Deformable registration of CT and cone-beam CT with local intensity matching.
Deformable registration of CT and cone-beam CT with local intensity matching.
复制标题
具有局部强度匹配的CT和锥形CT的可变形登记。
DOI：
10.1088/1361-6560/aa4f6d
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发表时间：
2017-02-07
影响因子：
3.5
通讯作者：
Lee J
中科院分区：
文献类型：
--
作者：
Park S;Plishker W;Quon H;Wong J;Shekhar R;Lee J
锥束CT(CBCT)是图像引导放射治疗和外科手术中广泛使用的一种术中成像方式。CBCT重建通常使用短扫描，然后进行滤波反投影。当中面(源-探测器旋转平面)上的数据是完整的时，离面-中面经历了不同程度的信息缺失，计算的重建结果是近似的。这会在不同的层面上造成不同的重建伪影，因此阻碍了CT和CBCT之间的准确配准。本文提出了一种通过迭代匹配局部CT和CBCT强度来精确配准CT和CBCT的方法。我们通过逐片匹配局部强度直方图结合基于强度的可变形配准来校正CBCT强度。以交替的方式重复校正-配准步骤，直到结果图像收敛。我们将灰度匹配融合到三种不同的可变形配准方法中：B样条法、DEMONS法和光流法，这三种方法被广泛应用于CT-CBCT配准。所有这三种配准方法都在图形处理器(GPU)上实现，以实现高效的并行计算。我们在25个头颈癌病例上测试了所提出的方法，并将其性能与最先进的配准方法进行了比较。通过计算归一化互相关(NCC)、结构相似指数(SSIM)和目标配准误差(TRE)来评价配准性能。我们的方法产生的总体NCC为0.96，SSIM为0.94，TRE为2.26 mm，分别比现有方法高出9%、12%和27%。实验结果还表明，我们的方法性能一致，比现有算法更准确，计算效率也更高。
Cone-beam CT (CBCT) is a widely used intra-operative imaging modality in image-guided radiotherapy and surgery. A short scan followed by a filtered-backprojection is typically used for CBCT reconstruction. While data on the mid-plane (plane of source-detector rotation) is complete, off-mid-planes undergo different information deficiency and the computed reconstructions are approximate. This causes different reconstruction artifacts at off-mid-planes depending on slice locations, and therefore impedes accurate registration between CT and CBCT. In this paper, we propose a method to accurately register CT and CBCT by iteratively matching local CT and CBCT intensities. We correct CBCT intensities by matching local intensity histograms slice by slice in conjunction with intensity-based deformable registration. The correction-registration steps are repeated in alternating way until the result image converges. We integrate the intensity matching into three different deformable registration methods, B-spline, demons, and optical flow that are widely used for CT-CBCT registration. All three registration methods were implemented on a graphics processing unit (GPU) for efficient parallel computation. We tested the proposed methods on twenty five head and neck cancer cases and compared the performance with state-of-the-art registration methods. Normalized cross correlation (NCC), structural similarity index (SSIM), and target registration error (TRE) were computed to evaluate the registration performance. Our method produced overall NCC of 0.96, SSIM of 0.94, and TRE of 2.26mm, outperforming existing methods by 9%, 12%, and 27%, respectively. Experimental results also show that our method performs consistently and is more accurate than existing algorithms, and also computationally efficient.