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Fully automated bone mineral density assessment from low-dose chest CT
Author(s): Shuang Liu; Jessica Gonzalez; Javier Zulueta; Juan P. de-Torres; David F. Yankelevitz; Claudia I. Henschke; Anthony P. Reeves
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Paper Abstract

A fully automated system is presented for bone mineral density (BMD) assessment from low-dose chest CT (LDCT). BMD assessment is central in the diagnosis and follow-up therapy monitoring of osteoporosis, which is characterized by low bone density and is estimated to affect 12.3 million US population aged 50 years or older, creating tremendous social and economic burdens. BMD assessment from DXA scans (BMDDXA) is currently the most widely used and gold standard technique for the diagnosis of osteoporosis and bone fracture risk estimation. With the recent large-scale implementation of annual lung cancer screening using LDCT, great potential emerges for the concurrent opportunistic osteoporosis screening. In the presented BMDCT assessment system, each vertebral body is first segmented and labeled with its anatomical name. Various 3D region of interest (ROI) inside the vertebral body are then explored for BMDCT measurements at different vertebral levels. The system was validated using 76 pairs of DXA and LDCT scans of the same subject. Average BMDDXA of L1-L4 was used as the reference standard. Statistically significant (p-value < 0.001) strong correlation is obtained between BMDDXA and BMDCT at all vertebral levels (T1 – L2). A Pearson correlation of 0.857 was achieved between BMDDXA and average BMDCT of T9-T11 by using a 3D ROI taking into account of both trabecular and cortical bone tissue. These encouraging results demonstrate the feasibility of fully automated quantitative BMD assessment and the potential of opportunistic osteoporosis screening with concurrent lung cancer screening using LDCT.

Paper Details

Date Published: 27 February 2018
PDF: 8 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750M (27 February 2018); doi: 10.1117/12.2293838
Show Author Affiliations
Shuang Liu, Cornell Univ. (United States)
Jessica Gonzalez, Clínica Univ. de Navarra (Spain)
Javier Zulueta, Clínica Univ. de Navarra (Spain)
Juan P. de-Torres, Clínica Univ. de Navarra (Spain)
David F. Yankelevitz, Icahn School of Medicine at Mount Sinai (United States)
Claudia I. Henschke, Icahn School of Medicine at Mount Sinai (United States)
Anthony P. Reeves, Cornell Univ. (United States)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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