteoporos Int, 2017, 28(11): 3283-3284. |
2 | KIMMEL D B, VENNIN S, DESYATOVA A, et al. Bone architecture, bone material properties, and bone turnover in non-osteoporotic post-menopausal women with fragility fracture[J]. Osteoporos Int, 2022, 33(5): 1125-1136. |
3 | ADAMS J E. Advances in bone imaging for osteoporosis[J]. Nat Rev Endocrinol, 2013, 9(1): 28-42. |
4 | PADLINA I, GONZALEZ-RODRIGUEZ E, HANS D, et al. The lumbar spine age-related degenerative disease influences the BMD not the TBS: the Osteolaus cohort[J]. Osteoporos Int, 2017, 28(3): 909-915. |
5 | AMNUAYWATTAKORN S, SRITARA C, UTAMAKUL C, et al. Simulated increased soft tissue thickness artefactually decreases trabecular bone score: a phantom study[J]. BMC Musculoskelet Disord, 2016, 17(1): 17. |
6 | RAJAN R, CHERIAN K E, KAPOOR N, et al. Trabecular bone score-an emerging tool in the management of osteoporosis[J]. Indian J Endocrinol Metab, 2020, 24(3): 237-243. |
7 | MESSINA C, RINAUDO L, CESANA B M, et al. Prediction of osteoporotic fragility re-fracture with lumbar spine DXA-based derived bone strain index: a multicenter validation study[J]. Osteoporos Int, 2021, 32(1): 85-91. |
8 | HUMBERT L, MARTELLI Y, FONOLLÀ R, et al. 3D-DXA: assessing the femoral shape, the trabecular macrostructure and the cortex in 3D from DXA images[J]. IEEE Trans Med Imaging, 2017, 36(1): 27-39. |
9 | CLOTET J, MARTELLI Y, DI GREGORIO S, et al. Structural parameters of the proximal femur by 3-dimensional dual-energy X-ray absorptiometry software: comparison with quantitative computed tomography[J]. J Clin Densitom, 2018, 21(4): 550-562. |
10 | HE Q F, SUN H, SHU L Y, et al. Radiographic predictors for bone mineral loss: cortical thickness and index of the distal femur[J]. Bone Joint Res, 2018, 7(7): 468-475. |
11 | LIM H K, HA H I, PARK S Y, et al. Comparison of the diagnostic performance of CT Hounsfield unit histogram analysis and dual-energy X-ray absorptiometry in predicting osteoporosis of the femur[J]. Eur Radiol, 2019, 29(4): 1831-1840. |
12 | LI Y L, WONG K H, LAW M W M, et al. Opportunistic screening for osteoporosis in abdominal computed tomography for Chinese population[J]. Arch Osteoporos, 2018, 13(1): 76. |
13 | POOLE K E, TREECE G M, PEARSON R A, et al. Romosozumab enhances vertebral bone structure in women with low bone density[J]. J Bone Miner Res, 2022, 37(2): 256-264. |
14 | SU Y B, WANG L, LIU X Y, et al. Lack of periosteal apposition in the head and neck of femur after menopause in Chinese women with high risk for hip fractures—a cross-sectional study with QCT[J]. Bone, 2020, 139: 115545. |
15 | 程晓光, 王亮, 曾强, 等. 中国定量CT骨质疏松症诊断指南(2018)[J]. 中华健康管理学杂志, 2019, 5(3): 195-200. |
CHEN X G, WANG L, ZENG Q, et al. Chinese guideline for the diagnosis of osteoporosis with quantitative computed tomography(2018)[J]. Chin J Health Manage, 2019, 5(3): 195-200. | |
