Biomedical subjects

M G Cawley

Publications and source records attributed to M G Cawley.

3 recordsLinked to original sources

PubMed1988

Improvements in snap-shot nuclear magnetic resonance imaging.

New variants of the ultra-high-speed echo-planar imaging technique have been used to obtain snap-shot images of adult patients and volunteers at 0.1 T. Modified pulsed-gradient sequences together with non-linear signal sampling and activity screened gradients have greatly improved the image quality obtainable by single-shot methods. A particular variant, modulus blipped echo-planar single-pulse technique (MBEST), although slightly slower than the blipped echo-planar single-pulse technique (BEST), is experimentally more robust and incorporates intrinsic T2 weighting. An account of these improvements together with some experimental results is presented.

Adult↗

PubMedDate not supplied

Data representation for subsequent image interpretation.

Radiological scans acquired using either the X-ray CT or the NMR imaging techniques provide a wealth of information about tissue behaviour under that imaging modality and contrast agent. To reason about the image in an interpretation stage the scans have to be converted from a pixel by pixel representation to a symbolic form. The technique used by us to generate such a description is region-based segmentation. Each region refers to a pixel or group of pixels having a common attribute. This work has provided a quantitative measure for the partial evaluation of the segmentation which can be applied independent of attribute or combination of attributes. From our initial studies of the behaviour of CT scans a precept for segmentation was developed. The segmentation employs a one-to-one map as an adaptive mechanism. The segmentation criterion at each point in the image therefore depends on the value at the corresponding point in the map. Any process can be used to generate this map, and so easily utilizes new operators as they are developed.

Artificial Intelligence↗

PubMedDate not supplied

A knowledge-based system paradigm for automatic interpretation of CT scans.

The interpretation of X-ray CT scans is a task which relies on specialized medical expertise, comprising anatomical, modality-dependent, non-visual and radiological knowledge. Most medical imaging techniques generate a single scan or sequence of two-dimensional scans. The radiologist's experience is gained by interpreting two-dimensional scans. The more complex three-dimensional anatomical knowledge becomes significant only when non-standard slice orientations are used. Hence, implicit in the radiologist's knowledge is the appearance of anatomical structures in standard two-dimensional planes, transverse, sagittal and coronal. That is, position with respect to both a coordinate reference system and other structures; intensity ranges for tissue types; contrast between structures; and size within the slices. Further to this, neurological landmarking is used to establish points of reference, i.e. more easily identifiable structures are first found and subsequent hypotheses are formed. With this in mind we have developed a knowledge-based system paradigm that partitions an image by applying the domain-dependent knowledge necessary (1) to set constraints on region-based segmentation and (2) to make explicit the expectation of the appearance of the anatomy under the imaging modality for use in the region grouping phase. This paradigm affords both expectation- and event-driven segmentation by representing grouping knowledge as production rules.

Algorithms↗