Cline Christopher C, Chen Xiao, Mailhe Boris, Wang Qiu, Pfeuffer Josef, Nittka Mathias, Griswold Mark A, Speier Peter, Nadar Mariappan S
Medical Imaging Technologies, Siemens Medical Solutions USA, Inc., Princeton, NJ, USA; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA.
Medical Imaging Technologies, Siemens Medical Solutions USA, Inc., Princeton, NJ, USA.
Magn Reson Imaging. 2017 Sep;41:29-40. doi: 10.1016/j.mri.2017.07.007. Epub 2017 Jul 14.
Existing approaches for reconstruction of multiparametric maps with magnetic resonance fingerprinting (MRF) are currently limited by their estimation accuracy and reconstruction time. We aimed to address these issues with a novel combination of iterative reconstruction, fingerprint compression, additional regularization, and accelerated dictionary search methods. The pipeline described here, accelerated iterative reconstruction for magnetic resonance fingerprinting (AIR-MRF), was evaluated with simulations as well as phantom and in vivo scans. We found that the AIR-MRF pipeline provided reduced parameter estimation errors compared to non-iterative and other iterative methods, particularly at shorter sequence lengths. Accelerated dictionary search methods incorporated into the iterative pipeline reduced the reconstruction time at little cost of quality.
目前，利用磁共振指纹识别（MRF）重建多参数图谱的现有方法受到估计精度和重建时间的限制。我们旨在通过迭代重建、指纹压缩、附加正则化和加速字典搜索方法的新颖组合来解决这些问题。这里描述的流程，即磁共振指纹识别的加速迭代重建（AIR-MRF），通过模拟以及体模和活体扫描进行了评估。我们发现，与非迭代方法和其他迭代方法相比，AIR-MRF流程提供了更低的参数估计误差，尤其是在较短序列长度时。纳入迭代流程的加速字典搜索方法在几乎不影响质量的情况下减少了重建时间。
