DiFiR-CT: Distance field representation to resolve motion artifacts in computed tomography.
DiFiR-CT: Distance field representation to resolve motion artifacts in computed tomography.
复制标题
DOI：
10.1002/mp.16157
复制
发表时间：
2023-03
期刊：
影响因子：
3.8
通讯作者：
Contijoch, Francisco
中科院分区：
文献类型：
--
作者：
Gupta, Kunal;Colvert, Brendan;Chen, Zhennong;Contijoch, Francisco
数据采集过程中的运动会导致CT重建中的伪影。在心脏成像等情况下，不仅运动是不可避免的，而且评估对象的运动具有临床意义。减少运动伪影通常是通过开发具有更快机架旋转的系统或通过测量和/或估计位移的算法来实现的。然而，由于物理条件的限制以及估计非刚性的、随时间变化的和患者特定的运动场的挑战，这些方法的成功有限。开发一种新的重建方法，该方法生成时间分辨的、无伪影的图像，而无需对运动进行估计或显式建模。我们描述了一种按合成分析的方法，该方法逐步回归与所获得的正弦图一致的解。在我们的方法中，我们关注对象边界的移动。边界不仅是图像伪影的来源，而且对象边界可以同时用于表示对象及其随时间的运动，而不需要显式的运动模型。我们用符号距离函数(SDF)来表示对象边界，该函数可以用神经网络有效地建模。因此，可以在空间和时间平滑约束下执行优化，而不需要显式的运动估计。我们展示了DifiR-CT在三种运动复杂性不断增加的成像场景中的应用：小圆圈的平移、椭圆形直径的心形变化和复杂的拓扑变形。与滤波反投影相比，DiFiR-CT可为所有三个运动提供高质量的图像重建，而无需调整超参数或改变结构。我们还评估了DifiR-CT对获取的正弦图中的噪声的稳健性，并发现其重建在广泛的噪声水平范围内都是准确的。最后，我们演示了如何将该方法用于多强度场景，并说明了提供真实初始化的初始分割的重要性。代码和补充电影可在https://kunalmgupta.github.io/projects/DiFiR-CT.html.上找到投影数据可以用于准确地估计时间演变的场景，而不需要使用神经隐式表示和合成分析方法的显式运动估计。
Motion during data acquisition leads to artifacts in computed tomography (CT) reconstructions. In cases such as cardiac imaging, not only is motion unavoidable, but evaluating the motion of the object is of clinical interest. Reducing motion artifacts has typically been achieved by developing systems with faster gantry rotation or via algorithms which measure and/or estimate the displacement. However, these approaches have had limited success due to both physical constraints as well as the challenge of estimating non-rigid, temporally varying, and patient-specific motion fields. To develop a novel reconstruction method which generates time-resolved, artifact-free images without estimation or explicit modeling of the motion. We describe an analysis-by-synthesis approach which progressively regresses a solution consistent with the acquired sinogram. In our method, we focus on the movement of object boundaries. Not only are the boundaries the source of image artifacts, but object boundaries can simultaneously be used to represent both the object as well as its motion over time without need for an explicit motion model. We represent the object boundaries via a signed distance function (SDF) which can be efficiently modeled using neural networks. As a result, optimization can be performed under spatial and temporal smoothness constraints without the need for explicit motion estimation. We illustrate the utility of DiFiR-CT in three imaging scenarios with increasing motion complexity: translation of a small circle, heart-like change in an ellipse’s diameter, and a complex topological deformation. Compared to filtered backprojection, DiFiR-CT provides high quality image reconstruction for all three motions without hyperparameter tuning or change to the architecture. We also evaluate DiFiR-CT’s robustness to noise in the acquired sinogram and found its reconstruction to be accurate across a wide range of noise levels. Lastly, we demonstrate how the approach could be used for multi-intensity scenes and illustrate the importance of the initial segmentation providing a realistic initialization. Code and supplemental movies are available at https://kunalmgupta.github.io/projects/DiFiR-CT.html. Projection data can be used to accurately estimate a temporally-evolving scene without the need for explicit motion estimation using a neural implicit representation and analysis-by-synthesis approach.
登录
查看更多内容
DOI：
10.1016/0735-1097(90)90282-t
发表时间：
1990-03-15
影响因子：
24
作者：
AGATSTON, AS;JANOWITZ, WR;DETRANO, R
通讯作者：
DETRANO, R
影响因子：
5.4
作者：
Lin E;Alessio A
通讯作者：
Alessio A
影响因子：
10.9
作者：
Lossau (Nee Elss), T.;Nickisch, H.;Grass, M.
通讯作者：
Grass, M.
影响因子：
3.8
作者：
Kim, Seungeon;Chang, Yongjin;Ra, Jong Beom
通讯作者：
Ra, Jong Beom
影响因子：
3.8
作者：
McCollough, Cynthia H.;Schmidt, Bernhard;Flohr, Thomas G.
通讯作者：
Flohr, Thomas G.