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National Research Council of Canada

The University of Sydney

Institute for Magnetic Resonance Research

Rajmund Somorjai

Carolyn Mountford

Peter Stanwell

Anne Woodhouse

Ian C P Smith

Peter Russell

Phillip John Siddall

Philip John Siddall

A system and method identifies different components of the pain experience (nociceptive (tissue damage), neuropathic (nerve damage) and psychological) and their relative contributions by the use of magnetic resonance spectroscopy (MRS) to measure absolute and relative concentrations of metabolites in specific brain regions in the central nervous system or brain. The system and method can be used as a diagnostic tool for the assessment of the relative contribution of different aspects of the pain experience as well as monitoring of response to interventions directed at modifying these components.

System and method for detecting pain and its components using magnetic resonance

System and method for detecting pain and its components using magnetic resonance spectroscopy

Robust classification methods analyze magnetic resonance spectroscopy (MRS) data (spectra) of fine needle aspirates taken from breast tumors. The resultant data when compared with the histopathology and clinical criteria provide computerized classification-based diagnosis and prognosis with a very high degree of accuracy and reliability. Diagnostic correlation performed between the spectra and standard synoptic pathology findings contain detail regarding the pathology (malignant versus benign), vascular invasion by the primary cancer and lymph node involvement of the excised axillary lymph nodes. The classification strategy consisted of three stages: pre-processing of MR magnitude spectra to identify optimal spectral regions, cross-validated Linear Discriminant Analysis, and classification aggregation via Computerised Consensus Diagnosis. Malignant tissue was distinguished from benign lesions with an overall accuracy of 93%. From the same spectrum, lymph node involvement was predicted with an accuracy of 95% and tumor vascularisation with an overall accuracy of 92%.

Magnetic resonance spectroscopy to classify tissue

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Rajmund Somorjai