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S L Rossnick

Publications and source records attributed to S L Rossnick.

2 recordsLinked to original sources

Magnetic resonance imaging-based brain morphometry: development and application to normal subjects.

A semiautomated computerized method of in vivo morphometric analysis that is based upon high-resolution three-dimensional magnetic resonance imaging has been developed. This morphometric method is efficient and is of greater analytical precision than any other morphometric method currently applied to living human tissue. Including error inherent in image data acquisition, the aggregate error of the methodology, as estimated by the phantom studies, ranges from 4.5 to 9.6%, with incremental error above 4.5% a function of magnetic resonance slice thickness. This method was applied to magnetic resonance scans of 7 normal volunteers. The derived volumes of whole brain and of individual substructures were closely concordant with previously published volumes of normal fresh (unfixed) brains obtained post mortem. This morphometric methodology is potentially applicable to any structure or lesion that can be visualized by magnetic resonance imaging.

Adolescent↗

Multispectral analysis of MR images of the breast.

Preliminary investigations were conducted into the potential of magnetic resonance (MR) images for tissue classification of the breast on the basis of relative signal intensity. Multispectral techniques originally developed by the National Aeronautics and Space Administration for satellite image analysis were used in sequence selection, image data correction, image standardization, and image interpretation. Numerous sequence combinations with varying repetition times (TR) and echo times (TE) were considered, and a triplet was selected consisting of long TR/long TE, short TR/short TE, and an opposed phase sequence with intermediate TR and TE. Correction to remove system-imposed intensity inhomogeneities was required for all images. Image standardization based on fat and pectoral muscle signals was necessary for intercase comparisons. Multispectral images obtained based on this analysis suggest the feasibility of intensity-based image classification.

Breast Neoplasms↗