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    • 66. 发明授权
    • Method for image reconstruction using low-dimensional-structure self-learning and thresholding
    • 使用低维结构自学习和阈值的图像重建方法
    • US08699773B2
    • 2014-04-15
    • US12909313
    • 2010-10-21
    • Mehmet AkcakayaReza Nezafat
    • Mehmet AkcakayaReza Nezafat
    • G06K9/00
    • G06T5/002G06T5/10G06T11/008G06T2200/12G06T2207/10081G06T2207/10088G06T2207/20056G06T2207/30004
    • A method for reconstructing an image of a subject from undersampled image data that is acquired with an imaging system, such as a magnetic resonance imaging system or computed tomography system, is provided. From the acquired undersampled image data, an image of the subject is reconstructed and used to guide further image reconstruction. For example, a low resolution image is reconstructed from a portion of the undersampled image data, such as from a portion corresponding to the center of k-space when MRI is used. From this image, a number of similarity clusters are produced and processed. The processing may be by hard thresholding, Wiener filtering, principal component pursuit, or other similar techniques. These processed similarity clusters are then used to reconstruct a final, target image of the subject using, for example, a weighted average combination of the similarity clusters.
    • 提供了一种用于使用诸如磁共振成像系统或计算机断层摄影系统的成像系统获取的欠采样图像数据来重建被摄体图像的方法。 从获取的欠采样图像数据中,重建对象的图像并用于指导进一步的图像重建。 例如,低分辨率图像从欠采样图像数据的一部分重建,例如从使用MRI时与k空间的中心相对应的部分。 从该图像中,生成并处理了许多相似性簇。 处理可以是通过硬阈值,维纳滤波,主成分追踪或其他类似技术。 然后使用这些处理的相似性群体使用例如相似性群集的加权平均组合来重建受试者的最终目标图像。