Long-range medical image registration through generalized mutual information (GMI): towards a fully automatic volumetric alignment.
Long-range medical image registration through generalized mutual information (GMI): towards a fully automatic volumetric alignment.
复制标题
通过广义互信息 (GMI) 进行远程医学图像配准：实现全自动体积对齐。
DOI：
10.1088/1361-6560/ac5298
复制
发表时间：
2022
影响因子：
3.5
通讯作者：
Murta,LuizOtavio
中科院分区：
文献类型：
--
作者：
Vianna,ViniciusPavanelli;Murta,LuizOtavio
目的 互信息 (MI) 被整合为一种强大的相似性度量，常用于医学图像配准。尽管 MI 提供了强大的配准功能，但当配准图像所需的变换由于 MI 局部最小值陷阱而太大时，它通常会失败。本文提出并评估了广义 MI (GMI)，使用 Tsallis 熵来改进仿射配准。方法我们使用可分离仿射变换来评估 GMI 度量输出空间，以寻求更好的梯度空间。平移使用的范围是 150 mm，旋转使用的范围是 360 mm，缩放使用的范围是 [0.5, 2]，偏斜使用的范围是 [− 1, 1]。使用梯度和轮廓曲线的 3D 可视化来评估数据。还使用模拟梯度下降算法来计算配准能力。然后通过对来自 HCP 数据集的大脑 T1 和 T2 MRI 的实际配准进行蒙特卡洛模拟来测试检测到的改进。 主要结果结果显示配准范围显着延长，在度量空间中没有局部最小值，平移配准能力为 100%，旋转配准能力为 88.2%，缩放配准能力为 100%，偏度配准能力为 100%。 Tsallis 熵在 1113 个双随机受试者 T1 和 T2 脑 MRI 的 2000 次平移配准的蒙特卡洛模拟中获得了 99.75% 的成功，而香农熵的成功率为 56.5%。 意义 Tsallis 熵可以通过远程平移配准、低成本插值和通过更好的梯度空间实现更快的配准来改善脑 MRI MI 仿射配准。
ObjectiveMutual information (MI) is consolidated as a robust similarity metric often used for medical image registration. Although MI provides a robust registration, it usually fails when the transform needed to register an image is too large due to MI local minima traps. This paper proposes and evaluates Generalized MI (GMI), using Tsallis entropy, to improve affine registration.ApproachWe assessed the GMI metric output space using separable affine transforms to seek a better gradient space. The range used was 150 mm for translations, 360 for rotations,[0.5, 2] for scaling, and [− 1, 1] for skewness. The data were evaluated using 3D visualization of gradient and contour curves. A simulated gradient descent algorithm was also used to calculate the registration capability. The improvements detected were then tested through Monte Carlo simulation of actual registrations with brain T1 and T2 MRI from the HCP dataset.Main resultsResults show significantly prolonged registration ranges, without local minima in the metric space, with a registration capability of 100% for translations, 88.2% for rotations, 100% for scaling and 100% for skewness. Tsallis entropy obtained 99.75% success in the Monte Carlo simulation of 2000 translation registrations with 1113 double randomized subjects T1 and T2 brain MRI against 56.5% success for the Shannon entropy.SignificanceTsallis entropy can improve brain MRI MI affine registration with long-range translation registration, lower-cost interpolation, and faster registrations through a better gradient space.