Low-Dose Chest CT–Based 3D Osteoporosis Assessment: Insights into Vertebral Microarchitecture

Low-Dose Chest CT–Based 3D Osteoporosis Assessment: Insights into Vertebral Microarchitecture | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Low-Dose Chest CT–Based 3D Osteoporosis Assessment: Insights into Vertebral Microarchitecture Hisaya Tanioka, Sayaka Tanioka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8390011/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Annual screening chest low-dose CT (LDCT) exams contain detailed bone anatomical information. There is a need for a simplified three-dimensional (3D) method to perform practical, passive, accurate, reproducible, and rapid osteoporosis screening from these data. Purpose To develop a simplified 3D method for osteoporosis screening from vertebral trabecular and cortical architecture using screening chest LDCT data, and to analyze bone microarchitecture according to shape, parameters, age, and gender. Materials and Methods Twenty-two subjects underwent screening chest LDCT. 3D vertebral histological images were reconstructed from LDCT data using interactive direct transparency thresholding volume rendering algorithms. Trabecular and cortical bone parameters were measured and compared with values from the literature. Age, gender, and each parameter’s relationships were analyzed using linear regression. Results Reconstructed 3D images of trabecular and cortical microstructures closely matched prior literature. Parameter values were generally larger in males, although Tb.Th and Tb.Sp showed no significant gender difference. Tb.Th, Tb.Sp, and Ct.Th declined faster with age in females than males, while postmenopausal leads to a reduction in Tb.N Conclusions Screening chest LDCT (~ 1.5 mSv) can provide superior vertebral imaging compared to lumbar spine radiography, revealing both trabecular and cortical architecture. This simplified 3D technique allows assessment of macro- and microstructural bone status, supporting osteoporosis diagnosis and management. Biophysics Trabecular bone Cortical bone Vertebra Osteoporosis CT thresholding 3D Imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Summary A simplified 3D thresholding method using low-dose chest CT enables practical, safe, and reproducible osteoporosis screening. It visualizes trabecular and cortical networks, showing faster horizontal trabecular thinning in females. Postmenopausal females exhibit reduced trabecular number, increased separation, and cortical thinning, with quantified attenuation changes reflecting microarchitectural bone loss. Introduction Bones are composed of two types. One is cortical bone as the compact out layer of the bone. The other is trabecular bone which is a mesh-like structure. Osteoporosis is bone thinning in cortical bone and trabecular bone with a reduction in bone mass due to depletion of calcium and bone proteins. An important fracture determinant of osteoporosis is bone strength which depends on the mineral content as well as the internal structure. Hence, trabecular bone loss will increase the weakness of bone strength. Several previous studies have shown that osteoporosis is mainly affected by trabecular bone and that the bone structure has great importance for biomechanical competence. Trabecular microarchitecture improves prediction of bone mechanical behavior and its heterogeneity has been previously described [ 1 – 4 ]. However, there is limited information about its contribution to vertebral fragility. It is unclear if a decrease in trabecular bone density or cortical bone thickness is related to vertebral insufficiency fractures. Therefore, to understand osteoporosis that we have to know the status of trabecular and cortical structures. Meanwhile, millions of chest screening exams using low-dose computed tomography (LDCT) are performed annually in the World. As these data include bone information, there are a few methods to extract this information. These methods use the extracted bone density information, which serve as osteoporosis screening. Reduction in overall bone density leads to local reductions in bone density and cortical thickness in vertebrae that match clinically observed fracture patterns in patients with vertebral insufficiency fractures. Current methods for opportunistic osteoporosis screening consist of quantitative measurements of vertebral mean attenuation in Hounsfield Units (HU) [ 5 , 6 ] and subjective visual assessment of color-coding CT images [ 7 ]. The colors green and red are applied to rapidly and accurately differentiate normal from abnormal bone density, respectively. However, these methods cannot provide us the status of trabecular and cortical structures. To understand osteoporosis truly, it is necessary to know in vivo macrostructure, microstructure, matrix properties, cellular composition and activity of the bone. Therefore, we need a practical, accurate and rapid technique for extracting these information. This study aims to develop a thresholding 3D method for qualitative and quantitative assessment of vertebral microarchitecture from screening chest low-dose computed tomography (LDCT) data, validate it against histological literature, and analyze age-, gender-, and postmenopausal-related microarchitectural changes. Materials and Methods Subjects This retrospective study was approved by the Tanioka Clinic Institutional Review Board in accordance with the 1964 Helsinki Declaration. Informed consent was waived. Twenty-two subjects (12 males, mean age 49.00 years, range 26–82; 12 females, mean age 56.25 years, range 17–82, including 9 postmenopausal, mean age 66.89, range 55–82) undergoing LDCT screening were included. Histological comparisons utilized previous studies [ 3 , 4 ]. Imaging Acquisition All scans were performed using a spiral CT scanner (ProSpeed AI; GE Systems, Milwaukee, WI, USA) with 120 kV, 60–80 mA, 1-mm section thickness, high-resolution reconstruction, 512 × 512 matrix, and 1.00 × 0.18 × 0.18 mm³ voxel size. DLP ranged from 60–150 mGy/cm, corresponding to 0.7–2.0 mSv effective dose. Volume Rendering Algorithm and Parameters Three-dimensional (3D) histological images of the vertebrae were reconstructed from CT data using a threshold-based, semitransparent direct volume-rendering (VR) algorithm. Optimal threshold and opacity values were determined by measuring radiodensity in three representative regions: cortex (C), bone marrow (B), and high-density spot (T) within the marrow, considered indicative of trabecular bone. Measurements were obtained in Hounsfield units (HU) from 1-mm-thick high-resolution axial CT slices at the T12 vertebral level. The results are summarized in Table 1 . Table 1 Measurements of CT values at the Cortex (C), bone marrow (B), and high-density spot (T) Case Sex Age Cortex (C) Bone Marrow (B) High-Density Spot (T) T/C 1 M 82 467.00 270.71 265.01 0.57 2 M 75 459.21 225.70 260.10 0.57 3 M 56 460.33 321.57 291.13 0.63 4 M 51 490.70 280.21 288.18 0.59 5 M 50 497.75 322.40 261.18 0.53 6 M 49 487.75 134.40 138.80 0.29 7 M 48 500.02 297.32 265.42 0.53 8 M 46 501.35 286.12 255.10 0.51 9 M 42 522.21 297.76 285.55 0.55 10 M 32 550.20 301.02 278.81 0.51 11 M 31 