Michael S. Hansen is a biomedical engineer with a PhD (2005), from University of Aarhus, Denmark. He did his postdoctoral training at the Centre for Medical Image Computing, University College London (UCL) and was subsequently a senior research fellow at UCLs Institute of Child Health and Great Ormond Street Hospital for Children, London, UK. In 2008 he joined Novartis Institutes for Biomedical Research where he ran a small-animal imaging laboratory. In 2009, he joined the NHLBI as a staff scientist and in 2016 he joined the NHLBI faculty as a tenure track investigator. Dr. Hansen is a reviewer for numerous journals and a member of the International Society for Magnetic Resonance in Medicine where he also serves on the Annual Meeting Program Committee.
Magnetic Resonance Imaging (MRI) has be become an established medical imaging modality. It is used widely for anatomical imaging, where it provides unparalleled soft tissue contrast combined with good spatial resolution. While MRI has proven its clinical usefulness for imaging the brain, spinal cord, musculoskeletal tissues, and to some extend the heart, it has not reached its full potential. Specifically it is plagued by long acquisition times, motion sensitivity, and poor patient ergonomics. Furthermore, MRI has the potential to provide much more than just anatomical imaging, e.g. image guidance for interventional procedures, multidimensional information about blood flow velocities, and tissue characterization. These additional capabilities have not been developed adequately to this date and consequently they are not as useable and reliable as they could be.
Dr Hansen’s laboratory is focused on the development and implementation of advanced MRI techniques for disease diagnosis and image guidance. Specifically, the aim is to leverage advanced acquisition and reconstruction techniques to improve imaging speed (real-time imaging), motion robustness (registration and correction), quantitation (parametric mapping or flow), and clinical workflow.
An example target application is pediatric cardiac MRI exams. Here the goal is be able to complete diagnostic studies in a short amount of time (minutes) without the need for breath-holds. This would enable studies to be done without anesthesia. Benefits of such shortened free-breathing exams would also translate into the adult populations, where shortness of breath is a common symptom in patients with heart disease. The work in the laboratory is aimed at identifying more efficient acquisition strategies. The acquired raw data from such exams are often incomplete and/or corrupted (e.g., by motion) and this puts more demands on the image reconstruction process. This increased computational need is met by deploying state-of-the-art computational resources (GPUs and cloud computing) in the clinical environment. The laboratory puts emphasis on deploying this technology in a way that is transparent to the clinical user. This makes the developed solutions suitable for deployment in hospital environments that are less familiar with research technology.
Another example application of the technology developed by Dr Hansen’s laboratory is MRI guided catheterization procedures. Catheter based interventions are traditionally done under X-ray fluoroscopic guidance. Some of the procedures are quite long and involve significant radiation doses. Furthermore, X-ray fluoroscopy has relatively poor soft tissue contrast characteristics. MRI is an interesting alternative for interventional procedures, but faster real-time imaging methods are needed in order to guide an intervention. Using the image reconstruction technology developed in the laboratory, it is possible to do interactive imaging where image quality trade-offs (signal to noise and image frame rate) can be tuned by the physician depending on the specific demands at a given point in the procedure. It is also possible to use advanced acquisition techniques such as non-Cartesian parallel imaging in real-time. The laboratory continues to explore new imaging strategies for interventional MRI including imaging instruments and techniques that improves safety of interventional devices such as metal guide wires that can heat up during regular MRI imaging.
In addition to developing specific solutions to such applications, Dr Hansen’s laboratory invests time and energy in developing framework infrastructure components that can benefit other laboratories working on similar problems or on MRI in general. As an example, the laboratory has developed a modular, open source medical image reconstruction framework called the Gadgetron (http://gadgetron.github.io) and is taking a leading role in defining a community standard for sharing MRI raw data across vendor platforms (http://ismrmrd.github.io). These efforts are part of a commitment to making MRI research vendor independent and reproducible.