GATE : a simulation toolkit for PET and SPECT

Monte Carlo simulation is an essential tool in emission tomography that can assist in the design of new medical imaging devices, the optimization of acquisition protocols, and the development or assessment of image reconstruction algorithms and correction techniques. GATE, the Geant4 Application for Tomographic Emission, encapsulates the Geant4 libraries to achieve a modular, versatile, scripted simulation toolkit adapted to the field of nuclear medicine. In particular, GATE allows the description of time-dependent phenomena such as source or detector movement, and source decay kinetics. This feature makes it possible to simulate time curves under realistic acquisition conditions and to test dynamic reconstruction algorithms. A public release of GATE licensed under the GNU Lesser General Public License can be downloaded at the address http://www-lphe.epfl.ch/GATE/

A Guide to the Brain Initiative Cell Census Network Data Ecosystem

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain

The automatic identification of hibernating myocardium

Delayed enhancement imaging is a recently described technique that enables for the first time, the direct observation of areas of myocardium that have scarred following infarction. When this information is combined with information about myocardial contraction, areas that are neither dead, nor contracting can be identified. Such areas will resume contraction following revascularisation (hibernating myocardium). The identification of such areas is consequently of great interest to clinicians. This paper describes how registration can be used to align the images prior to the identification of areas that will benefit from revascularisation. Patient data is used to demonstrate image alignment and image-derived information combination. This is then mapped onto patient-specific 2D and 3D representations of the heart

Automatic estimation of error in voxel-based registration

Image registration driven by similarity measures that are simple functions of voxel intensities is now widely used in medical applications. Validation of registration in general remains an unsolved problem; measurement of registration error usually requires manual intervention. This paper presents a general framework for automatically estimating the scale of spatial registration error. The error is estimated from a statistical analysis of the scale-space of a residual image constructed with the same assumptions used to choose the image similarity measure. The analysis identifies the most significant scale of voxel clusters in the residual image for a coarse estimate of error. A partial volume correction is then applied to estimate finer and sub-voxel displacements. We describe the algorithm and present the results of an evaluation on rigid-body registrations where the ground-truth error is known. Automated measures may ultimately provide a useful estimate of the scale of registration error

Rapid Coarse-to-Fine Matching Using Scale-Specific Priors

The Gibbs priors with potential equal to the membrane deflection and thin plate bending energies are explored in the Bayesian approach to image matching. Their smoothness properties are qualitatively demonstrated in a matching task. The priors are further evaluated by comparing their effect on the atlas-based localization of several subcortical structures in MRI data. Results of the localization study indicate that the implementation based on the membrane prior assumed over a fine mesh outperforms, both in speed and accuracy of the anatomic labeling, a plate-based approach that uses a comparable number of unknowns. Keywords: Image matching, Bayesian analysis, smoothness constraints, anatomic atlases, cerebral anatomy 1. INTRODUCTION Given two related images in the sense that they represent instances of the same scene, the image matching operation determines the transformation that maps each point in one image into its corresponding point in the other. Such inferences are of interest..