Literature DB >> 28716553

Regional analysis of age-related local bone loss in the spine of a healthy population using 3D voxel-based modeling.

Alexander Valentinitsch1, Stefano Trebeschi2, Eva Alarcón3, Thomas Baum4, Johannes Kaesmacher5, Claus Zimmer6, Cristian Lorenz7, Jan S Kirschke8.   

Abstract

Local variations in bone loss may be of great importance to individually predict osteoporotic fractures but are neglected by current densitometry techniques. The purpose of this study was to evaluate regional variations of normal bone loss at the spine among different age groups using voxel-based morphometry. Non-contrast MDCT scans of 16 patients under the age of 40 (mean age 26years) without spinal pathology were identified as a reference cohort, where each thoracolumbar vertebra was assessed individually. For comparison, 38 patients >40years were grouped by decades in 4 cohorts of 10 patients each, except the youngest, including 8 patients only. All spines were automatically detected, segmented and non-rigidly registered for spatially normalized vertebral bodies. Afterwards, statistical and T-score mapping was performed to highlight local density differences in comparison to the reference cohort. The calculated statistical maps of significantly affected density regions (ADR) started to highlight small local changes of volumetric bone mineral density (vBMD) distribution within the vertebra of L5 (ADR: 7.9%) in the fifties cohort. Regions near the endplates were most affected. The effect dramatically increased in the sixties cohort, where bone loss was most prominent from T12 to L2. In the seventies cohort, around 50% of voxels in T10 to L5 showed significantly decreased vBMD. In conclusion, ADR and local T-score maps of the spine showed age-related local variations in a healthy population, corresponding to known areas of fracture origination and increased fracture incidence. It thus might provide a powerful tool in diagnosis of osteoporosis.
Copyright © 2017. Published by Elsevier Inc.

Entities:  

Keywords:  Age-related bone loss; Computed tomography; Osteoporosis; Spine; Statistical mapping; T-score mapping; Voxel-based morphometry (VBM)

Mesh:

Year:  2017        PMID: 28716553     DOI: 10.1016/j.bone.2017.06.013

Source DB:  PubMed          Journal:  Bone        ISSN: 1873-2763            Impact factor:   4.398


  14 in total

Review 1.  X-ray-based quantitative osteoporosis imaging at the spine.

Authors:  M T Löffler; N Sollmann; K Mei; A Valentinitsch; P B Noël; J S Kirschke; T Baum
Journal:  Osteoporos Int       Date:  2019-11-14       Impact factor: 4.507

2.  Artificial intelligence-based radiomics on computed tomography of lumbar spine in subjects with fragility vertebral fractures.

Authors:  E Biamonte; R Levi; F Carrone; W Vena; A Brunetti; M Battaglia; F Garoli; G Savini; M Riva; A Ortolina; M Tomei; G Angelotti; M E Laino; V Savevski; M Mollura; M Fornari; R Barbieri; A G Lania; M Grimaldi; L S Politi; G Mazziotti
Journal:  J Endocrinol Invest       Date:  2022-06-25       Impact factor: 5.467

3.  Labeling Vertebrae with Two-dimensional Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy.

Authors:  Anjany Sekuboyina; Markus Rempfler; Alexander Valentinitsch; Bjoern H Menze; Jan S Kirschke
Journal:  Radiol Artif Intell       Date:  2020-03-25

4.  Anatomical Variation of Age-Related Changes in Vertebral Bone Marrow Composition Using Chemical Shift Encoding-Based Water-Fat Magnetic Resonance Imaging.

Authors:  Thomas Baum; Alexander Rohrmeier; Jan Syväri; Maximilian N Diefenbach; Daniela Franz; Michael Dieckmeyer; Andreas Scharr; Hans Hauner; Stefan Ruschke; Jan S Kirschke; Dimitrios C Karampinos
Journal:  Front Endocrinol (Lausanne)       Date:  2018-04-04       Impact factor: 5.555

Review 5.  Cortical Bone Mapping: Measurement and Statistical Analysis of Localised Skeletal Changes.

Authors:  Graham Treece; Andrew Gee
Journal:  Curr Osteoporos Rep       Date:  2018-10       Impact factor: 5.096

6.  Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures.

Authors:  A Valentinitsch; S Trebeschi; J Kaesmacher; C Lorenz; M T Löffler; C Zimmer; T Baum; J S Kirschke
Journal:  Osteoporos Int       Date:  2019-03-04       Impact factor: 4.507

7.  Prediction of Incidental Osteoporotic Fractures at Vertebral-Specific Level Using 3D Non-Linear Finite Element Parameters Derived from Routine Abdominal MDCT.

Authors:  Long Yu Yeung; Nithin Manohar Rayudu; Maximilian Löffler; Anjany Sekuboyina; Egon Burian; Nico Sollmann; Michael Dieckmeyer; Tobias Greve; Jan S Kirschke; Karupppasamy Subburaj; Thomas Baum
Journal:  Diagnostics (Basel)       Date:  2021-01-30

8.  A study of dynamic contrast-enhanced MR imaging features and influence factors of pelvic bone marrow in adult females.

Authors:  X Zhang; H Pang; Y Dong; D Shi; F Liu; Y Luo; T Yu; X Wang
Journal:  Osteoporos Int       Date:  2019-08-26       Impact factor: 4.507

9.  A Vertebral Segmentation Dataset with Fracture Grading.

Authors:  Maximilian T Löffler; Anjany Sekuboyina; Alina Jacob; Anna-Lena Grau; Andreas Scharr; Malek El Husseini; Mareike Kallweit; Claus Zimmer; Thomas Baum; Jan S Kirschke
Journal:  Radiol Artif Intell       Date:  2020-07-29

10.  MDCT-Based Finite Element Analyses: Are Measurements at the Lumbar Spine Associated with the Biomechanical Strength of Functional Spinal Units of Incidental Osteoporotic Fractures along the Thoracolumbar Spine?

Authors:  Nico Sollmann; Nithin Manohar Rayudu; Long Yu Yeung; Anjany Sekuboyina; Egon Burian; Michael Dieckmeyer; Maximilian T Löffler; Benedikt J Schwaiger; Alexandra S Gersing; Jan S Kirschke; Thomas Baum; Karupppasamy Subburaj
Journal:  Diagnostics (Basel)       Date:  2021-03-06
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