A semi-automated bone mineral density measurement tool, phantom-less QCT: Technique optimization and clinical validation

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This study aimed to evaluate the accuracy and precision of a new phantom-less quantitative computed tomography (PL-QCT) technique for measuring T12 to L1 bone mineral density using thoracolumbar CT scans. Materials and methods : Waist fat and muscle tissue were used as calibration references for bone mineral density measurements in PL-QCT. Automatic bone mineral density measurements in PL-QCT were conducted using crude segmentation, tissue-specific thresholds, and a two-dimensional convolution algorithm. Region-of-interest selection in PL-QCT measurement was cross-referenced with phantom-based QCT (PB-QCT) through asynchronous calibration of body film. Measurement consistency between PB-QCT and PL-QCT was compared. Results : A total of 568 participants, aged 20 to 85 years (mean age, 49.74 ± 16.80 years), were included. Consistency analysis showed a mean deviation of 0.31 mg/cm 3 in T12 to L1 BMD measurements between PB-QCT and PL-QCT, with the correlation coefficient (r) and Cronbach’s alpha values both ranging between 0.998 and 1.000. Bland–Altman analysis revealed that most T12 to L2 bone mineral density data points were within the 95% limits of agreement, demonstrating high accuracy. Conclusion : The PL-QCT system demonstrated excellent consistency in T12 to L2 vertebral BMD measurements when compared with the PB-QCT system. Overall, PL-QCT exhibited superior accuracy and precision, confirming its reliability as a tool to evaluate spinal bone mineral density. bone mineral density osteoporosis quantitative computed tomography vertebral body Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Osteoporosis is a systemic bone disease characterized by reduced bone mass, structural deterioration, and decreased bone strength, leading to increased bone fragility and fracture risk [ 1 ]. Osteoporosis poses a significant public health challenge, affecting approximately 200 million people worldwide. According to the International Osteoporosis Foundation, an osteoporotic fracture occurs every 3 seconds, resulting in approximately 8.9 million fractures annually [ 2 , 3 ]. As the global population ages, the prevalence of osteoporotic fractures and related diseases continues to rise. In 2010, nearly 158 million people were at high risk of osteoporotic fractures, a number expected to double by 2040 [ 4 ]. Bone mineral density (BMD) measurements provide critical information on bone strength, mass, and overall health. The primary clinical methods for quantitative bone assessment include dual-energy x-ray absorptiometry (DEXA) and quantitative computed tomography (QCT) [ 5 ]. DEXA is a two-dimensional planar projection imaging technology. Its measurements are influenced by numerous factors, such as body weight, scoliosis, bone hyperplasia, vertebral fractures, and vascular calcification. Studies have shown that DEXA may overestimate BMD in certain patients with osteoporosis because it measures areal BMD and provides an average of cortical and trabecular bone density. It cannot measure cancellous bone, which has a faster metabolism, separately [ 6 ]. In contrast, QCT assesses BMD using clinical CT scan data combined with a QCT quality control phantom and software analysis system. Recent studies suggest that thoracolumbar vertebral BMD alone can reflect whole-body BMD. QCT measures volumetric BMD of the central cancellous bone in the vertebral body, reported in mg/cm 3 . This method provides true volumetric BMD, making it more accurate and independent of factors such as spinal degeneration, scoliosis, and body weight [ 7 , 8 ]. QCT can be classified into phantom-based quantitative computed tomography (PB-QCT) and phantom-less quantitative computed tomography (PL-QCT). PB-QCT includes synchronously and asynchronously calibrated QCT systems. Asynchronous QCT provides results comparable to those of synchronously calibrated QCT [ 9 ]. However, body phantom-based QCT requires reference calibration using an image obtained while the patient is scanned. This approach is prone to beam-hardening and scattering effects, and asynchronous QCT cannot reliably control scanner stability during calibration [ 10 ]. PL-QCT eliminates the need for external phantom calibration. This system utilizes automatic algorithms incorporating coarse segmentation, tissue-specific thresholds, and two-dimensional convolution operations to automatically select and calculate the optimal regions of interest (ROI) for fat and muscles. This method improves the accuracy of PL-QCT BMD measurements. Furthermore, PL-QCT can be seamlessly integrated into CT workflows, enabling BMD evaluation during routine chest and abdominal scans with high precision [ 11 – 13 ]. This study aimed to verify the accuracy and precision of PL-QCT for measuring thoracolumbar BMD. Using asynchronous PB-QCT as the reference standard, we evaluated PL-QCT across varying age, sex, and bone mass categories to determine its potential as a reliable tool for clinical bone assessment. Materials and methods This study received approval from the ethics commit tee of Tianjin University, Tianjin Hospital (project approval number 2021 Medical Review 084 )and the 1964 Declaration of Helsinki. including its later amendments and comparable ethical guidelines. In compliance with HIPAA regulations, informed consent was not required for this retrospective study. Patient selection Patients who underwent chest or thoracolumbar CT scans between March 2020 and June 2023 were included. The exclusion criteria were as follows: (1) Patients with metal implants or bone cement injections after spinal surgery that could interfere with measurement accuracy; (2) Patients with spinal infections, inflammation, tumors, or metabolic diseases at the scan site; and (3) Cases with poor image quality unsuitable for diagnostic evaluation (Figure 1). QCT acquisition CT images were acquired using various CT devices, including the GE 64-slice spiral CT (CT660; GE Healthcare, Milwaukee, WI, USA) and the GE 128-slice spiral CT (Discovery CT750 HD GT2000 CHN; GE Healthcare). Chest CT scans: The scan field of view was 50 cm, with a reconstructed field of view of 38 cm. Tube voltage was set to 120 kv and tube current ranged from 200 to 300 mA, automatically adjusted based on the patient’s weight. The scanning range extended from the apex of the lungs to the lower margin of the second lumbar vertebrae. A standard algorithm was applied, with a slice thickness of 1.25 mm. Thoracolumbar CT scans: The scan field of view was 50 cm, with a reconstructed field of view of 38 cm. The tube voltage was 120 kv and the tube current 300 mA. The scanning range extended from the upper margin of the 11 th thoracic vertebrae to the lower margin of the second lumbar vertebrae. A standard algorithm was applied, with a slice thickness of 1.25 mm. Following the acquisition, the imaging data were processed using Bone’s QCT version 1.0 software (Bone’s Technology [Shenzhen] Limited China) and the PB-QCT workstation (Mindways QCT PRO version 6.1; Mindways Sofware, Inc., Austin, Texas, USA). PL-QCT data acquisition The specific algorithm for automatic ROI selection in PL-QCT combines advanced artificial intelligence and human self-modeling technologies developed by annotating tens of thousands of clinical datasets. Segmentation: A segmentation mask (M) for the fat and muscle regions was generated using a threshold-based segmentation algorithm based on clinical reference values (Figure. 2a). Convolutional filtering: Convolutional filtering was applied to the segmentation mask (M) to produce a convolutional graph (C) of fat and muscle regions, ensuring robust boundary identification (Figure. 