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An Adaptive MR-CT Registration Method for MRI-guided Prostate Cancer Radiotherapy

机译：MRI指导的前列腺癌放射治疗的自适应MR-CT配准方法

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摘要

Magnetic Resonance images (MRI) have superior soft tissue contrast compared with CT images. Therefore, MRI might be a better imaging modality to differentiate the prostate from surrounding normal organs. Methods to accurately register MRI to simulation CT images are essential, as we transition the use of MRI into the routine clinic setting. In this study, we present a finite element method (FEM) to improve the performance of a commercially available, B-spline-based registration algorithm in the prostate region. Specifically, prostate contours were delineated independently on ten MRI and CT images using the Eclipse treatment planning system. Each pair of MRI and CT images was registered with the B-spline-based algorithm implemented in the VelocityAI system. A bounding box that contains the prostate volume in the CT image was selected and partitioned into a tetrahedral mesh. An adaptive finite element method was then developed to adjust the displacement vector fields (DVFs) of the B-spline-based registrations within the box. The B-spline and FEM-based registrations were evaluated based on the variations of prostate volume and tumor centroid, the unbalanced energy of the generated DVFs, and the clarity of the reconstructed anatomical structures. The results showed that the volumes of the prostate contours warped with the B-spline-based DVFs changed 10.2% on average, relative to the volumes of the prostate contours on the original MR images. This discrepancy was reduced to 1.5% for the FEM-based DVFs. The average unbalanced energy was 2.65 and 0.38 mJ/cm3, and the prostate centroid deviation was 0.37 and 0.28 cm, for the B-spline and FEM-based registrations, respectively. Different from the B-spline-warped MR images, the FEM-warped MR images have clear boundaries between prostates and bladders, and their internal prostatic structures are consistent with those of the original MR images. In summary, the developed adaptive FEM method preserves the prostate volume during the transformation between the MR and CT images and improves the accuracy of the B-spline registrations in the prostate region. The approach will be valuable for development of high-quality MRI-guided radiation therapy.
机译：与CT图像相比，磁共振图像（MRI）具有出色的软组织对比度。因此，MRI可能是将前列腺与周围正常器官区分开的更好的成像方式。准确地将MRI记录到模拟CT图像中的方法至关重要，因为我们将MRI的使用过渡到常规的临床环境中。在这项研究中，我们提出了一种有限元方法（FEM），以改善在前列腺区域中基于B样条的市售配准算法的性能。具体来说，使用Eclipse治疗计划系统在十张MRI和CT图像上分别描绘出前列腺轮廓。每对MRI和CT图像都通过VelocityAI系统中实现的基于B样条的算法进行配准。选择在CT图像中包含前列腺体积的边界框，并将其划分为四面体网格。然后，开发了一种自适应有限元方法来调整框内基于B样条的套准的位移矢量场（DVF）。 B样条和基于FEM的配准是基于前列腺体积和肿瘤质心的变化，生成的DVF的不平衡能量以及重建的解剖结构的清晰度进行评估的。结果显示，相对于原始MR图像上的前列腺轮廓体积，使用基于B样条的DVF弯曲的前列腺轮廓体积平均变化10.2％。对于基于FEM的DVF，这种差异降低到1.5％。对于B样条和基于FEM的配准，平均不平衡能量分别为2.65和0.38 mJ / cm 3 ，前列腺质心偏差分别为0.37和0.28 cm。与B样条曲线扭曲的MR图像不同，FEM扭曲的MR图像在前列腺和膀胱之间具有清晰的边界，其内部前列腺结构与原始MR图像一致。总之，开发的自适应有限元方法可以在MR和CT图像之间的转换过程中保留前列腺体积，并提高B样条在前列腺区域中的定位准确性。该方法对于开发高质量的MRI指导的放射治疗将是有价值的。

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