We propose to develop statistical methods for the analysis of longitudinal magnetic reso- nance imaging (MRI) data for patients with multiple sclerosis (MS). Disease biomarkers identi?ed from MRI are necessary for studying disease progression in observational studies and for assessing treatment effects of therapies in clinical trials. We propose statistical methods for the analysis of longitudinal MRI intensity time courses that integrate information across multiple modalities. The proposed methods will harness the complex data structure of clinical MRI for identifying biomarkers that can be utilized in future studies and that are implementable in MS centers across the country.
Identi?cation of magnetic resonance imaging biomarkers for patients with multiple sclerosis (MS) that describe brain changes over time is crucial for observational studies of MS and treatment clinical trials. We propose to develop rigorous statistical methods for analyzing high-dimensional longitudinal imaging data before, during, and after the formation of white matter lesions in patients with MS.
| 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 |
| 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 |
| Reich, Brian J; Guinness, Joseph; Vandekar, Simon N et al. (2018) Fully Bayesian spectral methods for imaging data. Biometrics 74:645-652 |
| Fortin, Jean-Philippe; Cullen, Nicholas; Sheline, Yvette I et al. (2018) Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 167:104-120 |
| Oguz, Ipek; Carass, Aaron; Pham, Dzung L et al. (2018) Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation. Brainlesion (2017) 10670:3-14 |
| Dong, Mengjin; Oguz, Ipek; Subbana, Nagesh et al. (2017) Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion. Patch Based Tech Med Imaging (2017) 10530:138-145 |
Showing the most recent 10 out of 18 publications
