Computational Functional Anatomy (CFA) is the mathematical study of anatomical configurations and signals associated with anatomy and functions in anatomical coordinates using multi-modal images. Its ultimate goal is to identify image biomarkers associated with a specific disease.
Currently, most of our work focuses on the development of medical image analysis tools to assess anatomical shape and functions of the human brain in magnetic resonance imaging (MRI), including structural MRI, functional MRI, and diffusion tensor imaging (DTI). Special tools developed are listed:
- Brain Mapping
1. Large Deformation Diffeomorphic Metric Mapping (LDDMM) tools register images, landmark points, curves, and surface meshes.
2. Parallel Transport in Diffeomorphisms tool tracks longitudinal changes of anatomy.
- Shape Analysis Pipeline
The pipeline has several key components, structural delineation, segmentation denoising, surface momentum maps recording shape variation between anatomies, as well as random field statistical analysis for detecting group differences in anatomical shape. This pipeline fully automatically processes raw MRI scans and allows us to assess large scale MRI database.
- Random Field Analysis
- Spatial Smoothing Functional Signals on the Cortex
- Surface-based fMRI Analysis.
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Anqi Qiu, Marc Vaillant, Patrick Barta, J. Tilak Ratnanather, Michael I. Miller, "Region of Interest Based Analysis of Cortical Thickness Variation of Left Planum Temporale in Schizophrenia and Psychotic Bipolar Disorder", Human Brain Mapping, 29(8):973-985, 2008. |
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Anqi Qiu, Laurent Younes, Michael I. Miller, “Intrinsic and Extrinsic Analysis in Computational Anatomy”, NeuroImage, 39(4):1803-1814, 2008. |
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Anqi Qiu, Laurent Younes, Lei Wang, J. Tilak Ratnanather, Sarah K. Gillepsie, Gillian Kaplan, John Csernansky, Michael I. Miller, “Combining Anatomical Manifold Information via Diffeomorphic Metric Mappings for Studying Cortical Thinning of the Cingulate Gyrus in Schizophrenia”, Neuroimage, 37, 821-833, 2007. |
