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Single-scan patient-specific scatter correction in computed tomography using peripheral detection of scatter and compressed sensing scatter retrieval

机译:在计算机断层摄影中使用扫描的外围检测和压缩传感散射检索进行单扫描患者特定的散射校正

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摘要

>Purpose: X-ray scatter results in a significant degradation of image quality in computed tomography (CT), representing a major limitation in cone-beam CT (CBCT) and large field-of-view diagnostic scanners. In this work, a novel scatter estimation and correction technique is proposed that utilizes peripheral detection of scatter during the patient scan to simultaneously acquire image and patient-specific scatter information in a single scan, and in conjunction with a proposed compressed sensing scatter recovery technique to reconstruct and correct for the patient-specific scatter in the projection space.>Methods: The method consists of the detection of patient scatter at the edges of the field of view (FOV) followed by measurement based compressed sensing recovery of the scatter through-out the projection space. In the prototype implementation, the kV x-ray source of the Varian TrueBeam OBI system was blocked at the edges of the projection FOV, and the image detector in the corresponding blocked region was used for scatter detection. The design enables image data acquisition of the projection data on the unblocked central region of and scatter data at the blocked boundary regions. For the initial scatter estimation on the central FOV, a prior consisting of a hybrid scatter model that combines the scatter interpolation method and scatter convolution model is estimated using the acquired scatter distribution on boundary region. With the hybrid scatter estimation model, compressed sensing optimization is performed to generate the scatter map by penalizing the L1 norm of the discrete cosine transform of scatter signal. The estimated scatter is subtracted from the projection data by soft-tuning, and the scatter-corrected CBCT volume is obtained by the conventional Feldkamp-Davis-Kress algorithm. Experimental studies using image quality and anthropomorphic phantoms on a Varian TrueBeam system were carried out to evaluate the performance of the proposed scheme.>Results: The scatter shading artifacts were markedly suppressed in the reconstructed images using the proposed method. On the Catphan©504 phantom, the proposed method reduced the error of CT number to 13 Hounsfield units, 10% of that without scatter correction, and increased the image contrast by a factor of 2 in high-contrast regions. On the anthropomorphic phantom, the spatial nonuniformity decreased from 10.8% to 6.8% after correction.>Conclusions: A novel scatter correction method, enabling unobstructed acquisition of the high frequency image data and concurrent detection of the patient-specific low frequency scatter data at the edges of the FOV, is proposed and validated in this work. Relative to blocker based techniques, rather than obstructing the central portion of the FOV which degrades and limits the image reconstruction, compressed sensing is used to solve for the scatter from detection of scatter at the periphery of the FOV, enabling for the highest quality reconstruction in the central region and robust patient-specific scatter correction.
机译:>目的:X射线散射会导致计算机断层扫描(CT)的图像质量显着下降,这代表了锥形束CT(CBCT)和大型视野诊断扫描仪的主要局限。在这项工作中,提出了一种新颖的散射估计和校正技术,该技术在患者扫描期间利用散射的外围检测来在单次扫描中同时获取图像和特定于患者的散射信息,并结合建议的压缩传感散射恢复技术来实现。重建并校正投影空间中特定于患者的散射。>方法:该方法包括检测视野(FOV)边缘的患者散射,然后进行基于测量的压缩传感恢复散布在整个投影空间中。在原型实现中,瓦里安TrueBeam OBI系统的kV X射线源在投影FOV的边缘被遮挡,并且相应遮挡区域中的图像检测器用于散射检测。该设计使得能够在投影的数据的未阻塞的中心区域上捕获图像数据,并在阻塞的边界区域上散射数据。对于中心FOV上的初始散射估计,使用获取的边界区域上的散射分布来估计由结合了散射插值方法和散射卷积模型的混合散射模型组成的先验。使用混合散射估计模型,通过惩罚散射信号离散余弦变换的L1范数,执行压缩感测优化以生成散射图。通过软调谐从投影数据中减去估计的散射,并通过常规的Feldkamp-Davis-Kress算法获得散射校正的CBCT体积。在Varian TrueBeam系统上使用图像质量和拟人化幻像进行了实验研究,以评估该方案的性能。>结果:使用该方法,可显着抑制重建图像中的散射阴影伪影。在Catphan©504幻像上,所提出的方法将CT数的误差减少到13个Hounsfield单位,不进行散射校正的误差为10%,并且在高对比度区域中将图像对比度提高了2倍。在拟人模型上,校正后空间不均匀性从10.8%降低到6.8%。>结论:一种新颖的散射校正方法,可以无障碍地采集高频图像数据并同时检测患者特定的在这项工作中提出并验证了FOV边缘的低频散射数据。相对于基于分块器的技术,压缩的传感用于检测FOV外围的散射,从而解决散射问题,而不是阻塞会降低和限制图像重建的FOV中心部分,从而实现最高质量的重建。中心区域和针对患者的可靠散布校正。

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