Separation of collagen-bound and porous bone water transverse relaxation in mice: proposal of a multi-step approach.

Abstract

The separation and quantification of collagen-bound water (CBW) and pore water (PW) components of the cortical bone signal are important because of their different contribution to bone mechanical properties. Ultrashort TE (UTE) imaging can be used to exploit the transverse relaxation from CBW and PW, allowing their quantification. We tested, for the first time, the feasibility of UTE measurements in mice for the separation and quantification of the transverse relaxation of CBW and PW in vivo using three different approaches for T2 * determination. UTE sequences were acquired at 4.7 T in six mice with 10 different TEs (50-5000 μs). The transverse relaxation time T2 * of CBW (T2 *cbw ) and PW (T2 *pw ) and the CBW fraction (bwf) were computed using a mono-exponential (i), a standard bi-exponential (ii) and a new multi-step bi-exponential (iii) approach. Regions of interest were drawn at multiple levels of the femur and vertebral body cortical bone for each mouse. The sum of the normalized squared residuals (Res) and the homogeneity of variance were tested to compare the different methods. In the femur, approach (i) yielded mean T2 * ± standard deviation (SD) of 657 ± 234 μs. With approach (ii), T2 *cbw , T2 *pw and bwf were 464 ± 153 μs, 15 777 ± 10 864 μs and 57.6 ± 9.9%, respectively. For approach (iii), T2 *cbw , T2 *pw and bwf were 387 ± 108 μs, 7534 ± 2765 μs and 42.5 ± 6.2%, respectively. Similar values were obtained from vertebral bodies. Res with approach (ii) was lower than with the two other approaches (p <0.007), but T2 *pw and bwf variance was lower with approach (iii) than with approach (ii) (p <0.048). We demonstrated that the separation and quantification of cortical bone water components with UTE sequences is feasible in vivo in mouse models. The direct bi-exponential approach exhibited the best approximation to the measured signal curve with the lowest residuals; however, the newly proposed multi-step algorithm resulted in substantially lower variability of the computed parameters. Copyright © 2016 John Wiley & Sons, Ltd.