@article {828, title = {Axial 3D gradient-echo imaging for improved multiple sclerosis lesion detection in the cervical spinal cord at 3T.}, journal = {Neuroradiology}, volume = {55}, year = {2013}, month = {2013 Mar}, pages = {431-9}, abstract = {

INTRODUCTION: In multiple sclerosis (MS), spinal cord imaging can help in diagnosis and follow-up evaluation. However, spinal cord magnetic resonance imaging (MRI) is technically challenging, and image quality, particularly in the axial plane, is typically poor compared to brain MRI. Because gradient-recalled echo (GRE) images might offer improved contrast resolution within the spinal cord at high magnetic field strength, both without and with a magnetization transfer prepulse, we compared them to T2-weighted fast-spin-echo (T2-FSE) images for the detection of MS lesions in the cervical cord at 3T.

METHODS: On a clinical 3T MRI scanner, we studied 62 MS cases and 19 healthy volunteers. Axial 3D GRE sequences were performed without and with off-resonance radiofrequency irradiation. To mimic clinical practice, all images were evaluated in conjunction with linked images from a sagittal short tau inversion recovery scan, which is considered the gold standard for lesion detection in MS. Two experienced observers recorded image quality, location and size of focal lesions, atrophy, swelling, and diffuse signal abnormality independently at first and then in consensus.

RESULTS: The number and volume of lesions detected with high confidence was more than three times as high on both GRE sequences compared to T2-FSE (p < 0.0001). Approximately 5 \% of GRE scans were affected by artifacts that interfered with image interpretation, not significantly different from T2W-FSE.

CONCLUSIONS: Axial 3D GRE sequences are useful for MS lesion detection when compared to 2D T2-FSE sequences in the cervical spinal cord at 3T and should be considered when examining intramedullary spinal cord lesions.

}, keywords = {Adult, Aged, Algorithms, Cervical Vertebrae, Echo-Planar Imaging, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Male, Middle Aged, Multiple Sclerosis, Reproducibility of Results, Sensitivity and Specificity, Spinal Cord}, issn = {1432-1920}, doi = {10.1007/s00234-012-1118-5}, author = {Ozturk, Arzu and Aygun, Nafi and Smith, Seth A and Caffo, Brian and Calabresi, Peter A and Reich, Daniel S} } @article {812, title = {Likelihood-based population independent component analysis.}, journal = {Biostatistics}, volume = {14}, year = {2013}, month = {2013 Jul}, pages = {514-27}, abstract = {

Independent component analysis (ICA) is a widely used technique for blind source separation, used heavily in several scientific research areas including acoustics, electrophysiology, and functional neuroimaging. We propose a scalable two-stage iterative true group ICA methodology for analyzing population level functional magnetic resonance imaging (fMRI) data where the number of subjects is very large. The method is based on likelihood estimators of the underlying source densities and the mixing matrix. As opposed to many commonly used group ICA algorithms, the proposed method does not require significant data reduction by a 2-fold singular value decomposition. In addition, the method can be applied to a large group of subjects since the memory requirements are not restrictive. The performance of our approach is compared with a commonly used group ICA algorithm via simulation studies. Furthermore, the proposed method is applied to a large collection of resting state fMRI datasets. The results show that established brain networks are well recovered by the proposed algorithm.

}, keywords = {Adult, Algorithms, Attention Deficit Disorder with Hyperactivity, Biostatistics, Brain, Child, Computer Simulation, Databases, Factual, Humans, Imaging, Three-Dimensional, Likelihood Functions, Magnetic Resonance Imaging, Principal Component Analysis, Statistics, Nonparametric}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxs055}, author = {Eloyan, Ani and Crainiceanu, Ciprian M and Caffo, Brian S} }