Single and dual energy X-ray absorptiometry

Adams, Judith E.

doi:10.1007/pl00006861

Cited by 66 publications

(38 citation statements)

References 151 publications

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“…Localization for SXA is normally done using external landmarks without an image. It is now widely considered inferior to dual-energy X-ray absorptiometry, which uses a second energy beam to correct for absorption of X-ray energy by non-calcium containing tissues (Adams, 1997).…”

Section: Sxa (Single Energy X-ray Absorptiometry)mentioning

confidence: 99%

“…The method overcame the problems for radiographic photodensitometric techniques, caused by polychromatic X-rays and nonuniformity of film sensitivity and development, by using a single energy g-ray source (125I, photon energy 27.3 KeV) and a scintillation detector to measure transmitted photons (Adams 1997). SPA was used to advance bone measurement from the early days of measurement of bone size on radiographs of the hand or crude determinations of optical density from similar images.…”

Section: Spa (Single Photon Absorptiometry)mentioning

confidence: 99%

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Bone Density Measurement Using Computed Tomography

Çelenk¹,

Çelenk²

2012

Computed Tomography - Clinical Applications

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Section: Sxa (Single Energy X-ray Absorptiometry)mentioning

confidence: 99%

Section: Spa (Single Photon Absorptiometry)mentioning

confidence: 99%

Bone Density Measurement Using Computed Tomography

Çelenk¹,

Çelenk²

2012

Computed Tomography - Clinical Applications

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“…BMD can be measured at specific fracture-related skeletal sites by different quantitative techniques: single photon absorptiometry (SPA), dual photon absorptiometry (DPA), and Dual energy X-ray absorptiometry (DXA). DXA has become the de facto standard for diagnosing osteoporosis because of its precise and acceptably accurate measures of BMD [3], [4]. For this purpose, several clinically relevant sites are widely used: proximal femur, vertebrae, and distal radius.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Distal Radius Segmentation in DXA Using Modified ASM

Lee

Cho

2011

IEICE Trans. Inf. & Syst.

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SUMMARYThe active shape model (ASM) has been widely adopted by automated bone segmentation approaches for radiographic images. In radiographic images of the distal radius, multiple edges are often observed in the near vicinity of the bone, typically caused by the presence of thin soft tissue. The presence of multiple edges decreases the segmentation accuracy when segmenting the distal radius using ASM. In this paper, we propose an enhanced distal radius segmentation method that makes use of a modified version of ASM, reducing the number of segmentation errors. To mitigate segmentation errors, the proposed method emphasizes the presence of the bone edge and downplays the presence of a soft tissue edge by making use of Dual energy X-ray absorptiometry (DXA). To verify the effectiveness of the proposed segmentation method, experiments were performed with 30 distal radius patient images. For the images used, compared to ASM-based segmentation, the proposed method improves the segmentation accuracy with 47.4% (from 0.974 mm to 0.512 mm).

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“…Techniques for the determination of fracture risk traditionally use methods for measuring bone mineral density (BMD) like e.g. DXA [1]. However, there is still a significant overlap in bone mineral density between osteoporotic and normal individuals and BMD alone is not sufficient to predict bone failure load for an individual patient [2].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Biomechanical Parameters of the Proximal Femur Using Statistical Appearance Models and Support Vector Regression

Fritscher¹,

Schuler²,

Link

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract.Fractures of the proximal femur are one of the principal causes of mortality among elderly persons. Traditional methods for the determination of femoral fracture risk use methods for measuring bone mineral density. However, BMD alone is not sufficient to predict bone failure load for an individual patient and additional parameters have to be determined for this purpose. In this work an approach that uses statistical models of appearance to identify relevant regions and parameters for the prediction of biomechanical properties of the proximal femur will be presented. By using Support Vector Regression the proposed model based approach is capable of predicting two different biomechanical parameters accurately and fully automatically in two different testing scenarios.

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Single and dual energy X-ray absorptiometry

Cited by 66 publications

References 151 publications

Bone Density Measurement Using Computed Tomography

Bone Density Measurement Using Computed Tomography

Enhanced Distal Radius Segmentation in DXA Using Modified ASM

Prediction of Biomechanical Parameters of the Proximal Femur Using Statistical Appearance Models and Support Vector Regression

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