实时心肌声学造影定量分析若干图像处理问题的应用研究
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摘要
实时心肌声学造影(RT-MCE)有着很强的评价心肌灌注能力,而且已经成为冠状动脉疾病诊断和评价心肌存活性的有力工具。但是,目前的RT-MCE图像数据的定量分析技术还有待完善,尤其是MCE序列图像的降噪和MCE序列图像ROI区域的帧间校准这两个关键问题亟需得到解决。本课题给出了上述问题的具体解决方法。
MCE图像中含有严重的Speckle噪声,它们遮盖了对临床诊断较为重要的图像细节,并给后续的处理,如边缘检测、图像分割等造成了很大的麻烦。对于超声图像来说,Speckle噪声是无法避免的,而且其复杂的形成机制、多变的统计特性使得很多经典降噪算法无法发挥作用。这一现象引起了很多研究人员的广泛关注。有些学者将研究重点放在噪声模型的求解上,给出了瑞利分布(Rayleigh Distribution)、K分布(K-distribution)以及莱斯分布(Rician Distribution)等模型；有些学者则直接关注实际问题中的Speckle噪声的抑制,给出了SAR(synthetic aperture radar)图像、IVUS图像的降噪方法,这些方法主要有基于空间滤波、基于小波变换、基于扩散理论三种。一些学者将上述研究成果应用到MCE图像的降噪中,虽然具有很强的学术价值,但得到的临床应用效果却不是很理想。但前人的工作给我们提供了重要的信息：Speckle噪声的抑制必须要考虑图像的自身特性。本实验工作人员在前期工作中提出了一种能有效抑制IVUS序列图像血液Speckle噪声的自适应时空滤波方法。该方法的基本思想是对目标帧中每一个要检测的像素结合其空间和时间两方面的信息,构造一个二维ROI区域,然后根据此ROI区域内血液和组织频谱特性的差异进行降噪。自适应时空滤波较好的临床应用效果给了我们很大启发,通过引入S变换,并结合MCE图像的自身特性,本文最终给出了针对MCE图像的Speckle噪声抑制方法。实验结果表明,该方法提高了图像的信噪比,改善了图像的质量。
MCE序列图像定量分析的另一个关键问题是心肌ROI区域的帧间校准问题。目前应用最广泛的MCE定量分析方法是时间-强度曲线分析方法。准确的ROI位置是时间-强度曲线分析方法可靠性的必要前提。然而,由于心脏跳动、呼吸作用的影响,在初始帧上选取的ROI位置在后续帧上都存在不同程度的漂移。因此,为了保证MCE图像定量分析的准确性,必须首先保证图像序列中每一帧上的ROI都标记同一个心肌位置。本课题利用超声图像散斑模式的唯一性和稳定性并使用基于B-mode图像的散斑跟踪方法实现心肌ROI的帧间校准。基于图像的散斑跟踪方法主要有两种,光流场方法和块匹配方法。鉴于块匹配方法计算量小、实时性高及易于软硬件实现的优点,本课题采用块匹配方法求解出ROI区域在后续帧上的位置。为了有效地避免块匹配跟踪误差的帧间积累,本课题通过引入自适应卡尔曼滤波提出了一种新的参考块更新方法,提高了跟踪方法的整体精确度,给出了更加合理的时间-强度曲线。
Due to the strong ability for myocardial perfusion and myocardial viability assessment. Real Time Myocardial Contrast Echocardiography (RT-MCE) has been a powerful tool for the diagnosis of Coronary Artery Disease(CAD).However, further research on the quantitative analysis of RT-MCE is still required. At present, two relevant key problems:the denoising of the MCE images and the reposition of ROIs along the MCE images, need to be solved urgently. This subject will give specific solution to the issues above.
The heavy speckle noise in MCE images, either tends to obscure and mask diagnostically important details or decreases the efficiency of further image processing such as edge detection and image segmentation. For ultrasound images, the speckle noise is inevitable. Its complex formation mechanism and statistics properties make a lot of classic noise reduction algorithm can not play a role. This phenomenon has caused wide attention of many researchers. Some scholars focused on outling the noise model, and has presented several approximate models, such as Rayleigh Distribution, K-distribution and Rician Distribution. While some reseachers tried to give the de-noising algorithm for specific ultrasound imaging modals, e.g., synthetic aperture radar images and intravascular ultrasound images. Their algorithms are mainly based on spatial filter, wavelet transform and diffusion theory. The existed de-speckled methods are of great academic value, but their clinical application effect is not very ideal. The staff of our workgroup put forward a effective de-speckled method for IVUS images, namely adaptive temporal-spatial filtering method, which tries to distinguish vascular tissue and blood via the difference of their frequency spectrum. Motivated by its successful application effect, we finally found one method suppressing the speckle noise in MCE images. Simulation experiments show that our algorithm effectively improves the signal-to-noise ratio of the image and improves the quality of the image.
Another problem is how to reposition the ROIs in MCE imgaes. At present, the time-intensity curve is the most popular way in the quantitatve analysis of RT-MCE, which requires the ROIs locate in the right place. Unfortunately, as a result of heart beat and respiration, the ROI selected on the first frame often does not represent the same structures along cardiac cycle. To obtain reasonable time-intensity curves, the ROIs in the imgaes must tag the same myocardial tissue. We tended to use a B-mode image based speckle tracking technique to track ROIs along the sequence, which takes advantage of the uniqueness and stability of the speckle pattern. In light of the inherent simplicity and relative immunity to noise, a speckle tracking technique based on block matching was implemented. Then a new refference block updating method based on adaptive Kalman filtering was proposed to avoid the interframe tracking error's accumulation along the sequence, which improve the accuracy of the tracking method and ensure the quality of the time-intensity curve finally.
