Many studies of brain function with positron emission tomography (PET) involve the interpretation of a subtracted PET image, usually the difference between two images under base...
Abstract Current approaches to detecting significantly activated regions of cerebral tissue use statistical parametric maps, which are thresholded to render the probability of o...
Abstract A simple spatial-correlation model is presented for repeated measures data. Correlation between observations on the same subject is assumed to decay as a linear functio...
We present a fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques. The method...
Magnetic resonance imaging (MRI) has become the modality of choice for neuro-anatomical imaging. Quantitative analysis requires the accurate and reproducible labeling of all vox...
Abstract Explicit segmentation is required for many forms of quantitative neuroanatomic analysis. However, manual methods are time‐consuming and subject to errors in both accura...
A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue cl...
We present a unified statistical theory for assessing the significance of apparent signal observed in noisy difference images. The results are usable in a wide range of applicat...
After conception and implementation of any new medical image processing algorithm, validation is an important step to ensure that the procedure fulfills all requirements set for...
Recently, there has been a rapid growth in the use of 3D multi-modal correlative imaging for studies of the human brain. Regional cerebral blood flow (CBF) changes indicate brai...
Although numerous methods to register brains of different individuals have been proposed, no work has been done, as far as we know, to evaluate and objectively compare the perfo...
h-index: Number of publications with at least h citations each.