2b, c). Distance-based refinement: A distance-based convolutional operation refined the convolutional graph (C), prioritizing points closer to the vertebral body center. This step produced an adjusted convolutional graph (C^), in which closer distances yielded higher convolutional values. Elliptical calibration: The maximum two-dimensional coordinate (x^, y^) in the refined convolutional graph (C^) was identified and an elliptical calibration circle was placed at this coordinate. This allowed extraction of the CT values for fat and muscle features. These CT values were then compared with their respective density reference values to establish a linear relationship between CT values and bone density, enabling the BMD to be calculated. BMD measurements using PL-QCT were performed with the Bone’s QCT version 1.0 software. Two trained technicians (WYZ and MHF) performed the measurements using a self-developed algorithm for vertebral segment analysis [13]. Measurer 1 assessed the placement of the PL-QCT ROI. When differences in single-segment BMD were close to or exceeded 10 mg/cm 3 , adjustment was made to the ROI of the cancellous bone to minimize errors caused by measurement location. Measurer 2 relied on automatic ROI selection for measurements. The measurement process was as follows: The CT level was adjusted to the middle layer of the vertebrae to be measured using the software. The function button was then clicked and all ROIs were automatically selected. The calibration ROI was automatically positioned within the subcutaneous fat and paravertebral muscle ranges; in contrast, the bone ROI was placed within the vertebral range (Figure. 3a). The reliability of subcutaneous fat and paravertebral muscle ROI selection was supported by the calibration curve (Figure. 3b). This calibration curve was adjusted and aligned with the ROI placement methodology used by Mindways, ensuring that the cancellous bone was consistently measured from the 12 th thoracic to the 2 nd lumbar vertebrae (Figure. 3c). PB-QCT data acquisition PB-QCT data were measured using Mindways QCT PRO version 6.1 by two highly experienced radiologists: MXH, with 12 years of experience in musculoskeletal imaging, and WZ, with 33 years of experience in osteomuscular imaging. The average of two measurements was used. The measurement process was as follows: Select “New 3D spine exam analysis” in the QCT software interface. Open the patient’s QCT file, confirm their information, and enter their height and weight. Select vertebrae T12 to L2 and adjust the rotation to align the vertebral endplates horizontally. Center the ROI marker on the vertebral body. In the “ROIs” section, adjust the red ROI box to exclude bone islands, veins, and lesion areas. Save the results under “Report” after confirming and recording the measurements (Figure. 4). Statistical analysis SPSS Statistics for Windows version 26.0 (IBM Inc., Armonk, NY, USA) was used for statistical analysis. Normally distributed variables are expressed as mean ± standard deviation; skewed variables are presented as medians with interquartile ranges. A paired sample t-test was conducted to assess BMD differences between the two methods, with statistical significance defined as p < 0.05. Cronbach’s alpha was used to evaluate the consistency between the two methods. Comparative data are presented as absolute mean differences, along with 95% confidence interval (CI) and 95% prediction intervals (agreement). Systematic bias between methods was analyzed using Bland–Altman and XY plots. Results Statistical information A total of 568 patients with consecutive PB-QCT and PL-QCT measurements from the chest and thoracolumbar CT scans were included. Patient ages ranged from 20 to 85 years, with a mean age of 49.74 ± 16.80 years and a mean body mass index of 24.50 ± 3.96 kg/m 2 . The detailed data are presented in Table 1 . Table 1 Demographic information Category Number Percentage (%) Sex Male 272 48.0 Female 296 52.0 Age (years) 80 4 0.7 BMD (mg/cm 3 ) > 120 mg/cm 3 318 56.0 80–120 mg/cm 3 152 26.8 < 80 mg/cm 3 98 17.2 BMD: Bone mineral density BMD measurements obtained via the PL-QCT software were slightly higher than those obtained using the PB-QCT method. Differences were observed at the following vertebral levels: T12: Mean difference 0.38 mg/cm3, P < 0.001. L1: Mean difference 0.32 mg/cm3, P < 0.001. L2: Mean difference 0.22 mg/cm3, P = 0.02. The detailed values are presented in Table 2 . Table 2 Comparison of differences between PL-QCT software and PB-QCT in the measurement of BMD in T12, L1, and L2 vertebrae SD standard deviation, BMD bone mineral density.Mean BMD ± SD (mg/cm 3 ), Mean ± SD of PL and PB(mg/cm 3 ) Mean difference in the value of PL-QCT and PB-QCT ± SD (mg/cm 3 ), PL-QCT phantom-less quantitative computed tomography, PB-QCT phantom-based quantitative computed tomography with body film Mean BMD ± SD Mean ± SD of PL and PB t P T12 PL-QCT 133.49 ± 47.86 0.38 ± 2.15 5.11 < 0.001 PB-QCT 133.11 ± 48.03 L1 PL-QCT 129.91 ± 48.71 0.32 ± 1.51 2.28 < 0.001 PB-QCT 129.59 ± 48.96 L2 PL-QCT 125.47 ± 49.63 0.22 ± 2.33 4.24 0.02 PB-QCT 125.25 ± 49.92 Consistency verification The PL-QCT results showed excellent agreement with PB-QCT measurements. For the T12 to L2 levels, intraclass correlation coefficients ranged from 0.998 to 0.999; in contrast, Cronbach’s alpha values were between 0.999 and 1. This high degree of consistency is summarized in Table 3 . The PL-QCT results measured by Measurer 2 showed strong agreement with the PB-QCT measurements. At the T12 to L2 level, the intragroup correlation coefficients ranged from 0.999 to 1.000 and Cronbach’s alpha from 0.999 to 1.000. The PL-QCT results of Measurers 1 and 2 were in good agreement. At the T12 to L2 level, the measured intragroup correlation coefficients ranged from 0.998 to 0.999 and Cronbach’s coefficients from 0.999 to 1.000. Table 3 Comparison of the consistency between PL-QCT and PB-QCT measured by Measurers 1 and 2 Cronbach's Alpha 95% Confidence Interval P T12 PL-QCT_ Measurer 1 & PB-QCT 0.999 0.998 (95%CI:0.998 ~ 0.999) < 0.001 PL-QCT_ Measurer 2 & PB-QCT 0.999 0.999 (95%CI:0.999 ~ 1.000) < 0.001 PL-QCT_Measurer1&PL-QCT_ Measurer 2 0.999 0.998 (95%CI:0.998 ~ 0.999) < 0.001 L1 PL-QCT_ Measurer 1 & PB-QCT 1.000 0.999 (95%CI:0.999 ~ 1.000) < 0.001 PL-QCT_ Measurer 2 & PB-QCT 1.000 1.000(95%CI:0.999 ~ 1.000) < 0.001 PL-QCT_Measurer1&PL-QCT_ Measurer 2 1.000 0.999 (95%CI:0.998 ~ 0.999) < 0.001 L2 PL-QCT_ Measurer 1 & PB-QCT 0.999 0.998 (95%CI:0.998 ~ 0.999) < 0.001 PL-QCT_ Measurer 2 & PB-QCT 0.999 0.999 (95%CI:0.998 ~ 0.999) < 0.001 PL-QCT_Measurer1&PL-QCT_ Measurer 2 1.000 0.999 (95%CI:0.998 ~ 0.999) < 0.001 PL-QCT phantom-less quantitative computed tomography, PB-QCT phantom-based quantitative computed tomography with body film Bland–Altman and XY diagram analysis results Bland–Altman analysis revealed a mean BMD difference between PL-QCT and PB-QCT of 0.22 mg/cm 3 to 0.38 mg/cm 3 at the T12, L1, and L2 levels. Most data points fell within the 95% limits of agreement (LOA), indicating strong consistency between the two measurement systems. The points within the consistency limit were evenly distributed in horizontal bands with no significant upward or downward trend, suggesting no obvious linear relationship between the BMD difference measured by PL-QCT and PB-QCT and the mean BMD values. The BMD difference between the two methods remained stable and did not change with the absolute BMD values. Bland–Altman analysis of BMD measurement results at the T12 level The mean difference between the two methods was 0.38, with a maximum difference of 19.41, a minimum difference of − 32.18, and 95% LOA ranging from − 3.82 to 4.58. Most data points (97.18%, 552/568) fell within the LOA range and the data were evenly distributed around the 0 line (Fig. 5 a). Bland–Altman analysis of BMD measurements at the L1 level The mean difference was 0.32, with a maximum difference of 16.14, a minimum difference of − 6.25, and 95% LOA ranging from − 2.64 to 3.29. Most data points (94.89%, 539/568) fell within the LOA range, with even distribution