Imaging of the Osteoporotic Spine - Quantitative Approaches in Diagnostics and for the Prediction of the Individual Fracture Risk.

Osteoporosis is a highly prevalent systemic skeletal disease that is characterized by low bone mass and microarchitectural bone deterioration. It predisposes to fragility fractures that can occur at various sites of the skeleton, but vertebral fractures (VFs) have been shown to be particularly common. Prevention strategies and timely intervention depend on reliable diagnosis and prediction of the individual fracture risk, and dual-energy X-ray absorptiometry (DXA) has been the reference standard for decades. Yet, DXA has its inherent limitations, and other techniques have shown potential as viable add-on or even stand-alone options. Specifically, three-dimensional (3 D) imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), are playing an increasing role. For CT, recent advances in medical image analysis now allow automatic vertebral segmentation and value extraction from single vertebral bodies using a deep-learning-based architecture that can be implemented in clinical practice. Regarding MRI, a variety of methods have been developed over recent years, including magnetic resonance spectroscopy (MRS) and chemical shift encoding-based water-fat MRI (CSE-MRI) that enable the extraction of a vertebral body's proton density fat fraction (PDFF) as a promising surrogate biomarker of bone health. Yet, imaging data from CT or MRI may be more efficiently used when combined with advanced analysis techniques such as texture analysis (TA; to provide spatially resolved assessments of vertebral body composition) or finite element analysis (FEA; to provide estimates of bone strength) to further improve fracture prediction. However, distinct and experimentally validated diagnostic criteria for osteoporosis based on CT- and MRI-derived measures have not yet been achieved, limiting broad transfer to clinical practice for these novel approaches. KEY POINTS:: · DXA is the reference standard for diagnosis and fracture prediction in osteoporosis, but it has important limitations.. · CT- and MRI-based methods are increasingly used as (opportunistic) approaches.. · For CT, particularly deep-learning-based automatic vertebral segmentation and value extraction seem promising.. · For MRI, multiple techniques including spectroscopy and chemical shift imaging are available to extract fat fractions.. · Texture and finite element analyses can provide additional measures for vertebral body composition and bone strength.. CITATION FORMAT: · Sollmann N, Kirschke JS, Kronthaler S et al. Imaging of the Osteoporotic Spine - Quantitative Approaches in Diagnostics and for the Prediction of the Individual Fracture Risk. Fortschr Röntgenstr 2022; 194: 1088 - 1099.

The Founder's Lecture 2009: advances in imaging of osteoporosis and osteoarthritis.

The objective of this review article is to provide an update on new developments in imaging of osteoporosis and osteoarthritis over the past three decades. A literature review is presented that summarizes the highlights in the development of bone mineral density measurements, bone structure imaging, and vertebral fracture assessment in osteoporosis as well as MR-based semiquantitative assessment of osteoarthritis and quantitative cartilage matrix imaging. This review focuses on techniques that have impacted patient management and therapeutic decision making or that potentially will affect patient care in the near future. Results of pertinent studies are presented and used for illustration. In summary, novel developments have significantly impacted imaging of osteoporosis and osteoarthritis over the past three decades.

Regeneration of the donor side after autogenous fibula transplantation in 53 patients: evaluation by dual x-ray absorptiometry.

We determined the degree of fibular regeneration at the donor site, using radiographs and dual x-ray absorptiometry, in 53 patients who underwent autogenous nonvascularized fibular transplantation for tumor reconstruction in long bones (mean follow-up 15 (3-26) years). Logistic regression was used to determine whether gender, age at transplantation, time since transplantation, bone mineral density (BMD), and length of the graft were associated with fibular regeneration. 26 patients had spontaneous complete bone regeneration. Younger age at transplant was the only predictor of fibular regeneration. In predicting fibular regeneration, sensitivity was 96% and specificity 74%, using 15 years of age as a cut-off. In the long-term follow-up, we found only gradual changes in the BMD and the values ranged from 24% to 217%. We found no correlations of bone mineral density with age, gender, length of the graft, or time since transplantation.