Faculty Bibliography

We propose a novel method to automatically compute the symmetry plane and correct the 3D orientation of patient brain images. Many images of the brain are clinically unreadable because of the misalignment of the patient's head in the scanner. We proposed an algorithm that represents the brain volume as a re-parameterized surface point cloud where each location has been parameterized by its elevation (latitude), azimuth (longitude) and radius. The removal of the interior contents of the brain makes this approach perform robustly in the presence of the brain pathologies, e.g. tumor, stroke and bleed. Thus, we decompose the symmetry plane computation problem into a surface matching routine. The search for the best matching surface is implemented in a multi-resolution paradigm so as to decrease computational time considerably. Spatial affine transform then is performed to rotate the 3D brain images and align them within the coordinate system of the scanner. The corrected brain volume is re-sliced such that each planar image represents the brain at the same axial level.

We present an improvement of an automated generic methodology for symmetry identification, asymmetry quantification, and segmentation of brain pathologies, utilizing the inherent bi-fold mirror symmetry in brain imagery. In the pipeline of operations starting with detection of the symmetry axis, hemisphere-wise cross registration, statistical correlation and quantification of asymmetries, we segment a target brain pathology. The detection of pathological difference left to right in brain imagery is complicated by normal variations as well as geometric misalignment in anatomical structures between two hemispheres. Introducing hemisphere-wise registration and spatial correlation makes our approach perform robustly in the presence of normal asymmetries and systematic artifacts such as bias field and acquisition noise.

The focus of this project is to improve our understanding of the relationships between brain structure and function in patients presenting with anterior visual pathway compression using functional MRI (fMRI), visual field(VF) maps and diffusion tensor imaging (DTI). Significant visual loss can occur when large pituitary lesions compress the optic chiasm. Surgical resection of these lesions decompresses the chiasm and can lead to visual recovery. In this preliminary study, we selected patients presenting with slowly progressive visual loss secondary to a compressive pituitary region mass. Using preoperative DTI data, we reconstructed white matter projections of the optic radiations and demonstrated a structural correlation with functional vision as quantified by formal visual field mapping and fMRI. The structural data generated through a fiber tracking algorithm may represent a potentially powerful tool to better understand functional visual deficits in patients with anterior visual pathway compression. Furthermore, we believe that specific patterns in preoperative DTI data may predict the likelihood of postoperative visual recovery in a select group of patients.

RATIONALE AND OBJECTIVES/OBJECTIVE:Perfusion-weighted computed tomography (CTP) is a relatively recent innovation that estimates a value for cerebral blood flow (CBF) using a series of axial head CT images tracking the time course of a signal from an intravenous contrast bolus. MATERIALS AND METHODS/METHODS:CTP images were obtained using a standard imaging protocol and were analyzed using commercially available software. A novel computer-based method was used for objective quantification of side-to-side asymmetries of CBF values calculated from CTP images. RESULTS:Our method corrects for the inherent variability of the CTP methodology seen in the subarachnoid hemorrhage (SAH) patient population to potentially aid in the diagnosis of cerebral vasospasm (CVS). This method analyzes and quantifies side-to-side asymmetry of CBF and presents relative differences in a construct termed a Relative Difference Map (RDM). To further automate this process, we have developed a unique methodology that enables a computer to delineate vascular territories within a brain image, regardless of the size and shape of the brain. CONCLUSIONS:While both the quantification of image symmetry using RDMs and the automated assignment of vascular territories were initially designed for the analysis of CTP images, it is likely that they will be useful in a variety of applications.