16 | SCHULTE F A, CHRISTEN P, BADILATTI S D, et al. Virtual supersampling as post-processing step preserves the trabecular bone morphometry in human peripheral quantitative computed tomography scans[J]. PLoS One, 2019, 14(2): e0212280. |
17 | HANSEN S, SHANBHOGUE V, FOLKESTAD L, et al. Bone microarchitecture and estimated strength in 499 adult Danish women and men: a cross-sectional, population-based high-resolution peripheral quantitative computed tomographic study on peak bone structure[J]. Calcif Tissue Int, 2014, 94(3): 269-281. |
18 | SAMELSON E J, BROE K E, XU H F, et al. Cortical and trabecular bone microarchitecture as an independent predictor of incident fracture risk in older women and men in the Bone Microarchitecture International Consortium (BoMIC): a prospective study[J]. Lancet Diabetes Endocrinol, 2019, 7(1): 34-43. |
19 | BURT L A, LIANG Z Y, SAJOBI T T, et al. Sex- and site-specific normative data curves for HR-pQCT[J]. J Bone Miner Res, 2016, 31(11): 2041-2047. |
20 | ALVARENGA J C, CAPARBO V F, DOMICIANO D S, et al. Age-related reference data of bone microarchitecture, volumetric bone density, and bone strength parameters in a population of healthy Brazilian men: an HR-pQCT study[J]. Osteoporos Int, 2022, 33(6): 1309-1321. |
21 | RAMCHAND S K, DAVID N L, LEE H, et al. Effects of combination denosumab and high-dose teriparatide administration on bone microarchitecture and estimated strength: the DATA-HD HR-pQCT study[J]. J Bone Miner Res, 2021, 36(1): 41-51. |
22 | NILSSON A G, SUNDH D, JOHANSSON L, et al. Type 2 diabetes mellitus is associated with better bone microarchitecture but lower bone material strength and poorer physical function in elderly women: a population-based study[J]. J Bone Miner Res, 2017, 32(5): 1062-1071. |
23 | MANSKE S L, DAVISON E M, BURT L A, et al. The estimation of second-generation HR-pQCT from first-generation HR-pQCT using in vivo cross-calibration[J]. J Bone Miner Res, 2017, 32(7): 1514-1524. |
24 | SODICKSON A D, KERALIYA A, CZAKOWSKI B, et al. Dual energy CT in clinical routine: how it works and how it adds value[J]. Emerg Radiol, 2021, 28(1): 103-117. |
25 | ZHOU S W, ZHU L, YOU T, et al. In vivo quantification of bone mineral density of lumbar vertebrae using fast kVp switching dual-energy CT: correlation with quantitative computed tomography[J]. Quant Imaging Med Surg, 2021, 11(1): 341-350. |
26 | SHEN W, SCHERZER R, GANTZ M, et al. Relationship between MRI-measured bone marrow adipose tissue and hip and spine bone mineral density in African-American and Caucasian participants: the CARDIA study[J]. J Clin Endocrinol Metab, 2012, 97(4): 1337-1346. |
27 | LI J, CHEN X, LU L Y, et al. The relationship between bone marrow adipose tissue and bone metabolism in postmenopausal osteoporosis[J]. Cytokine Growth Factor Rev, 2020, 52: 88-98. |
