Search Results (148)

Construction material identification is important for automated construction monitoring, digital twin generation, and building information modelling. Terrestrial laser scanning (TLS) provides dense geometric information together with LiDAR intensity measurements; however, reliable material discrimination remains challenging because intensity is affected by acquisition geometry and surface texture may vary depending on the spatial scale at which it is characterised. This study proposes a TLS-only machine-learning framework combining a reflectance-related intensity–geometry regression descriptor with multiscale geometric roughness features. Plane-residual roughness and normal-variation roughness were calculated using local cube neighbourhoods with side lengths of 0.10, 0.20, and 0.30 m. To avoid ambiguity associated with surface-normal orientation, the normal-variation descriptor was calculated using orientation-invariant angular differences between neighbouring surface normals. The training dataset was balanced using distance-stratified random undersampling, while the held-out test dataset retained its original class distribution. FLAML was used for automated model selection and hyperparameter optimisation using three-fold cross-validation with macro F1-score as the optimisation metric. The final stacking classifier achieved an overall accuracy of 90.44%, balanced accuracy of 87.42%, and macro F1-score of 87.85% on 1,296,822 held-out test points. The held-out test data were acquired from different scanner positions and spatially distinct material regions from those used for training, with no shared point samples; however, both datasets originated from the same general study area, and broader cross-site generalisation therefore requires further independent validation. Most material classes showed strong discrimination, although Carpet remained challenging because of confusion with Asphalt. The results demonstrate the potential of combining TLS-derived radiometric information with multiscale geometric surface descriptors for construction material classification without relying on RGB colour information. Full article

Background/Objectives: Cerebral small vessel disease (SVD) is a heterogeneous condition in which similar conventional MRI findings may be associated with different clinical manifestations and pathogenetic mechanisms. Previously, hierarchical clustering of structural MRI features in patients with severe white matter hyperintensities (Fazekas 3) identified two MRI phenotypes, designated MRI Type 1 and MRI Type 2. Diffusion MRI (dMRI) may provide additional information about the microstructural differences between these phenotypes. To compare white matter microstructure between MRI Type 1 and MRI Type 2 of sporadic age-related SVD using signal-based and biophysical dMRI models. Methods: This cross-sectional study included 75 patients with SVD and 36 age- and sex-matched healthy controls. Among the patients with SVD, 43 had MRI Type 1 and 32 had MRI Type 2. All participants underwent structural and multi-shell dMRI on a 3 Tesla MRI scanner. Diffusion metrics were derived using multiple models: Diffusion Tensor Imaging (DTI), Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), White Matter Tract Integrity (WMTI), and the Multi-compartment Spherical Mean Technique (MC-SMT). Tract-profile analysis was performed in three corpus callosum segments: the forceps major, forceps minor, and body. Group differences were assessed using age- and sex-adjusted general linear models with correction for multiple comparisons. The combined discriminative value of dMRI metrics was evaluated using regularized Elastic Net logistic regression with repeated nested five-fold cross-validation. Results: After adjustment for age and sex, the overall group effect remained significant for 45 of 48 global dMRI measures following Benjamini–Hochberg correction. Compared with MRI Type 2, MRI Type 1 showed lower fractional anisotropy (FA), neurite density index (NDI), intra-axonal volume fraction (INTRA), axonal water fraction (AWF), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK), and higher mean diffusivity (MD), radial diffusivity (RD), extra-axonal mean diffusivity (EXTRA_MD), extra-axonal transverse diffusivity (EXTRA_TRANS), and extra-axonal radial diffusivity (radEAD). These differences were generally most pronounced in the body of the corpus callosum. In the segmental analysis, 131 of 144 values showed a significant overall group effect after correction, and 108 demonstrated significant differences between MRI Type 1 and MRI Type 2. The largest effects were observed in the 60–80% interval of the corpus callosum body, particularly for AWF, MK, INTRA, EXTRA_TRANS, RK, FA, RD, radEAD, and MD. An Elastic Net model combining age, sex, and 48 global dMRI measures discriminated MRI Type 1 from MRI Type 2 with an internally validated area under the curve of 0.866 (95% CI, 0.762–0.953), accuracy of 86.7%, sensitivity of 75.0%, and specificity of 95.3%. Ten dMRI features showed a selection frequency of at least 70% across repeated model construction. Conclusions: MRI Type 1 is characterized by more severe and spatially extensive corpus callosum microstructural abnormalities than MRI Type 2, despite broadly similar vascular risk-factor profiles. The findings support the heterogeneity of sporadic age-related SVD and indicate that combined signal-based and biophysical dMRI metrics may improve MRI phenotyping. The observed associations should be interpreted as indirect markers of tissue microstructure and require confirmation in larger, independent, and longitudinal cohorts. Full article

