Respiratory motion correction in dynamic MRI using robust data decomposition registration - Application to DCE-MRI.

Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15-62% reduction following registration) in tissue time-intensity curves and improved areas under the curve (AUC60) at early enhancement

Improving Accuracy of Information Extraction from Quantitative Magnetic Resonance Imaging

Quantitative MRI offers the possibility to produce objective measurements of tissue physiology at different scales. Such measurements are highly valuable in applications such as drug development, treatment monitoring or early diagnosis of cancer. From microstructural information in diffusion weighted imaging (DWI) or local perfusion and permeability in dynamic contrast (DCE-) MRI to more macroscopic observations of the local intestinal contraction, a number of aspects of quantitative MRI are considered in this thesis. The main objective of the presented work is to provide pre-processing techniques and model modification in order to improve the reliability of image analysis in quantitative MRI. Firstly, the challenge of clinical DWI signal modelling is investigated to overcome the biasing effect due to noise in the data. Several methods with increasing level of complexity are applied to simulations and a series of clinical datasets. Secondly, a novel Robust Data Decomposition Registration technique is introduced to tackle the problem of image registration in DCE-MRI. The technique allows the separation of tissue enhancement from motion effects so that the latter can be corrected independently. It is successfully applied to DCE-MRI datasets of different organs. This application is extended to the correction of respiratory motion in small bowel motility quantification in dynamic MRI data acquired during free breathing. Finally, a new local model for the arterial input function (AIF) is proposed. The estimation of the arterial blood contrast agent concentration in DCE-MRI is augmented using prior knowledge on local tissue structure from DWI. This work explores several types of imaging using MRI. It contributes to clinical quantitative MRI analysis providing practical solutions aimed at improving the accuracy and consistency of the parameters derived from image data