My current research is improving magnetic resonance imaging
(MRI), more specifically, developing innovative algorithms to make
the images look better. In MRI, because the time needed to obtain
the necessary images is long, patients have to stay in the MRI
scanner — motionless — for a period of time. In many
cases, this means that patients are asked to hold their breath.
When doctors want to look at an organ that can’t be still,
such as a beating heart, the images have poor quality.
I develop complicated algorithms to generate high-quality images from only a small portion of the data that is commonly collected. Collecting less data means that a shorter time is needed to perform an MRI scan. The algorithms take advantage of the observations that most data in an image — whether an MRI scan or a vacation photo — is redundant. So she only acquires the key, non-redundant information in the first place, but without knowing what the image looks like exactly. To obtain the image from reduced data, mathematical models describing the redundancy are used.
My research has been recognized by a CAREER award from the National Science Foundation. I am collaborating with industrial partners to transfer this technology into clinical MRI systems to benefit all patients.
Compressed sensing and its applications in biomedical imaging, magnetic resonance imaging, image reconstruction