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Achieving accurate automated image registration for prostate radiotherapy.

机译：为前列腺放射治疗实现准确的自动图像配准。

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Prostate cancer is the second most common type of cancer in men in the United States affecting about 0.4% of men. If detected early, prostate cancer is treatable. Three dimensional (3D) conformal radiation therapy, which is employed for the treatment of localized cancers, allows radiation dose escalation and improved local tumor control while sparing surrounding healthy tissue. 3D conformal radiation therapy is susceptible, however, to geometric (targeting) uncertainties in treatment delivery. Geometric uncertainties can be reduced by matching low resolution two-dimensional (2D) portal images acquired during treatment to a higher resolution 3D planning computed tomography (CT) image to determine deviations from the intended patient setup.; The objective of this study was to develop an automated 3D to 2D image registration framework to determine patient setup deviations in radiation therapy. The underlying assumption of this study is that CT and portal images are related modalities. Three registration methods, line-based correlation, intensity-based correlation and maximum likelihood matching, were formulated based on the imaging physics and different assumptions on signal and noise characteristics of the images.; The methods were validated on accurately collected kilovoltage cone beam CT (CBCT) images and kilovoltage radiographs and megavoltage portal images of an anthropomorphic phantom of the pelvis. Additional validation of the line-based algorithm was performed on phantom, simulated and patient data acquired with conventional CT scanners. Estimates of setup deviations obtained with the three methods were accurate to at least 0.25mm and 0.1° on the CBCT and kilovoltage radiographs and at least 0.7mm and 0.2° on the CBCT and portal images. The accuracy of the line-based correlation algorithm on the additional data tested was better than 1mm and 1°.; This study achieved setup verification accuracy that far exceeds current clinical expectations. The results obtained support the assumptions and arguments made. Namely, the relationship between kilovoltage and megavoltage images can be approximated to be linear, that useful signal is present at the higher spatial frequencies of the images employed, and that the noise in the images can be assumed to be independent and Gaussian.
机译：前列腺癌是美国男性中第二大最常见的癌症，影响约0.4％的男性。如果及早发现，则可以治愈前列腺癌。用于治疗局部癌症的三维（3D）保形放射疗法可在保持周围健康组织的同时，提高放射剂量并改善局部肿瘤控制。但是，3D保形放射疗法易受治疗交付中几何（目标）不确定性的影响。通过将在治疗期间获取的低分辨率二维（2D）门图像与更高分辨率的3D规划计算机断层扫描（CT）图像相匹配，以确定与预期患者设置之间的偏差，可以减少几何不确定性。这项研究的目的是开发一种自动的3D到2D图像配准框架，以确定放射治疗中的患者设置偏差。这项研究的基本假设是CT和门户图像是相关的方式。基于成像物理学和对图像信号和噪声特性的不同假设，提出了三种配准方法：基于线的相关性，基于强度的相关性和最大似然匹配。该方法在骨盆拟人化体模的精确收集的千伏锥束CT（CBCT）图像，千伏X射线照片和兆伏门图像上得到了验证。基于行的算法的其他验证是对使用常规CT扫描仪采集的幻像，模拟和患者数据进行的。用这三种方法获得的设置偏差的估计值在CBCT和千伏射线照片上精确到至少0.25mm和0.1°，在CBCT和门户图像上至少精确到0.7mm和0.2°。基于行的相关算法在测试的其他数据上的精度优于1mm和1°。该研究获得的设置验证准确性远远超过当前的临床期望。获得的结果支持所作的假设和论点。即，千伏和兆伏图像之间的关系可以近似地是线性的，在所使用的图像的较高空间频率处存在有用信号，并且可以假定图像中的噪声是独立的和高斯的。

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