Amplitude-modulation magnetic particle imaging (AM-MPI) has strong potential for real-time navigation of magnetic nanoparticles because it provides tracer-specific spatial feedback using a narrowband acquisition scheme with relatively low excitation power and simplified signal detection. However, improving spatial resolution by increasing the selection-field gradient becomes increasingly difficult as scanner dimensions increase. This study investigates whether field orientation can be used to improve and control the spatial resolution of three-dimensional AM-MPI with field-free-point (FFP) and field-free-line (FFL) encoding. Four scan-receive configurations were analyzed using a matrix point-spread-function (PSF) model, followed by two-point phantom simulations at equal physical and FWHM-normalized source separations. With a common y-directed scan, FFP-y produced a single-peaked collinear h_yy response with FWHM values of 1.29 mm along y and 5.89 mm along x and z. FFL-y retained the same y-direction FWHM while reducing the z-direction FWHM to 2.94 mm. FFP-x selected the transverse h_xy component, producing a central null and a multi-lobe response, whereas FFL-x was a null channel for the adopted field geometry. At equal physical spacing, the first tested separation satisfying the adopted Rayleigh-type criterion (M ≥ 0.26) was 3 mm along y for both FFP-y and FFL-y, 5 mm along z for FFL-y, and 7 mm along x and z for FFP-y. FFL-y also provided better z-direction separability than FFP-y. After normalization by the corresponding directional FWHM, the single-peaked responses showed similar two-point separability, indicating that the differences observed at equal physical spacing were mainly associated with directional PSF width. These results show that field orientation affects both the topology and directional resolution of the AM-MPI response and can be considered together with selection-field design when optimizing AM-MPI systems for nanoparticle navigation. Full article

Coarse root systems govern tree anchorage, yet remain among the least documented components of tree architecture: excavation is irreversible, and established 3D methods rely on specialist scanners and lengthy post-processing. We evaluated whether a consumer smartphone records exposed coarse root architecture metrically, and which traits agree most closely with manual measurement. Four fully exposed Scots pine (Pinus sylvestris L.) root systems in northwestern Poland were scanned with an iPhone 17 Pro running Scaniverse, at about 30 min of acquisition and 5 h of processing per tree. Clouds were registered, cleaned and oriented to magnetic north in CloudCompare; of eight architectural metrics, four were validated against manual references at 95 cross-sections on 44 roots, and four were exploratory. Visible root length (root-mean-square error, RMSE, 22.2 cm, 8.4%), azimuth (RMSE 3.58°, mean absolute error 2.47°) and depth (RMSE 3.18 cm, 14.9%) agreed most closely with the reference; 70 of 77 first-order roots were detected with no false positives. Diameter was the weakest metric and the only one dependent on the operator (RMSE 0.46 and 0.29 cm for two operators on the same clouds). Smartphone LiDAR thus turns an irreversible excavation into a permanent, measurable record of the traits relevant to anchorage, provided that centimetre-level diameters are not required. Full article

Terrestrial laser scanning (TLS), also referred to as terrestrial LiDAR, has become an essential close-range remote sensing technique for high-precision engineering surveying, deformation monitoring, industrial inspection, and cultural heritage documentation. The geometric reliability of TLS point clouds strongly depends on the effective compensation of instrumental systematic errors through in situ self-calibration. However, conventional target-based self-calibration often suffers from strong coupling between calibration parameters and exterior orientation parameters, whereas recently developed coplanarity-constrained formulations generally require highly redundant target networks, limiting their field efficiency. To address this limitation, this study proposes a variance inflation factor (VIF)-driven minimal network design strategy for efficient in situ geometric self-calibration of TLS systems. Unlike the commonly used geometric dilution of precision, VIF provides a dimensionless statistical alternative that effectively resolves the dimensional inconsistency inherent in traditional GDOP when handling mixed angular and distance parameters. A differential evolution algorithm is employed to search for hybrid calibration networks that minimize parameter coupling while preserving the physical interpretability of the National Institute of Standards and Technology (NIST) 10-parameter instrumental error model. Five digital twin simulation experiments and a physical validation experiment using a Faro Focus 350 scanner were conducted to evaluate the proposed method. The results show that the optimized network substantially reduces the number of required targets while maintaining high calibration accuracy. The final configuration, which combines VIF-optimized target placement with a dual-station height-difference constraint, reduces the condition number of the normal equations to below 60 and yields a mean system VIF close to 10. The maximum parameter correlation coefficient among the key calibration parameters is constrained to approximately 0.75, indicating near-optimal parameter decoupling under the limited field-of-view geometry of the instrument. These findings demonstrate that the proposed VIF-driven network design provides a highly effective strategy for field-efficient TLS self-calibration and improves the geometric reliability of terrestrial LiDAR point clouds in high-precision remote sensing applications. Full article

