Tomographic image reconstruction is generally an ill-posed linear inverse problem. Such ill-
posed inverse problems are typically regularized using prior knowledge of the sought-after …

There remains an important need for the development of image reconstruction methods that
can produce diagnostically useful images from undersampled measurements. In magnetic …

A rapid and cost-effective noninvasive tool to detect and characterize neural silences can be
of important benefit in diagnosing and treating many disorders. We propose an algorithm …

Despite the relative successes in the surgical treatment of pharmacoresistant epilepsy, there
is rather little research on the neural (re) organization that potentially subserves behavioral …

Although several previous studies have used resting-state functional magnetic resonance
imaging and diffusion tensor imaging to report topological changes in the brain in epilepsy, it …

A quantitative method of analyzing EEG signals after stroke onset can help monitor disease
progression and tailor treatments. In this work, we present an EEG-based imaging algorithm …

Knowledge gaps for functional outcomes after multilobar resective and disconnective pediatric
epilepsy surgery: Conference Proceedings of the Patient-Centered Stakeholder Meeting 2019 …

Deep learning (DL) methods are currently being explored to recover images from sparse-
view, limited-data, and undersampled acquisitions in medical applications. Although …

Tomographic image reconstruction is generally an ill-posed inverse problem. Such inverse
problems are typically regularized using prior knowledge of the sought-after object property …

Despite the relative successes in the surgical treatment of pharmacoresistant epilepsy, there
is rather little research on the neural (re) organization that potentially subserves behavioral …