Gravel Pierre, Després Philippe, Beaudoin Gilles, de Guise Jacques A
Génie de la production automatisée, Ecole de technologie supérieure, Montréal, Canada.
Phys Med Biol. 2006 May 21;51(10):2415-39. doi: 10.1088/0031-9155/51/10/005. Epub 2006 Apr 26.
We have developed a restoration method for radiographs that enhances image sharpness and reveals bone microstructures that were initially hidden in the soft-tissue glare. The method is two fold: the image is first deconvolved using the Richardson-Lucy algorithm and is then divided with a signal modelling the soft-tissue distribution to increase the overall contrast. Each step has its own merits but the power of the restoration method lies in their combination. The originality of the method is its reliance on a priori information at each step in the processing. We have measured and modelled analytically the point-spread function of a low-dose gas microstrip x-ray detector at several beam energies. We measured the relationship between the local image intensity and the noise variance for these images. The soft-tissue signal was also modelled using a minimum-curvature filtering technique. These results were then combined into an image deconvolution procedure that uses wavelet filtering to reduce restoration noise while keeping the enhanced small-scale features. The method was applied successfully to images of a human-torso phantom and improved the contrast of small details on the bones and in the soft tissues. We measured a mean 54% increase in signal to noise ratio and a mean 105% increase in contrast to noise ratio in the 70 and 140 kVp images we analysed. The method was designed to facilitate the analysis of radiographs by relying on two levels of visual inspection. The contrast of the full image is first enhanced by division with the signal modelling the soft-tissue distribution. Based on the result, a radiologist might decide to zoom in on a given image section. The full restoration method is then applied to that region of interest. Indeed, full image deconvolution is often unnecessary since enhanced small-scale details are not visible at large scale; only the section of interest is processed which is more efficient.
我们开发了一种用于X光片的恢复方法，该方法可增强图像清晰度，并揭示最初隐藏在软组织眩光中的骨骼微观结构。该方法有两个步骤：首先使用理查森-露西算法对图像进行去卷积，然后用模拟软组织分布的信号进行除法运算，以提高整体对比度。每个步骤都有其优点，但恢复方法的强大之处在于它们的结合。该方法的独特之处在于其在处理的每个步骤都依赖先验信息。我们在多个光束能量下测量并通过解析建模了低剂量气体微带X光探测器的点扩散函数。我们测量了这些图像中局部图像强度与噪声方差之间 的关系。软组织信号也使用最小曲率滤波技术进行建模。然后将这些结果组合到一个图像去卷积过程中，该过程使用小波滤波来减少恢复噪声，同时保留增强的小尺度特征。该方法成功应用于人体躯干模型的图像，并改善了骨骼和软组织中小细节的对比度。在我们分析的70和140 kVp图像中，我们测得信噪比平均提高了54%，对比噪声比平均提高了105%。该方法旨在通过依赖两个层次的视觉检查来促进X光片的分析。首先通过用模拟软组织分布的信号进行除法运算来增强全图像的对比度。基于该结果，放射科医生可能会决定放大给定的图像区域。然后将完整的恢复方法应用于该感兴趣区域。实际上，通常不需要对全图像进行去卷积，因为增强的小尺度细节在大尺度下是不可见的；只处理感兴趣的部分会更有效。
