Abstract: Disclosed is a method for computerizing delineation and/or multi-label classification of an ECG signal, including: applying a neural network to the ECG, labelling the ECG, and optionally displaying the labels according to time with the ECG signal.

Abstract: Disclosed is a method for computerizing delineation and/or multi-label classification of an ECG signal, including: applying a neural network to the ECG, labelling the ECG, and optionally displaying the labels according to time with the ECG signal.

Abstract: A method for computerizing delineation and/or multi-label classification of an ECG signal, includes: applying a neural network to the ECG whereby labelling the ECG, and optionally displaying the labels according to time, optionally with the ECG signal.

Abstract: A method for computerizing delineation and/or multi-label classification of an ECG signal, includes: applying a neural network to the ECG whereby labelling the ECG, and optionally displaying the labels according to time, optionally with the ECG signal.

Abstract: A method for computerizing delineation and/or multi-label classification of an ECG signal, includes: applying a neural network to the ECG whereby labelling the ECG, and optionally displaying the labels according to time, optionally with the ECG signal.

Abstract: A method for computerizing delineation and/or multi-label classification of an ECG signal, includes: applying a neural network to the ECG whereby labelling the ECG, and optionally displaying the labels according to time, optionally with the ECG signal.

Abstract: A method for parallel magnetic resonance imaging (MRI) reconstruction of digital images includes providing a set of acquired k-space MR image data v, a redundant Haar wavelet matrix W satisfying WTW=I, wherein I is an identity matrix, a regularization parameter ??0, and a counter limit k, initializing a variable z0=Wv, and intermediate quantities p0=q0=0, calculating yi=arg minz½?z?(pi+zi)?22+??z?1 for 0?i?k, wherein z denotes values of an MR image sought to be reconstructed, updating pi+1=(pi+zi)?yi, updating zi+1=arg minz½?z?(qi+zi)?22+g(z), wherein g ? ( z ) = { 0 , z = WW T ? z , + ? , otherwise ; and updating qi+1=(qi+yi)?zi?l, wherein x=WTz is a solution of min x ? 1 2 ? ? Wx - Wv ? 2 2 + ? ? ? Wx ? 1 that specifies a reconstruction of the MR image.

Abstract: A method for image reconstruction includes receiving under-sampled k-space data, determining a data fidelity term of a first image of the under-sampled k-space data in view of a second image of the under-sampled k-space data, wherein a time component separated the first image and the second image, determining a spatial penalization on redundant Haar wavelet coefficients of the first image in view of the second image, and optimizing the first image according the data fidelity term and the spatial penalization, wherein the spatial penalization selectively penalizes temporal coefficients and an optimized image of the first image is output.

Abstract: A method for parallel magnetic resonance imaging (MRI) reconstruction of digital images includes providing a set of acquired k-space MR image data v, a redundant Haar wavelet matrix W satisfying WTW=I, wherein I is an identity matrix, a regularization parameter ??0, and a counter limit k, initializing a variable z0=Wv, and intermediate quantities p0=q0=0, calculating yi=arg minz 1/2?z?(pi+zi)?22+??z?1 for 0?i?k, wherein z denotes values of an MR image sought to be reconstructed, updating pi+1=(pi+zi)?yi, updating zi+1=arg minz 1/2?z?(qi+zi)?22+g(z), wherein g ? ( z ) = { 0 , z = WW T ? z , + ? , otherwise ; and updating qi+1=(qi+yi)?zi?1, wherein x=WTz is a solution of min x ? 1 2 ? ? Wx - Wv ? 2 2 + ? ? ? Wx ? 1 that specifies a reconstruction of the MR image.

Abstract: A method for image reconstruction includes receiving under-sampled k-space data, determining a data fidelity term of a first image of the under-sampled k-space data in view of a second image of the under-sampled k-space data, wherein a time component separated the first image and the second image, determining a spatial penalization on redundant Haar wavelet coefficients of the first image in view of the second image, and optimizing the first image according the data fidelity term and the spatial penalization, wherein the spatial penalization selectively penalizes temporal coefficients and an optimized image of the first image is output.