552.21 268.86 245.38 0.49 12 M 26 560.20 298.78 290.27 0.52 Mean ± SD M 49.00 ± 16.57 504.08 ± 35.33 275.40 ± 51.41 260.41 ± 41.19 0.52 1 F 82 490.00 141.41 206.50 0.42 2 F 75 456.50 83.02 184.50 0.40 3 F 74 460.00 172.18 255.75 0.56 4 F 71 455.26 156.61 200.10 0.44 5 F 64 507.40 136.14 201.21 0.40 6 F 63 555.00 283.24 245.21 0.44 7 F 60 495.25 274.21 234.14 0.47 8 F 58 501.24 287.27 245.56 0.49 9 F 55 485.56 295.41 235.48 0.48 10 F 32 488.81 305.02 250.07 0.51 11 F 24 499.00 312.20 246.00 0.49 12 F 17 490.00 312.00 235.50 0.48 Mean ± SD F 56.25 ± 20.98 490.34 ± 26.99 229.89 ± 84.45 228.34 ± 23.71 0.47 Overall 52.63 ± 18.85 497.21 ± 31.54 252.65 ± 72.22 244.38 ± 36.73 0.49 The opacity ratio between cortical and trabecular bone was calculated, and the relationship was expressed as a scatter plot and linear regression model (Fig. 1 ): Y = 0.0019X + 0.0426; r = 0.9276; p < 0.001. The model was derived under the assumption of normal distribution. In the VR process, structures with HU values above the upper threshold were assigned high opacity. The transparency ratio between cortex (C) and high-density spot (T) determined the final threshold settings. An opacity of 0.3–0.8 within the bone interior provided optimal visualization of the trabecular network. The HU value corresponding to the trabecular region was obtained from the regression equation, resulting in a final threshold range of 130–400 HU. The relationship was best represented by an upward monotonic (increasing) curve algorithm (Fig. 1 ). Images were rendered only within the specified threshold range. Transparency was set to 0% below 130 HU, ensuring exclusion of non-bone structures, and to 100% above 400 HU to visualize high-density cortical regions. Since the measured trabecular (T) values were below 300 HU, trabecular imaging was not affected by the upper limit. Opacity values in the VR process ranged from 0% to 100%, depending on the HU-defined threshold range. Threshold and opacity parameters were defined as follows: Cortical and trabecular bone threshold: 130–400 HU Opacity range: 0–100% The scatter plot shows the correlation between the high-density spot (T) and cortical-to-trabecular opacity ratio (T/C). The linear regression model (y = 0.0019x + 0.0426; R² = 0.8605; p < 0.001) demonstrates a strong positive association between the measured Hounsfield unit (HU) values of trabecular regions and their corresponding opacity levels used for volume rendering. This regression model defined the opacity variation applied in the reconstruction algorithm for three-dimensional vertebral imaging. Postprocessing 3D visualization was performed using GE Advantage Navigator Software (v2), which allows interactive adjustment of opacity and color in under five minutes. Measurements of distances and volumes were conducted directly within the 3D environment, similar to platforms like OsiriX or LiveVolume. A sample console image is shown in Fig. 2 . Qualitative Imaging Analysis T12 vertebra images were evaluated against anatomical references and literature [ 1 – 4 ] for trabecular and cortical architecture. Quantitative Imaging Analysis Measurements from T12 horizontal view: Trabecular thickness (Tb.Th) : Mean diameter of trabeculae (mm). Trabecular separation (Tb.Sp) : Mean distance between trabeculae (mm). Trabecular number (Tb.N) : Inverse of mean spacing between trabecular midlines (1/mm). Cortical thickness (Ct.Th) : Ventral shell thickness of the vertebral body (mm). Statistical analysis Normality of the measurement data was assessed using the Kolmogorov–Smirnov test. Variables that followed a normal distribution were expressed as mean ± standard deviation (SD). The effects of age, sex, and menopausal status were evaluated using univariate linear regression analysis. The Welch test was applied to verify the equivalence of the present results with previously reported histological data and to examine gender-related differences in each parameter. A p-value of < 0.05 was considered statistically significant. All analyses were performed using Microsoft Excel 2013 (Microsoft Corporation, Redmond, WA, USA). Results Volume Rendering Algorithm and Parameters Threshold values of 130–400 HU and opacity settings from 0–100% were applied. Using an upward-curve algorithm with monochromatic shading, trabecular and cortical bone structures were successfully visualized. Qualitative Imaging Analysis The reconstructed three-dimensional images clearly depicted trabecular and cortical architectures consistent with established anatomical and histological references (Figs. 2 – 5 ). The cortical shell surrounded the spongy cancellous core, and osteonal structures were identifiable. Age-related cortical porosity was also observed. Quantitative Imaging Analysis All measurement data satisfied the assumptions of normality and homogeneity of variance; therefore, parametric tests were used for statistical analyses. Age-related regression analysis is summarized in Table 2 . Table 2 Age-related regression analysis Parameter Males (n = 12) Females (n = 12) Postmenopausal (n = 9) Tb.Th (mm/year) -0.0054 -0.0086 -0.0088 Tb.Sp (mm/year) + 0.0117 + 0.0158 + 0.0280 Tb.N (1/mm/year) -0.0061 -0.0062 -0.0138 Ct.Th (mm/year) -0.0068 -0.0084 -0.0012 Comparison with Previous Histological Data The Welch test was used to compare parameters obtained in this study with those from previous histological reports and to assess gender differences. In the reference histological data, cortical thickness was measured in 13 female cadavers (aged 54–87 years; mean 74.6 ± 9.4 years), with a mean T10 cortical thickness of 0.38 ± 0.06 mm [ 3 ]. Microstructural parameters from 43 females without fracture (mean age 64.4 ± 5.5 years) were: Tb.Th = 0.368 ± 0.046 mm, Tb.Sp = 0.667 ± 0.129 mm, and Tb.N = 0.970 ± 0.090 1/mm [ 4 ]. Table 3 presents the Welch test results comparing the present study with the previous literature. No significant differences were observed for most parameters, except for male Tb.Sp (p = 0.036), postmenopausal Tb.N (p = 0.033), and postmenopausal Ct.Th (p = 0.009). Table 3 Comparison of Measured Vertebral Parameters with Previous Literature (Welch Test) Parameter This Study (Mean ± SD, n) Previous Reports (Mean ± SD, n) Significance P value t f Tb.Th (mm) 0.421 ± 0.159 (24) — NS 0.114 1.651 22.453 Males 0.450 ± 0.131 (12) — NS 0.082 1.928 22.453 Females 0.392 ± 0.183 (12) 0.368 ± 0.046(43) NS 0.604 0.535 10.424 Postmenopausal 0.300 ± 0.087 (9) — NS 0.604 -0.534 8.188 Tb.Sp (mm) 0.583 ± 0.303 (24) — NS 0.406 -0.848 24.839 Males 0.483 ± 0.221 (12) — p < 0.05 0.036 -2.374 11.749 Females 0.683 ± 0.356 (12) 0.667 ± 0.129(43) NS 0.646 0.473 10.700 Postmenopausal 0.833 ± 0.269 (9) — NS 0.105 1.868 8.785 Tb.N (1/mm) 0.999 ± 0.164 (24) — NS 0.731 0.348 27.747 Males 1.053 ± 0.160 (12) — NS 0.109 1.735 11.851 Females 0.946 ± 0.156 (12) 0.970 ± 0.090(43) NS 0.200 -1.358 11.935 Postmenopausal 0.891 ± 0.142 (9) — p < 0.05 0.033 -2.461 10.112 Ct.Th (mm) 0.367 ± 0.163(24) — NS 0.365 -0.349 32.097 Males 0.392 ± 0.040(12) — NS 0.28 0.592 21.021 Females 0.347 ± 0.054 (12) 0.38 ± 0.06 (13) NS 0.081 -1.447 22.989 Postmenopausal 0.256 ± 0.124 (9) — P < 0.01 0.009 -2.783 10.617 Number (n) Gender Comparison