Medical imaging is a cornerstone of basic science and clinical practice. To discover new mechanisms and markers of disease and their crucial implications for clinical practice, large multi-center imaging studies are acquiring terabytes of complex multi-modality imaging data cross-sectionally and longitudinally over decades. The statistical analysis of data from such studies is challenging due to the complex structure of the imaging data acquired and the ultra-high dimensionality. Furthermore, the heterogeneity of anatomy, pathology, and imaging protocols causes instability and failure of many current state-of-the-art image analysis methods. This grant proposes statistical frameworks for studying populations through biomedical imaging, scalable and robust methods for the identification and accurate quantification of pathology, and analytic tools for the cross-sectional and longitudinal examination of etiology and disease progression. These techniques will be applied to address key goals of the motivating large and multi- center studies of multiple sclerosis and Alzheimer's disease conducted at Johns Hopkins Hospital, the National Institute of Neurological Disorders and Stroke, and across the globe. The project will create methods for uncovering and quantifying brain lesion pathology, incidence, and trajectory. Methods developed under this grant will be targeted towards these neuroimaging goals, but will form the basis for statistical image analysis methods applicable broadly in the biomedical sciences.
This project involves the development of statistical frameworks and methods for the analysis of complex ultra-high-dimensional biomedical imaging. Methods developed are applied to study the clinical management and etiology of multiple sclerosis and Alzheimer's disease longitudinally and cross-sectionally.
| Dworkin, J D; Linn, K A; Oguz, I et al. (2018) An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions. AJNR Am J Neuroradiol 39:626-633 |
| Dworkin, J D; Sati, P; Solomon, A et al. (2018) Automated Integration of Multimodal MRI for the Probabilistic Detection of the Central Vein Sign in White Matter Lesions. AJNR Am J Neuroradiol 39:1806-1813 |
| Urbanek, Jacek K; Zipunnikov, Vadim; Harris, Tamara et al. (2018) Prediction of sustained harmonic walking in the free-living environment using raw accelerometry data. Physiol Meas 39:02NT02 |
| Valcarcel, Alessandra M; Linn, Kristin A; Vandekar, Simon N et al. (2018) MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions. J Neuroimaging 28:389-398 |
| Valcarcel, Alessandra M; Linn, Kristin A; Khalid, Fariha et al. (2018) A dual modeling approach to automatic segmentation of cerebral T2 hyperintensities and T1 black holes in multiple sclerosis. Neuroimage Clin 20:1211-1221 |
| Fleishman, Greg M; Valcarcel, Alessandra; Pham, Dzung L et al. (2018) Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation. Brainlesion (2017) 10670:43-54 |
| Papinutto, Nico; Bakshi, Rohit; Bischof, Antje et al. (2018) Gradient nonlinearity effects on upper cervical spinal cord area measurement from 3D T1 -weighted brain MRI acquisitions. Magn Reson Med 79:1595-1601 |
| Kim, Janet S; Maity, Arnab; Staicu, Ana-Maria (2018) Additive Nonlinear Functional Concurrent Model. Stat Interface 11:669-685 |
| Dworkin, Jordan D; Shinohara, Russell T; Bassett, Danielle S (2018) The landscape of NeuroImage-ing research. Neuroimage 183:872-883 |
| Reardon, P K; Seidlitz, Jakob; Vandekar, Simon et al. (2018) Normative brain size variation and brain shape diversity in humans. Science 360:1222-1227 |
Showing the most recent 10 out of 76 publications