around the 0 line (Fig. 5 b). Bland–Altman analysis of BMD measurements at the L2 level The mean difference between was 0.22, with a maximum difference of 39.73, a minimum difference of − 32.18, and 95% LOA ranging from − 4.34 to 4.79. Most data points (97.36%, 553/568) were within the 95% LOA range, with even distribution around the 0 line (Fig. 5 c). XY chart analysis results The general trend of the correlation between the PL-QCT and PB-QCT measurement systems for BMD values at the T12 level, the correlation coefficient was 0.9990, with most points closely aligned with the fitting curve (Fig. 5 d). At the L1 level, the correlation coefficient was 0.9995, with data points tightly clustered around the fitting curve (Fig. 5 e). At the L2 level, the correlation coefficient was 0.9989, with most points closely scattered around the fitting curve (Fig. 5 f). Most data points were closely aligned with the fitting curve, indicating strong agreement between the measurements from the two methods. Discussion Our findings indicate that in a large, diverse sample of individuals with varying sex, age, and bone content, the PL-QCT system developed by our research group demonstrates high consistency with PB-QCT in measuring vertebral body BMD from T12 to L2, with a correlation coefficient of 0.998 to 1.000 and Cronbach’s alpha ranging from 0.998 to 1.000. Bland–Altman analysis revealed that most data points for T12 to L2 BMD measurements were within the 95% LOA, suggesting high consistency between the two measurement methods. BMD values from PL-QCT at T12 to L2 were slightly higher than those from PB-QCT, with a deviation of approximately 0.31 mg/cm 3 , which did not affect the diagnosis of osteoporosis or bone content analysis. Although no significant differences were found between the two BMD measurement systems, the consistency and correlation were strong. Therkildsen et al. [ 14 ] reported that the BMD measurements from a general PB-QCT system were slightly higher than those from PL-QCT, which contrasts with our findings. This discrepancy may be due to differences in the PL-QCT systems used and slight variations in the algorithms employed. The BMD deviation between PL-QCT and PB-QCT in our study was approximately 0.31 mg/cm 3 ; in contrast, Mueller et al. [ 11 ] reported a deviation of 0.9 mg/cm 3 between the Philips BMD PL-QCT system and PB-QCT. In the present study, the PL-QCT measurements were closer to those of PB-QCT, which could be attributed to the automatic ROI selection used in our system. The selection of subcutaneous fat and paravertebral muscle ROIs is a critical factor in BMD measurement [ 15 , 16 ], and the differences in fat or muscle mass across individual tests can lead to inconsistent BMD results. However, the automatic PL-QCT system developed by our research group utilizes rough segmentation, tissue-specific thresholds, and two-dimensional convolution operations to perform effective filtering, providing peak curves of the normal distribution of Hounsfield unit values for bone, muscle, and fat. This helps with the automatic selection of subcutaneous fat and paravertebral muscle ROIs, identification of the vertebral center, and selection of the middle layer of the vertebra for analysis. As a result, the consistency of BMD is guaranteed, improving the accuracy of the PL-QCT system [ 14 , 17 ]. The traditional PB-QCT system is more accurate than DEXA [ 18 , 19 ] because it measures true volumetric bone density without being influenced by factors such as spinal degeneration, scoliosis, or body weight. DEXA measurements are susceptible to inaccuracies caused by spinal degeneration, vertebral deformation, abdominal aortic calcification, and other factors, particularly in older adults. Traditional PB-QCT also allows for the measurement of both trabecular and cortical BMD, providing an earlier and more accurate reflection of BMD changes compared to DEXA. It can be used to monitor treatment responses for osteoporosis, predict fracture risk, and aid in preoperative orthopedic planning. Individuals at risk of osteoporotic fractures [ 20 – 22 ] often undergo CT scans for other conditions [ 23 ]: PB-QCT can be performed simultaneously with these clinical CT scans, avoiding additional radiation exposure or scan time [ 24 ]. However, PB-QCT is prone to hardening artifacts due to the use of a hydroxyapatite body film and asynchronous calibration can cause scanner instability [ 25 , 26 ]. Additionally, traditional PB-QCT is bound to a specific CT scanner and can only be used with that machine. In contrast, the PL-QCT system developed by our research group, in addition to maintaining the advantages of traditional PB-QCT, can automatically measure BMD, avoid body film artifacts and scanner instability, and be applied to multiple CT scanners. This flexibility allows for system upgrades and integration of measurement results into imaging reports. The automated PL-QCT system has the potential for further clinical applications in BMD-related studies, improving the prediction accuracy of osteoporosis and facilitating early diagnosis. This study does have some limitations. The sample size was insufficient for age-based subgroup analyses and, being a single-center study, the findings may not be applicable to broader populations. Future multicenter studies with larger sample sizes are necessary. Additionally, comparisons with other bone densitometry were not performed; future research should include multimodal consistency testing. To conclude, the semi-automated PL-QCT system developed by our research group yields results that are accurate and consistent with PB-QCT measurements. This system, which can be integrated directly into imaging report systems without the need for an external body film, shows great promise for BMD evaluation and osteoporosis diagnosis. Declarations Author contributions Fengling Zhu was responsible for the experimental design, statistical analysis of data, figure creation, writing of the manuscript. Yuanzhi Weng, Hanfei Miao, Hua Xu,and Jun Han collected the data collection and conducted the statistical analysis. Hui Yu, Zhi Wang, and Xianghong Meng provided research guidance and reviewed the manuscript. Funding Not applicable. Conflict of interest All authors have no conflicts of interest. Data availability Data is provided within the manuscript. Ethics approval and consent to participate This study received approval from the ethics commit tee of Tianjin University, Tianjin Hospital (project approval number 2021 Medical Review 084)and the 1964 Declaration of Helsinki. including its later amendments and comparable ethical guidelines. In compliance with HIPAA regulations, informed consent was not required for this retrospective study. Consent for publication Informed consent to publish was obtained from the participants for publication of the images. Competing interests The authors declare no competing interests. References Sözen T, Özışık L, Başaran NÇ. An overview and management of osteoporosis. Eur J Rheumatol 2017;4:46-56. Anish RJ, Nair A. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7635189","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524579044,"identity":"a9cef8d1-5a09-47a6-bd67-62466d3a9aa0","order_by":0,"name":"Fengling Zhu","email":"","orcid":"","institution":"Tianjin University, Tianjin Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fengling","middleName":"","lastName":"Zhu","suffix":""},{"id":524579045,"identity":"49ae0465-a414-4fda-9c65-7450aae1414b","order_by":1,"name":"Yuanzhi Weng","email":"","orcid":"","institution":"The University of Hong 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1","display":"","copyAsset":false,"role":"figure","size":125232,"visible":true,"origin":"","legend":"\u003cp\u003eInclusion and exclusion criteria\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7635189/v1/aa4942ee6e082e1c13cb6622.png"},{"id":92859281,"identity":"5e61049e-9d49-4fdc-bd46-527b6473979d","added_by":"auto","created_at":"2025-10-06 12:00:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":253713,"visible":true,"origin":"","legend":"\u003cp\u003eExample of a CT scan processed using the PL-QCT system.