Gender-based comparisons using the Welch test are summarized in Table 4 . Males generally exhibited higher values for Tb.Th and Ct.Th and lower values for Tb.Sp than females. Significant gender differences were observed for Tb.N and Ct.Th. Table 4 Gender comparison in each parameter using the Welch test Parameter Males (n = 12) (Mean ± SD) Female (n = 12) (Mean ± SD) Significance P value t f Tb.Th (mm) 0.450 ± 0.131 0.392 ± 0.183 NS 0.190 0.896 19.952 Tb.Sp (mm) 0.483 ± 0.221 0.683 ± 0.356 NS 0.101 -1.733 16.958 Tb.N (1/mm) 1.053 ± 0.160 0.946 ± 0.156 p < 0.05 0.033 2.290 19.978 Ct.Th (mm) 0.392 ± 0.40 0.347 ± 0.054 P < 0.05 0.039 2.236 16.698 Summary of Findings were as follows. The developed thresholding 3D reconstruction method accurately reproduced trabecular and cortical microstructures. Quantitative parameters showed close agreement with previous histological data. Significant differences were limited to specific subgroups (male Tb.Sp, postmenopausal Tb.N, and Ct.Th). Male vertebrae tended to have thicker cortices and denser trabecular networks than those of females. Discussion This technique was developed by transforming screening chest low-dose CT (LDCT) data using a threshold-based, semitransparent direct volume-rendering algorithm. A threshold range of 130–400 Hounsfield units (HU) included most trabecular bone values and was therefore appropriate for bone histological imaging. Thresholds below this range allowed visualization of soft tissues within the bone structure. The reconstructed three-dimensional (3D) vertebral histological images showed close agreement with standard anatomical atlases and published histological descriptions. Statistical comparison revealed no significant differences between the present measurements and those reported in previous histological studies, supporting the reliability of the reconstructed 3D vertebral models [ 1 – 4 ]. The vertebra consisted of light, spongy cancellous tissue covered by a thin layer of compact bone perforated by multiple foramina. Coronal and axial sections of the reconstructed images clearly demonstrated the spatial relationship between cortical and trabecular bone in three dimensions. Radiation dose Although MDCT is the available technique to analyze vertebral microstructure in vivo, high radiation exposure (28 mSv to 62 mSv) cannot be avoided [ 4 ]. In this technique, the radiation dose converted from the DLP value was 0.7 to 2.1 mSv, and the average dose was 1.5 mSv [ 9 ]. This dose is larger than that for QCT (0.025–0.360 mSv) [ 10 ]. It is approximately equal to the average effective dose of the thoracic spine (1.0 mSv) or the average effective dose of the lumbar spine (1.5 mSv) [ 11 ]. So, this technique is safe for daily routine practice. Age-related microstructural changes and gender differences In our horizontal view study, for males, females, and postmenopausal females, Tb.Th decreased linearly with age at a rate of 0.0054 mm, 0.0086 mm, and 0.0088 mm per year, respectively; Tb.Sp increased linearly with age at a rate of 0.0117 mm, 0.0158 mm, and 0.0280 mm per year, respectively; Tb.N decreased linearly with age at a rate of 0.0061, 0.0062, and 0.00138 per year, respectively; Ct.Th decreased linearly with age at a rate of 0.0068 mm, 0.0084 mm, and 0.0012 mm per year, respectively. Age-related changes of horizontal Tb.Th decrease in thickness with aging is faster in females than in males, as previously reported [ 12 ]. The bone attenuation due to decreases in Tb.N and increases in Tb.Sp, with thinning of Tb.Th has concluded from our results like the previous report for age-related trabecular bone loss [ 13 ]. Postmenopausal females show decrease in Tb.Th, Tb.N, and more increase Tb.Sp than females. That is, postmenopausal females have fragile trabecular bone with more space. There was significant difference between genders in Ct.Th decrease in thickness with age. However, in postmenopausal period, Ct.Th decrease in thickness is slowly. Significant differences are found between the genders. These findings support the hypothesis of two major age-related processes that lead to bone loss [ 14 ]. One is a reduction in trabecular bone loss. The second process that contributes to bone loss is cortical bone loss. Trabecular bone loss is caused by thinning and loss of trabecular bone. Limitation The study has several limitations. The sample size was small (12 males and 12 females), limiting statistical inference. However, the relatively small sample size and single-center design of our study limit the generalizability of these results. Further multicenter studies with larger cohorts are needed to validate the clinical utility of this thresholding 3D-CT imaging. Finally, although radiation dose (0.7–2.0 mSv) is safe for routine screening, lower than MDCT (62 mSv) and comparable to thoracic/lumbar spine imaging [ 9 – 11 ], repeated imaging warrants caution. Conclusions This study evaluated the feasibility of using data obtained from low-dose computed tomography (LDCT) for lung cancer screening to visualize and measure parameters of the thoracic vertebral cortical bone and trabeculae. By comparing these findings with previous pathological studies, the authors demonstrated that this method could be used to assess vertebral bone condition. In other words, it suggests that LDCT scans performed for cancer screening may also provide simultaneous insight into the state of the thoracic spine. However, since this investigation was conducted at a single institution with a limited number of cases, further multicenter studies are required to validate the findings. Abbreviations LDCT: Low-dose computed tomography MDCT: Multi-detector computed tomography VR: Volume rendering HU: Hounsfield units DLP: Dose length product Tb.Th: Trabecular thickness Tb.Sp: Trabecular separation Tb.N: Trabecular number Ct.Th: Cortical thickness SD: Standard deviation Declarations Data availability Data are available upon request from the corresponding author. Authors' contributions HT and ST carried out the design of the study, evaluated the created images, the imaging of reproducibility, measured each parameter, performed statistical analysis, and prepared the manuscript. Compliance with Ethical Standards Conflict of Interest: All authors of this manuscript declare no relationship with any companies, whose products or services may be related the subject matter of the article. Funding: All authors state that this work has not received any funding. Statistics and Biometry: No complex statistical methods were necessary for this paper. Informed consent: Informed consent was waived for this retrospective study in which we analyzed previously obtained screening chest LDCT data at one institution. Ethical approval: All procedures performed in studies involving human participants were accorded with the Ethics Committee of Tanioka Clinic, Japan, and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Institutional Review Board approval at Tanioka Clinic was obtained. Methodology: Retrospective; experimental, performed at one institution References Martini FH (2006) Osseous tissue and bone structure. In: Martini FH. Fundamentals of Anatomy & Physiology. 