Department of Imaging/AIM program, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA.
Med Phys. 2009 Dec;36(12):5467-79. doi: 10.1118/1.3253301.
Cardiac computed tomography (CT) and single photon emission computed tomography (SPECT) provide clinically complementary information in the diagnosis of coronary artery disease (CAD). Fused anatomical and physiological data acquired sequentially on separate scanners can be coregistered to accurately diagnose CAD in specific coronary vessels.
A fully automated registration method is presented utilizing geometric features from a reliable segmentation of gated myocardial perfusion SPECT (MPS) volumes, where regions of myocardium and blood pools are extracted and used as an anatomical mask to de-emphasize the inhomogeneities of intensity distribution caused by perfusion defects and physiological variations. A multiresolution approach is employed to represent coarse-to-fine details of both volumes. The extracted voxels from each level are aligned using a similarity measure with a piecewise constant image model and minimized using a gradient descent method. The authors then perform limited nonlinear registration of gated MPS to adjust for phase differences by automatic cardiac phase matching between CT and MPS. For phase matching, they incorporate nonlinear registration using thin-plate-spline-based warping. Rigid registration has been compared with manual alignment (n=45) on 20 stress/rest MPS and coronary CTA data sets acquired from two different sites and five stress CT perfusion data sets. Phase matching was also compared to expert visual assessment.
As compared with manual alignment obtained from two expert observers, the mean and standard deviation of absolute registration errors of the proposed method for MPS were 4.3 +/- 3.5, 3.6 +/- 2.6, and 3.6 +/- 2.1 mm for translation and 2.1 +/- 3.2 degrees, 0.3 +/- 0.8 degree, and 0.7 +/- 1.2 degrees for rotation at site A and 3.8 +/- 2.7, 4.0 +/- 2.9, and 2.2 +/- 1.8mm for translation and 1.1 +/- 2.0 degrees, 1.6 +/- 3.1 degrees, and 1.9 +/- 3.8 degrees for rotation at site B. The results for CT perfusion were 3.0 +/- 2.9, 3.5 +/- 2.4, and 2.8 +/- 1.0 mm for translation and 3.0 +/- 2.4 degrees, 0.6 +/- 0.9 degree, and 1.2 +/- 1.3 degrees for rotation. The registration error shows that the proposed method achieves registration accuracy of less than 1 voxel (6.4 x 6.4 x 6.4 mm) misalignment. The proposed method was robust for different initializations in the range from -80 to 70, -80 to 70, and -50 to 50 mm in the x-, y-, and z-axes, respectively. Validation results of finding best matching phase showed that best matching phases were not different by more than two phases, as visually determined.
The authors have developed a fast and fully automated method for registration of contrast cardiac CT with gated MPS which includes nonlinear cardiac phase matching and is capable of registering these modalities with accuracy <10 mm in 87% of the cases.
心脏计算机断层扫描（CT）和单光子发射计算机断层扫描（SPECT）在冠状动脉疾病（CAD）的诊断中提供临床互补信息。在单独的扫描仪上顺序采集的融合解剖和生理数据可以进行配准，以准确诊断特定冠状动脉中的 CAD。
提出了一种完全自动化的配准方法，利用门控心肌灌注 SPECT（MPS）容积可靠分割的几何特征，提取心肌和血池区域，并用作解剖掩模，以减轻灌注缺陷和生理变化引起的强度分布不均匀性。采用多分辨率方法表示两个体积的粗到细细节。从每个水平提取的体素使用具有分段常数图像模型的相似性度量进行对齐，并使用梯度下降方法最小化。然后，作者通过 CT 和 MPS 之间的自动心脏相位匹配对门控 MPS 进行有限的非线性配准，以调整相位差。对于相位匹配，他们使用基于薄板样条的变形进行非线性配准。在从两个不同站点采集的 20 个应激/休息 MPS 和冠状动脉 CTA 数据集以及五个应激 CT 灌注数据集上，将刚性配准与手动对准（n=45）进行了比较。相位匹配也与专家视觉评估进行了比较。
与两名专家观察员从手动对齐获得的结果相比，该方法用于 MPS 的平均和标准偏差的绝对配准误差为 4.3 +/- 3.5、3.6 +/- 2.6 和 3.6 +/- 2.1mm 用于平移，2.1 +/- 3.2 度、0.3 +/- 0.8 度和 0.7 +/- 1.2 度用于旋转，站点 A 和 3.8 +/- 2.7、4.0 +/- 2.9 和 2.2 +/- 1.8mm 用于平移，1.1 +/- 2.0 度、1.6 +/- 3.1 度和 1.9 +/- 3.8 度用于旋转，站点 B。CT 灌注的结果分别为 3.0 +/- 2.9、3.5 +/- 2.4 和 2.8 +/- 1.0mm 用于平移，3.0 +/- 2.4 度、0.6 +/- 0.9 度和 1.2 +/- 1.3 度用于旋转。注册误差表明，该方法的注册精度小于 1 个体素（6.4 x 6.4 x 6.4mm）的偏差。该方法在 x、y 和 z 轴的范围分别为-80 到 70、-80 到 70 和-50 到 50mm 时，对于不同的初始化是稳健的。最佳匹配相位的验证结果表明，最佳匹配相位相差不超过两个相位，与视觉确定的结果相同。
作者开发了一种快速且完全自动化的方法，用于将对比心脏 CT 与门控 MPS 配准，其中包括非线性心脏相位匹配，并且能够以<10mm 的精度在 87%的情况下对这些模式进行配准。
