Normalization of Magnetic Resonance Images and its Application to the Diagnosis of the Scoliotic Spine
Due to its excellent soft tissue contrast and novel innovative acquisition sequences, Magnetic Resonance Imaging has become one of the most popular imaging modalities in health care. However, associated acquisition artifacts can significantly reduce image quality. Consequently, this imperfections can disturb the assessment of the acquired images. In the worst case, they may even lead to false decisions by the physician. Moreover, they can negatively influence an automatic processing of the data, e.g., image segmentation or registration. The most commonly observed artifacts are intensity inhomogeneities and a missing sequence-dependent general intensity scale.
In this thesis, several novel techniques for the correction of the intensity variations are introduced. Further on, we demonstrate their advantages in a clinical application. Many state–of–the–art approaches for correction of inhomogeneities lack either generalizability, efficiency, or accuracy. We present novel methods that overcome these drawbacks by introducing prior knowledge in the objective function and by mapping the optimization process onto a divide–and–conquer like strategy. The experiments show that we can increase the average separability of tissue classes in clinical relevant 3-d angiographies by approximately 18.2% whereas state–of–the–art methods could only achieve 11.6 %. The mapping of the intensities of a newly acquired image to a general intensity scale has to preserve the structural characteristics of the image’s histogram. Further, it has to be invertible. Hence, many standardization approaches estimate a rather coarse intensity transformation. We propose several methods for standardization that are closely related to image registration techniques. These methods compute a perintensity mapping. In addition, the methods presented are the only ones known that do a joint standardization and that can handle images with a very large field–of–view. The experiments show that our method achieves an average intensity overlap of the major tissue classes of T1w images of about 86.2%. The most commonly used state–of–the–art method resulted in only 70.1% overlap.
In order to illustrate the applicability and importance of the proposed normalization techniques, we introduce a system for the computer-aided assessment of anomalies in the scoliotic spine. It is based on the segmentation of the spinal cord using Markov random field theory. All required steps are presented, from the pre-processing to the visualization of the results. In order to evaluate the system, we use the angle between automatically computed planes through the vertebrae and planes estimated by medical experts. This results in a mean angle difference of less than six degrees being accurate enough to be applicable in a clinical environment.