7th ed. Pearson Education, San Francisco.183–203 Mosekilde L, Ebbesen EN, Tornvig L et al (2000) Trabecular bone structure and strength - remodeling and repair. 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10:21:44","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93717,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8390011/v1/9a411652a0cbf69576d6090e.html"},{"id":98780871,"identity":"4c80174b-4f43-4bce-ae3c-0cc9189aa92e","added_by":"auto","created_at":"2025-12-22 12:31:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77863,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between the high-density spot and opacity value.\u003c/p\u003e","description":"","filename":"LDCTFIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-8390011/v1/fc291b94e4cd2209609538cd.png"},{"id":98780493,"identity":"ff0a6c82-8ef8-4bae-8dfb-fb19466c56ed","added_by":"auto","created_at":"2025-12-22 12:31:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1210033,"visible":true,"origin":"","legend":"\u003cp\u003eConsole processing image of a 55-year-old female.\u003c/p\u003e","description":"","filename":"LDCTFIG2.png","url":"https://assets-eu.researchsquare.com/files/rs-8390011/v1/14c2d6853db7c31edc5c5986.png"},{"id":98779228,"identity":"47377e1b-12f7-4489-afee-f3570847ee1e","added_by":"auto","created_at":"2025-12-22 12:30:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1193871,"visible":true,"origin":"","legend":"\u003cp\u003eThree-dimensional trabecular bone structure and its measurements displayed on the monitor.\u003c/p\u003e","description":"","filename":"LDCTFIG3.png","url":"https://assets-eu.researchsquare.com/files/rs-8390011/v1/c33a474df922cbb75a0796ae.png"},{"id":98767200,"identity":"a3db545b-c7e8-4ebb-98ce-a9ea4861ad7f","added_by":"auto","created_at":"2025-12-22 10:21:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2259186,"visible":true,"origin":"","legend":"\u003cp\u003ePosterior right supra-lateral view of the 3D reconstructed trabecular microstructure at T12 from a 55-year-old female.\u003c/p\u003e","description":"","filename":"LDCTFIG4.png","url":"https://assets-eu.researchsquare.com/files/rs-8390011/v1/223e6fb3492a5dd1e75565d1.png"},{"id":98767197,"identity":"2b38c2ed-7616-44a4-9ec0-3eb0775455cf","added_by":"auto","created_at":"2025-12-22 10:21:44","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":160241,"visible":true,"origin":"","legend":"\u003cp\u003eSuperior (right) and posterior-supra-lateral (left) views of the 3D reconstructed trabecular microstructure at T12 from an 82-year-old male.\u003c/p\u003e","description":"","filename":"LDCTFIG5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8390011/v1/2e4efa3face8f4822366f98f.jpg"},{"id":98783577,"identity":"7713b975-fb4b-4995-8202-ca7143c55f71","added_by":"auto","created_at":"2025-12-22 12:42:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6527947,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8390011/v1/beb4e033-5b1b-476e-8be5-6f1028d50d0c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eLow-Dose Chest CT–Based 3D Osteoporosis Assessment: Insights into Vertebral Microarchitecture\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Summary","content":"\u003cp\u003eA simplified 3D thresholding method using low-dose chest CT enables practical, safe, and reproducible osteoporosis screening. It visualizes trabecular and cortical networks, showing faster horizontal trabecular thinning in females. Postmenopausal females exhibit reduced trabecular number, increased separation, and cortical thinning, with quantified attenuation changes reflecting microarchitectural bone loss.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eBones are composed of two types. One is cortical bone as the compact out layer of the bone. The other is trabecular bone which is a mesh-like structure. Osteoporosis is bone thinning in cortical bone and trabecular bone with a reduction in bone mass due to depletion of calcium and bone proteins. An important fracture determinant of osteoporosis is bone strength which depends on the mineral content as well as the internal structure. Hence, trabecular bone loss will increase the weakness of bone strength. Several previous studies have shown that osteoporosis is mainly affected by trabecular bone and that the bone structure has great importance for biomechanical competence. Trabecular microarchitecture improves prediction of bone mechanical behavior and its heterogeneity has been previously described [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, there is limited information about its contribution to vertebral fragility. It is unclear if a decrease in trabecular bone density or cortical bone thickness is related to vertebral insufficiency fractures. Therefore, to understand osteoporosis that we have to know the status of trabecular and cortical structures.\u003c/p\u003e \u003cp\u003eMeanwhile, millions of chest screening exams using low-dose computed tomography (LDCT) are performed annually in the World. As these data include bone information, there are a few methods to extract this information. These methods use the extracted bone density information, which serve as osteoporosis screening. Reduction in overall bone density leads to local reductions in bone density and cortical thickness in vertebrae that match clinically observed fracture patterns in patients with vertebral insufficiency fractures. Current methods for opportunistic osteoporosis screening consist of quantitative measurements of vertebral mean attenuation in Hounsfield Units (HU) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and subjective visual assessment of color-coding CT images [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The colors green and red are applied to rapidly and accurately differentiate normal from abnormal bone density, respectively. However, these methods cannot provide us the status of trabecular and cortical structures. To understand osteoporosis truly, it is necessary to know in vivo macrostructure, microstructure, matrix properties, cellular composition and activity of the bone. Therefore, we need a practical, accurate and rapid technique for extracting these information.\u003c/p\u003e \u003cp\u003eThis study aims to develop a thresholding 3D method for qualitative and quantitative assessment of vertebral microarchitecture from screening chest low-dose computed tomography (LDCT) data, validate it against histological literature, and analyze age-, gender-, and postmenopausal-related microarchitectural changes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eThis retrospective study was approved by the Tanioka Clinic Institutional Review Board in accordance with the 1964 Helsinki Declaration. Informed consent was waived. Twenty-two subjects (12 males, mean age 49.00 years, range 26\u0026ndash;82; 12 females, mean age 56.25 years, range 17\u0026ndash;82, including 9 postmenopausal, mean age 66.89, range 55\u0026ndash;82) undergoing LDCT screening were included. Histological comparisons utilized previous studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImaging Acquisition\u003c/h3\u003e\n\u003cp\u003eAll scans were performed using a spiral CT scanner (ProSpeed AI; GE Systems, Milwaukee, WI, USA) with 120 kV, 60\u0026ndash;80 mA, 1-mm section thickness, high-resolution reconstruction, 512 \u0026times; 512 matrix, and 1.00 \u0026times; 0.18 \u0026times; 0.18 mm\u0026sup3; voxel size. DLP ranged from 60\u0026ndash;150 mGy/cm, corresponding to 0.7\u0026ndash;2.0 mSv effective dose.