Inactive

Statement of Need an Purpose: The purpose of this acquisition is to purchase service to develop a graph-based model for multispectral MRI datasets, based on current state-of-the art on related structu... Statement of Need an Purpose: The purpose of this acquisition is to purchase service to develop a graph-based model for multispectral MRI datasets, based on current state-of-the art on related structures for RGB images. Period of Performance: 8/15/2024 - 8/14/2025 Background Information and Objective: The magnetic resonance imaging (MRI) section of the National Institute on Aging (NIA) Intramural Research Program (IRP) specializes in studies of tissue response to aging, and age-related pathology. As part of this program in brain mapping in particular, NIA has a need to work with intrinsically lengthy and relatively low-resolution mapping modalities, including diffusion tensor imaging (DTI). In addition to this, depending on imaging parameters, T1- and T2-weighted images may also require lengthy imaging times and hence may exhibit low resolution. There is an ongoing need to develop methods for increased resolution without corresponding increases in imaging time. These will become central elements in our work on non-invasive diagnosis of brain tissue pathology and understanding microstructural changes that occur with aging. One of the major open questions in aging research is how the brain and brain stem change with age, and what differentiates between healthy and non-healthy aging. This incorporates characterization of age-related pathology and disease, including Alzheimer’s disease. The MRI Section has made major advances over the past several years using data stabilization methods. However, these do not directly address the problem of the tradeoff between imaging resolution and data acquisition time. Initial progress has been made in this area using the Laplacian eigenmap dimensionality reduction technique, in which features are extracted from low- and high-resolution imaging modalities, matched in an embedding space, and used to recover a high-resolution version of the low-resolution input image. However, this imposes what is in effect an artificial limit on the algorithm, since multispectral (i.e. multi-weighted) image sets are often obtained routinely, and in some instances without additional imaging time (as for a stack of T2-weighted images). Thus, there is a need to extend the current methods to make full use of multispectral images for super-resolution of MRI images. This would provide a means for obtaining improved image quality for our studies of aging and age-related pathology. Please refer to the attached Statement of Work for specific Requirements.