Seeger Matthias, Nickisch Hannes, Pohmann Rolf, Schölkopf Bernhard
Department of Computer Science, Saarland University, Saarbrücken, Germany.
Magn Reson Med. 2010 Jan;63(1):116-26. doi: 10.1002/mrm.22180.
The optimization of k-space sampling for nonlinear sparse MRI reconstruction is phrased as a Bayesian experimental design problem. Bayesian inference is approximated by a novel relaxation to standard signal processing primitives, resulting in an efficient optimization algorithm for Cartesian and spiral trajectories. On clinical resolution brain image data from a Siemens 3T scanner, automatically optimized trajectories lead to significantly improved images, compared to standard low-pass, equispaced, or variable density randomized designs. Insights into the nonlinear design optimization problem for MRI are given.
用于非线性稀疏MRI重建的k空间采样优化被表述为一个贝叶斯实验设计问题。通过对标准信号处理原语的一种新颖松弛来近似贝叶斯推理，从而得到一种针对笛卡尔和螺旋轨迹的高效优化算法。在来自西门子3T扫描仪的临床分辨率脑图像数据上，与标准低通、等间距或可变密度随机设计相比，自动优化的轨迹能显著改善图像。文中给出了对MRI非线性设计优化问题的见解。