\u003c/p\u003e\n\u003ch3\u003eVolume Rendering Algorithm and Parameters\u003c/h3\u003e\n\u003cp\u003eThree-dimensional (3D) histological images of the vertebrae were reconstructed from CT data using a threshold-based, semitransparent direct volume-rendering (VR) algorithm.\u003c/p\u003e \u003cp\u003eOptimal threshold and opacity values were determined by measuring radiodensity in three representative regions: cortex (C), bone marrow (B), and high-density spot (T) within the marrow, considered indicative of trabecular bone.\u003c/p\u003e \u003cp\u003eMeasurements were obtained in Hounsfield units (HU) from 1-mm-thick high-resolution axial CT slices at the T12 vertebral level. The results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurements of CT values at the Cortex (C), bone marrow (B), and high-density spot (T)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCortex (C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBone Marrow (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh-Density Spot (T)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT/C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e 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colname=\"c4\"\u003e \u003cp\u003e487.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e297.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e265.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e501.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e286.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e255.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e522.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e297.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e285.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e550.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e301.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e278.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e552.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e268.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e245.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e560.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e298.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e290.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.00\u0026thinsp;\u0026plusmn;\u0026thinsp;16.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e504.08\u0026thinsp;\u0026plusmn;\u0026thinsp;35.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e275.40\u0026thinsp;\u0026plusmn;\u0026thinsp;51.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e260.41\u0026thinsp;\u0026plusmn;\u0026thinsp;41.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e490.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e206.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e456.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e184.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e460.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e172.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e255.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e455.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e156.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e200.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e507.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e136.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e201.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e555.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e283.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e245.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e495.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e274.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e234.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e501.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e287.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e245.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e485.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e295.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e235.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e488.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e305.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e250.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e499.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e312.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e246.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e490.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e312.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e235.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.25\u0026thinsp;\u0026plusmn;\u0026thinsp;20.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e490.34\u0026thinsp;\u0026plusmn;\u0026thinsp;26.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e229.89\u0026thinsp;\u0026plusmn;\u0026thinsp;84.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e228.34\u0026thinsp;\u0026plusmn;\u0026thinsp;23.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.63\u0026thinsp;\u0026plusmn;\u0026thinsp;18.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e497.21\u0026thinsp;\u0026plusmn;\u0026thinsp;31.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e252.65\u0026thinsp;\u0026plusmn;\u0026thinsp;72.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e244.38\u0026thinsp;\u0026plusmn;\u0026thinsp;36.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe opacity ratio between cortical and trabecular bone was calculated, and the relationship was expressed as a scatter plot and linear regression model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.0019X\u0026thinsp;+\u0026thinsp;0.0426; r\u0026thinsp;=\u0026thinsp;0.9276; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003eThe model was derived under the assumption of normal distribution.\u003c/p\u003e \u003cp\u003eIn the VR process, structures with HU values above the upper threshold were assigned high opacity. The transparency ratio between cortex (C) and high-density spot (T) determined the final threshold settings.\u003c/p\u003e \u003cp\u003eAn opacity of 0.3\u0026ndash;0.8 within the bone interior provided optimal visualization of the trabecular network. The HU value corresponding to the trabecular region was obtained from the regression equation, resulting in a final threshold range of 130\u0026ndash;400 HU.\u003c/p\u003e \u003cp\u003eThe relationship was best represented by an upward monotonic (increasing) curve algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImages were rendered only within the specified threshold range. Transparency was set to 0% below 130 HU, ensuring exclusion of non-bone structures, and to 100% above 400 HU to visualize high-density cortical regions.\u003c/p\u003e \u003cp\u003eSince the measured trabecular (T) values were below 300 HU, trabecular imaging was not affected by the upper limit.