New computational tools are critical to leverage progress in computational technologies to impact biomedical imaging with MRI. This work seeks to develop, evaluate and apply new MRI methods for high-end diffusion imaging. These high angular resolution methods work where diffusion tensor imaging (DTI) methods fail - in crossing or touching fibers, which corresponds too much of the brain. This is of particular value for assessing white matter tract integrity after stroke. Two small studies have shown that advanced """"""""post-DTI"""""""" methods can give valuable information predicting the degree of recovery from stroke. However, the post-DTI methods require impractical scan duration. Thus this project aims to (1) develop advanced acquisition and reconstruction methods for rapid (undersampled in k- and q-space) diffusion measurements. (2) To characterize rapid acquisition and reconstruction methods for advanced diffusion imaging. (3) To determine white matter tract integrity changes over time when patients with stroke are treated with a neurogenic drug, sildenafil. This will allow for determining the predictive power of the high angular resolution post-DTI methods and give insight into changes in response to the drug therapy.
These aims will be accomplished by developing software tools to provide post-DTI type data from short, clinically practical, acquisition times. Advanced reconstruction methods that include low rank and spatiotemporal constraints with intensity reordering, dictionary-based approaches, and parametric models will be developed and tested to obtain diffusion information in short time frames. The integrity of the white matter tracts will be estimated and the repeatability and accuracy of the new methods will be determined relative to full long scan acquisitions. The serial application of the methods to patients with stroke undergoing standard therapy and novel drug therapy will then provide new information regarding changes associated with neuronal remodeling and possibly differentiate responders from non-responders. The resulting software tools will be integrated into the insight toolkit (ITK) and provided for use to the research community. The relevance to public health is that stroke is a leading cause of disability. The development and use of accurate and repeatable measurements of diffusion will accelerate evaluation of clinical therapies. The project will also provide new tools and knowledge for better management of patients with stroke. The proposed approach can be extended to improve a wide range of multi-image MRI applications, such as the response of tumors to therapy and the response of the brain to stimuli.
This proposal offers new methods to quantify white matter integrity in people with stroke. If such measurements can be made more accurately, this will allow for more informed treatments and monitoring of stroke disability and will improve public health. The proposed methods will be applied along with measurements of brain function to patients with stroke that are undergoing standard therapy or therapy augmented with a neurogenic drug to improve their management and to improve our understanding of brain remodeling and plasticity.
|Ning, Lipeng; Laun, Frederik; Gur, Yaniv et al. (2015) Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use? Med Image Anal 26:316-31|
|Adluru, Ganesh; Gur, Yaniv; Chen, Liyong et al. (2015) MRI reconstruction of multi-image acquisitions using a rank regularizer with data reordering. Med Phys 42:4734-44|
|Welsh, Christopher L; DiBella, Edward V R; Hsu, Edward W (2015) Higher-Order Motion-Compensation for In Vivo Cardiac Diffusion Tensor Imaging in Rats. IEEE Trans Med Imaging 34:1843-53|
|Adluru, Ganesh; Gur, Yaniv; Anderson, Jeffrey S et al. (2014) Assessment of white matter microstructure in stroke patients using NODDI. Conf Proc IEEE Eng Med Biol Soc 2014:742-5|
|Welsh, Christopher L; Dibella, Edward V R; Adluru, Ganesh et al. (2013) Model-based reconstruction of undersampled diffusion tensor k-space data. Magn Reson Med 70:429-40|