Feasibility of T2* mapping for the evaluation of hip joint cartilage at 1.5T using a three-dimensional (3D), gradient-echo (GRE) sequence: a prospective study

This study defines the feasibility of utilizing three-dimensional (3D) gradient-echo (GRE) MRI at 1.5T for T(2)* mapping to assess hip joint cartilage degenerative changes using standard morphological MR grading while comparing it to delayed gadolinium-enhanced MRI of cartilage (dGEMRIC). MRI was obtained from 10 asymptomatic young adult volunteers and 33 patients with symptomatic femoroacetabular impingement (FAI). The protocol included T(2)* mapping without gadolinium-enhancement utilizing a 3D-GRE sequence with six echoes, and after gadolinium injection, routine hip sequences, and a dual-flip-angle 3D-GRE sequence for dGEMRIC T(1) mapping. Cartilage was classified as normal, with mild changes, or with severe degenerative changes based on morphological MRI. T(1) and T(2)* findings were subsequently correlated. There were significant differences between volunteers and patients in normally-rated cartilage only for T(1) values. Both T(1) and T(2)* values decreased significantly with the various grades of cartilage damage. There was a statistically significant correlation between standard MRI and T(2)* (T(1)) (P < 0.05). High intraclass correlation was noted for both T(1) and T(2)*. Correlation factor was 0.860 to 0.954 (T(2)*-T(1) intraobserver) and 0.826 to 0.867 (T(2)*-T(1) interobserver). It is feasible to gather further information about cartilage status within the hip joint using GRE T(2)* mapping at 1.5T.

Stochastic rank correlation: a robust merit function for 2D/3D registration of image data obtained at different energies

In this article, the authors evaluate a merit function for 2D/3D registration called stochastic rank correlation (SRC). SRC is characterized by the fact that differences in image intensity do not influence the registration result; it therefore combines the numerical advantages of cross correlation (CC)-type merit functions with the flexibility of mutual-information-type merit functions. The basic idea is that registration is achieved on a random subset of the image, which allows for an efficient computation of Spearman's rank correlation coefficient. This measure is, by nature, invariant to monotonic intensity transforms in the images under comparison, which renders it an ideal solution for intramodal images acquired at different energy levels as encountered in intrafractional kV imaging in image-guided radiotherapy. Initial evaluation was undertaken using a 2D/3D registration reference image dataset of a cadaver spine. Even with no radiometric calibration, SRC shows a significant improvement in robustness and stability compared to CC. Pattern intensity, another merit function that was evaluated for comparison, gave rather poor results due to its limited convergence range. The time required for SRC with 5% image content compares well to the other merit functions; increasing the image content does not significantly influence the algorithm accuracy. The authors conclude that SRC is a promising measure for 2D/3D registration in IGRT and image-guided therapy in general.