The quantification of bone mineral density using photon counting computed tomography and its implications for detecting bone remodeling
Bibliographic record
Abstract
HR-pQCT has become standard practice when quantifying volumetric BMD (vBMD) in vivo. Yet, it is only accessible to peripheral sites, with small fields of view and lengthy scanning times. This limits general applicability in clinical workflows. The goal of this study was to assess the potential of photon counting CT (PCCT) in quantitative bone imaging. Using the European Forearm Phantom, PCCT was calibrated to hydroxyapatite (HA) density. Eight cadaveric forearms were scanned twice with PCCT and once with HR-pQCT. The dominant forearm of two volunteers was scanned twice with PCCT. In each scan, the carpals were delineated. At bone level, accuracy was assessed with a paired measurement of total vBMD (Tt.vBMD) calculated with PCCT and HR-pQCT. At voxel-level, repeatability was assessed by image registration and voxel-wise subtraction of the ex vivo PCCT scans. In an ideal scenario, this difference would be zero; any deviation was interpreted as falsely detected remodeling. For clinical usage, the least detectable remodeling was determined by finding a threshold in the PCCT difference image that resulted in a classification of bone formation and resorption below acceptable noise levels (<0.5%). The paired measurement of Tt.vBMD had a Pearson correlation of 0.986. Compared to HR-pQCT, PCCT showed a bias of 7.46 mgHA/cm3. At voxel-level, the repeated PCCT scans showed a bias of 17.66 mgHA/cm3 and a standard error of 96.23 mgHA/cm3. Least detectable remodeling was found to be 250 mgHA/cm3, for which 0.37% of the voxels was incorrectly classified as newly added or resorbed bone. In vivo, this volume increased to 0.97%. Based on the cadaver data, we conclude that PCCT can be used to quantify vBMD and bone turnover. We provided proof of principle that this technique is also accurate in vivo, hence, that it has high potential for clinical applications.
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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 | 0.002 | 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".