\u003c/p\u003e \u003cp\u003eOpacity values in the VR process ranged from 0% to 100%, depending on the HU-defined threshold range.\u003c/p\u003e \u003cp\u003eThreshold and opacity parameters were defined as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCortical and trabecular bone threshold: 130\u0026ndash;400 HU\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOpacity range: 0\u0026ndash;100%\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe scatter plot shows the correlation between the high-density spot (T) and cortical-to-trabecular opacity ratio (T/C). The linear regression model (y\u0026thinsp;=\u0026thinsp;0.0019x\u0026thinsp;+\u0026thinsp;0.0426; R\u0026sup2; = 0.8605; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) demonstrates a strong positive association between the measured Hounsfield unit (HU) values of trabecular regions and their corresponding opacity levels used for volume rendering. This regression model defined the opacity variation applied in the reconstruction algorithm for three-dimensional vertebral imaging.\u003c/p\u003e\n\u003ch3\u003ePostprocessing\u003c/h3\u003e\n\u003cp\u003e3D visualization was performed using GE Advantage Navigator Software (v2), which allows interactive adjustment of opacity and color in under five minutes. Measurements of distances and volumes were conducted directly within the 3D environment, similar to platforms like OsiriX or LiveVolume. A sample console image is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eQualitative Imaging Analysis\u003c/h3\u003e\n\u003cp\u003eT12 vertebra images were evaluated against anatomical references and literature [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] for trabecular and cortical architecture.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Imaging Analysis\u003c/h2\u003e \u003cp\u003eMeasurements from T12 horizontal view:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTrabecular thickness (Tb.Th)\u003c/b\u003e: Mean diameter of trabeculae (mm).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTrabecular separation (Tb.Sp)\u003c/b\u003e: Mean distance between trabeculae (mm).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTrabecular number (Tb.N)\u003c/b\u003e: Inverse of mean spacing between trabecular midlines (1/mm).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCortical thickness (Ct.Th)\u003c/b\u003e: Ventral shell thickness of the vertebral body (mm).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eNormality of the measurement data was assessed using the Kolmogorov\u0026ndash;Smirnov test. Variables that followed a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). The effects of age, sex, and menopausal status were evaluated using univariate linear regression analysis.\u003c/p\u003e \u003cp\u003eThe Welch test was applied to verify the equivalence of the present results with previously reported histological data and to examine gender-related differences in each parameter. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were performed using Microsoft Excel 2013 (Microsoft Corporation, Redmond, WA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVolume Rendering Algorithm and Parameters\u003c/h2\u003e \u003cp\u003eThreshold values of 130\u0026ndash;400 HU and opacity settings from 0\u0026ndash;100% were applied. Using an upward-curve algorithm with monochromatic shading, trabecular and cortical bone structures were successfully visualized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eQualitative Imaging Analysis\u003c/h2\u003e \u003cp\u003eThe reconstructed three-dimensional images clearly depicted trabecular and cortical architectures consistent with established anatomical and histological references (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe cortical shell surrounded the spongy cancellous core, and osteonal structures were identifiable. Age-related cortical porosity was also observed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Imaging Analysis\u003c/h2\u003e \u003cp\u003eAll measurement data satisfied the assumptions of normality and homogeneity of variance; therefore, parametric tests were used for statistical analyses.\u003c/p\u003e \u003cp\u003eAge-related regression analysis is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge-related regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemales (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePostmenopausal (n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.Th (mm/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.Sp (mm/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.0117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.0158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.0280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.N (1/mm/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCt.Th (mm/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison with Previous Histological Data\u003c/h2\u003e \u003cp\u003eThe Welch test was used to compare parameters obtained in this study with those from previous histological reports and to assess gender differences.\u003c/p\u003e \u003cp\u003eIn the reference histological data, cortical thickness was measured in 13 female cadavers (aged 54\u0026ndash;87 years; mean 74.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4 years), with a mean T10 cortical thickness of 0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 mm [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMicrostructural parameters from 43 females without fracture (mean age 64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5 years) were: Tb.Th\u0026thinsp;=\u0026thinsp;0.368\u0026thinsp;\u0026plusmn;\u0026thinsp;0.046 mm, Tb.Sp\u0026thinsp;=\u0026thinsp;0.667\u0026thinsp;\u0026plusmn;\u0026thinsp;0.129 mm, and Tb.N\u0026thinsp;=\u0026thinsp;0.970\u0026thinsp;\u0026plusmn;\u0026thinsp;0.090 1/mm [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the Welch test results comparing the present study with the previous literature. No significant differences were observed for most parameters, except for male Tb.Sp (p\u0026thinsp;=\u0026thinsp;0.036), postmenopausal Tb.N (p\u0026thinsp;=\u0026thinsp;0.033), and postmenopausal Ct.Th (p\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Measured Vertebral Parameters with Previous Literature (Welch Test)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThis Study\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrevious Reports (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.Th (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.421\u0026thinsp;\u0026plusmn;\u0026thinsp;0.159 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.450\u0026thinsp;\u0026plusmn;\u0026thinsp;0.131 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.392\u0026thinsp;\u0026plusmn;\u0026thinsp;0.183 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.368\u0026thinsp;\u0026plusmn;\u0026thinsp;0.046(43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.300\u0026thinsp;\u0026plusmn;\u0026thinsp;0.087 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.Sp (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.583\u0026thinsp;\u0026plusmn;\u0026thinsp;0.303 