Abstract: A system and method identifies Post Traumatic Stress Disorder 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 PTSD. These chemical changes in the brain of those people suffering from PTSD are different from those suffering from head injury, chronic pain and other neurological conditions.

Abstract: Robust classification methods analyse magnetic resonance spectroscopy (MRS) data (spectra) of fine needle aspirates taken from breast tumours. 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%.

Abstract: Robust classification methods analyse 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%.

Abstract: 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.

Abstract: 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.

Abstract: Robust classification methods analyse magnetic resonance spectroscopy (MRS) data (spectra) of fine needle aspirates taken from breast tumours. 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%.

Abstract: 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%.

Abstract: Robust classification methods analyse magnetic resonance spectroscopy (MRS) data (spectra) of fine needle aspirates taken from breast tumours. 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%.

Abstract: A statistical classifier identifies microorganisms, such as bacteria and fungi, using magnetic resonance spectroscopy, with multivariate analysis.

Abstract: In a method for determining chemical states of living animal or human tissue using nuclear magnetic resonance with a homogeneous constant magnetic field, the tissue is measured by means of a high-resolution nuclear magnetic resonance measurement and the measured values obtained are then evaluated by comparison with measured values from resonance measurements on comparable tissue to determine whether the chemical state of the tissue corresponds to a chemical normal state or to a deviating abnormal state. In order to enable a determination of chemical intermediate states to be made, it is proposed to compare the measured values obtained with such a series of reference measurements which also contain the detectable characteristic transition states between the chemical normal state and the abnormal end state and that the chemical transition state of the measured tissue sample is determined from the comparison.