Malone researchers develop novel medical imaging techniques to improve surgical procedures, radiation therapy, and the diagnosis and treatment of neurological disorders such as epilepsy and autism spectrum disorders.
PI: Jeff Siewerdsen
The overall goal of the SpineCloud project is to gain understanding and predictive power of the factors underlying spine surgery outcomes – particularly the unacceptably broad variability associated with spinal fusion. Such capability will yield evidence-based insight and decision support to improve patient selection, enable more optimal surgical planning, identify perioperative sentinel factors, and help to guide the most effective post-operative, therapeutic, and rehabilitative process on a patient-specific basis. The SpineCloud project aims to curate a database consisting of patient demographic data, image and specific anatomy, surgical procedures, and pathologies. By developing this data-intensive approach to future spine surgeries, SpineCloud will provide more favorable and consistent outcomes for patients.
PI: Archana Venkataraman
Epilepsy affects nearly 3.5 million people in the United States and is linked to a five-fold increase in mortality. While epilepsy is often controlled with medication, 20-40% of patients are medically refractory and continue to experience seizures in spite of drug therapies. The alternative for many of these patients is to surgically remove the areas of the brain responsible for triggering an epileptic seizure. However, the gold-standard procedure to localize the epileptic foci requires a craniotomy and implantation of electrode grids directly onto the cortical surface. This evaluation procedure is understandably traumatic and increases the patient risk for infection and injury.
Hence, our goal is to automatically and noninvasively localize the seizure onset zones, which will both streamline the pre-surgical planning process and reduce the amount of time the patients spend in the hospital. As such, we have developed a novel Coupled Hidden Markov Model to detect and localize epileptic seizures in clinical EEG recordings. This model tracks the spatio-temporal propagation of a seizure to back-infer the onset. We have validated our approach on data from the Johns Hopkins Hospital and the Children’s Hospital in Boston. Ongoing work focuses on generalizing the performance via hierarchical graphical models and deep learning approaches. Moving forward, we will combine multiple snapshots of the brain, such as structural MRI, functional MRI and EEG in order to refine the localization.
PI: Muyinatu “Bisi” Bell
Approximately 600,000 hysterectomies are performed each year in the US to remove the uterus. This surgery requires cauterization and clipping of the uterine arteries, located close to and overlapping the ureter, which can cause accidental injury to the ureter. We are developing the imaging technology needed visualize the uterine arteries and the nearby ureter, which are both hidden by surrounding tissue. Our goal is to eliminate the risk of accidental ureteral injury during robotic hysterectomies performed with the da Vinci surgical robot. The concept for this intraoperative photoacoustic imaging technology includes optical fibers that surround a da Vinci surgical tool and a transvaginal ultrasound probe placed to receive the resulting sound waves, as demonstrated in the picture:
News | Medical Imaging
She is one of 36 junior faculty members across the U.S. to receive the one-year award, which aims to enrich the research and growth of those in the first years of tenure-track positions.
Bell was recognized as this year’s Outstanding Young Engineer by the Maryland Academy of Sciences and the Maryland Science Center on May 8, 2019.
She was named to the annual list for her pioneering work detecting the source of epileptic seizures in the brain.