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Biomedical subjects

U Raff

Publications and source records attributed to U Raff.

10 recordsLinked to original sources
PubMed1991

Three monochrome displays from a single, true color video display controller.

Some nuclear medicine computer displays, as well as many image processing workstations are "true color" machines characterized by independent memory and grey scale mapping for each of the red, green and blue color channels. Other color image display systems produce a color image from a single grey scale map composed of red, green, and blue intensity values ("pseudo color"). In the true color system the final image is obtained by overlays of three independent color images. In an effort to present complete nuclear medicine studies for diagnosis from cathode-ray tubes (CRTs) we have employed a true color display to present three times as much spatial information as the system was designed for by directing each color output from the display controller to a different monochrome black and white (b/w) monitor. Therefore our system displays a 512 x 512 x 24-bit true-color image, or three 512 x 512 x 8-bit monochrome images, or any combination of smaller size matrices. Monitor requirements, cabling, and general software considerations are detailed here. The ability to display complete nuclear medicine studies on CRTs (as currently presented on film) has been provided by adding monitors and software revisions to a commercially available nuclear medicine computer system.

Color↗
PubMed1979

Reversal of digitalis effects by specific antibodies.

Highly digoxin-specific or ouabain-specific antibodies can readily be obtained by immunizing rabbits or sheep with repeated injections of the glycoside coupled to protein carriers. By virtue of their binding capacity digoxin-specific antibodies are capable of removing digoxin concentrations from red blood cells and renal tissue specimens. As evidenced by various experiments with human erythrocytes and isolated cardiac preparations in vitro, digoxin effects are rapidly reversible in the presence of digoxin-specific antibodies. In vivo antidigoxin antibodies can protect animals from digoxin effects and promptly abolish established toxic effects, associated with marked alterations of digoxin pharmacokinetics. However, due to the large molecular weight, complete antibodies cannot be eliminated via the renal route. The use of antigen-binding (Fab) fragments of digoxin-specific antibodies offer the advantage of rapid renal elimination of bound and inactivated digoxin. So far, due to potential immune reactions, the clinical use of purified digoxin-specific antibodies of Fab fragments is restricted to life-threatening accidental or suicidal digoxin or digitoxin poisoning.

Animals↗
PubMed1977

[The influence of antiarrhythmic drugs on the exercise-ecg (author's transl)].

The influence of five most common antiarrhythmic drugs on exercise-electrocadiogram was investigated: prajmaliumbitartrat, quinidinbisulfate, diphenylhydantoine, propranolol, and verapamil. All exercise-tests were performed in eight healthy males. During exercise and the subsequent resting period, the changes in the following parameters were analysed: blood pressure, heart rate, P-Q-time, Q-T-time, and the formal course of the Ecg as well as individual reactions. None of the drugs produced a pathological ECG. As for the analysed parameters, diphenylhydantoine was entirely neutral, whereas propranolol caused the most significant changes. Propranolol lowered heart rate by more than 20% and blood pressure by 10%. Verapamil had a less pronounced effect on diastolic blood pressure and heart rate. Prajmaliumbitartrat and quinidine had no definite effect during exercise and resting period.

Adult↗
PubMedDate not supplied

Lesion detection in radiologic images using an autoassociative paradigm: preliminary results.

An area of artificial intelligence that has gained recent attention is the neural network approach to pattern recognition and classification. The use of neural networks in radiologic lesion detection is explored by employing what is known in the literature as the "novelty filter." This filter uses a linear algebraic model, whereupon in neural network terms, images of normal patterns become "training vectors" and are stored as columns of a matrix. An image of an abnormal pattern is introduced and the abnormality or the "novelty" is extracted. A noniterative technique has been applied. In a preliminary experiment, autoassociative recall was tested using alphabetic characters as training vectors. The second experiment used sections of transverse magnetic resonance (MR) images (TR = 3000 ms, TE = 40 ms) of normal patients as the training vectors. A section of a transverse MR brain image with multiple sclerosis lesions was introduced to the filter and the abnormalities were extracted. In conclusion, a neural network based lesion detector may have great promise in medical pattern recognition.

Humans↗
PubMedDate not supplied

Automated lesion detection and lesion quantitation in MR images using autoassociative memory.

Previous efforts concerning lesion extraction in radiologic images indicated that autoassociative memory models can be a valuable tool in automated lesion detection. Preliminary results are expanded to resolve the technical problems of image registration and magnification. Instead of operating on selected portions of the MR images, each entire image matrix is operated upon as image vector comprising all stacked columns of the matrix. Spin density weighted images (TR = 3000 ms and TE = 40 ms) of 42 normal subjects were remapped and standardized with respect to location and magnification. All image vectors were orthonormalized to span a linear manifold. Standardized abnormal image vectors were then tested by the stored autoassociative memory and the abnormalities (novelties) were extracted by application of an autocorrelation matrix to the input vector. The autocorrelation matrix is computed using image vectors from normal subjects. The lesions (multiple sclerosis and tumors) are then identified as the orthogonal component to the linear manifold spanned by the basis vectors of the normal brain scans. Lesion extraction has been achieved with the intention of quantitating and staging diseased parenchyma after automated edge detection.

Algorithms↗