28 | LIU Z H, ZHANG Y T, LIU Z, et al. Dual-energy computed tomography virtual noncalcium technique in diagnosing osteoporosis: correlation with quantitative computed tomography[J]. J Comput Assist Tomogr, 2021, 45(3): 452-457. |
29 | WICHMANN J L, BOOZ C, WESARG S, et al. Dual-energy CT-based phantomless in vivo three-dimensional bone mineral density assessment of the lumbar spine[J]. Radiology, 2014, 271(3): 778-784. |
30 | BOOZ C, HOFMANN P C, SEDLMAIR M, et al. Evaluation of bone mineral density of the lumbar spine using a novel phantomless dual-energy CT post-processing algorithm in comparison with dual-energy X-ray absorptiometry[J]. Eur Radiol Exp, 2017, 1(1): 11. |
31 | CHANG G, HONIG S, LIU Y X, et al. 7 Tesla MRI of bone microarchitecture discriminates between women without and with fragility fractures who do not differ by bone mineral density[J]. J Bone Miner Metab, 2015, 33(3): 285-293. |
32 | RAJAPAKSE C S, HOTCA A, NEWMAN B T, et al. Patient-specific hip fracture strength assessment with microstructural MR imaging-based finite element modeling[J]. Radiology, 2017, 283(3): 854-861. |
33 | RAJAPAKSE C S, FARID A R, KARGILIS D C, et al. MRI-based assessment of proximal femur strength compared to mechanical testing[J]. Bone, 2020, 133: 115227. |
34 | ZHANG L Y, WANG L, FU R S, et al. In vivo assessment of age- and loading configuration-related changes in multiscale mechanical behavior of the human proximal femur using MRI-based finite element analysis[J]. J Magn Reson Imaging, 2021, 53(3): 905-912. |
35 | JERBAN S, MA Y J, JANG H, et al. Water proton density in human cortical bone obtained from ultrashort echo time (UTE) MRI predicts bone microstructural properties[J]. Magn Reson Imaging, 2020, 67: 85-89. |
36 | BAUM T, YAP S P, KARAMPINOS D C, et al. Does vertebral bone marrow fat content correlate with abdominal adipose tissue, lumbar spine bone mineral density, and blood biomarkers in women with type 2 diabetes mellitus?[J]. J Magn Reson Imaging, 2012, 35(1): 117-124. |
37 | HE J, FANG H, LI X N. Vertebral bone marrow fat content in normal adults with varying bone densities at 3T magnetic resonance imaging[J]. Acta Radiol, 2019, 60(4): 509-515. |
38 | LI X J, SHET K, XU K P, et al. Unsaturation level decreased in bone marrow fat of postmenopausal women with low bone density using high resolution magic angle spinning (HRMAS) 1H NMR spectroscopy[J]. Bone, 2017, 105: 87-92. |
39 | WOODS G N, EWING S K, SCHAFER A L, et al. Saturated and unsaturated bone marrow lipids have distinct effects on bone density and fracture risk in older adults[J]. J Bone Miner Res, 2022, 37(4): 700-710. |
40 | GUO Y H, CHEN Y J, ZHANG X T, et al. Magnetic susceptibility and fat content in the lumbar spine of postmenopausal women with varying bone mineral density[J]. J Magn Reson Imaging, 2019, 49(4): 1020-1028. |
