Advances in imaging approaches to fracture risk evaluation

Fragility fractures are a growing problem worldwide, and current methods for diagnosing osteoporosis do not always identify individuals who require treatment to prevent a fracture and may misidentify those not a risk. Traditionally, fracture risk is assessed using dual-energy X-ray absorptiometry, which provides measurements of areal bone mineral density at sites prone to fracture. Recent advances in imaging show promise in adding new information that could improve the prediction of fracture risk in the clinic. As reviewed herein, advances in quantitative computed tomography (QCT) predict hip and vertebral body strength; high-resolution HR-peripheral QCT (HR-pQCT) and micromagnetic resonance imaging assess the microarchitecture of trabecular bone; quantitative ultrasound measures the modulus or tissue stiffness of cortical bone; and quantitative ultrashort echo-time MRI methods quantify the concentrations of bound water and pore water in cortical bone, which reflect a variety of mechanical properties of bone. Each of these technologies provides unique characteristics of bone and may improve fracture risk diagnoses and reduce prevalence of fractures by helping to guide treatment decisions.

Introduction

Bone fractures are a widespread problem that affects over 75 million people in the world, with more than 2.3 million osteoporotic fractures per year globally.1, 2 Over a lifetime, the risk of a fracture is around 40% for women in developed countries.³ The costs associated with bone fractures were estimated to be $19 billion in 2005 in the United States alone and are projected to increase by 50% by the year 2025.⁴ In the EU, costs in 2010 were estimated to be €37 billion and are expected to increase by 25% in 2025.¹ An increase in fracture risk occurs with aging for both women and men.2, 4 Fractures are a large problem with certain diseases and conditions, such as post-menopausal women and diabetes. Diabetes, in particular, has a rapidly increasing prevalence,⁵ leading to even higher costs and an increasing need for comprehensive clinical procedures to accurately measure and diagnose fracture risk.

The most common imaging parameter used to diagnose high fracture risk is low bone mineral density (BMD) assessed by dual-energy X-ray absorptiometry (DXA) of the hip, spine, and distal radius. Examples of DXA images acquired in the radius and the hip are shown in Fig 1. DXA measures the transmission of X-ray beams through tissue at 2 different mean photon energies. The difference in dependence of X-ray attenuation on photon energy between bone mineral and soft tissues then allows for an estimate of BMD.⁶ Because DXA uses 2D projection images, the resulting BMD values are areal estimates, computed in units of mineral mass per image pixel area. In clinical practice, however, DXA BMD is typically evaluated as a T-score (tabulated over a standard region of interest), defined as an individual's BMD relative to the standard deviation of BMD values of a young healthy population of the same ethnicity and sex.⁷ The World Health Organization has defined osteoporosis as having a T-score lower than −2.5 or having a previous fragility fracture, and osteopenia is defined as having a T-score between −1 and −2.5.

DXA is a fast, inexpensive, and well-studied method that has very low radiation dose (5–20 μSv), but it also has many limitations. Areal BMD varies significantly based on anatomical structure, so the results are biased by bone size and orientation. Degenerative disc disease or aortic calcifications can lead to an increased apparent BMD and falsely lower apparent fracture risk,8, 9 whereas other imaging artifacts arising from excess soft tissue in obese patients or prosthetic implants in the background can also alter DXA results. In addition, DXA does not fully explain the increase in fracture risk with age¹⁰ or diabetes.¹¹ Moreover, in a study of nearly 150,000 post-menopausal women (50–104 years), 82% of those that reported a fracture within 1 year had a baseline T-score greater than −2.5 (DXA at peripheral sites, namely heel, finger, or forearm).¹²

