A CORRELATED NOISE-REDUCTION ALGORITHM FOR DUAL-ENERGY DIGITAL SUBTRACTION ANGIOGRAPHY
A CORRELATED NOISE-REDUCTION ALGORITHM FOR DUAL-ENERGY DIGITAL SUBTRACTION ANGIOGRAPHY
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DOI：
10.1118/1.596436
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发表时间：
1989-11-01
期刊：
影响因子：
3.8
通讯作者：
MISTRETTA, CA
中科院分区：
文献类型：
--
作者：
MCCOLLOUGH, CH;VANLYSEL, MS;MISTRETTA, CA
长期以来，人们已经认识到，传统的时间减影数字减影血管造影术（DSA）中的运动伪影问题可以使用能量减影技术来克服。在所研究的各种能量减法技术中，非k边缘双能量减法提供了最佳的信噪比（SNR）。然而，该技术仅实现了时间DSA SNR的55%。平均噪声较高的高能图像的降噪技术产生不同程度的噪声改善，同时最小限度地影响碘对比度和分辨率。然而，当适当消除材料特定图像中存在的相关噪声时，双能量DSA碘SNR会有更显著的改善。这里提出的相关降噪（CNR）算法直接来自Kalender的双能量计算机断层扫描工作，Kalender明确利用材料特定图像中的噪声相关性来降低噪声。该结果与使用Macovski描述的两级滤波过程的线性版本所获得的结果相同，在该两级滤波过程中，选择性图像被滤波以减少高频噪声，并且被添加到已经用高频带通滤波器处理的加权的、高SNR的非选择性图像。本文介绍的双能量DSA CNR算法结合了选择性组织和碘图像，在充分保留碘空间分辨率的同时显著提高了碘SNR。 理论计算预测，与传统的双能量图像相比，SNR提高了2-4倍。实现的改善因子取决于X射线束谱和算法中使用的模糊核的大小。在大模糊核的限制下，噪声降低的双能量图像具有接近利用低能量图像和高能量图像的线性组合可实现的最大值的碘SNR，具有基本上降低低空间频率组织信号的附加优点。体模测量证实了CNR技术的SNR的预测增加，而通过常规双能量和CNR方法处理的图像证明了CNR算法的噪声抑制和它可能引入的组织边缘伪影。
It has long been recognized that the problems of motion artifacts in conventional time subtraction digital subtraction angiography (DSA) may be overcome using energy subtraction techniques. Of the variety of energy subtraction techniques investigated, non-k-edge dual-energy subtraction offers the best signal-to-noise ratio (SNR). However, this technique achieves only 55% of the temporal DSA SNR. Noise reduction techniques that average the noisier high-energy image produce various degrees of noise improvement while minimally affecting iodine contrast and resolution. A more significant improvement in dual-energy DSA iodine SNR, however, results when the correlated noise that exists in material specific images is appropriately cancelled. The correlated noise reduction (CNR) algorithm presented here follows directly from the dual-energy computed tomography work of Kalender who made explicit use of noise correlations in material specific images to reduce noise. The results are identical to those achieved using a linear version of the two-stage filtering process described by Macovski in which the selective image is filtered to reduce high-frequency noise and added to a weighted, high SNR, nonselective image which has been processed with a high-frequency bandpass filter. The dual-energy DSA CNR algorithm presented here combines selective tissue and iodine images to produce a significant increase in the iodine SNR while fully preserving iodine spatial resolution. Theoretical calculations predict a factor of 2-4 improvement in SNR compared to conventional dual-energy images. The improvement factor achieved is dependent upon the x-ray beam spectra and the size of blurring kernel used in the algorithm. In the limit of large blurring kernels, the noise-reduced dual-energy image has an iodine SNR approaching the maximum value achievable with a linear combination of the low- and high-energy images, with the additional advantage that low spatial frequency tissue signals are substantially reduced. Phantom measurements confirm the predicted increase in SNR of the CNR technique while images processed both by conventional dual-energy and CNR methods demonstrate the noise suppression of the CNR algorithm and the tissue edge artifacts which it may introduce.