We present iCluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The output is a small number of template images that represent different modes in a population. This is in contrast with traditional approaches that assume a single template to construct atlases. We validate and explore the algorithm in two experiments. First, we employ iCluster to partition a data set of 416 whole brain MR volumes of subjects aged 18-96 years into three sub-groups, which mainly correspond to age groups.
We present iCluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The output of the algorithm is a small number of template images that represent different modes in a population. This is in contrast with traditional, hypothesis-driven computational anatomy approaches that assume a single template to construct an atlas. We derive the algorithm based on a generative model of an image population as a mixture of deformable template images. We validate and explore our method in four experiments.
Alzheimer's disease is a neurodegenerative disease that is associated with the abnormal accumulation of amyloid-β. Much is known about regional brain atrophy in Alzheimer's disease, yet our knowledge about the network nature of Alzheimer's disease-associated amyloid-β accumulation is limited. We use stepwise connectivity analysis of Pittsburgh Compound B positron emission tomography images to reveal the network properties of amyloid-β deposits in normal elderly subjects and clinical patients with Alzheimer's disease.
In this paper, we explore the use of fiber bundles extracted from diffusion MR images for a nonlinear registration algorithm. We employ a white matter atlas to automatically label major fiber bundles and to establish correspondence between subjects. We propose a polyaffine framework to calculate a smooth and invertible nonlinear warp field based on these correspondences, and derive an analytical solution for the reorientation of the tensor fields under the polyaffine transformation.
Multi-atlas techniques are commonplace in medical image segmentation due to their high performance and ease of implementation. Locally weighting the contributions from the different atlases in the label fusion process can improve the quality of the segmentation. However, how to define these weights in a principled way in inter-modality scenarios remains an open problem. Here we propose a label fusion scheme that does not require voxel intensity consistency between the atlases and the target image to segment.
This paper presents the relevance voxel machine (RVoxM), a dedicated Bayesian model for making predictions based on medical imaging data. In contrast to the generic machine learning algorithms that have often been used for this purpose, the method is designed to utilize a small number of spatially clustered sets of voxels that are particularly suited for clinical interpretation. RVoxM automatically tunes all its free parameters during the training phase, and offers the additional advantage of producing probabilistic prediction outcomes.
We present a systematic, multiparameter study of Rb/(129)Xe spin-exchange optical pumping (SEOP) in the regimes of high xenon pressure and photon flux using a 3D-printed, clinical-scale stopped-flow hyperpolarizer. In situ NMR detection was used to study the dynamics of (129)Xe polarization as a function of SEOP-cell operating temperature, photon flux, and xenon partial pressure to maximize (129)Xe polarization (PXe). PXe values of 95 ± 9%, 73 ± 4%, 60 ± 2%, 41 ± 1%, and 31 ± 1% at 275, 515, 1000, 1500, and 2000 Torr Xe partial pressure were achieved.
The first magnetic resonance imaging studies of laser-polarized (129)Xe, dissolved in the blood and tissue of the lungs and the heart of Sprague-Dawley rats, are described. (129)Xe resonances at 0, 192, 199, and 210 ppm were observed and assigned to xenon in gas, fat, tissue, and blood, respectively. One-dimensional chemical-shift imaging (CSI) reveals xenon magnetization in the brain, kidney, and lungs. Coronal and axial two-dimensional CSI show (129)Xe dissolved in blood and tissue in the thorax. Images of the blood resonance show xenon in the lungs and the heart ventricle.
The feasibility of brain MRI with laser-polarized 129Xe in a small animal model is demonstrated. Naturally abundant 129Xe is polarized and introduced into the lungs of Sprague-Dawley rats. Polarized xenon gas dissolves in the blood and is transported to the brain where it accumulates in brain tissue. Spectroscopic studies reveal a single, dominant, tissue-phase NMR resonance in the head at 194.5 ppm relative to the gas phase resonance. Images of 129Xe in the rat head were obtained with 98-microliter voxels by 2D chemical shift imaging and show that xenon is localized to the brain.
The pathophysiology of idiopathic Parkinson disease (PD) is traditionally characterized as substantia nigra degeneration, but careful examination of the widespread neuropathological changes suggests individual differences in neuronal vulnerability. A major limitation to studies of disease progression in PD has been that conventional MRI techniques provide relatively poor contrast for the structures that are affected by the disease, and thus are not typically used in experimental or clinical studies.