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.483\u0026thinsp;\u0026plusmn;\u0026thinsp;0.221 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.683\u0026thinsp;\u0026plusmn;\u0026thinsp;0.356 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.667\u0026thinsp;\u0026plusmn;\u0026thinsp;0.129(43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.833\u0026thinsp;\u0026plusmn;\u0026thinsp;0.269 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.N (1/mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.999\u0026thinsp;\u0026plusmn;\u0026thinsp;0.164 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.053\u0026thinsp;\u0026plusmn;\u0026thinsp;0.160 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.946\u0026thinsp;\u0026plusmn;\u0026thinsp;0.156 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.970\u0026thinsp;\u0026plusmn;\u0026thinsp;0.090(43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.891\u0026thinsp;\u0026plusmn;\u0026thinsp;0.142 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCt.Th (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.367\u0026thinsp;\u0026plusmn;\u0026thinsp;0.163(24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.392\u0026thinsp;\u0026plusmn;\u0026thinsp;0.040(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.347\u0026thinsp;\u0026plusmn;\u0026thinsp;0.054 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.256\u0026thinsp;\u0026plusmn;\u0026thinsp;0.124 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNumber (n)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGender Comparison\u003c/h2\u003e \u003cp\u003eGender-based comparisons using the Welch test are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eMales generally exhibited higher values for Tb.Th and Ct.Th and lower values for Tb.Sp than females. Significant gender differences were observed for Tb.N and Ct.Th.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGender comparison in each parameter using the Welch test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.Th (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.450\u0026thinsp;\u0026plusmn;\u0026thinsp;0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.392\u0026thinsp;\u0026plusmn;\u0026thinsp;0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.Sp (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.483\u0026thinsp;\u0026plusmn;\u0026thinsp;0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.683\u0026thinsp;\u0026plusmn;\u0026thinsp;0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.N (1/mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.053\u0026thinsp;\u0026plusmn;\u0026thinsp;0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.946\u0026thinsp;\u0026plusmn;\u0026thinsp;0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCt.Th (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.392\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.347\u0026thinsp;\u0026plusmn;\u0026thinsp;0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSummary of Findings were as follows.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe developed thresholding 3D reconstruction method accurately reproduced trabecular and cortical microstructures.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eQuantitative parameters showed close agreement with previous histological data.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSignificant differences were limited to specific subgroups (male Tb.Sp, postmenopausal Tb.N, and Ct.Th).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMale vertebrae tended to have thicker cortices and denser trabecular networks than those of females.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis technique was developed by transforming screening chest low-dose CT (LDCT) data using a threshold-based, semitransparent direct volume-rendering algorithm.\u003c/p\u003e \u003cp\u003eA threshold range of 130\u0026ndash;400 Hounsfield units (HU) included most trabecular bone values and was therefore appropriate for bone histological imaging. Thresholds below this range allowed visualization of soft tissues within the bone structure.\u003c/p\u003e \u003cp\u003eThe reconstructed three-dimensional (3D) vertebral histological images showed close agreement with standard anatomical atlases and published histological descriptions. Statistical comparison revealed no significant differences between the present measurements and those reported in previous histological studies, supporting the reliability of the reconstructed 3D vertebral models [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe vertebra consisted of light, spongy cancellous tissue covered by a thin layer of compact bone perforated by multiple foramina. Coronal and axial sections of the reconstructed images clearly demonstrated the spatial relationship between cortical and trabecular bone in three dimensions.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRadiation dose\u003c/h2\u003e \u003cp\u003eAlthough MDCT is the available technique to analyze vertebral microstructure in vivo, high radiation exposure (28 mSv to 62 mSv) cannot be avoided [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In this technique, the radiation dose converted from the DLP value was 0.7 to 2.1 mSv, and the average dose was 1.5 mSv [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This dose is larger than that for QCT (0.025\u0026ndash;0.360 mSv) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is approximately equal to the average effective dose of the thoracic spine (1.0 mSv) or the average effective dose of the lumbar spine (1.5 mSv) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. So, this technique is safe for daily routine practice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAge-related microstructural changes and gender differences\u003c/h2\u003e \u003cp\u003eIn our horizontal view study, for males, females, and postmenopausal females, Tb.Th decreased linearly with age at a rate of 0.0054 mm, 0.0086 mm, and 0.0088 mm per year, respectively; Tb.Sp increased linearly with age at a rate of 0.0117 mm, 0.0158 mm, and 0.0280 mm per year, respectively; Tb.N decreased linearly with age at a rate of 0.0061, 0.0062, and 0.00138 per year, respectively; Ct.Th decreased linearly with age at a rate of 0.0068 mm, 0.0084 mm, and 0.0012 mm per year, respectively.\u003c/p\u003e \u003cp\u003eAge-related changes of horizontal Tb.Th decrease in thickness with aging is faster in females than in males, as previously reported [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The bone attenuation due to decreases in Tb.N and increases in Tb.Sp, with thinning of Tb.Th has concluded from our results like the previous report for age-related trabecular bone loss [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Postmenopausal females show decrease in Tb.Th, Tb.N, and more increase Tb.Sp than females. That is, postmenopausal females have fragile trabecular bone with more space. There was significant difference between genders in Ct.Th decrease in thickness with age. However, in postmenopausal period, Ct.Th decrease in thickness is slowly. Significant differences are found between the genders.