To overcome some of the limitations of DXA, it is now standard of care to consider additional risk factors in the diagnosis and treatment of osteoporosis. This is often done using algorithms that incorporate known risk factors, such as The World Health Organization's Fracture Risk Algorithm (FRAX) tool.¹³ This online tool calculates the 10-year probability of a major osteoporotic fracture and of a hip fracture based on relevant risk factors (eg, age, sex, history of fracture, smoking status, alcohol consumption, and various diseases associated with high fracture) with or without hip BMD. The FRAX model is widely used in the clinic and is continuing to be expanded to include more countries. However, FRAX does not include all ethnicities or diseases, for instance type-2 diabetes, and is only designed to help guide clinical decisions. Other algorithms, such as Garvan and QFracture, have also been introduced as an alternative to FRAX. The Garvan algorithm¹⁴ was developed in Australia, includes the probability of suffering a fracture within both 5 and 10 years, and uses the history and frequency of previous fractures and falls. However, it does not include other risk factors and has only been tested on Australian and Canadian populations. The QFracture method¹⁵ was developed in the United Kingdom and includes more risk factors than FRAX, such as various diseases, history of falls, and a 5-point scale for history of smoking and alcohol use. However, it does not include previous fractures in the model and is limited to studies in the UK. In addition to the risk factor models, the trabecular bone score (TBS) is a gray-level texture measure that is derived from experimental variograms of DXA images of the lumbar spine. TBS is an indirect index of trabecular architecture and has shown promise in adding to the predictive power of DXA.¹⁶ Although both TBS and risk factor algorithms are useful tools, the primary limitation of these measures is that they lack additional information about the composition of the bone itself.17, 18

Changes in both cortical and trabecular bone alter bone strength. With aging, there can be a thinning of the cortices, due to endosteal resorption, that leads to an increase in fracture risk.¹⁹ Aging can also lead to deterioration of the trabecular architecture (eg, fenestrations of the trabeculae lowering the connectivity), thereby weakening the bone.²⁰ Because bone loss usually begins in trabecular bone, clinicians are often interested in looking at trabecular bone measures to detect early changes in bone quality. Some more recent imaging methods have aimed to look at properties beyond areal BMD from DXA.21, 22

Regardless of type, the bone tissue is comprised of 3 principal components: (1) mineral (primarily crystals of calcium phosphate with carbonate and hydroxyl substitutions), (2) organic matrix (primarily type 1 collagen, noncollagenous proteins, and lipids), and (3) water (existing in porous spaces and bound to the matrix). The mineral component imparts strength and stiffness and is the component of bone to which DXA is sensitive. However, the mineral component of bone alone is brittle; the plasticity or ductility of the bone comes from the hydrated organic matrix. During plastic deformation (ie, post-yield strain), energy is dissipated until the bone fractures.19, 23 Along with bone structure, both the strength and plasticity of the bone tissue contribute to fracture resistance. Although increases in fracture risk are usually attributed to a decrease in BMD, changes in collagen organization or condition also affect fracture risk. For example, as a person ages, the collagen integrity of their bones decreases, which results in increased brittleness of the bone,24, 25 leading to a significant increase in fracture risk. A major challenge in bone imaging is finding useful surrogates that are sensitive to bone brittleness.

This article reviews additional imaging techniques that probe properties of bone that have the potential to help better diagnose fracture risk in clinical settings. The methods discussed are quantitative computed tomography (QCT) including high-resolution peripheral QCT (HR-pQCT), quantitative ultrasound (QUS), micromagnetic resonance imaging (μMRI), and other quantitative MRI methods that provide information about the composition of the tissue. QCT methods provide 3D bone structure and volumetric BMD, which in-turn can also support the use of numerical methods to predict bone strength. High-resolution HR-peripheral QCT (HR-pQCT) and micro-magnetic resonance imaging (μMRI) both assess the micro-architecture of trabecular bone. HR-pQCT also provides volumetric BMD, but at the cost of radiation exposure; μMRI has no ionizing radiation, allowing for repeated measurements, but has lower resolution and does not report BMD. Both HR-pQCT and μMRI have also been used in combination with μFEA to help improve bone strength predictions. QUS estimates of bone quality based on the ultrasound wave characteristics through bone tissue. Quantitative MRI methods can assess 3D bone structure, bone marrow fat content, and cortical bone water compartments including bound water and pore water components.