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Modeling patterns of anatomical deformations in prostate patients undergoing radiation therapy with an endorectal balloon

机译:前列腺患者患者含有胸腔疗法的前列腺患者解剖变形的建模模式

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External beam radiation therapy (EBRT) treats cancer by delivering daily fractions of radiation to a target volume. For prostate cancer, the target undergoes day-to-day variations in position, volume, and shape. For stereotactic photon and for proton EBRT, endorectal balloons (ERBs) can be used to limit variations. To date, patterns of non-rigid variations for patients with ERB have not been modeled. We extracted and modeled the patient-specific patterns of variations, using regularly acquired CT-images, non-rigid point cloud registration, and principal component analysis (PCA). For each patient, a non-rigid point-set registration method, called Coherent Point Drift, (CPD) was used to automatically generate landmark correspondences between all target shapes. To ensure accurate registrations, we tested and validated CPD by identifying parameter values leading to the smallest registration errors (surface matching error 0.13±0.09 mm). PCA demonstrated that 88±3.2% of the target motion could be explained using only 4 principal modes. The most dominant component of target motion is a squeezing and stretching in the anterior-posterior and superior-inferior directions. A PCA model of daily landmark displacements, generated using 6 to 10 CT-scans, could explain well the target motion for the CT-scans not included in the model (modeling error decreased from 1.83±0.8 mm for 6 CT-scans to 1.6±0.7 mm for 10 CT-scans). PCA modeling error was smaller than the naive approximation by the mean shape (approximation error 2.66±0.59 mm). Future work will investigate the use of the PCA-model to improve the accuracy of EBRT techniques that are highly susceptible to anatomical variations such as, proton therapy.
机译:外部光束放射治疗(EBRT)通过将每日辐射馏分递送到靶体积来治疗癌症。对于前列腺癌,目标在日常变化的位置,体积和形状进行。对于立体定位光子和用于质子EBRT,胸腔气球(ERB)可用于限制变化。迄今为止,ERB患者的非刚性变化模式尚未进行建模。我们用定期获得的CT图像,非刚度云登记和主成分分析(PCA)利用定期获得的患者特定的变化模式进行了提取和建模。对于每个患者,使用一种称为相干点漂移(CPD)的非刚性点设置登记方法来自动生成所有目标形状之间的地标对应。为确保准确的注册,我们通过识别导致最小注册错误的参数值来测试和验证CPD(表面匹配误差0.13±0.09 mm)。 PCA证明,只有4个主模式,可以解释88±3.2%的目标运动。目标运动的最主导组件是挤压和伸展在前后 - 后和上下方向上。使用6到10 CT扫描生成的日常地标位移的PCA模型可以很好地解释模型中未包含的CT扫描的目标运动(建模误差从1.83±0.8 mm降低,6 CT扫描到1.6° 10 CT扫描0.7毫米)。 PCA建模误差小于平均形状的天真近似(近似误差2.66±0.59 mm)。未来的工作将调查PCA模型的使用,以提高EBRT技术的准确性,这些技术高易受诸如质子疗法的解剖学变化。

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