arXiv subjects

Ludovic Humbert

Publications and source records attributed to Ludovic Humbert.

3 recordsLinked to original sources

arXiv2026

Short-Term Precision and Least Significant Change of 3D-DXA Cortical and Trabecular Proximal Femur Measurements Across Hologic DXA Scanner Models

3D-DXA provides volumetric and compartment-specific hip measurements from standard DXA scans. However, for reliable longitudinal interpretation, establishing scanner-specific short-term precision data is essential. This study assessed the short-term precision of 3D-DXA-derived parameters using repeated acquisitions from five clinical Hologic DXA scanner datasets. Duplicate hip DXA acquisitions were collected at five clinical centers using two Horizon Wi scanners, two Horizon A scanners, and one Discovery W scanner. Each subject was scanned twice with complete repositioning between acquisitions. Conventional total hip and femoral neck aBMD were obtained using APEX software, and 3D-Shaper software was used to derive integral vBMD, trabecular vBMD, and cortical sBMD. Precision error was expressed as RMS-SD and RMS-CV, and LSC was calculated at the 95% confidence level. For total hip aBMD, absolute LSC values ranged from 0.018 to 0.037 g/cm$^2$. Femoral neck aBMD showed higher error, with LSC values ranging from 0.031 to 0.049 g/cm$^2$. For 3D-DXA parameters, absolute LSC values ranged from 9.955 to 17.859 mg/cm$^2$ for integral vBMD, 8.662 to 18.487 mg/cm$^2$ for trabecular vBMD, and 4.444 to 8.875 mg/cm$^2$ for cortical sBMD. Precision errors for 3D-Shaper parameters varied moderately across the five clinical datasets. Short-term precision of 3D-Shaper-derived measurements was broadly comparable with previously published precision data. The LSC values reported here can support interpretation of longitudinal changes in integral vBMD, trabecular vBMD, and cortical sBMD on Hologic scanners.

q-bio.QM↗

arXiv2026

3D-DXA Cortical and Trabecular Parameters: Agreement Between Hologic Densitometers in Clinical Practice

Background: Three-dimensional dual-energy X-ray absorptiometry reconstructs three-dimensional maps of the proximal femur's density distribution from standard hip scans, enabling the estimation of trabecular and cortical bone parameters. The aim of this study was to assess the agreement of these three-dimensional cortical and trabecular femur parameters across different series and models of Hologic densitometers. Methodology: The study cohort was composed of 103 women and men recruited from four clinical centers in Spain and France. Subjects had duplicated hip scans using different Hologic scanners from the Horizon, Discovery, and QDR4500 series. Analyses were performed using 3D-Shaper software. Inter-scanner agreement was evaluated using Deming regression and Bland-Altman analysis. Results: The parameters demonstrated strong inter-device agreement across all clinical centers and scanner models, with coefficients of determination greater than 0.91. Absolute biases were less than 2.5 mg$/$cm$^3$ for integral volumetric bone mineral density, less than 2.9 mg$/$cm$^3$ for trabecular volumetric bone mineral density, and less than 1.7 mg$/$cm$^2$ for cortical surface bone mineral density. No statistically significant bias was found between parameters obtained from different scanners. Furthermore, the observed bias was lower than the expected least significant change, indicating that inter-scanner variability across these devices is not clinically significant. Conclusions: This study demonstrated excellent agreement for standard and three-dimensional derived bone parameters at the hip across Hologic densitometers. These findings support their suitability for clinical use.

q-bio.QM↗

arXiv2018

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.

cs.CV↗