Background/Objectives: Natural head position (NHP) is an important reference for orthodontic and orthognathic assessment. Although postoperative changes in head posture have been reported previously, simultaneous three-dimensional changes across yaw, pitch, and roll in different skeletal classes remain incompletely characterized. This exploratory study quantified six-month changes in three-dimensional head orientation and compared their direction and magnitude between skeletal Class II and Class III patients. Methods: Fifty-four adults (17 Class II, 37 Class III) undergoing Le Fort I osteotomy and bilateral sagittal split osteotomy were evaluated preoperatively (T0) and 6 months postoperatively (T1). Standardized 3D facial surface acquisitions were obtained using a calibrated structured-light scanner and a fixed external Cartesian laser reference. T0 and T1 datasets were registered using stable upper-facial landmarks (nasion and bilateral exocanthia), and changes in yaw, pitch, and roll were calculated within the common external reference system. Results: Statistically significant T0–T1 changes were observed across all three axes in both skeletal groups (p < 0.01). Class II patients showed mean changes of +2.28° in yaw, −1.30° in pitch, and +2.89° in roll, whereas Class III patients showed +4.61°, +1.96°, and +4.04°, respectively. Between-group differences were significant for yaw, pitch, and roll, with an opposite mean pitch direction and larger mean yaw and roll changes in Class III. Conclusions: At six months, this retrospective cohort demonstrated direction-specific changes in three-dimensional head orientation. Because repeated acquisitions, study-specific observer-reliability and measurement-error analyses, an untreated comparator, an independent validation cohort, and functional or patient-reported outcomes were not available, the findings should be interpreted as exploratory measurements rather than evidence of physiological normalization, neuromuscular adaptation, or clinical benefit. Formal prospective validation is required before the workflow can support predictive or patient management recommendations. Full article

This study addresses the influence of process conditions on the dimensional accuracy, geometric deviations, and surface quality of PLA parts manufactured using high-dynamics material extrusion (MEX) technology. The aim was to identify the dominant sources of variability and to assess within-condition manufacturing consistency and inter-machine consistency. The investigation considered two 3D printers, nine build locations on the working platform, two printing strategies (layer-by-layer and model-by-model), and model face orientation. Additionally, an exploratory comparison of aligned and random seam configurations and an analysis of local temperature variations within the build chamber were performed. Regular dodecahedron geometries were manufactured using a Bambu Lab P1S system and processed under identical high-quality printing parameters. Dimensional measurements were performed using a Linear 100 universal length measuring machine, while full-field geometric deviations were acquired using a GOM Scan 1 structured-light 3D scanner. Surface roughness (Ra) was measured with a MarSurf XR 20 profilometer. Part orientation is the dominant source of dimensional variability, representing the largest relative contribution to linear deviation in the mixed-effects model (ΔR² = 0.776), while local temperature variations near the printing zone were associated with location-dependent dimensional deviations. Within the supplementary temperature dataset, the regression model including temperature and printers explained 66% of the variability in mean linear dimension. This association provides indirect evidence of a thermal contribution but does not establish direct causality. The layer-by-layer strategy provided better dimensional stability than the model-by-model approach. In the exploratory seam comparison, seam configuration did not explain the orientation-dependent LD pattern. Surface roughness variability was primarily geometry-driven (ΔR² = 0.852). Variability between independent manufacturing series and specimens for linear deviation and Ra was low after accounting for the investigated factors, indicating consistent process performance under constant settings; however, the present design did not allow measurement repeatability and reproducibility to be separated. In conclusion, dimensional accuracy in high-dynamics MEX is strongly associated with part orientation, while thermal variations may represent an additional contributing factor; however, the observed correlation between thermal conditions and dimensional variability does not establish direct causality. Full article

