Proximal femur specimens: automated 3D trabecular bone mineral density analysis at multidetector CT--correlation with biomechanical strength measurement.
OBJECTIVE
To prospectively evaluate an automated volume of interest (VOI)-fitting algorithm for quantitative computed tomography (CT) of proximal femur specimens, correlate bone mineral density (BMD) with biomechanically determined bone strength in vitro, and compare that correlation with those observed at dual-energy x-ray absorptiometry (DXA) measurement of BMD.


METHODS
The study was compliant with institutional and legislative requirements; donors had dedicated their body for education and research before death. Multidetector CT and DXA scans were acquired in 178 proximal femur specimens harvested from human cadavers (91 women, 87 men; mean age at death, 79 years +/- 10.2; range, 52-100 years). An automated VOI-fitting algorithm was used to calculate BMD and bone mineral content (BMC) in the head, neck, and trochanter from CT findings and pixel distribution parameters. The femur failure load (FL) was determined by using a mechanical test. Quantitative CT BMD, quantitative CT pixel distribution parameters, DXA BMD, and FL were correlated at multiple regression analysis.


RESULTS
Mean precision errors in quantitative CT BMD measurements at segmentation with repositioning were 0.56%, 2.26%, and 0.61% for the head, neck, and trochanter, respectively. For the head, neck, and trochanter, respectively, r values were 0.77, 0.53, and 0.59 for the correlation between quantitative CT BMD and FL and 0.74, 0.55, and 0.65 for the correlation between quantitative CT BMC and FL (P < .001). Values ranged from 0.77 to 0.80 for correlations between DXA BMD and FL and from 0.73 to 0.82 for correlations between DXA BMC and FL (P < .001). In a multiple regression model that included quantitative CT pixel distributions, adjusted multivariate correlation coefficient values for correlations with FL increased to up to 0.88.


CONCLUSIONS
Regional BMD of the proximal femur can be determined in vitro from quantitative CT data with high precision by using an automated VOI-fitting algorithm. The best multiple regression model for predicting FL included DXA BMD and regional quantitative CT BMD measurements.
Huber
Markus B
MB
Musculoskeletal and Quantitative Imaging Research, Department of Radiology, University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107, USA. mbh@radiology.ucsf.edu
Carballido-Gamio
Julio
J
Bauer
Jan S
JS
Baum
Thomas
T
Eckstein
Felix
F
Lochmüller
Eva M
EM
Majumdar
Sharmila
S
Link
Thomas M
TM
D015502:Absorptiometry, Photon; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001696:Biomechanical Phenomena; D015519:Bone Density; D002102:Cadaver; D005260:Female; D005269:Femur / Q000000981:diagnostic imaging* / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D011857:Radiographic Image Interpretation, Computer-Assisted; D018709:Statistics, Nonparametric; D013314:Stress, Mechanical; D014057:Tomography, X-Ray Computed