Neuro engineering

1. Medical-image Analysis and Statistical Interpretation

2. Kurt Schilling, PhD.,
Research Assistant Professor
Research Interest
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 di
ff
usion MRI data.

4. Neuroimaging
Flow chart of steps to create SIMEX Estimated Bias and True Bias maps. Variables refer to terms de
fi
ned
explicitly by equations in the Theory section.

5. Neuroimaging
Research Objective
1. Develop new analysis techniques to model tissue architecture from quantitative and multi-modal MRI imaging data
2. Exploit high performance computing to perform very large-scale image analysis.
3. Design robust system architectures to capture algorithmic failure in image analysis algorithms.
4. Investigate techniques to characterize uncertainty in registration, segmentation and model
fi
tting.

6. Neuroimaging Outside the Brain (Ophthalmological and Spinal)
(Left) (A) In the proposed multi-atlas segmentation pipeline, atlases are non-rigidly registered to the cropped target image.
Non-local statistical label fusion is used to combine the registered labels to segment a target scan. Right) A. Shows
example data for control and disease cohorts. ICD9 data is collected at each visit for each subject. ICD9 data for the
disease cohort is censored from time point (tdx-2) onwards. (B) Structural volume measurements are computed based on
the 3-D segmentation. Illustrations of 7 orbital structures are shown. (B. ICD9s are mapped to PheWAS phenotypes and
data frames are formed for each phenotype. C. Logistic regression is performed for each phenotype. D. Plot showing
signi
fi
cant ICD9 phenotypes of glaucoma with respect to the control group.

7. Neuroimaging Outside the Brain (Ophthalmological and Spinal)
Research Objective
1. Create image processing methods to address resolution, distortion, and
fi
eld of view di
ffi
culties inherent with
optic nerve imaging.
2. Develop quantitative analysis techniques to improve multi-modal imaging (MRI, CT, di
ff
usion MRI, MT, CEST,
etc.) of the optic nerve.
3. Identify structural and quantitative biomarkers to improve prognostic assessments, aid in navigation, and
better understand etiology of optic nerve disease.

8. Abdominal Imaging
Illustrations of the proposed system in use. (a) Surgeon using the system. (b) Virtual hand interacting with 3D abdominal
model. (c) Navigation of axial slices. (d) Navigation of sagittal slices.

9. Abdominal Imaging
Research Objective
1. Develop and evaluate algorithms for the automated labeling of abdominal structures in patient populations
using current generation clinically acquired CT data.
2. Create new labeling paradigms so that automated methods can be e
ffi
ciently learned from expertly labeled
training data.
3. Identify biomarkers based on structural imaging to improve accuracy of prognosis, guide treatment selection,
and improve patient outcomes.