Background/Objectives: Prostate cancer (PCa) frequently metastasizes to bone, leading to severe clinical complications and reduced quality of life. Accurate and robust imaging-based characterization of bone lesions is therefore critical for diagnosis and treatment planning. Radiomics has emerged as a powerful tool for extracting quantitative information from medical images; however, classical radiomics features are often affected by inter-scanner variability, segmentation dependence, and limited ability to describe lesions with complex biological heterogeneity. This study aims to introduce a translational graph-based radiomics approach designed to extract novel quantitative descriptors with improved robustness and clinical reliability. Methods: A graph representation was derived from segmented Positron Emission Tomography/Computed Tomography (PET/CT) bone lesions by generating a point cloud followed by Delaunay triangulation to preserve geometric information. Graph signal processing techniques were applied to extract three classes of features: orientation, connectivity, and transform-based descriptors. The dataset included PET/CT scans from 50 PCa patients acquired using two different scanners, comprising 92 bone lesions classified as benign or malignant. Correlation analysis with classical radiomics features was performed to assess information redundancy. Robustness against batch effects and segmentation variability was evaluated. Classification performance was tested using Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) models based on proposed features, classical features, and their combination. Results: The proposed features captured non-redundant information compared to classical radiomics and demonstrated superior robustness to scanner-related batch effects and segmentation variability. In classification tasks, models using the proposed features consistently outperformed those based on classical radiomics. Using LDA, the proposed features achieved a mean balanced accuracy of 69.68% and a mean Area Under the Curve (AUC) of 72.14%. With SVM, they achieved a mean balanced accuracy of 65.16% and a mean AUC of 66.49%, exceeding the performance of classical and combined feature sets. Conclusions: This study presents a translational graph-based radiomics framework that extends beyond conventional methodologies, improving robustness and diagnostic performance. The proposed approach shows promise as an integrative tool for more reliable PET/CT-based characterization of bone lesions in prostate cancer. Full article

Background/Objectives: Accurate visualization of peroneal perforator vessels prior to autologous transplantation of osteomyocutaneous fibular flap is essential for surgical success. Our aim was to improve and simplify pre-surgical diagnostics of peroneal perforators using a dedicated dual-energy Computed Tomography Angiography (CTA) protocol and semiautomatic Vessel Unfolding Reconstruction algorithm (VUR). Methods: CTA of both lower legs was performed in 22 patients using dual-energy acquisitions from a third-generation dual-source CT scanner and a high iodine flux (7 mL/s, 350 mg/mL). Low-energy virtual monoenergetic reconstructions (40 keV) were automatically reconstructed from the scanner and used for centerline labeling of the peroneal arteries and their perforators on a post-processing console using a dedicated vascular workflow. Separate segmentation and curved multiplanar reconstructions (MPRs) of each identified peroneal perforator vessel were regarded as the gold standard. Traditional visualization techniques of the entire volume like thin-slice maximum intensity projections (MIPs) or the volume rendering technique (VRT) were compared to a new VUR algorithm that aimed to adjust the visualization plane to the course of the vessels. The identified numbers and lengths of the perforator arteries were compared between curved MPRs, thin-slice MIPs in oblique coronal orientation, posterior-view VRT and coronal VUR of each lower leg. Results: The VUR algorithm was feasible in all patients and the same quantity of peroneal perforator vessels could be detected in comparison to the gold standard. The mean number of perforator vessels per lower leg was 2.6. Mean perforator length in VUR was slightly shorter by 1.6% and did not significantly differ from curved MPRs (p = 0.54), whereas length values from oblique coronal MIP and VRT reconstructions were significantly shorter (both p < 0.001). Conclusions: The combination of virtual monoenergetic reconstructions and the VUR algorithm enables comprehensive and precise depiction of small peroneal perforator vessels prior to autologous fibular flap transplantation, representing a diagnostic tool comparable to traditional visualization methods. Full article

Rice straw, a byproduct of global rice production (~530 million tons annually), is generated at 80–100 million tons per year, yet a significant portion is incinerated or discarded, causing environmental problems. This study investigated the sound absorption properties of straw from IR8, a high-yielding long-grain rice variety. The normal incidence sound absorption coefficient was measured at three bulk densities (0.140, 0.150, and 0.160 g/cm³) for bundled rice straw structures. Cross-sectional images obtained using a micro-computed tomography (CT) scanner were then used to theoretically estimate the sound absorption coefficient. Each CT cross-section, oriented perpendicular to the incident sound wave direction, was modeled as a clearance between two parallel planes. The characteristic impedance and propagation constant were calculated from this model, and the normal incidence sound absorption coefficient was determined using the transfer matrix method with measured tortuosity incorporated. The experimental and theoretical absorption peaks showed similar trends across bulk densities. A parameter study was also conducted by scaling cross-sectional images according to the diameter ratios of Koshihikari short-grain rice straw and Yumekaori wheat straw relative to IR8. Additionally, reducing the number of CT images to as few as ten adequately approximated the full dataset for a 20 mm thick sample. Full article

