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SYSTEM, METHOD AND COMPUTER-ACCESSIBLE MEDIUM FOR IMAGE RECONSTRUCTION OF NON-CARTESIAN MAGNETIC RESONANCE IMAGING INFORMATION USING DEEP LEARNING
SYSTEM, METHOD AND COMPUTER-ACCESSIBLE MEDIUM FOR IMAGE RECONSTRUCTION OF NON-CARTESIAN MAGNETIC RESONANCE IMAGING INFORMATION USING DEEP LEARNING
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机译：基于深度学习的非笛卡尔磁共振成像信息图像重建的系统，方法和计算机可访问介质
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摘要
An exemplary system, method, and computer-accessible medium for generating a Cartesian equivalent image(s) of a portion(s) of a patient(s), can include, for example, receiving non-Cartesian sample information based on a magnetic resonance imaging (MRI) procedure of the portion(s) of the patient(s), and automatically generating the Cartesian equivalent image(s) from the non-Cartesian sample information using a deep learning procedure(s). The non-Cartesian sample information can be Fourier domain information. The non-Cartesian sample information can be undersampled non-Cartesian sample information. The MRI procedure can include an ultra-short echo time (UTE) pulse sequence. The UTE pulse sequence can include a delay(s) and a spoiling gradient. The Cartesian equivalent image(s) can be generated by reconstructing the Cartesian equivalent image(s). The Cartesian equivalent image(s) can be reconstructed using a sampling density compensation with a tapering of over a particular percentage of a radius of a k-space, where the particular percentage can be about 50%.
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