Detection of sub-clinical heart disease for patients identified with rare genetic variants associated with inherited heart conditions.
Geisinger is among the leaders in “GenomeFIRST” medicine—using information for a person’s genetic sequence to clinically screen for diseases such as cancer and heart disease. With many inherited heart diseases, not everyone with these genetic risk factors will develop disease, but it is often difficult to distinguish these “healthy” people from those who will develop heart disease, but do not yet have symptoms. Our teams are collaborating with researchers and physicians in Geisinger’s Clinical Genomics Department to develop novel advanced imaging approaches that can provide important insights to these challenging clinical evaluations.
Quantifying how the heart deforms during contraction, and understanding what that can tell us about patients with heart disease.
How efficiently the heart is pumping blood is one of the best known markers of heart health. With cardiac magnetic resonance imaging (MRI), there are many sophisticated ways of measuring this heart function in great detail. For example, DISI researchers specialize in displacement sensitive MRI, known as “DENSE”, which can quantitatively measure the motion of heart tissue with very high resolution. By collecting data from medical imaging, and linking these images to outcomes data from Geisinger’s EHR, they hope to learn how this detailed motion data can help physicians make better predictions of heart health.
Estimating the influence of sulcogyral patterns in neurodevelopmental and psychiatric disorders.
The brain is made up of ridges and grooves that can form characteristic patterns. In the Troiani lab, we use anatomical tracing techniques and magnetic resonance imaging (MRI) to identify these patterns. We then use these patterns as a proxy for whether neurons in the underlying brain tissue are connected and sending signals in a standard or atypical way. In a recent study, we found that having a certain sulcal connectivity pattern within the orbitofrontal cortex of the brain (the region just above the eyes) is protective against several brain disorders, including Schizophrenia, ADHD, and Bipolar Disorder. By characterizing how these patterns vary within patient populations, we hope to better understand how this metric can be useful as a marker of risk for complex neurodevelopmental and psychiatric disorders.
Using quantitative cross-sectional neuroimaging to extract subtle information from routine protocols
In general, diagnostic neuroradiology is still qualitative in nature. That is, there are relatively few areas in which imaging data quantitatively represents a single tissue property and for the modalities that are or can be quantitative, that information is rarely used by radiologists to aid in decision making. Computed Tomography (CT) is one potential source of quantitative data. In one project, we are mining Geisinger’s extensive imaging database to investigate subtle shifts in CT image content in various settings using cross-modality tools. In another project, magnetic resonance imaging (MRI) signal intensities are being normalized using both internal and external references to support better disease discrimination as well as to enable comparisons across time, patients, and equipment. Through these projects, we are exploring ways in which quantitative or semi-quantitative information can be obtained with little or no impact on the patient experience with the goal that these methods may someday be used to inform care.