Identification of Vertebral Fractures

  • Mohamed Jarraya
  • Daichi Hayashi
  • James F. Griffith
  • Ali Guermazi
  • Harry K. Genant
Part of the Medical Radiology book series (MEDRAD)


Osteoporosis is characterized by reduced bone mass and microarchitectural deterioration of bone, leading to an increase in bone fragility and susceptibility to low-traumatic or atraumatic fractures, most commonly vertebral fractures. Osteoporotic vertebral fractures have a significant impact on morbidity, mortality, and healthcare costs. Vertebral fracture is an independent and significant predictor of increased risk for further fractures. The occurrence of vertebral fracture is often clinically asymptomatic, and many of these fractures, therefore, remain undiagnosed. Several techniques are available for their reliable identification on radiographs. The two most widely used methods are the semiquantitative (SQ) assessment, which is based on visual evaluation, and the quantitative approach, which is based on morphometric criteria. Genant’s SQ approach is an accurate and reproducible method, tested and applied in many clinical studies. The newest generation of fan-beam dual energy X-ray absorptiometry (DXA) systems delivering lateral spine images of higher resolution offer a practical alternative to radiographs for vertebral fracture analysis. The advantages of DXA over radiography are its minimal radiation exposure and the practicalities of a one-step image acquisition allowing concurrent evaluation of vertebral fracture and bone mineral density, which are important criteria when assessing the risk of osteoporotic fracture. Standard computed tomography (CT) is not primarily used to detect vertebral fracture, though it often leads to the fortuitous detection of asymptomatic fracture. Magnetic resonance imaging (MRI) is an increasingly used modality for assessing the age and other important aspects of vertebral fracture.


Vertebral Fracture Vertebral Body Vertebral Deformity Vertebral Height Vertebral Fracture Assessment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg  2013

Authors and Affiliations

  • Mohamed Jarraya
    • 1
  • Daichi Hayashi
    • 1
  • James F. Griffith
    • 2
  • Ali Guermazi
    • 3
  • Harry K. Genant
    • 4
  1. 1.Department of RadiologyBoston UniversityBoston, MAUSA
  2. 2.Department of Imaging and Interventional RadiologyThe Chinese University of Hong KongHong KongChina
  3. 3.Department of RadiologyBoston UniversityBoston, MAUSA
  4. 4.Department of RadiologyUniversity of California San FranciscoSan FranciscoUSA

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