[go: up one dir, main page]

Lunttila et al., 2003 - Google Patents

Regularization in cardiac source imaging

Lunttila et al., 2003

Document ID
4698505813275628847
Author
Lunttila T
Nenonen J
Somersalo E
Publication year
Publication venue
International Workshop on Functional Imaging and Modeling of the Heart

External Links

Snippet

Estimation of bioelectric currents in the heart involves the solution of an ill-posed inverse problem in electro-and magnetocardiography. The problem becomes linear in respect to current magnitudes when the equivalent sources are constrained into a pre-determined grid …
Continue reading at link.springer.com (other versions)

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0536—Impedance imaging, e.g. by tomography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7239—Details of waveform analysis using differentiation including higher order derivatives
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402—Electrocardiography, i.e. ECG
    • A61B5/0452—Detecting specific parameters of the electrocardiograph cycle
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7285—Specific aspects of physiological measurement analysis for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10104—Positron emission tomography [PET]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/004—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection

Similar Documents

Publication Publication Date Title
US9125581B2 (en) Continuous modeling for dipole localization from 2D MCG images with unknown depth
Hauk Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data
US8553956B2 (en) 3D current reconstruction from 2D dense MCG images
Messnarz et al. A new spatiotemporal regularization approach for reconstruction of cardiac transmembrane potential patterns
Fuchs et al. Linear and nonlinear current density reconstructions
Haufe et al. Combining sparsity and rotational invariance in EEG/MEG source reconstruction
CN102682425B (en) Magnetocardiogram system and method for establishing magnetocardiogram image
US9089274B2 (en) Denoise MCG measurements
Schuler et al. Reducing line-of-block artifacts in cardiac activation maps estimated using ECG imaging: A comparison of source models and estimation methods
CN106132288B (en) Three-dimensional cardiac contour reconstruction method
CN110811596B (en) Noninvasive cardiac potential reconstruction method based on low rank and sparse constraint and non-local total variation
Ha et al. Three-dimensional reconstruction of a cardiac outline by magnetocardiography
Maksymenko et al. Strategies for statistical thresholding of source localization maps in magnetoencephalography and estimating source extent
Ghosh et al. Bayesian inference for brain source imaging with joint estimation of structured low-rank noise
Lunttila et al. Regularization in cardiac source imaging
Ponasso et al. High-Definition MEG Source Estimation using the Reciprocal Boundary Element Fast Multipole Method
El Houari et al. Investigating transmembrane current source formulation for solving the ECG inverse problem
Plis et al. Modeling spatiotemporal covariance for magnetoencephalography or electroencephalography source analysis
Erenler Comparison of ML and MAP parameter estimation techniques for the solution of inverse electrocardiography problem
Coll-Font et al. A common-ground review of the potential for machine learning approaches in electrocardiographic imaging based on probabilistic graphical models
Kim et al. Independent component analysis for synthetic aperture magnetometry in magnetocardiography
Cai et al. A novel scanning algorithm for MEG/EEG imaging using covariance partitioning and noise learning
Cai et al. Robust estimation of noise for electromagnetic brain imaging with the champagne
Elaff Modeling The Invers Electrophysiology of the Heart Activation As Anisotropic Volume Source Inside Inhomogeneous Volume Conductor
Joud et al. SOS-MUSIC, a subspace approach for EEG source imaging promoting sparsity of active sources