imec-Vision Lab, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium.
Med Phys. 2021 Oct;48(10):6362-6374. doi: 10.1002/mp.14765. Epub 2021 Aug 18.
Adjoint image warping is an important tool to solve image reconstruction problems that warp the unknown image in the forward model. This includes four-dimensional computed tomography (4D-CT) models in which images are compared against recorded projection images of various time frames using image warping as a model of the motion. The inversion of these models requires the adjoint of image warping, which up to now has been substituted by approximations. We introduce an efficient implementation of the exact adjoints of multivariate spline based image warping, and compare it against previously used alternatives.
Using symbolic computer algebra, we computed a list of 64 polynomials that allow us to compute a matrix representation of trivariate cubic image warping. By combining an on-the-fly computation of this matrix with a parallelized implementation of columnwise matrix multiplication, we obtained an efficient, low memory implementation of the adjoint action of 3D cubic image warping. We used this operator in the solution of a previously proposed 4D-CT reconstruction model in which the image of a single subscan was compared against projection data of multiple subscans by warping and then projecting the image. We compared the properties of our exact adjoint with those of approximate adjoints by warping along inverted motion.
Our method requires halve the memory to store motion between subscans, compared to methods that need to compute and store an approximate inverse of the motion. It also avoids the computation time to invert the motion and the tunable parameter of the number of iterations used to perform this inversion. Yet, a similar and often better reconstruction quality was obtained in comparison with these more expensive methods, especially when the motion is large. When compared against a simpler method that is similar to ours in computational demands, our method achieves a higher reconstruction quality in general.
Our implementation of the exact adjoint of cubic image warping improves efficiency and provides accurate reconstructions.
伴随图像变形是解决图像重建问题的重要工具,该问题通过正向模型中的未知图像变形来比较记录的各时间帧投影图像。这包括四维计算机断层扫描(4D-CT)模型,其中通过图像变形作为运动模型来比较各时间帧的图像。这些模型的反演需要伴随图像变形,而到目前为止,这种伴随一直被近似取代。我们引入了基于多元样条的图像变形的精确伴随的有效实现,并与之前使用的替代方法进行了比较。
使用符号计算机代数,我们计算了一组 64 个多项式,这些多项式允许我们计算三变量三次图像变形的矩阵表示。通过实时计算该矩阵与列矩阵乘法的并行化实现相结合,我们获得了三变量三次图像变形伴随作用的高效、低内存实现。我们在先前提出的 4D-CT 重建模型中使用该运算符,该模型将单个子扫描的图像与多个子扫描的投影数据通过变形进行比较,然后对图像进行投影。我们通过沿反运动变形来比较我们的精确伴随与近似伴随的性质。
与需要计算和存储运动的近似逆的方法相比,我们的方法将存储子扫描之间的运动所需的内存减半。它还避免了计算运动逆的时间和用于执行此逆的迭代次数的可调参数。然而,与这些更昂贵的方法相比,通常可以获得相似甚至更好的重建质量,尤其是当运动较大时。与在计算需求上与我们相似的更简单的方法相比,我们的方法通常可以实现更高的重建质量。
我们对三次图像变形的精确伴随的实现提高了效率并提供了准确的重建。