\u003c/p\u003e\n\u003cp\u003ea. The green region represents fat, the red region muscle, and the blue region other tissues. b. Convolution map of muscle. c. Convolution map of fat.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7635189/v1/0bbeb2fb0e015c165f035dd2.png"},{"id":92857569,"identity":"fa4f65c8-e26d-41d5-a6d8-f796c03436d5","added_by":"auto","created_at":"2025-10-06 11:44:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":347244,"visible":true,"origin":"","legend":"\u003cp\u003eThe measurement process used for phantom-less quantitative computed tomography. a. Automatic selection of the region of interest for bones. b. Peak curves of the normal distribution of mean Hounsfield unit values for bone, fat, and muscle.c. Automatic selection of the phantom-less quantitative computed tomography region of interest.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7635189/v1/7ba3458939a797f724e40811.png"},{"id":92858161,"identity":"9446c731-b264-43b3-a193-4fb1ce8f683c","added_by":"auto","created_at":"2025-10-06 11:52:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136062,"visible":true,"origin":"","legend":"\u003cp\u003eThe measurement process used for phantom-based quantitative computed tomography. Example of the bone area of interest for phantom-based quantitative computed tomography.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7635189/v1/69c496e01a8bf28ddd806a46.png"},{"id":92857568,"identity":"667a47a8-3e7e-456f-9a58-7d9bbfaaa327","added_by":"auto","created_at":"2025-10-06 11:44:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":256023,"visible":true,"origin":"","legend":"\u003cp\u003eBland–Altman and XY diagram analysis, a,b,c, is the Bland–Altman analysis of BMD measurement results at the T12, L1, L2 level, respectively, d,e,f is the XY chart analysis of T12, L1, L2 level, respectively, PL-QCT, phantom-less quantitative computed tomography, PB-QCT, phantom-based quantitative computed tomography with body film\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7635189/v1/4974ad870184e5f9e9bc7108.png"},{"id":92860429,"identity":"c3ceb0f0-9b85-4c37-ad04-d1267bc2aba1","added_by":"auto","created_at":"2025-10-06 12:09:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1938543,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7635189/v1/4a267275-fbc0-4e24-8a26-11cfdd3ee591.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A semi-automated bone mineral density measurement tool, phantom-less QCT: Technique optimization and clinical validation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoporosis is a systemic bone disease characterized by reduced bone mass, structural deterioration, and decreased bone strength, leading to increased bone fragility and fracture risk [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Osteoporosis poses a significant public health challenge, affecting approximately 200\u0026nbsp;million people worldwide. According to the International Osteoporosis Foundation, an osteoporotic fracture occurs every 3 seconds, resulting in approximately 8.9\u0026nbsp;million fractures annually [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As the global population ages, the prevalence of osteoporotic fractures and related diseases continues to rise. In 2010, nearly 158\u0026nbsp;million people were at high risk of osteoporotic fractures, a number expected to double by 2040 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBone mineral density (BMD) measurements provide critical information on bone strength, mass, and overall health. The primary clinical methods for quantitative bone assessment include dual-energy x-ray absorptiometry (DEXA) and quantitative computed tomography (QCT) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDEXA is a two-dimensional planar projection imaging technology. Its measurements are influenced by numerous factors, such as body weight, scoliosis, bone hyperplasia, vertebral fractures, and vascular calcification. Studies have shown that DEXA may overestimate BMD in certain patients with osteoporosis because it measures areal BMD and provides an average of cortical and trabecular bone density. It cannot measure cancellous bone, which has a faster metabolism, separately [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn contrast, QCT assesses BMD using clinical CT scan data combined with a QCT quality control phantom and software analysis system. Recent studies suggest that thoracolumbar vertebral BMD alone can reflect whole-body BMD. QCT measures volumetric BMD of the central cancellous bone in the vertebral body, reported in mg/cm\u003csup\u003e3\u003c/sup\u003e. This method provides true volumetric BMD, making it more accurate and independent of factors such as spinal degeneration, scoliosis, and body weight [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eQCT can be classified into phantom-based quantitative computed tomography (PB-QCT) and phantom-less quantitative computed tomography (PL-QCT). PB-QCT includes synchronously and asynchronously calibrated QCT systems. Asynchronous QCT provides results comparable to those of synchronously calibrated QCT [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, body phantom-based QCT requires reference calibration using an image obtained while the patient is scanned. This approach is prone to beam-hardening and scattering effects, and asynchronous QCT cannot reliably control scanner stability during calibration [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePL-QCT eliminates the need for external phantom calibration. This system utilizes automatic algorithms incorporating coarse segmentation, tissue-specific thresholds, and two-dimensional convolution operations to automatically select and calculate the optimal regions of interest (ROI) for fat and muscles. This method improves the accuracy of PL-QCT BMD measurements. Furthermore, PL-QCT can be seamlessly integrated into CT workflows, enabling BMD evaluation during routine chest and abdominal scans with high precision [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study aimed to verify the accuracy and precision of PL-QCT for measuring thoracolumbar BMD. Using asynchronous PB-QCT as the reference standard, we evaluated PL-QCT across varying age, sex, and bone mass categories to determine its potential as a reliable tool for clinical bone assessment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThis study received approval from the ethics commit tee of Tianjin University, Tianjin Hospital (project approval number 2021 \u003cem\u003eMedical Review 084\u003c/em\u003e)and the 1964 Declaration of Helsinki. including its later amendments and comparable ethical guidelines. In compliance with HIPAA regulations, informed consent was not required for this retrospective study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients who underwent chest or thoracolumbar CT scans between March 2020 and June 2023 were included. The exclusion criteria were as follows: (1) Patients with metal implants or bone cement injections after spinal surgery that could interfere with measurement accuracy; (2) Patients with spinal infections, inflammation, tumors, or metabolic diseases at the scan site; and (3) Cases with poor image quality unsuitable for diagnostic evaluation (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQCT acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCT images were acquired using various CT devices, including the GE 64-slice spiral CT (CT660; GE Healthcare, Milwaukee, WI, USA) and the GE 128-slice spiral CT (Discovery CT750 HD GT2000 CHN; GE Healthcare).