An adapted fan volume sampling scheme for 3-D algebraic reconstruction in linear tomosynthesis
An adapted fan volume sampling scheme for 3-D algebraic reconstruction in linear tomosynthesis
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DOI：
10.1109/tns.2002.803683
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发表时间：
2002-10-01
影响因子：
1.8
通讯作者：
Magnin, I
中科院分区：
文献类型：
--
作者：
Bleuet, P;Guillemaud, R;Magnin, I
研究了X射线源沿有限直线沿着平移时，探测器运动和不运动时的重建过程。这个过程称为线性断层合成，导致有限的视角，这导致垂直空间分辨率差。为了提高这一分辨率，我们使用迭代代数重建方法，这是通常用于从减少数量的投影断层重建。对于有噪声的投影，这样的算法产生质量差的重建。为了防止这种情况，我们使用第一个对象的先验知识，由分段平滑约束。为了减少与重建和正则化过程相关的计算时间，我们引入了第二个几何先验知识，基于X射线源的线性轨迹。这种线性源轨迹允许我们在体积的扇形组织中重建一系列二维（2-D）平面。使用这种适应风扇体积采样方案，我们通过将初始三维（3-D）问题转化为一系列的2-D问题来减少计算时间。显然，算法变得直接可并行化。专注于特定的兴趣区域也变得更容易。正则化过程可以很容易地实现与该计划。我们使用实验预测来测试该算法。重建对象的质量是保守的，而计算时间大大减少，即使没有任何并行化的算法。
We study the reconstruction process when the X-ray source translates along a finite straight line, the detector moving or not. This process, called linear tomosynthesis, induces a limited angle of view, which causes the vertical spatial resolution to be poor. To improve this resolution, we use iterative algebraic reconstruction methods, which are commonly used for tomographic reconstruction from a reduced number of projections. With noisy projections, such algorithms produce poor quality reconstructions. To prevent this, we use a first object prior knowledge, consisting of piecewise smoothness constraint. To reduce the computation time associated with both reconstruction and regularization processes, we introduce a second geometrical prior knowledge, based on the linear trajectory of the X-ray source. This linear source trajectory allows us, to reconstruct a series of two-dimensional (2-D) planes in a fan organization of the volume. Using this adapted fan volume sampling scheme, we reduce the computation time by transforming the initial three-dimensional (3-D) problem into a series of 2-D problems. Obviously, the algorithm becomes directly parallelizable. Focusing on a particular region of interest becomes easier too. The regularization process can easily be implemented with this scheme. We test the algorithm using experimental projections. The quality of the reconstructed object is conserved, while the computation time is considerably reduced, even without any parallelization of the algorithm.