Background:
Osteoporosis is defined as a metabolic bone disease characterized by compromised bone strength
predisposing an individual to an increased risk for fracture. The prevalence of osteoporosis is
increasing in our ageing societies and classified as public health problem as osteoporotic fractures,
particularly at the spine and hip, are associated with a high morbidity and mortality. However,
osteoporosis is frequently becoming evident not until an associated fragility fracture occurs. Thus, the
initiation of therapy including effective drug treatment is delayed and the clinical goal to prevent
osteoporotic fractures fails.
The World Health Organisation (WHO) defines osteoporosis by measurements of bone mineral
density (BMD) at the spine and hip using dual-energy X-ray absorptiometry (DXA). However, BMD
values of subjects with versus without osteoporotic fractures overlap. Therefore, the FRAX® algorithm
has been established calculating a 10-year fracture probability taking additional clinical risk factors into
account.
State-of-the-Art:
Recent advances in the field of quantitative osteoporosis imaging allow a dedicated analysis of the
mineralized and non-mineralized bone component. Magnetic Resonance Spectroscopy (MRS) and
chemical shift encoding-based water-fat Magnetic Resonance Imaging (MRI) are able to reveal the
bone marrow composition. These measurements have been proposed as advanced imaging
biomarkers for osteoporosis-associated fracture risk in patients with type 2 diabetes who have an
increased fracture risk despite normal or even elevated BMD. Furthermore, advanced Multi-Detector
Computed Tomography (MDCT)-based imaging biomarkers have emerged including Finite Element
Models (FEM), trabecular bone texture analysis, and deep-learning approaches to improve fracture
risk prediction. Opportunistic osteoporosis screening in non-dedicated routine abdominal MDCT
exams has received scientific interest over the last years as such an approach is cost and time saving
and reduces radiation exposure. Lastly, the muscle-bone interaction has drawn considerable interest,
but clear evidence for improvement of fracture risk prediction by taking the adjacent muscle
compartments into account are still lacking.
Scope of Research Topic:
Based on these developments in the field of quantitative osteoporosis imaging and computational
tools, there is the justified expectation that imaging allows an individualized and anatomical location
dependent assessment of bone health and prediction of osteoporotic fracture risk. This Research
Topic welcomes original articles, reviews, method articles, hypotheses, perspectives, and technology
& code articles.
Specific topics are investigations in humans ex- and in-vivo which demonstrate
(I) advances in image acquisition including DXA, (low-dose) MDCT, high-resolution peripheral
Quantitative Computed Tomography (hr-pQCT), and MR techniques,
(II) novel post-processing methods for image reconstruction (e.g. MR-based bone marrow fat
quantifications or CT-based iterative image reconstruction algorithms),
(III) advances in automated image segmentation (particularly deep-learning approaches) with focus on
the clinically most important fracture sites, i.e. spine and hip,
(IV) newly or further developed data analytics tools for bone strength prediction including Finite
Element Modelling and deep-learning approaches,
and
(V) insights in the muscle-bone interaction in osteoporosis and opportunistic osteoporosis
screening possibilities in clinical routine image data.
Background:
Osteoporosis is defined as a metabolic bone disease characterized by compromised bone strength
predisposing an individual to an increased risk for fracture. The prevalence of osteoporosis is
increasing in our ageing societies and classified as public health problem as osteoporotic fractures,
particularly at the spine and hip, are associated with a high morbidity and mortality. However,
osteoporosis is frequently becoming evident not until an associated fragility fracture occurs. Thus, the
initiation of therapy including effective drug treatment is delayed and the clinical goal to prevent
osteoporotic fractures fails.
The World Health Organisation (WHO) defines osteoporosis by measurements of bone mineral
density (BMD) at the spine and hip using dual-energy X-ray absorptiometry (DXA). However, BMD
values of subjects with versus without osteoporotic fractures overlap. Therefore, the FRAX® algorithm
has been established calculating a 10-year fracture probability taking additional clinical risk factors into
account.
State-of-the-Art:
Recent advances in the field of quantitative osteoporosis imaging allow a dedicated analysis of the
mineralized and non-mineralized bone component. Magnetic Resonance Spectroscopy (MRS) and
chemical shift encoding-based water-fat Magnetic Resonance Imaging (MRI) are able to reveal the
bone marrow composition. These measurements have been proposed as advanced imaging
biomarkers for osteoporosis-associated fracture risk in patients with type 2 diabetes who have an
increased fracture risk despite normal or even elevated BMD. Furthermore, advanced Multi-Detector
Computed Tomography (MDCT)-based imaging biomarkers have emerged including Finite Element
Models (FEM), trabecular bone texture analysis, and deep-learning approaches to improve fracture
risk prediction. Opportunistic osteoporosis screening in non-dedicated routine abdominal MDCT
exams has received scientific interest over the last years as such an approach is cost and time saving
and reduces radiation exposure. Lastly, the muscle-bone interaction has drawn considerable interest,
but clear evidence for improvement of fracture risk prediction by taking the adjacent muscle
compartments into account are still lacking.
Scope of Research Topic:
Based on these developments in the field of quantitative osteoporosis imaging and computational
tools, there is the justified expectation that imaging allows an individualized and anatomical location
dependent assessment of bone health and prediction of osteoporotic fracture risk. This Research
Topic welcomes original articles, reviews, method articles, hypotheses, perspectives, and technology
& code articles.
Specific topics are investigations in humans ex- and in-vivo which demonstrate
(I) advances in image acquisition including DXA, (low-dose) MDCT, high-resolution peripheral
Quantitative Computed Tomography (hr-pQCT), and MR techniques,
(II) novel post-processing methods for image reconstruction (e.g. MR-based bone marrow fat
quantifications or CT-based iterative image reconstruction algorithms),
(III) advances in automated image segmentation (particularly deep-learning approaches) with focus on
the clinically most important fracture sites, i.e. spine and hip,
(IV) newly or further developed data analytics tools for bone strength prediction including Finite
Element Modelling and deep-learning approaches,
and
(V) insights in the muscle-bone interaction in osteoporosis and opportunistic osteoporosis
screening possibilities in clinical routine image data.
