Prof. Mehmet Akçakaya is the recipient of 2 grants from the National Institutes of Health: a Research Project Grant (R01) from the National Heart, Lung, and Blood Institute (NHLBI), and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) Trailblazer Award. Both awards will support Mehmet’s research in improving accuracy, reducing risk, and increasing patient comfort during cardiac imaging.
R01 Grant to Develop Rapid Cardiac MRI
The R01 grant (the NIH’s original grant mechanism) is awarded by the NHLBI (National Heart, Lung, and Blood Institute), and supports health-related research and development based on the mission of the NIH. Prof. Mehmet Akçakaya has received a grant amount close to $2.5 million spread over 5 years.
Titled, “Rapid Comprehensive Cardiac MRI Exam for Diagnosis of Coronary Artery Disease,” Mehmet’s research in this R01 award addresses the lengthy exam times involved in cardiac MRI (CMR) by developing and validating techniques for rapid CMR for comprehensive assessment of coronary artery disease.
Under the R01 project, Mehmet and his team will develop and validate novel acquisition and reconstruction strategies using ideas from MRI physics, such as simultaneous excitation of multiple volumes, as well as from machine learning, including new methods for scan-specific deep learning reconstruction previously developed in his lab.
The clinical gold standards for the diagnosis and treatment of coronary artery disease, the leading cause of death in the United States, are catheter-based procedures, such as x-ray coronary angiography (XCA) for anatomic assessment, or fractional flow reserve (FFR) for physiologic assessment. These procedures have inherent risks due to their invasive nature. Additionally, large studies have indicated that nearly two-thirds of patients referred for their initial elective invasive XCA did not have any significant stenoses i.e., narrowing of arteries. All of this indicates the importance of developing and using non-invasive diagnostic tools.
Currently, CMR is the only non-invasive imaging procedure that is capable of providing a comprehensive assessment of coronary artery disease (CAD) in a single examination, without requiring ionizing radiation. It provides an assessment of myocardial perfusion, cardiac function and viability, as well as angiographic evaluation of stenoses. CMR can be used repeatedly as clinically indicated. However, despite its potential to serve as a non-invasive gatekeeper to costly invasive procedures, lengthy examination times have prevented CMR from being widely deployed. And while several other accelerated imaging techniques have been proposed, these still require trade-offs between coverage, resolution, and signal-to-noise ratio.
Under the current project, Mehmet and his team will develop and validate novel acquisition and reconstruction strategies that will enable a highly accelerated high-resolution whole heart CMR exam for comprehensive CAD assessment in under 10 minutes. These methods use ideas from MRI physics such as simultaneous excitation of multiple volumes, as well as from machine learning, including new methods for scan-specific deep learning reconstruction developed in Mehmet’s lab.
On successful completion, the project has the potential to transform CMR into a leading rapid non-invasive tool for safe and accurate diagnosis of CAD, improving the healthcare of several million patients with chest pain and other CAD symptoms annually.
Trailblazer Award to Develop Alternate Techniques to Assess Myocardial Fibrosis
The NIBIB Trailblazer Award supports new and early stage investigators pursuing research at the “interface of the life sciences with engineering and the physical sciences.” The grant amounts to more than $550, 000 spread over 3 years.
Titled, “Novel Quantitative MRI Techniques for the Assessment of Cardiac Fibrosis without Gadolinium Contrast,” Mehmet’s Trailblazer Award addresses challenges associated with the use of gadolinium-based contrast agents (GBCA) in contemporary cardiac MRI (CMR) by developing novel techniques that assess scar formation in the heart without resorting to GBCA.
The Trailblazer Award project has the potential to transform the way CMR is performed for the assessment of myocardial fibrosis, and will eliminate the need for gadolinium-based contrast agents in a cardiac MRI.
The clinical gold standard for assessing cardiac fibrosis (scarring of cardiac muscle) is late gadolinium enhancement (LGE) CMR. LGE images are acquired approximately 20 minutes after the patient is administered a GBCA intravenously. Although this is a widely used assessment method, there are several concerns about GBCA including its effects on patients with renal impairment, allergic reactions to the contrast agent in some patients, as well as higher costs and patient discomfort due to the presence of an intravenous line. These are compelling concerns that have driven Mehmet to pursue the development of assessment strategies that do not require the administration of GBCA.
Quantitative CMR techniques have attracted some interest as alternatives for identifying myocardial fibrosis without resorting to gadolinium. These methods characterize the underlying tissue by acquiring multiple images with different contrast and generating voxel-wise maps of the tissue properties. Native T1 mapping, magnetization transfer (MT) imaging, and rotating frame relaxation mapping are methods that have shown promise while also displaying some limitations.
In this project, Mehmet will lead his research team to develop novel quantitative CMR techniques that unleash the full potential of MT imaging and rotating frame relaxation in assessing myocardial fibrosis. While the potential of both ideas have been demonstrated on other anatomies, neither have been explored for imaging human heart tissue.
The project, if successful, has the potential to transform the way CMR is performed for the assessment of myocardial fibrosis, and will eliminate the need for gadolinium-based contrast agents in a cardiac MRI.
For both projects, Prof. Mehmet Akçakaya will work with researchers from the Center for Magnetic Resonance Research in the Department of Radiology, and the Cardiovascular Division in the Department of Medicine, at the University of Minnesota Twin Cities.
Prof. Mehmet Akçakaya’ s research is interdisciplinary in nature, and lies at the intersection of signal processing, computational imaging, machine learning and MRI physics. His contributions to these fields include theoretical guarantees for sparse signal processing, new reconstruction methods that learn anatomy-specific structures in MRI, high-precision techniques for quantitative MRI, and subject-specific deep learning MRI reconstruction. His applications focus on heart and brain MRI, and he collaborates with scientists across multiple departments, including the Departments of Radiology, and Medicine. Previously, he has been a recipient of the NIH K99/R00 award (2012/15), an NSF CAREER award (2017), and a McKnight Land-Grant professorship (2018).