伽玛相机轫致形变图像的神经网络恢复

M. Kallergi, W. Qian, L. Clarke, Huai-Dong Li
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引用次数: 0

摘要

采用一种新的恢复技术对含磷-32 (32Ρ)的球形幻影图像进行了滤波,该技术采用阶统计非线性滤波器进行噪声抑制,并采用神经网络进行图像恢复。该方法在保留图像内容的同时有效地降低了图像噪声,并根据源到检测器的距离或源大小对数据进行计数和恢复,从而获得了有效的32P衰减系数。在误差图像和衰减测量方面,将新滤波器与维纳滤波器进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural network restoration of gamma camera bremsstrahlung images
Planar gamma camera Images of spherical phantoms filled with phosphorous-32 (32Ρ) are filtered with a new restoration technique which uses an order-statistic non-linear filter for noise suppression and a neural network for image restoration. The proposed method reduces Image noise effectively while preserving Image content and counts and restores the data consistently with source-to-detector distance or source size resulting in an effective attenuation coefficient for 32P. The new filter is compared to the Wiener filter for restoration in terms of error Images and attenuation measurements.
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