41 | MOMENI M, ASADZADEH M, MOWLA K, et al. Sensitivity and specificity assessment of DWI and ADC for the diagnosis of osteoporosis in postmenopausal patients[J]. Radiol med, 2020, 125(1): 68-74. |
42 | RAUM K, GRIMAL Q, VARGA P, et al. Ultrasound to assess bone quality[J]. Curr Osteoporos Rep, 2014, 12(2): 154-162. |
43 | MOAYYERI A, ADAMS J E, ADLER R A, et al. Quantitative ultrasound of the heel and fracture risk assessment: an updated meta-analysis[J]. Osteoporos Int, 2012, 23(1): 143-153. |
44 | MCCLOSKEY E V, KANIS J A, ODÉN A, et al. Predictive ability of heel quantitative ultrasound for incident fractures: an individual-level meta-analysis[J]. Osteoporos Int, 2015, 26(7): 1979-1987. |
45 | LIU Z J, ZHANG C, MA C, et al. Automatic phantom-less QCT system with high precision of BMD measurement for osteoporosis screening: technique optimisation and clinical validation[J]. J Orthop Transl, 2022, 33: 24-30. |
46 | LI Y C, CHEN H H, LU H H S, et al. Can a deep-learning model for the automated detection of vertebral fractures approach the performance level of human subspecialists?[J]. Clin Orthop Relat Res, 2021, 479(7): 1598-1612. |
47 | FANG Y J, LI W, CHEN X J, et al. Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks[J]. Eur Radiol, 2021, 31(4): 1831-1842. |
[1] | 吴兵, 李小敏, 柳思宇, 赵露露, 武文, 郝永强, 艾松涛. 改良3D打印病理切片盒在骨肿瘤病理拼接中的应用初探[J]. 上海交通大学学报(医学版), 2023, 43(2): 180-187. |
[2] | 吴炯睿, 高益鸣. 上颌窦动脉解剖结构的锥形术计算机断层扫描分析[J]. 上海交通大学学报(医学版), 2023, 43(2): 201-207. |
[3] | 柳思宇, 吴兵, 李小敏, 赵露露, 陈骏, 艾松涛. 弥散加权成像对隆突性皮肤纤维肉瘤术前规划的价值初探[J]. 上海交通大学学报(医学版), 2022, 42(8): 1095-1102. |
[4] | 陈立奇, 薛卓维, 吴氢凯. 基于磁共振成像的女性盆底器官三维数字模型重建的研究进展[J]. 上海交通大学学报(医学版), 2022, 42(3): 381-386. |
[5] | 王亦欢, 李若坤, 种欢欢, 严福华. 钆塞酸二钠增强磁共振成像在肝细胞癌生物学行为评估中的应用进展[J]. 上海交通大学学报(医学版), 2022, 42(1): 130-134. |
[6] | 陈翠, 金叶, 王琳, 李红丽, 万财凤, 姜立新. 30例乳腺化生性癌的多种影像学对比分析[J]. 上海交通大学学报(医学版), 2022, 42(1): 70-76. |
[7] | 刘子豪, 唐文芳, 王辉. 儿童脑肿瘤氨基酸PET显像的研究进展[J]. 上海交通大学学报(医学版), 2021, 41(7): 949-952. |
[8] | 蔡苗苗, 高艳虹. 肌少-骨质疏松症的研究进展[J]. 上海交通大学学报(医学版), 2021, 41(5): 678-683. |
[9] | 罗 虹1, 2,武红彦1, 3,谭 玺1, 3,戴红卫1, 2, 3,黄 兰1, 2, 3. 肥胖和肥胖抵抗大鼠的正畸牙移动速率及压力侧骨改建差异研究[J]. 上海交通大学学报(医学版), 2020, 40(8): 1055-1062. |
[10] | 季莹莹,薛 彬,黄 悦,张剑蔚. 小儿磁共振成像检查中咪达唑仑口服复合右美托咪定滴鼻镇静的安全性和有效性[J]. 上海交通大学学报(医学版), 2020, 40(8): 1098-1102. |
[11] | 夏志鹏1,袁 瑛2,杨 希2,顾 豪2,林晓曦2#,陶晓峰1#. 动态增强磁共振成像对静脉畸形硬化治疗中硬化剂选择的参考价值[J]. 上海交通大学学报(医学版), 2020, 40(7): 873-880. |
[12] | 葛晓乾1,李 晓2,赵辉林2,孙贝贝2,许建荣2,刘晓晟2. 术前颈动脉斑块动态增强磁共振成像对支架置入后再狭窄发生的预测价值[J]. 上海交通大学学报(医学版), 2020, 40(7): 901-907. |
[13] | 周悦玲,丁 巍,艾红兰,卢建新,丁 峰,胡 春. 维持性血液透析的终末期肾病患者脑结构性异常及认知功能分析[J]. 上海交通大学学报(医学版), 2020, 40(7): 962-967. |
[14] | 王峰伟1,沈秋明1,拉巴仓拉1,施 悦1,张舒娴1,王沪雯1,常睿捷1,杨颖华2,万和平3,沈 恬1,蔡 泳1. 上海市社区中老年居民骨质疏松症预防相关路径分析[J]. 上海交通大学学报(医学版), 2020, 40(4): 525-. |
[15] | 万良荣,黄 干,刘建军. 99mTcO4- SPECT/CT定量显像在测定毒性弥漫性甲状腺肿患者SUV值和甲状腺体积中的应用[J]. 上海交通大学学报(医学版), 2020, 40(12): 1637-1640. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||