New approach for improved radiological diagnosis of pathology by means of fast and robust parameter quantification in 3D Magnetic Resonance Imaging (MRI)

• Applicant: Dr. Felix Breuer
• Subject Area: Medical Physics, Biomedical Technology ; Human Cognitive and Systems Neuroscience
• Term: from 2014 to 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 259831630

Since the acquisition of the first human magnetic resonance (MR) images in the early 1980s, MRI has played an ever-increasing diagnostic role in medicine due to its non-invasive nature, its rich information content and, in particular, the wide array of available contrasts. In current clinical practice, several individual MRI acquisitions are performed sequentially in order to generate multiple images with varying contrast. However, this procedure is time consuming, prone to successive misalignments and registration artifacts and it usually delivers an anisotropic spatial resolution. In addition, these images often have suboptimal contrast and, unlike in computed tomography (CT), do not typically contain quantitative information. Our goal is to fundamentally change the way clinical MRI exams are carried out. This approach will allow us to obtain quantitative information on spin density and relaxation times (T1 & T2) in a single imaging experiment using an ultra-fast 3D MRI sequence with isotropic spatial resolution. From these voxel-wise quantitative 3D volume data sets, synthetic images of virtually any contrast and at any angular orientation desired can be generated after the actual image acquisition. This has the potential to profoundly improve the detection of various pathologies by MRI and can be expected enable a rapid translation into standardized clinical examination protocols. After implementation and validation on various MR systems with field strengths of 1.5T and 3T, we will apply the new technique to patients diagnosed with Multiple Sclerosis (MS) and Fabrys disease (FD) in order to demonstrate the expected improvements in lesion detection and classification in the central nervous system (CNS). In order to make the methodology robust against motion and thus available for uncooperative and pediatric patients we will extend the methodology by a motion detection and correction scheme. This will also allow the translation of the methodology to e.g. abdominal applications, such as the liver where respiratory motion makes quantitative imaging difficult. The quantitative -- one-stop-shop -- approach has the potential to increase patient comfort, scan and cost efficiency as well as diagnostic fidelity for various standard clinical applications.

• DFG Programme: Research Grants
• International Connection: USA
• Participating Persons: Professor Dr. Vikas Gulani, Ph.D.; Dr. György Homola