Evidence map›Paper›PMID 39315381›Full record

ArticleJBMR plus2024

HR-pQCT cross-calibration using standard vs. Laplace-Hamming binarization approach.

Saghi Sadoughi, Aditya Subramanian, Gabriella Ramil, Minhao Zhou, Andrew J Burghardt, Galateia J Kazakia

Abstract read
In one paragraph

Article in JBMR plus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. The impact of segmentation approach on HR-pQCT microarchitectural and biomechanical metrics depends on bone structure.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025
    Article
  4. Time-lapse HR-pQCT reliably assesses and monitors local bone turnover in patients with chronic kidney disease.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Saghi SadoughiBone Quality Research Lab, Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., Suite 350, San Francisco, CA 94107, United States.
Aditya SubramanianBone Quality Research Lab, Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., Suite 350, San Francisco, CA 94107, United States.
Gabriella RamilBone Quality Research Lab, Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., Suite 350, San Francisco, CA 94107, United States.
Minhao ZhouBone Quality Research Lab, Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., Suite 350, San Francisco, CA 94107, United States.ORCID https://orcid.org/0000-0001-8084-3638
Andrew J BurghardtBone Quality Research Lab, Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., Suite 350, San Francisco, CA 94107, United States.ORCID https://orcid.org/0000-0002-6343-4944
Galateia J KazakiaBone Quality Research Lab, Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., Suite 350, San Francisco, CA 94107, United States.ORCID https://orcid.org/0000-0002-7327-6425

Funding

Skeletal Biology and Biomechanics (SBB) CoreP30AR075055 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI EDWARD C HSIAO · 2019 to 2026
$7.1M
NIAMS NIH HHS P30 AR075055
6 · The paper itself

Abstract

High-resolution peripheral quantitative computed tomography (HR-pQCT) has emerged as a powerful imaging technique for characterizing bone microarchitecture in the human peripheral skeleton. The second-generation HR-pQCT scanner provides improved spatial resolution and a shorter scan time. However, the transition from the first-generation (XCTI) to second-generation HR-pQCT scanners (XCTII) poses challenges for longitudinal studies, multi-center trials, and comparison to historical data. Cross-calibration, an established approach for determining relationships between measurements obtained from different devices, can bridge this gap and enable the utilization and comparison of legacy data. The goal of this study was to establish cross-calibration equations to estimate XCTII measurements from XCTI data, using both the standard and Laplace-Hamming (LH) binarization approaches. Thirty-six volunteers (26-85 yr) were recruited and their radii and tibiae were scanned on both XCTI and XCTII scanners. XCTI images were analyzed using the manufacturer's standard protocol. XCTII images were analyzed twice: using the manufacturer's standard protocol and the LH segmentation approach previously developed and validated by our team. Linear regression analysis was used to establish cross-calibration equations. Results demonstrated strong correlations between XCTI and XCTII density and geometry outcomes. For most microstructural outcomes, although there were considerable differences in absolute values, correlations between measurements obtained from different scanners were strong, allowing for accurate cross-calibration estimations. For some microstructural outcomes with a higher sensitivity to spatial resolution (eg, trabecular thickness, cortical pore diameter), XCTII standard protocol resulted in poor correlations between the scanners, while our LH approach improved these correlations and decreased the difference in absolute values and the proportional bias for other measurements. For these reasons and due to the improved accuracy of our LH approach compared with the standard approach, as established in our previous study, we propose that investigators should use the LH approach for analyzing XCTII scans, particularly when comparing to XCTI data.

Indexed as

cross-calibrationestimation errorGaussian binarizationHR-pQCTLaplace-Hamming binarization

Identifiers

PMID39315381
PMCPMC11417609

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.