This work presents the development and validation of an automated 3D scanning device based on two opposed consumer-grade Apple TrueDepth sensors integrated into a controlled rotational architecture, designed for the digitization of complex freeform surfaces such as the external cranial geometry. The system design was guided by a prior metrological characterisation of the sensor’s distance-dependent behaviour and complemented by an additional study of the influence of surface orientation, from which a suitable operating window for complete head acquisition was derived. On this basis, a mechatronic system was implemented comprising a mechanical structure, electronic hardware, a control architecture, and a calibration procedure that registers the local point clouds from both sensors into a common global coordinate system. Geometric validation was performed using symmetric and asymmetric cranial phantoms digitized with both the proposed device and a professional reference scanner. Surface comparison revealed localized discrepancies concentrated in fine anatomical details, while the cranial vault showed good overall agreement, with RMS deviations of 0.314 mm and 0.286 mm for the symmetric and asymmetric phantoms, respectively. Morphometric consistency was assessed through the cranial vault asymmetry index (CVAI), for which both systems produced the same general trend with a maximum difference of 0.2%. These results demonstrate the feasibility of the proposed system as a geometrically consistent and morphometrically reliable instrument for head surface digitization under controlled laboratory conditions. Full article

Dynamic three-dimensional (3-D) reconstruction of small objects moving at high speed is fundamentally limited by the number of viewpoints that a fixed camera array can provide at any single time instant. When the camera count is insufficient, single-frame multi-view stereo produces incomplete or inaccurate geometry. This paper proposes a multi-frame temporal integration approach that overcomes this limitation by exploiting the rigid-body assumption: because a falling object maintains its shape across consecutive frames, images captured at different time instants can be combined into a single, viewpoint-enriched reconstruction. A three-layer circular array of 32 synchronized RGB cameras captures 1440 × 1080 images at 160 fps, and a free-fall-oriented algorithm automatically detects active frames, selects informative temporal windows, and feeds the accumulated multi-frame images into a structure-from-motion and multi-view stereo (SfM-MVS) pipeline, effectively multiplying the number of viewpoints without additional hardware. The algorithm simultaneously recovers the 6-DOF pose trajectory of each object from the SfM-estimated camera parameters. Progressive accumulation experiments on freely falling soybeans (approximately 9–10 mm diameter) show that a single 32-camera frame already achieves an F-score exceeding 0.97 at a 0.5 mm threshold against an industrial structured-light scanner reference, and that accumulating additional temporal frames reaches a stable convergence plateau with both objects reaching a plateau F-score of 0.984. Beyond approximately one to two accumulated frames, additional frames yield diminishing returns, confirming that a small number of temporal frames is sufficient for convergent sub-millimeter accuracy. Across 30 independent free-fall trials with three objects, the system achieves an overall mean error of $0.146\pm 0.033$ mm and an overall F-score of $0.980\pm 0.006$—a mean relative error of approximately 1.6% on 8–10 mm targets—and fine surface features such as structural cracks are resolved at a fidelity sufficient for visual defect identification. These results establish rigid-body multi-frame temporal integration as an effective strategy for high-throughput, non-contact 3-D inspection of small objects in motion. Full article

