Automated method for classification and quantification of human

Details Automated method for classification and quantification of human Automated method for classification and quantification of human

1995-02-23

1999-03-30

Smith, Ruth S.

Surgery

Truss

Pad

395 21, 395924, A61B 5055

Patent

active

058875883

ABSTRACT:
A method for analyzing human brain physiology and pathology according to metabolic data obtained by non-invasive means. Metabolism is detected using in vivo proton nuclear magnetic resonance spectroscopy (.sup.1 H MRS) using pulse sequences that provide nuclear magnetic resonance signals from a living human brain. Factors which might decrease performance of artificial neuronal network analysis, e.g. the residual water signal, are eliminated from the nuclear magnetic resonance signal, e.g., by means of a singular value decomposition algorithm. Moreover, the artificial neuronal network analysis accepts as input all of the metabolite resonances, not just selected features, for analysis. The artificial neuronal network analysis yields metabolite concentrations and the nature of tissue type in focal lesions, such as tumors, stroke lesions, epileptic foci, traumatic scars, and also in more diffuse pathologies such as metabolic brain disorders as well as in post-therapeutic changes. The method can provide automated classification procedure of human brain tissue histology for clinical use.

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Cross et al., "Introduction to neural networks", The Lancet, vol. 346, Oct. 21, 1995, pp. 1075-1079.
William G. Baxt, "Application of artificial neural networks to clinical medicine", The Lancet, vol. 346, Oct. 28, 1995, pp. 1135-1138.
Usenius et al., "Automated classification of human brain tumours in neural network analysis using in vivo .sup.1 H magnetic resonance spectroscopic metabolite phenotypes", NeuroReport, vol. 7, No. 10, Jul. 1996, pp. 1597-1600.

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