Magnetic Resonance Imaging (MRI) has become a highly valued diagnostic tool because it provides excellent visualization of soft tissue without exposing patients to harmful, ionizing radiation. Unfortunately, MRI is a relatively slow technique, which makes it hard to image moving structures such as the heart and patients that have difficulty lying still. Cardiac MRI in children is thus a particularly challenging subject.
Much of the research in this field is focused on developing new, fast image reconstruction techniques that can convert incomplete and/or corrupted datasets to clinically useful images. Until now, this work has been confined to software environments provided by MRI vendors, which has made it challenging to utilize appropriate computational resources and to share algorithms in the community. At the National Heart, Lung, and Blood Institute, we have developed tools that allow scientists to convert data from most available MRI vendors to a vendor neutral format  and we have developed advanced image reconstruction tools that can be used to prototype and deploy novel image reconstruction methods in a clinical environment [2,3].
Since the image reconstruction tools are Open Source and work on multiple platforms, they can be deployed on a variety of hardware platforms. For example, applications with modest computational demands can be deployed on individual workstations and more demanding applications can leverage graphics processors (GPUs) or cloud based clusters. These different resources can be deployed in a manner that is transparent to the user. The availability of cloud computing in a clinical setting, makes it possible to use motion corrected, free breathing imaging when evaluating cardiac function. The video below shows example images acquired during free breathing and motion corrected using cloud computing.
It is also possible to perform advanced image reconstruction for MRI guided procedures. An example of an MRI guided procedure that uses this technology is the right heart diagnostic catheterization, which has been deployed at the NHLBI and at Children’s National Medical Center, Washington, DC. The video below shows example images from such a procedure.
1. Inati, S. J, Naegele, J. D., Zwart, N. R., Roopchansingh, V., Lizak, M. J., Hansen, D. C., Liu, C.-Y., Atkinson, D., Kellman, P., Kozerke, S., Xue, H., Campbell-Washburn, A. E., Sørensen, T. S. and Hansen, M. S. (2016), ISMRM Raw data format: A proposed standard for MRI raw datasets. Magn Reson Med. doi: 10.1002/mrm.26089
2. Hansen, M. S. and Sørensen, T. S. (2013), Gadgetron: An open source framework for medical image reconstruction. Magn Reson Med, 69: 1768–1776. doi: 10.1002/mrm.24389
3. Xue, H., Inati, S., Sørensen, T. S., Kellman, P. and Hansen, M. S. (2015), Distributed MRI reconstruction using gadgetron-based cloud computing. Magn Reson Med, 73: 1015–1025. doi: 10.1002/mrm.25213