Background Medical imaging plays a major role in the diagnosis and treatment of cardiovascular disease. The process of generating a medical image consists of two parts; data acquisition and image reconstruction. Image reconstruction transforms the acquired raw data signals into images that can be interpreted by clinician to aid in the diagnosis of a disease or used to guide a procedure. The raw data is frequently corrupted by instrument imperfections and patient motion. It is also common for datasets to be incomplete since there is a limited amount of time or other patient exposure available for data acquisition. Consequently, modern image reconstruction software is fairly complex. The source code for these complex image reconstruction algorithms is most often proprietary information retained by the system vendors and scientists working in the field of medical image reconstructions are forced to implement their own versions of existing algorithms that they then build on and improve. Because of this reimplementation and lack of open standards, many of the published literature on medical image reconstruction is not reproducible. The overarching goal of this project is to develop novel advanced image reconstruction algorithms and to do that in such a way that other scientists (and system vendors) can reproduce the presented results and use the methods in future work. The Laboratory of Imaging Technology, NHLBI, is particularly focused on Magnetic Resonance Imaging (MRI) techniques, but the developed principles apply to other techniques as well. Goals/Objectives The Laboratory of Imaging Technology develops and maintains two major software packages that support ongoing research projects. The first is the ISMRM Raw Data format (https://ismrmrd.github.io), which is an open raw data standard for MR experiments. It is a requirement for sharing algorithms and methods that there is common understanding of how to describe the raw data and this package provides a framework for this. We also aim to maintain data conversion tools from major device manufacturers proprietary formats to this vendor-independent format. The second software package is the Gadgetron (https://gadgetron.github.io), which is an advanced image reconstruction package that contains toolboxes and a streaming pipeline architecture for processing the raw data that is acquired by the imaging instrument.
We aim to expand this software package and support the growing user base around the world. There are a number of technical innovations that we are currently pursuing: - Expansion of the ISMRMRD format to include waveforms and telemetry from other instruments - Formal definition and implementation of ISMRMRD communication protocol - The use of cloud computing for MRI reconstruction - MRI raw data compression - Correction of measurement system imperfections - Tight integration of the Gadgetron with specific vendor instruments In addition to these infrastructure goals, we are developing and testing new imaging reconstruction techniques to solve specific clinical questions: - Real-time imaging sequences for interventional MRI - Real-time measurements of blood flow - Motion corrected, free-breathing techniques for measuring cardiac function and parametric maps. - Quantitative assessment of myocardial perfusion Progress in fiscal year 2017 In the past year we, in collaboration with Peter Kellman, have made significant progress in the deployment of cloud based MRI reconstructions to clinical collaborator sites using the Microsoft Azure cloud. In order to achieve this, we have made improvements in the infrastructure that enables automatic scaling of the Gadgetron cloud deployment to accommodate computational demand. We have also made significant progress in the area of raw data compression. We have developed an algorithm that allows us to compress the size of raw data in order to stream data more effectively. This work is specifically valuable for streaming large MRI datasets to the cloud for reconstruction. Furthermore, we have extended the Gadgetron framework to enable inline reconstruction using other researcher-driven open source image reconstruction packages. We have also made progress in fast spiral image reconstruction with an inline adaptive deblurring algorithm implemented for real-time data processing. We have initiated work on the second generation of the ISMRM Raw Data format, extending the definition to include more data types needed for MR image reconstruction, and redesigning our protocols to include these changes. We continue to work with the MRI community more broadly on both the Gadgetron and ISMRM Raw Data software packages. In the area of myocardial perfusion, we continue to make progress on the fast, fully automated, inline quantitative mapping package in the Gadgetron and the clinical validation of these methods.
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