\u003c/p\u003e \u003cp\u003eThese findings support the hypothesis of two major age-related processes that lead to bone loss [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. One is a reduction in trabecular bone loss. The second process that contributes to bone loss is cortical bone loss. Trabecular bone loss is caused by thinning and loss of trabecular bone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eThe study has several limitations. The sample size was small (12 males and 12 females), limiting statistical inference. However, the relatively small sample size and single-center design of our study limit the generalizability of these results. Further multicenter studies with larger cohorts are needed to validate the clinical utility of this thresholding 3D-CT imaging. Finally, although radiation dose (0.7\u0026ndash;2.0 mSv) is safe for routine screening, lower than MDCT (62 mSv) and comparable to thoracic/lumbar spine imaging [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], repeated imaging warrants caution.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study evaluated the feasibility of using data obtained from low-dose computed tomography (LDCT) for lung cancer screening to visualize and measure parameters of the thoracic vertebral cortical bone and trabeculae. By comparing these findings with previous pathological studies, the authors demonstrated that this method could be used to assess vertebral bone condition. In other words, it suggests that LDCT scans performed for cancer screening may also provide simultaneous insight into the state of the thoracic spine. However, since this investigation was conducted at a single institution with a limited number of cases, further multicenter studies are required to validate the findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eLDCT: Low-dose computed tomography\u003c/li\u003e\n \u003cli\u003eMDCT: Multi-detector computed tomography\u003c/li\u003e\n \u003cli\u003eVR: Volume rendering\u003c/li\u003e\n \u003cli\u003eHU: Hounsfield units\u003c/li\u003e\n \u003cli\u003eDLP: Dose length product\u003c/li\u003e\n \u003cli\u003eTb.Th: Trabecular thickness\u003c/li\u003e\n \u003cli\u003eTb.Sp: Trabecular separation\u003c/li\u003e\n \u003cli\u003eTb.N: Trabecular number\u003c/li\u003e\n \u003cli\u003eCt.Th: Cortical thickness\u003c/li\u003e\n \u003cli\u003eSD: Standard deviation\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHT and ST carried out the design of the study, evaluated the created images, the imaging of reproducibility, measured each parameter, performed statistical analysis, and prepared the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eAll authors of this manuscript declare no relationship with any companies, whose products or services may be related the subject matter of the article. \u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eAll authors state that this work has not received any funding. \u003cstrong\u003eStatistics and Biometry:\u0026nbsp;\u003c/strong\u003eNo complex statistical methods were necessary for this paper. \u003cstrong\u003eInformed consent:\u0026nbsp;\u003c/strong\u003eInformed consent was waived for this retrospective study in which we analyzed previously obtained screening chest LDCT data at one institution. \u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eAll procedures performed in studies involving human participants were accorded with the Ethics Committee of Tanioka Clinic, Japan, and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Institutional Review Board approval at Tanioka Clinic was obtained. \u003cstrong\u003eMethodology:\u0026nbsp;\u003c/strong\u003eRetrospective; experimental, performed at one institution\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMartini FH (2006) Osseous tissue and bone structure. In: Martini FH. Fundamentals of Anatomy \u0026amp; Physiology. 7th ed. Pearson Education, San Francisco.183\u0026ndash;203\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMosekilde L, Ebbesen EN, Tornvig L et al (2000) Trabecular bone structure and strength - remodeling and repair. 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Int J Endocrinol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2013/213234\u003c/span\u003e\u003cspan address=\"10.1155/2013/213234\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Tanioka Clinic","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Trabecular bone, Cortical bone, Vertebra, Osteoporosis, CT, thresholding 3D Imaging","lastPublishedDoi":"10.21203/rs.3.rs-8390011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8390011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAnnual screening chest low-dose CT (LDCT) exams contain detailed bone anatomical information. There is a need for a simplified three-dimensional (3D) method to perform practical, passive, accurate, reproducible, and rapid osteoporosis screening from these data.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo develop a simplified 3D method for osteoporosis screening from vertebral trabecular and cortical architecture using screening chest LDCT data, and to analyze bone microarchitecture according to shape, parameters, age, and gender.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMaterials and Methods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTwenty-two subjects underwent screening chest LDCT. 3D vertebral histological images were reconstructed from LDCT data using interactive direct transparency thresholding volume rendering algorithms. Trabecular and cortical bone parameters were measured and compared with values from the literature. Age, gender, and each parameter\u0026rsquo;s relationships were analyzed using linear regression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eReconstructed 3D images of trabecular and cortical microstructures closely matched prior literature. Parameter values were generally larger in males, although Tb.Th and Tb.Sp showed no significant gender difference. Tb.Th, Tb.Sp, and Ct.Th declined faster with age in females than males, while postmenopausal leads to a reduction in Tb.N\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eScreening chest LDCT (~\u0026thinsp;1.5 mSv) can provide superior vertebral imaging compared to lumbar spine radiography, revealing both trabecular and cortical architecture. This simplified 3D technique allows assessment of macro- and microstructural bone status, supporting osteoporosis diagnosis and management.\u003c/p\u003e","manuscriptTitle":"Low-Dose Chest CT–Based 3D Osteoporosis Assessment: Insights into Vertebral Microarchitecture","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:21:39","doi":"10.21203/rs.3.rs-8390011/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6961c99a-fd4d-415d-be3a-23a6eea5e932","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59854288,"name":"Biophysics"}],"tags":[],"updatedAt":"2025-12-22T10:21:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 10:21:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8390011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8390011","identity":"rs-8390011","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}