MCE图像中含有严重的Speckle噪声,它们遮盖了对临床诊断较为重要的图像细节,并给后续的处理,如边缘检测、图像分割等造成了很大的麻烦。对于超声图像来说,Speckle噪声是无法避免的,而且其复杂的形成机制、多变的统计特性使得很多经典降噪算法无法发挥作用。这一现象引起了很多研究人员的广泛关注。有些学者将研究重点放在噪声模型的求解上,给出了瑞利分布(Rayleigh Distribution)、K分布(K-distribution)以及莱斯分布(Rician Distribution)等模型；有些学者则直接关注实际问题中的Speckle噪声的抑制,给出了SAR(synthetic aperture radar)图像、IVUS图像的降噪方法,这些方法主要有基于空间滤波、基于小波变换、基于扩散理论三种。一些学者将上述研究成果应用到MCE图像的降噪中,虽然具有很强的学术价值,但得到的临床应用效果却不是很理想。但前人的工作给我们提供了重要的信息：Speckle噪声的抑制必须要考虑图像的自身特性。本实验工作人员在前期工作中提出了一种能有效抑制IVUS序列图像血液Speckle噪声的自适应时空滤波方法。该方法的基本思想是对目标帧中每一个要检测的像素结合其空间和时间两方面的信息,构造一个二维ROI区域,然后根据此ROI区域内血液和组织频谱特性的差异进行降噪。自适应时空滤波较好的临床应用效果给了我们很大启发,通过引入S变换,并结合MCE图像的自身特性,本文最终给出了针对MCE图像的Speckle噪声抑制方法。实验结果表明,该方法提高了图像的信噪比,改善了图像的质量。
MCE序列图像定量分析的另一个关键问题是心肌ROI区域的帧间校准问题。目前应用最广泛的MCE定量分析方法是时间-强度曲线分析方法。准确的ROI位置是时间-强度曲线分析方法可靠性的必要前提。然而,由于心脏跳动、呼吸作用的影响,在初始帧上选取的ROI位置在后续帧上都存在不同程度的漂移。因此,为了保证MCE图像定量分析的准确性,必须首先保证图像序列中每一帧上的ROI都标记同一个心肌位置。本课题利用超声图像散斑模式的唯一性和稳定性并使用基于B-mode图像的散斑跟踪方法实现心肌ROI的帧间校准。基于图像的散斑跟踪方法主要有两种,光流场方法和块匹配方法。鉴于块匹配方法计算量小、实时性高及易于软硬件实现的优点,本课题采用块匹配方法求解出ROI区域在后续帧上的位置。为了有效地避免块匹配跟踪误差的帧间积累,本课题通过引入自适应卡尔曼滤波提出了一种新的参考块更新方法,提高了跟踪方法的整体精确度,给出了更加合理的时间-强度曲线。
Due to the strong ability for myocardial perfusion and myocardial viability assessment. Real Time Myocardial Contrast Echocardiography (RT-MCE) has been a powerful tool for the diagnosis of Coronary Artery Disease(CAD).However, further research on the quantitative analysis of RT-MCE is still required. At present, two relevant key problems:the denoising of the MCE images and the reposition of ROIs along the MCE images, need to be solved urgently. This subject will give specific solution to the issues above.
The heavy speckle noise in MCE images, either tends to obscure and mask diagnostically important details or decreases the efficiency of further image processing such as edge detection and image segmentation. For ultrasound images, the speckle noise is inevitable. Its complex formation mechanism and statistics properties make a lot of classic noise reduction algorithm can not play a role. This phenomenon has caused wide attention of many researchers. Some scholars focused on outling the noise model, and has presented several approximate models, such as Rayleigh Distribution, K-distribution and Rician Distribution. While some reseachers tried to give the de-noising algorithm for specific ultrasound imaging modals, e.g., synthetic aperture radar images and intravascular ultrasound images. Their algorithms are mainly based on spatial filter, wavelet transform and diffusion theory. The existed de-speckled methods are of great academic value, but their clinical application effect is not very ideal. The staff of our workgroup put forward a effective de-speckled method for IVUS images, namely adaptive temporal-spatial filtering method, which tries to distinguish vascular tissue and blood via the difference of their frequency spectrum. Motivated by its successful application effect, we finally found one method suppressing the speckle noise in MCE images. Simulation experiments show that our algorithm effectively improves the signal-to-noise ratio of the image and improves the quality of the image.
Another problem is how to reposition the ROIs in MCE imgaes. At present, the time-intensity curve is the most popular way in the quantitatve analysis of RT-MCE, which requires the ROIs locate in the right place. Unfortunately, as a result of heart beat and respiration, the ROI selected on the first frame often does not represent the same structures along cardiac cycle. To obtain reasonable time-intensity curves, the ROIs in the imgaes must tag the same myocardial tissue. We tended to use a B-mode image based speckle tracking technique to track ROIs along the sequence, which takes advantage of the uniqueness and stability of the speckle pattern. In light of the inherent simplicity and relative immunity to noise, a speckle tracking technique based on block matching was implemented. Then a new refference block updating method based on adaptive Kalman filtering was proposed to avoid the interframe tracking error's accumulation along the sequence, which improve the accuracy of the tracking method and ensure the quality of the time-intensity curve finally.
引文
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