Background: Intraoral scanning (IOS) errors seldom originate from a single-point failure; instead, they arise from interactions among hardware performance, reconstruction software, and operator-dependent acquisition behaviors. James Reason’s layered defense (Swiss-cheese) model provides a systems-oriented lens to trace how residual vulnerabilities can propagate into clinically relevant surface distortions. Objectives: To quantify within-scanner precision of IOS under repeatability conditions in a controlled in vitro setting and to propose a Reason-based framework that maps defensive layers, barriers, and residual failure modes along the IOS workflow. Methods: A controlled in vitro design was implemented to minimize clinical confounders. A standardized partially edentulous maxillary reference specimen was scanned repeatedly with three IOS systems under fixed environmental conditions using a standardized scanning strategy. Within each IOS, precision was quantified from repeated scans using surface deviation metrics, including root mean square (RMS) deviation, percentile-based dispersion, and the percentage of points within a predefined tolerance band. Residual vulnerabilities were organized into a systems-oriented error framework by defensive layer (hardware, software/processing, and acquisition/operator) and workflow stage (pre-scan preparation, acquisition, reconstruction/registration, and export/verification). Results: Deviation-based precision metrics revealed scanner-specific dispersion patterns, including differences in RMS magnitude and tail behavior (percentile spread), suggesting scanner-specific patterns of residual distortion under the tested conditions. Tolerance-based metrics further showed that threshold selection materially influences interpretability and perceived clinical relevance. In vitro IOS precision assessed under repeatability conditions should be interpreted as an emergent output of multiple interacting defensive layers rather than as the isolated performance of a single component. Coupling deviation-based precision metrics with Reason’s layered defense model yields a clinically actionable framework for quality control, helping anticipate where residual risk is most likely to accumulate and where mitigation checkpoints can be implemented. Conclusions: In vitro IOS repeatability should be interpreted as an emergent output of multiple interacting defensive layers rather than the isolated performance of a single component. Coupling repeatability metrics with Reason’s layered defense model supports a framework for quality-oriented interpretation, helping anticipate where residual risk is most likely to accumulate and where mitigation checkpoints can be implemented. Full article

Shipyards are transitioning toward Industry 4.0 more slowly than other industrial sectors, and this inertia often limits the adoption of reliable digital workflows for reverse engineering. Within the wider research aimed at supporting the digital transition of shipbuilding operations, this study presents a dedicated methodology for evaluating 3D scan quality by combining three complementary indicators describing geometric completeness, agreement with a reference model, and measurement accuracy and variability. A purpose-designed test sample representative of shipbuilding geometrical challenges was manufactured in metal by CNC methods and in PLA through additive manufacturing. Two scanning systems, a field-oriented portable device and a metrology-oriented fixed system, were evaluated under raw surface conditions and with tracking enhancement strategies (optical markers and scanning spray). Results show that reflective surfaces represent a critical scenario, where tracking enhancement is essential to obtain continuous reconstruction and reliable dimensional correspondence. Conversely, with low-reflectivity surfaces, high-quality reconstructions can also be achieved with portable systems, with tracking enhancements mainly improving uniformity and repeatability. Overall, the proposed workflow provides a quantitative basis to support scanner selection, which involves a compromise between portability and achievable metrological performance, for shipyards reverse engineering applications. Full article

Background: Local bone quality of the proximal humerus is a key determinant of fracture risk and implant stability in osteoporotic bone. Beyond established HU-based calibration, CT-osteoabsorptiometry (CT-OAM)-derived indices and microarchitecture-oriented workflows warrant systematic cross-modality evaluation. Methods: Twelve proximal humeral heads from six body donors (age 65–86 years; bilateral specimens) were analyzed using paired clinical CT and high-resolution micro-CT. Bone quality was quantified by (i) a HU-calibrated cancellous vBMD method (Krappinger et al.), (ii) a CT-OAM-inspired workflow reporting an ROI-averaged mean-intensity index in arbitrary units (a.u.), and (iii) a calibrated Bone Microarchitecture Analysis (BMA) workflow in Analyze 15.0. Paired tests, linear regression, and repeated-measures ANOVA after z-standardization were applied. Results: HU calibration yielded a mean trabecular vBMD of 114.37 ± 35.15 mg/cm³ on clinical CT. The BMA workflow produced higher CT-based values (207.37 ± 23.78 mg/cm³, p < 0.001) and markedly higher micro-CT values (469.34 ± 30.99 a.u.), indicating a systematic level shift between calibration frameworks. The CT-OAM index averaged 166.94 ± 40.12 a.u. on clinical CT and 455.89 ± 132.63 a.u. on micro-CT. Cross-modality agreement was very strong for CT-OAM (R² = 0.888) and moderate for BMA (R² = 0.502). After z-standardization, no significant differences were detected between the three CT-based approaches. Conclusions: A CT-OAM-inspired ROI-mean index and a BMA-based workflow provide complementary, transferable readouts of proximal humeral bone quality across clinical CT and micro-CT, with stronger cross-modality rank consistency for CT-OAM. Absolute density values differ systematically between calibration frameworks and should not be interpreted as directly interchangeable. These approaches support opportunistic, site-specific bone quality assessment from routine CT, but require prospective validation against fixation-related outcomes and robust scanner-independent standardization. Full article