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MRI of Bone Metastases

  • Gerwin Paul Schmidt
  • Andrea Baur-Melnyk
Part of the Medical Radiology book series (MEDRAD)

Abstract

The skeletal system is a frequent target of metastatic spread from various primary tumors such as carcinoma of the breast, lung and prostate cancer. Therefore, it is highly important to accurately assess skeletal metastases in order to facilitate adequate therapy and predict patients’ prognosis. However, only pronounced destruction of bone with loss of mineral content exceeding 50% is readily visible in radiographic examinations. Computed tomography (CT) is definitely more sensitive than radiography and it is the imaging modality of choice to evaluate the extent of destruction of trabecular and cortical bone and to assess stability and fracture risk. Magnetic resonance imaging (MRI), on the other hand, allows visualization of bone marrow structure, such as hematopoietic—and fat cell components. Moreover, tumor infiltration into the spinal canal and paravertebral soft tissues is clearly depicted. The combination of unenhanced T1-weighted-spin echo- and turbo-STIR-sequences has shown to be most useful for the detection of bone marrow abnormalities and is able to discriminate benign from malignant bone marrow changes. Compared with other imaging modalities like radiography, CT or bone scintigraphy, it is the most sensitive technique for the detection of bone marrow pathologies, even if trabecular bone is not destroyed. Recently, multi-channel whole-body MRI (WB-MRI) scanners have been introduced and allow for head-to-toe assessment of the whole skeletal system without compromises in image quality compared with dedicated examinations of limited anatomical areas. Accordingly, WB-MRI has become a useful and sensitive alternative to standard whole-body imaging procedures such as skeletal scintigraphy or whole-body CT.

Keywords

Positron Emission Tomography Apparent Diffusion Coefficient Bone Metastasis Bone Scintigraphy Skeletal Metastasis 
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

  1. 1.Department of Clinical RadiologyUniversity Hospitals Munich/Grosshadern, LMUMunichGermany

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