A comparative study of minimum norm inverse methods for MEG imaging

Conference · Mon Jul 01 00:00:00 EDT 1996

OSTI ID:400181

Leahy, R M; Mosher, J C; Phillips, J W

The majority of MEG imaging techniques currently in use fall into the general class of (weighted) minimum norm methods. The minimization of a norm is used as the basis for choosing one from a generally infinite set of solutions that provide an equally good fit to the data. This ambiguity in the solution arises from the inherent non- uniqueness of the continuous inverse problem and is compounded by the imbalance between the relatively small number of measurements and the large number of source voxels. Here we present a unified view of the minimum norm methods and describe how we can use Tikhonov regularization to avoid instabilities in the solutions due to noise. We then compare the performance of regularized versions of three well known linear minimum norm methods with the non-linear iteratively reweighted minimum norm method and a Bayesian approach.

• Research Organization: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: National Insts. of Health, Bethesda, MD (United States); National Eye Inst., Bethesda, MD (United States)
• DOE Contract Number: W-7405-ENG-36
• OSTI ID: 400181
• Report Number(s): LA-UR-96-1941; CONF-9602101-7; ON: DE96012735; CNN: NIH Grant R01-MH53213; NEI Grant R01-EY08610-04
• Resource Relation: Conference: Biomagnetism conference, Santa Fe, NM (United States), Feb 1996; Other Information: PBD: [1996]
• Country of Publication: United States
• Language: English

Related Subjects

55 BIOLOGY AND MEDICINE
BASIC STUDIES
99 MATHEMATICS
COMPUTERS
INFORMATION SCIENCE
MANAGEMENT
LAW
MISCELLANEOUS
44 INSTRUMENTATION
INCLUDING NUCLEAR AND PARTICLE DETECTORS
IMAGE PROCESSING
STATISTICAL MODELS
BIOMEDICAL RADIOGRAPHY
WEIGHTING FUNCTIONS
STATISTICS
PHANTOMS
COMPUTERIZED SIMULATION
TWO-DIMENSIONAL CALCULATIONS
SPATIAL RESOLUTION