I do research using nuclear imaging modalities which include PET, SPECT, and CT. I collaborate with researchers from a wide range of diciplinaries.

My research is focused on translation of advanced, quantitative MRI methods to the human population at both high and low field strenghts.  Specifically, I am interested in developing advanced quantitative MRI methods to study under-represented structures of the human nervous system (optic nerve, spinal cord, and peripheral nerves) as they pertain to human disease.

We are currently involved in a number of research projects, all of which are targeted at understanding the pathophysiology of human disease through the development of advanced, quantitative MRI biomarkers for neurological diseases of the Brain, Spinal Cord, Optic Nerves, and Peripheral nerves.  Specifically, we are focused on ultra-high field (7T) CEST, Diffusion (conventional diffusion tensor and advanced diffusion weighted MRI), Magnetization Transfer, and quantiative T2.  

Some of my current work includes cancer imaging, radiomics, data science, deep learning, parallel computing, optimization, applied math, physics, astronomy, and astrobiology.

My interests lie in improving Magnetic Resonance Imaging performance by developing methods to minimize image artifacts and improve scanning efficiency on high field imaging systems. My research areas include real time shimming, continuously moving table imaging, motion correction and field monitoring.

My current work involves developing methods for rapid whole-body imaging using continuously moving table MRI methods. My past work has included realtime head motion correction using NMR probes for high resolution neuroimaging, dynamic B0 shimming at high field and developing models for prospective shim eddy current compensation.

My research is focused on image processing, biomedical modeling of magnetic resonance imaging (MRI) data, and mapping the human brain, with a focus on diffusion MRI data.

Dr. Schilling's research is focused on image processing, biomedical modeling of magnetic resonance imaging (MRI) data, and mapping the human brain, with a focus on diffusion MRI data

I study the structure and function of the brain. I am particularly interested in methods of measuring the timing of functional MRI signals and the correlations between them, and in applications of these methods to the study of neurological and mental disorders.

I am currently involved in a series of studies of hippocampal structure and function in epilepsy and psychosis, using ultra high field MRI with new methods of measuring functional connectivity and statistical approaches appropriate for the large data sets involved. Recent methodological work is focused on understanding dynamic variation in functional connectivity over short time scales (minute to minute).

Our group's research interests focus on the use of synthetic and colloidal chemistry in the development of novel molecular probes. Through our use of emerging imaging technologies, we foresee such probes being employed to investigate and define the mechanisms that lead to pathological diseases.

Our ongoing investigation emphasizes on cell therapy using a transgenic and preclinical mouse model. In addition, we have employed integrated nanotechnology-based platforms to achieve a multifunctional and multiplexed vaccine delivery system for cancer therapy. Aside from highlighting the contrast properties and carrier features available in nanotechnology, the overall goals of our studies are to (i) provide microanatomical and functional imaging feedback of the therapeutic process and (ii) realize an approach for longitudinal treatment and monitoring.     Our second project focuses on the development of a novel synthetic chemistry approach to the generation of probes intended specifically for imaging Alzheimer's disease (AD). The significance of this work lies in the development of a versatile vehicle which, after being loaded with imaging cargo, can be delivered to the brain. Furthermore, our laboratory currently maintains a clone of a double transgenic mouse model of AD to enable testing of these probes. Considering the tremendous contribution made by imaging in understanding the pathogenesis of AD, the results obtained through this research will have a strong influence on current efforts to find reliable biomarkers of this disease.

My research has two major thrusts: 1.) the development of high-resolution, multi-pinhole SPECT capabilities, and 2.) the use of CT, SPECT, and PET in preclinical research studies across a wide range of applications.

We are developing novel semiconductor radiation detector technology for use in innovative multi-pinhole SPECT systems. 2.) We are involved in a number of collaborative projects in which we our role is to refine and validate image acquisition and analysis techniques to improve quantitative PET and SPECT capabilities.

I am interested in translating quantitative MR techniques to the clinic to better understand the pathological changes in the brain and spinal cord associated with neurological diseases such as multiple sclerosis.

My research is focused on developing quantitative MR methodologies for the brain and spinal cord with a current emphasis on improving thoracic and lumbar spinal cord MRI. This work is also part of an effort to improve our understanding of the effects of biological sex on pathological changes associated with neurological diseases such as multiple sclerosis.

I am interested in the development of new fMRI methods including advances in both image acquisition and image analysis. I am particularly interested in methods involving imaging at ultra high field (7T).

Currently, I have several parallel research paths I am pursuing. First, I am developing methods for pushing the spatial resolution of whole brain fMRI data. The goal of this project is to obtain images with the highest possible isotropic spatial resolution while minimizing geometric distortions of the images and maintaining reasonable temporal resolution. Second, I am developing methods for similarly pushing the temporal resolution of fMRI data while maintaining spatial resolutions similar to those typically used at lower field strengths. These acquisitions will be used for measuring cognitive latencies and validating methods for removing physiological noise.