The "hidden noise" problem in MR image reconstruction.
The "hidden noise" problem in MR image reconstruction.
复制标题
MR图像重建中的“隐藏噪声”问题。
DOI：
10.1002/mrm.30100
复制
发表时间：
2024
影响因子：
3.3
通讯作者：
Haldar,JustinP
中科院分区：
文献类型：
--
作者：
Wang,Jiayang;An,Di;Haldar,JustinP
现代图像重建方法的性能通常使用定量误差指标来判断，如均方根误差和结构相似性指数，这些指标通过将重建图像与完全采样的参考数据进行比较来计算。在实践中，参考数据将包含噪声，并不是真正的黄金标准。在这项工作中，我们证明了参考数据中存在的“隐藏噪声”可以在很大程度上混淆用于对不同图像重建结果进行排序的标准方法。我们检查了使用典型噪声参考数据获得的性能指标与使用更高质量参考数据获得的性能指标之间是否存在相关性。在性能指标上，与更高质量的参考数据最佳匹配的重建与与典型的噪声参考数据最佳匹配的重建有很大的不同。如果使用关于噪声参考数据的性能来选择采用哪些重建方法/参数，则这导致次优重建结果。这些问题减少时，采用替代误差指标，更好地考虑noise.ConclusionReference数据包含隐藏的噪声可以大大误导图像重建方法的排名时，使用传统的误差指标，但这个问题可以减轻与替代误差指标。
PurposeThe performance of modern image reconstruction methods is commonly judged using quantitative error metrics like root mean squared‐error and the structural similarity index, which are calculated by comparing reconstructed images against fully sampled reference data. In practice, the reference data will contain noise and is not a true gold standard. In this work, we demonstrate that the “hidden noise” present in reference data can substantially confound standard approaches for ranking different image reconstruction results.MethodsUsing both experimental and simulated k‐space data and several different image reconstruction techniques, we examined whether there was correlation between performance metrics obtained with typical noisy reference data versus those obtained with higher‐quality reference data.ResultsFor conventional performance metrics, the reconstructions that matched best with the higher‐quality reference data were substantially different from the reconstructions that matched best with typical noisy reference data. This leads to suboptimal reconstruction results if the performance with respect to noisy reference data is used to select which reconstruction methods/parameters to employ. These issues were reduced when employing alternative error metrics that better account for noise.ConclusionReference data containing hidden noise can substantially mislead the ranking of image reconstruction methods when using conventional error metrics, but this issue can be mitigated with alternative error metrics.
影响因子：
3.3
作者：
ROEMER, PB;EDELSTEIN, WA;MUELLER, OM
通讯作者：
MUELLER, OM
影响因子：
3.3
作者：
Lustig, Michael;Donoho, David;Pauly, John M.
通讯作者：
Pauly, John M.