\u003c/p\u003e\n\u003cp\u003eChest CT scans: The scan field of view was 50 cm, with a reconstructed field of view of 38 cm. Tube voltage was set to 120 kv and tube current ranged from 200 to 300 mA, automatically adjusted based on the patient\u0026rsquo;s weight. The scanning range extended from the apex of the lungs to the lower margin of the second lumbar vertebrae. A standard algorithm was applied, with a slice thickness of 1.25 mm.\u003c/p\u003e\n\u003cp\u003eThoracolumbar CT scans: The scan field of view was 50 cm, with a reconstructed field of view of 38 cm. The tube voltage was 120 kv and the tube current 300 mA. The scanning range extended from the upper margin of the 11\u003csup\u003eth\u003c/sup\u003e thoracic vertebrae to the lower margin of the second lumbar vertebrae. A standard algorithm was applied, with a slice thickness of 1.25 mm.\u003c/p\u003e\n\u003cp\u003eFollowing the acquisition, the imaging data were processed using Bone\u0026rsquo;s QCT version 1.0 software (Bone\u0026rsquo;s Technology [Shenzhen] Limited China) and the PB-QCT workstation (Mindways QCT PRO version 6.1; Mindways Sofware, Inc., Austin, Texas, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePL-QCT data acquisition\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe specific algorithm for automatic ROI selection in PL-QCT combines advanced artificial intelligence and human self-modeling technologies developed by annotating tens of thousands of clinical datasets.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eSegmentation: A segmentation mask (M) for the fat and muscle regions was generated using a threshold-based segmentation algorithm based on clinical reference values (Figure. 2a).\u003c/li\u003e\n \u003cli\u003eConvolutional filtering: Convolutional filtering was applied to the segmentation mask (M) to produce a convolutional graph (C) of fat and muscle regions, ensuring robust boundary identification (Figure. 2b, c).\u003c/li\u003e\n \u003cli\u003eDistance-based refinement: A distance-based convolutional operation refined the convolutional graph (C), prioritizing points closer to the vertebral body center. This step produced an adjusted convolutional graph (C^), in which closer distances yielded higher convolutional values.\u003c/li\u003e\n \u003cli\u003eElliptical calibration: The maximum two-dimensional coordinate (x^, y^) in the refined convolutional graph (C^) was identified and an elliptical calibration circle was placed at this coordinate. This allowed extraction of the CT values for fat and muscle features. These CT values were then compared with their respective density reference values to establish a linear relationship between CT values and bone density, enabling the BMD to be calculated.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBMD measurements using PL-QCT were performed with the Bone\u0026rsquo;s QCT version 1.0 software. Two trained technicians (WYZ and MHF) performed the measurements using a self-developed algorithm for vertebral segment analysis [13].\u003c/p\u003e\n\u003cp\u003eMeasurer 1 assessed the placement of the PL-QCT ROI. When differences in single-segment BMD were close to or exceeded 10 mg/cm\u003csup\u003e3\u003c/sup\u003e, adjustment was made to the ROI of the cancellous bone to minimize errors caused by measurement location. Measurer 2 relied on automatic ROI selection for measurements. The measurement process was as follows: The CT level was adjusted to the middle layer of the vertebrae to be measured using the software. The function button was then clicked and all ROIs were automatically selected. The calibration ROI was automatically positioned within the subcutaneous fat and paravertebral muscle ranges; in contrast, the bone ROI was placed within the vertebral range (Figure. 3a). The reliability of subcutaneous fat and paravertebral muscle ROI selection was supported by the calibration curve (Figure. 3b). This calibration curve was adjusted and aligned with the ROI placement methodology used by Mindways, ensuring that the cancellous bone was consistently measured from the 12\u003csup\u003eth\u003c/sup\u003e thoracic to the 2\u003csup\u003end\u003c/sup\u003e lumbar vertebrae (Figure. 3c).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePB-QCT data acquisition\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePB-QCT data were measured using Mindways QCT PRO version 6.1 by two highly experienced radiologists: MXH, with 12 years of experience in musculoskeletal imaging, and WZ, with 33 years of experience in osteomuscular imaging. The average of two measurements was used. The measurement process was as follows:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eSelect \u0026ldquo;New 3D spine exam analysis\u0026rdquo; in the QCT software interface.\u003c/li\u003e\n \u003cli\u003eOpen the patient\u0026rsquo;s QCT file, confirm their information, and enter their height and weight.\u003c/li\u003e\n \u003cli\u003eSelect vertebrae T12 to L2 and adjust the rotation to align the vertebral endplates horizontally. Center the ROI marker on the vertebral body.\u003c/li\u003e\n \u003cli\u003eIn the \u0026ldquo;ROIs\u0026rdquo; section, adjust the red ROI box to exclude bone islands, veins, and lesion areas.\u003c/li\u003e\n \u003cli\u003eSave the results under \u0026ldquo;Report\u0026rdquo; after confirming and recording the measurements (Figure. 4).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS Statistics for Windows version 26.0 (IBM Inc., Armonk, NY, USA) was used for statistical analysis. Normally distributed variables are expressed as mean \u0026plusmn; standard deviation; skewed variables are presented as medians with interquartile ranges. A paired sample t-test was conducted to assess BMD differences between the two methods, with statistical significance defined as p \u0026lt; 0.05. Cronbach\u0026rsquo;s alpha was used to evaluate the consistency between the two methods. Comparative data are presented as absolute mean differences, along with 95% confidence interval (CI) and 95% prediction intervals (agreement). Systematic bias between methods was analyzed using Bland\u0026ndash;Altman and XY plots.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical information\u003c/h2\u003e\n \u003cp\u003eA total of 568 patients with consecutive PB-QCT and PL-QCT measurements from the chest and thoracolumbar CT scans were included. Patient ages ranged from 20 to 85 years, with a mean age of 49.74\u0026thinsp;\u0026plusmn;\u0026thinsp;16.80 years and a mean body mass index of 24.50\u0026thinsp;\u0026plusmn;\u0026thinsp;3.96 kg/m\u003csup\u003e2\u003c/sup\u003e. The detailed data are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic information\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u0026ndash;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u0026ndash;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u0026ndash;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMD (mg/cm\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;120 mg/cm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u0026ndash;120 mg/cm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;80 mg/cm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBMD: Bone mineral density\u003c/p\u003e\n \u003cp\u003eBMD measurements obtained via the PL-QCT software were slightly higher than those obtained using the PB-QCT method. Differences were observed at the following vertebral levels:\u003c/p\u003e\n \u003cp\u003eT12: Mean difference 0.38 mg/cm3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n \u003cp\u003eL1: Mean difference 0.32 mg/cm3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n \u003cp\u003eL2: Mean difference 0.22 mg/cm3, P\u0026thinsp;=\u0026thinsp;0.02.