Optimizing texture measures quantifying bone structures as well as MR-sequences at 3 Tesla: an integrative statistical approach

Abstract

High resolution MR-scanners working with magnetic field strengths of 3 Tesla are clinically available nowadays. They offer the possibility to obtain 3D images with unprecedented spatial resolution and/or signal-to-noise-ratio (SNR) allowing for an accurate visualization of the trabecular bone structure. It has been demonstrated that scaling indices are well suited to quantify these structures, especially to discriminate between plate-like and rod-like structural elements, which is crucial for the diagnosis of osteoporosis. Until now image quality has mainly been assessed by visual impression or by measures based on the SNR. In this work we present a methodology to assess different MR-sequences with respect to the texture measure that is used later in the image analysis. We acquired for a bone specimen HR-MR-sequences with different spatial resolution and signal to noise ratio. For these data sets we selected two volumes of interest (VOI) of same size located in the trabecular bone and in the background of the image. For both VOIs the scaling indices are calculated for different scale parameters. Subsequently the 'texture contrast' between structure and background is calculated by comparing the probability distributions of the scaling indices using a quadratic distance measure. By means of the contrast the optimal set of scale parameters is determined. By comparing the contrast for the different MR sequences the best suited ones are determined. It turns out that sequences with slightly lower spatial resolution but better signal to noise ration yield a better texture contrast than sequences with the best spatial resolution. The presented methodology offers the possibility to optimize simultaneously texture measures and MR-sequences, which will allow for an adapted and thus optimized analysis of image structures, e.g. trabecular bone, in the HR-MR data.