Chengjia Wang

Automatic multi-parametric MR registration method using mutual information based on adaptive asymmetric k-means binning

Multi-parametric MR image registration combines different imaging sequences to enhance visualisation and analysis. However, alignment of the different acquisitions is challenging, due to contrast-dependent anatomical information and abundant artefacts. For two decades, voxel-based registration has been dominated by methods based on mutual information, calculated from the joint image histogram. In this paper, we propose a modified framework - based on an asymmetric cluster-to-image mutual information metric - that increases registration speed and robustness. A new parameter, the homogeneous dynamic intensity range, is used to determine to which image clustering is applied. The framework also includes a semi-automatic 3D region of interest, multi-resolution wavelet decomposition, and particle swarm optimization. Performance of the framework, and its individual components, were evaluated on two diverse datasets, comprising cardiac and neonatal brain datasets. The results demonstrated the method was more robust and accurate than mutual information alone.

Quantitative myocardial inflammation assessed using a novel USPIO-Magnetic Resonance Imaging acquisition and analysis protocol

Background Ultrasmall superparamagnetic particles of iron-oxide (SUPIO) particles can be used as a magnetic resonance imaging contrast (MRI) agent. Due to their dextran coating and small diameter (<30 nm), they are phagocytised by inflammatory cells [1]. The aim of this study was to assess whether a novel acquisition/registration USPIO-MRI protocol could be used to assess myocardial cellular inflammation. Myocardial infarction and cardiac surgery was used to model myocardial inflammation [2]. An influx of macrophages can be seen in post-mortem histology, however the dynamics of in vivo patho-physiology is uncertain [3].