\u003c/p\u003e\n \u003cp\u003eThe detailed values are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of differences between PL-QCT software and PB-QCT in the measurement of BMD in T12, L1, and L2 vertebrae SD standard deviation, BMD bone mineral density.Mean BMD\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mg/cm\u003csup\u003e3\u003c/sup\u003e), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of PL and PB(mg/cm\u003csup\u003e3\u003c/sup\u003e) Mean difference in the value of PL-QCT and PB-QCT\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mg/cm\u003csup\u003e3\u003c/sup\u003e), PL-QCT phantom-less quantitative computed tomography, PB-QCT phantom-based quantitative computed tomography with body film\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean BMD\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of PL and PB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eT12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL-QCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133.49\u0026thinsp;\u0026plusmn;\u0026thinsp;47.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB-QCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133.11\u0026thinsp;\u0026plusmn;\u0026thinsp;48.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL-QCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129.91\u0026thinsp;\u0026plusmn;\u0026thinsp;48.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB-QCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129.59\u0026thinsp;\u0026plusmn;\u0026thinsp;48.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL-QCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125.47\u0026thinsp;\u0026plusmn;\u0026thinsp;49.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB-QCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125.25\u0026thinsp;\u0026plusmn;\u0026thinsp;49.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eConsistency verification\u003c/h3\u003e\n\u003cp\u003eThe PL-QCT results showed excellent agreement with PB-QCT measurements. For the T12 to L2 levels, intraclass correlation coefficients ranged from 0.998 to 0.999; in contrast, Cronbach\u0026rsquo;s alpha values were between 0.999 and 1. This high degree of consistency is summarized in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The PL-QCT results measured by Measurer 2 showed strong agreement with the PB-QCT measurements. At the T12 to L2 level, the intragroup correlation coefficients ranged from 0.999 to 1.000 and Cronbach\u0026rsquo;s alpha from 0.999 to 1.000. The PL-QCT results of Measurers 1 and 2 were in good agreement. At the T12 to L2 level, the measured intragroup correlation coefficients ranged from 0.998 to 0.999 and Cronbach\u0026rsquo;s coefficients from 0.999 to 1.000.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of the consistency between PL-QCT and PB-QCT measured by Measurers 1 and 2\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCronbach\u0026apos;s Alpha\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eT12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL-QCT_ Measurer 1 \u0026amp; PB-QCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998 (95%CI:0.998\u0026thinsp;~\u0026thinsp;0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL-QCT_ Measurer 2 \u0026amp; PB-QCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999 (95%CI:0.999\u0026thinsp;~\u0026thinsp;1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL-QCT_Measurer1\u0026amp;PL-QCT_ Measurer 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998 (95%CI:0.998\u0026thinsp;~\u0026thinsp;0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL-QCT_ Measurer 1 \u0026amp; PB-QCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999 (95%CI:0.999\u0026thinsp;~\u0026thinsp;1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL-QCT_ Measurer 2 \u0026amp; PB-QCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000(95%CI:0.999\u0026thinsp;~\u0026thinsp;1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL-QCT_Measurer1\u0026amp;PL-QCT_ Measurer 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999 (95%CI:0.998\u0026thinsp;~\u0026thinsp;0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL-QCT_ Measurer 1 \u0026amp; PB-QCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998 (95%CI:0.998\u0026thinsp;~\u0026thinsp;0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL-QCT_ Measurer 2 \u0026amp; PB-QCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999 (95%CI:0.998\u0026thinsp;~\u0026thinsp;0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL-QCT_Measurer1\u0026amp;PL-QCT_ Measurer 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999 (95%CI:0.998\u0026thinsp;~\u0026thinsp;0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003ePL-QCT phantom-less quantitative computed tomography, PB-QCT phantom-based quantitative computed tomography with body film\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBland\u0026ndash;Altman and XY diagram analysis results\u003c/h2\u003e\n \u003cp\u003eBland\u0026ndash;Altman analysis revealed a mean BMD difference between PL-QCT and PB-QCT of 0.22 mg/cm\u003csup\u003e3\u003c/sup\u003e to 0.38 mg/cm\u003csup\u003e3\u003c/sup\u003e at the T12, L1, and L2 levels. Most data points fell within the 95% limits of agreement (LOA), indicating strong consistency between the two measurement systems. The points within the consistency limit were evenly distributed in horizontal bands with no significant upward or downward trend, suggesting no obvious linear relationship between the BMD difference measured by PL-QCT and PB-QCT and the mean BMD values. The BMD difference between the two methods remained stable and did not change with the absolute BMD values.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eBland\u0026ndash;Altman analysis of BMD measurement results at the T12 level\u003c/h2\u003e\n \u003cp\u003eThe mean difference between the two methods was 0.38, with a maximum difference of 19.41, a minimum difference of \u0026minus;\u0026thinsp;32.18, and 95% LOA ranging from \u0026minus;\u0026thinsp;3.82 to 4.58. Most data points (97.18%, 552/568) fell within the LOA range and the data were evenly distributed around the 0 line (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eBland\u0026ndash;Altman analysis of BMD measurements at the L1 level\u003c/h2\u003e\n \u003cp\u003eThe mean difference was 0.32, with a maximum difference of 16.14, a minimum difference of \u0026minus;\u0026thinsp;6.25, and 95% LOA ranging from \u0026minus;\u0026thinsp;2.64 to 3.29. Most data points (94.89%, 539/568) fell within the LOA range, with even distribution around the 0 line (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eBland\u0026ndash;Altman analysis of BMD measurements at the L2 level\u003c/h2\u003e\n \u003cp\u003eThe mean difference between was 0.22, with a maximum difference of 39.73, a minimum difference of \u0026minus;\u0026thinsp;32.18, and 95% LOA ranging from \u0026minus;\u0026thinsp;4.34 to 4.79. Most data points (97.36%, 553/568) were within the 95% LOA range, with even distribution around the 0 line (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eXY chart analysis results\u003c/h2\u003e\n \u003cp\u003eThe general trend of the correlation between the PL-QCT and PB-QCT measurement systems for BMD values at the T12 level, the correlation coefficient was 0.9990, with most points closely aligned with the fitting curve (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed). At the L1 level, the correlation coefficient was 0.9995, with data points tightly clustered around the fitting curve (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ee). At the L2 level, the correlation coefficient was 0.9989, with most points closely scattered around the fitting curve (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ef). Most data points were closely aligned with the fitting curve, indicating strong agreement between the measurements from the two methods.