University of ArizonaGraduate Student Research Assistant
Aug. 2017 - Jul. 2024Tucson, Arizona, United StatesImaging modalities such as Magnetic Resonance Imaging (MRI) are utilized to image various biological and anatomical structures as well as physiological functions. Acquiring these data lead to the detection, diagnosis, and treatment of disease(s). However, the ability to perform these functions at a high efficacy rate requires high quality and resolution images. The process of acquiring such images is a challenging task through conventional image processing techniques. In the case of MRI, this imaging modality is highly sensitive to subject motion, which leads to motional-induced artifacts. This is primarily due to the large amount of time it takes to acquire sufficiently large data to output a high-quality image. Thus, the problem that needs to be addressed is how to achieve high quality and resolution images without exposing subjects to prolonged scan times or having to compromise image quality. This is where artificial intelligence and deep learning come into play. In this position I am devising deep convolutional neural networks and developing novel and vendor-agnostic imaging protocols that can greatly decrease scan times and improve image quality and resolution.