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기록

SEMI-SUPERVISED DENOISING AND DEALIASING FOR MAGNETIC RESONANCE IMAGING

발명심사 중
4조회수
15청구항 · 3 독립항
§ Ⅰ

개요

발명자

Michael Sofka; Jo Schlemper

IPC 분류

G1R 33/56G1R 33/565G6N 3/464G6T 5/70G6V 10/774G16H 30/40

CPC 분류

G1R33/5608G1R33/56545G6N3/464G6T5/70G6V10/774G16H30/40

Systems and methods for training a denoising and dealiasing machine-learning model to generate denoised and dealiased image data are provided. The present disclosure provides techniques for training a denoising and dealiasing machine-learning (ML) model to generate denoised and dealiased imaging data. A method includes (1) training a first ML model using a first training dataset comprising first image data to obtain a second ML model; and (2) training (a) the second ML model or (b) a third ML model using a second training dataset to obtain a fourth ML model. The second training dataset includes (i) the first image data and (ii) training image data obtained by applying at least one of the second ML model or the third ML model to second image data. The denoising and dealiasing ML model may be either the fourth ML model or derived from the fourth ML model.

원문 (중국어)

Systems and methods for training a denoising and dealiasing machine-learning model to generate denoised and dealiased image data are provided. The present disclosure provides techniques for training a denoising and dealiasing machine-learning (ML) model to generate denoised and dealiased imaging data. A method includes (1) training a first ML model using a first training dataset comprising first image data to obtain a second ML model; and (2) training (a) the second ML model or (b) a third ML model using a second training dataset to obtain a fourth ML model. The second training dataset includes (i) the first image data and (ii) training image data obtained by applying at least one of the second ML model or the third ML model to second image data. The denoising and dealiasing ML model may be either the fourth ML model or derived from the fourth ML model.