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings indicate that in a large, diverse sample of individuals with varying sex, age, and bone content, the PL-QCT system developed by our research group demonstrates high consistency with PB-QCT in measuring vertebral body BMD from T12 to L2, with a correlation coefficient of 0.998 to 1.000 and Cronbach\u0026rsquo;s alpha ranging from 0.998 to 1.000. Bland\u0026ndash;Altman analysis revealed that most data points for T12 to L2 BMD measurements were within the 95% LOA, suggesting high consistency between the two measurement methods. BMD values from PL-QCT at T12 to L2 were slightly higher than those from PB-QCT, with a deviation of approximately 0.31 mg/cm\u003csup\u003e3\u003c/sup\u003e, which did not affect the diagnosis of osteoporosis or bone content analysis. Although no significant differences were found between the two BMD measurement systems, the consistency and correlation were strong.\u003c/p\u003e\u003cp\u003eTherkildsen et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] reported that the BMD measurements from a general PB-QCT system were slightly higher than those from PL-QCT, which contrasts with our findings. This discrepancy may be due to differences in the PL-QCT systems used and slight variations in the algorithms employed. The BMD deviation between PL-QCT and PB-QCT in our study was approximately 0.31 mg/cm\u003csup\u003e3\u003c/sup\u003e; in contrast, Mueller et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] reported a deviation of 0.9 mg/cm\u003csup\u003e3\u003c/sup\u003e between the Philips BMD PL-QCT system and PB-QCT. In the present study, the PL-QCT measurements were closer to those of PB-QCT, which could be attributed to the automatic ROI selection used in our system. The selection of subcutaneous fat and paravertebral muscle ROIs is a critical factor in BMD measurement [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and the differences in fat or muscle mass across individual tests can lead to inconsistent BMD results. However, the automatic PL-QCT system developed by our research group utilizes rough segmentation, tissue-specific thresholds, and two-dimensional convolution operations to perform effective filtering, providing peak curves of the normal distribution of Hounsfield unit values for bone, muscle, and fat. This helps with the automatic selection of subcutaneous fat and paravertebral muscle ROIs, identification of the vertebral center, and selection of the middle layer of the vertebra for analysis. As a result, the consistency of BMD is guaranteed, improving the accuracy of the PL-QCT system [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe traditional PB-QCT system is more accurate than DEXA [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] because it measures true volumetric bone density without being influenced by factors such as spinal degeneration, scoliosis, or body weight. DEXA measurements are susceptible to inaccuracies caused by spinal degeneration, vertebral deformation, abdominal aortic calcification, and other factors, particularly in older adults. Traditional PB-QCT also allows for the measurement of both trabecular and cortical BMD, providing an earlier and more accurate reflection of BMD changes compared to DEXA. It can be used to monitor treatment responses for osteoporosis, predict fracture risk, and aid in preoperative orthopedic planning. Individuals at risk of osteoporotic fractures [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] often undergo CT scans for other conditions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]: PB-QCT can be performed simultaneously with these clinical CT scans, avoiding additional radiation exposure or scan time [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, PB-QCT is prone to hardening artifacts due to the use of a hydroxyapatite body film and asynchronous calibration can cause scanner instability [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, traditional PB-QCT is bound to a specific CT scanner and can only be used with that machine. In contrast, the PL-QCT system developed by our research group, in addition to maintaining the advantages of traditional PB-QCT, can automatically measure BMD, avoid body film artifacts and scanner instability, and be applied to multiple CT scanners. This flexibility allows for system upgrades and integration of measurement results into imaging reports. The automated PL-QCT system has the potential for further clinical applications in BMD-related studies, improving the prediction accuracy of osteoporosis and facilitating early diagnosis.\u003c/p\u003e\u003cp\u003eThis study does have some limitations. The sample size was insufficient for age-based subgroup analyses and, being a single-center study, the findings may not be applicable to broader populations. Future multicenter studies with larger sample sizes are necessary. Additionally, comparisons with other bone densitometry were not performed; future research should include multimodal consistency testing.\u003c/p\u003e\u003cp\u003eTo conclude, the semi-automated PL-QCT system developed by our research group yields results that are accurate and consistent with PB-QCT measurements. This system, which can be integrated directly into imaging report systems without the need for an external body film, shows great promise for BMD evaluation and osteoporosis diagnosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFengling Zhu was responsible for the experimental design, statistical analysis of data, figure creation, writing of the manuscript. Yuanzhi Weng, Hanfei Miao, Hua Xu,and Jun Han collected the data collection and conducted the statistical analysis. Hui Yu, Zhi Wang, and Xianghong Meng provided research guidance and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the ethics commit tee of Tianjin University, Tianjin Hospital (project approval number 2021 Medical Review 084)and the 1964 Declaration of Helsinki. including its later amendments and comparable ethical guidelines. In compliance with HIPAA regulations, informed consent was not required for this retrospective study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent to publish was obtained from the participants for publication of the images.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eS\u0026ouml;zen T, \u0026Ouml;zışık L, Başaran N\u0026Ccedil;. An overview and management of osteoporosis. Eur J Rheumatol 2017;4:46-56.\u003c/li\u003e\n\u003cli\u003eAnish RJ, Nair A. Osteoporosis management-current and future perspectives - A systemic review. J Orthop 2024;53:101-113.\u003c/li\u003e\n\u003cli\u003eCamacho PM, Petak SM, Binkley N, Diab DL, Eldeiry LS, Farooki A, Harris ST, Hurley DL, Kelly J, Lewiecki EM, Pessah-Pollack R, McClung M, Wimalawansa SJ, Watts NB. College of Endocrinology Clinical Practice guidelines for the diagnosis and treatment of postmenopausal Osteoporosis-2020 update. Endocr Pract 2020;26 (Supplement 1):1-46.\u003c/li\u003e\n\u003cli\u003eOd\u0026eacute;n A, McCloskey EV, Kanis JA, Harvey NC, Johansson H. Burden of high fracture probability worldwide: secular increases 2010\u0026ndash;2040. Osteoporos Int 2015;26:2243-2248.\u003c/li\u003e\n\u003cli\u003eRobertson A, Godavitarne C, Peters J. Bone quantification. Orthop Trauma 2017;31:326-329.\u003c/li\u003e\n\u003cli\u003eCata\u0026ntilde;o Jimenez S, Saldarriaga S, Chaput CD, Giambini H. Dual-energy estimates of volumetric bone mineral densities in the lumbar spine using quantitative computed tomography better correlate with fracture properties when compared to single-energy BMD outcomes. Bone 2020;130:115100.\u003c/li\u003e\n\u003cli\u003eLin W, He C, Xie F, Chen T, Zheng G, Yin H, Chen H, Wang Z. Discordance in lumbar bone mineral density measurements by quantitative computed tomography and dual-energy x-ray absorptiometry in postmenopausal women: A prospective comparative study. Spine J 2023;23:295-304.\u003c/li\u003e\n\u003cli\u003eKulkarni AG, Thonangi Y, Pathan S, Gunjotikar S, Goparaju P, Talwar I, Jaggi S, Shah S, Shah N, Kursija G. Should Q-CT Be the gold standard for detecting spinal osteoporosis? Spine 2022;47:E258-E264.\u003c/li\u003e\n\u003cli\u003eWang L, Su Y, Wang Q, Duanmu Y, Yang M, Yi C, Cheng X. Validation of asynchronous quantitative bone densitometry of the spine: accuracy, short-term reproducibility, and a comparison with conventional quantitative computed tomography. Sci Rep 2017;7:6284.