THE EFFECT OF RECONSTRUCTION KERNEL AND MONOCHROMATIC ENERGY PAIRS USED IN DUAL ENERGY CT IMAGING OF THE PROXIMAL HUMERUS
Bibliographic record
Abstract
INTRODUCTION Dual-energy computed tomography (DECT) allows for more accurate volumetric vBMD by accounting for marrow alterations with aging, disease and acute injuries. Tissue alterations, including vBMD, have been identified as potential biomarkers for early shoulder OA. Reconstruction kernel and energy pair images used in DECT alter vBMD and resulting estimated bone stiffness in image-based finite element models (FEMs). Prior to clinical investigation, the effect of imaging parameters must be understood. OBJECTIVE This study investigated how varying reconstruction kernel, and DECT monochromatic energy pair combinations influenced 1) vBMD, and 2) FEM estimated stiffness in the proximal humerus of cadaveric models. METHODS Cadaveric specimens (n = 7; 14 shoulders) were scanned bilaterally using DECT (GE Revolution HD GSI) with a K 2 HPO 4 calibration phantom. DECT images were reconstructed using bone sharpening (BONE) and standard (STD) kernels. Simulated monochromatic images were created at 40, 90, and 140 keV using the manufacturers GSI software and combined into energy pairs (40/90, 90/140, 40/140 keV). Images were processed with custom Python scripts and 3D Slicer software to segment and extract vBMD values in proximal humeral head and diaphysis locations. Image-based FEMs were used to compare estimated bone stiffness across models generated from each image. Results were compared using a two-way RM-ANOVA. RESULTS The highest vBMD values occurred in the humeral shaft diaphysis across all kernel and energy pair combinations (Table 1). There were significant differences in vBMD across energy pairs and kernels within the diaphysis region, with the greatest vBMD occurring with the 90/140 keV energy pair. No significant differences in mean vBMD values across energy pair combinations occurred for the anatomic neck. Increased vBMD input to FEMs resulted in similar trends, with the highest FEM stiffness in the diaphysis region, and those generated from 90/140 keV DECT images (Table 2). Significant differences remained in the diaphysis with no difference in the anatomic neck FEMs. CONCLUSION Higher vBMD values in the diaphysis reflect its cortical bone density, with significant differences by kernel and energy pair. Lower vBMD values in the anatomic neck, a trabecular-rich region, occur partially due to the heterogeneous composition, with minimal cortical bone. The BONE kernel at higher energy pairs (e.g., 90/140 keV) improved contrast but resulted in the greatest vBMD, a trend that was not observed with the other two energy pairs. Trends in vBMD persisted in FEMs indicating choice of energy pair combination has a large effect on vBMD and FEM stiffness in regions of high cortical bone, with the 90/140 keV energy pair, but little effect on trabecular regions within the proximal humerus of the cadavers evaluated in this study. The results of this study indicate that when generating DECT images from simulated monochromatic energy images for vBMD and image-based FEM estimated stiffness, 40/90 and 40/140 keV energy pairs have minimal influence across trabecular and cortical regions of the proximal humerus, while those generated with 90/140 keV have larger values, which may be partially explained by increased noise. Future studies will explore the validation of FEM models and precision measurements of vBMD in cross-sectional and longitudinal cohorts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".