\u003c/li\u003e\n\u003cli\u003eEngelke K. Quantitative computed tomography-current status and new developments. J Clin Densitom 2017;20:309-321.\u003c/li\u003e\n\u003cli\u003eMueller DK, Kutscherenko A, Bartel H, Vlassenbroek A, Ourednicek P, Erckenbrecht J. Phantom-less QCT BMD system as screening tool for osteoporosis without additional radiation. Eur J Radiol 2011;79:375-381.\u003c/li\u003e\n\u003cli\u003eBartenschlager S, Dankerl P, Chaudry O, Uder M, Engelke K. BMD accuracy errors specific to phantomless calibration of CT scans of the lumbar spine. Bone 2022;157:116304.\u003c/li\u003e\n\u003cli\u003eLiu ZJ, Zhang C, Ma C, Qi H, Yang ZH, Wu HY, Yang KD, Lin JY, Wong TM, Li ZY, Li CH, Ding Y. Automatic phantom-less QCT system with high precision of BMD measurement for osteoporosis screening: technique optimisation and clinical validation. J Orthop Translat 2022;33:24-30.\u003c/li\u003e\n\u003cli\u003eTherkildsen J, Thygesen J, Winther S, Svensson M, Hauge EM, B\u0026ouml;ttcher M, Ivarsen P, J\u0026oslash;rgensen HS. Vertebral bone mineral density measured by quantitative computed tomography with and without a calibration phantom: A comparison between 2 different software solutions. J Clin Densitom 2018;21:367-374.\u003c/li\u003e\n\u003cli\u003eYu WJ, Zhang Z, Fu WZ, He JW, Wang C, Zhang ZL. Association between LGR4 polymorphisms and peak bone mineral density and body composition. J Bone Miner Metab 2020;38:658-669.\u003c/li\u003e\n\u003cli\u003eGurusamy P, Larsen BA, Allen RT, Ward SR, Allison MA, Hughes-Austin JM. Density and fat fraction of the psoas, paraspinal, and oblique muscle groups are associated with lumbar vertebral bone mineral density in a multi-ethnic Community-Living Population: the Multi-Ethnic Study of Atherosclerosis. J Bone Miner Res 2022;37:1537-1544.\u003c/li\u003e\n\u003cli\u003eGuo DM, Weng YZ, Yu ZH, Li SH, Qu WR, Liu XN, Qi H, Ma C, Tang XF, Li RY, Han Q, Xu H, Lu WW, Qin YG. Semi-automatic proximal humeral trabecular bone density assessment tool: technique application and clinical validation. Osteoporos Int 2024;35:1049-1059.\u003c/li\u003e\n\u003cli\u003eDeshpande N, Hadi MS, Lillard JC, Passias PG, Linzey JR, Saadeh YS, LaBagnara M, Park P. Alternatives to DEXA for the assessment of bone density: A systematic review of the literature and future recommendations. J Neurosurg Spine 2023;38:436-445.\u003c/li\u003e\n\u003cli\u003eBoehm E, Kraft E, Biebl JT, Wegener B, Stahl R, Feist-Pagenstert I. Quantitative computed tomography has higher sensitivity detecting critical bone mineral density compared to dual-energy x-ray absorptiometry in postmenopausal women and elderly men with osteoporotic fractures: A real-life study. Arch Orthop Trauma Surg 2024;144:179-188.\u003c/li\u003e\n\u003cli\u003eProst S, Pesenti S, Fuentes S, Tropiano P, Blondel B. Treatment of osteoporotic vertebral fractures. Orthop Traumatol Surg Res 2021;107:102779.\u003c/li\u003e\n\u003cli\u003eLiu XY, Feng M, Zhang XL, Zou T, Huang Z, Yang JD, Sun HH. Are sandwich vertebrae prone to refracture after percutaneous vertebroplasty or kyphoplasty? A meta-analysis. Int J Spine Surg 2024;18:83-90.\u003c/li\u003e\n\u003cli\u003eHollensteiner M, Sandriesser S, Bliven E, von R\u0026uuml;den C, Augat P. Biomechanics of osteoporotic fracture fixation. Curr Osteoporos Rep 2019;17:363-374.\u003c/li\u003e\n\u003cli\u003eImamudeen N, Basheer A, Iqbal AM, Manjila N, Haroon NN, Manjila S. Management of osteoporosis and spinal fractures: contemporary guidelines and evolving paradigms. Clin Med Res 2022;20:95-106.\u003c/li\u003e\n\u003cli\u003eCheng X, Yuan H, Cheng J, Weng X, Xu H, Gao J, Huang M, W\u0026aacute;ng YXJ, Wu Y, Xu W, Liu L, Liu H, Huang C, Jin Z, Tian W, Bone and Joint Group of Chinese Society of Radiology, Chinese Medical Association (CMA), Musculoskeletal Radiology Society of Chinese Medical Doctors Association, Osteoporosis Group of Chinese Orthopedic Association, Bone Density Group of Chinese Society of Imaging Technology, CMA*. Chinese expert consensus on the diagnosis of osteoporosis by imaging and bone mineral density. Quant Imaging Med Surg 2020;10:2066-2077.\u003c/li\u003e\n\u003cli\u003eLink TM, Lang TF. Axial QCT: clinical applications and new developments. J Clin Densitom 2014;17:438-448.\u003c/li\u003e\n\u003cli\u003eHeyde CE, Roth A, Putzier M. [Osteoporotic vertebral body fractures]. Orthopadie (Heidelb) 2023;52:808-817.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"bone mineral density, osteoporosis, quantitative computed tomography, vertebral body","lastPublishedDoi":"10.21203/rs.3.rs-7635189/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7635189/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e: As the global population ages, osteoporosis poses a significant public health challenge. This study aimed to evaluate the accuracy and precision of a new phantom-less quantitative computed tomography (PL-QCT) technique for measuring T12 to L1 bone mineral density using thoracolumbar CT scans.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods\u003c/strong\u003e: Waist fat and muscle tissue were used as calibration references for bone mineral density measurements in PL-QCT. Automatic bone mineral density measurements in PL-QCT were conducted using crude segmentation, tissue-specific thresholds, and a two-dimensional convolution algorithm. Region-of-interest selection in PL-QCT measurement was cross-referenced with phantom-based QCT (PB-QCT) through asynchronous calibration of body film. Measurement consistency between PB-QCT and PL-QCT was compared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 568 participants, aged 20 to 85 years (mean age, 49.74 ± 16.80 years), were included. Consistency analysis showed a mean deviation of 0.31 mg/cm\u003csup\u003e3\u003c/sup\u003e in T12 to L1 BMD measurements between PB-QCT and PL-QCT, with the correlation coefficient (r) and Cronbach’s alpha values both ranging between 0.998 and 1.000. Bland–Altman analysis revealed that most T12 to L2 bone mineral density data points were within the 95% limits of agreement, demonstrating high accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The PL-QCT system demonstrated excellent consistency in T12 to L2 vertebral BMD measurements when compared with the PB-QCT system. Overall, PL-QCT exhibited superior accuracy and precision, confirming its reliability as a tool to evaluate spinal bone mineral density.\u003c/p\u003e","manuscriptTitle":"A semi-automated bone mineral density measurement tool, phantom-less QCT: Technique optimization and clinical validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 11:44:51","doi":"10.21203/rs.3.rs-7635189/v1","editorialEvents":[{"type":"communityComments","content":7},{"type":"editorInvitedReview","content":"","date":"2025-10-19T18:39:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T04:20:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128629378132978460226060071299840494551","date":"2025-09-24T15:19:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260657140345308539741036968692062397628","date":"2025-09-24T14:37:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-24T08:20:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-24T08:15:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-22T06:11:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-20T09:36:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2025-09-20T09:33:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe27f697-9948-4b48-a7ef-ec0fbb60eebc","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[{"value":"featured","date":"2025-10-07 14:46:10"}],"updatedAt":"2025-10-06T11:44:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 11:44:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7635189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7635189","identity":"rs-7635189","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}