基于数值优化的双输入延迟补偿定量肝脏灌注映射的快速线性最小二乘拟合。
Vastly accelerated linear least-squares fitting with numerical optimization for dual-input delay-compensated quantitative liver perfusion mapping.
机构信息
Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
Department of Radiology, Weill Cornell Medicine, New York, New York, USA.
出版信息
Magn Reson Med. 2018 Apr;79(4):2415-2421. doi: 10.1002/mrm.26888. Epub 2017 Aug 22.
PURPOSE
To propose an efficient algorithm to perform dual input compartment modeling for generating perfusion maps in the liver.
METHODS
We implemented whole field-of-view linear least squares (LLS) to fit a delay-compensated dual-input single-compartment model to very high temporal resolution (four frames per second) contrast-enhanced 3D liver data, to calculate kinetic parameter maps. Using simulated data and experimental data in healthy subjects and patients, whole-field LLS was compared with the conventional voxel-wise nonlinear least-squares (NLLS) approach in terms of accuracy, performance, and computation time.
RESULTS
Simulations showed good agreement between LLS and NLLS for a range of kinetic parameters. The whole-field LLS method allowed generating liver perfusion maps approximately 160-fold faster than voxel-wise NLLS, while obtaining similar perfusion parameters.
CONCLUSIONS
Delay-compensated dual-input liver perfusion analysis using whole-field LLS allows generating perfusion maps with a considerable speedup compared with conventional voxel-wise NLLS fitting. Magn Reson Med 79:2415-2421, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
目的
提出一种有效的算法，对肝脏进行双重输入室模型建模，以生成灌注图。
方法
我们采用全场线性最小二乘法（LLS），以延迟补偿的双输入单室模型拟合非常高时间分辨率（每秒四帧）的对比增强 3D 肝脏数据，以计算动力学参数图。使用模拟数据和健康受试者及患者的实验数据，在准确性、性能和计算时间方面，将全场 LLS 与传统的体素非线性最小二乘法（NLLS）进行比较。
结果
模拟结果表明，对于一系列动力学参数，LLS 与 NLLS 具有很好的一致性。与体素 NLLS 相比，全场 LLS 方法可以将肝脏灌注图的生成速度提高约 160 倍，同时获得相似的灌注参数。
结论
使用全场 LLS 进行延迟补偿的双输入肝脏灌注分析，可以与传统的体素 NLLS 拟合相比，显著提高灌注图的生成速度。磁共振医学 79：2415-2421，2018。© 2017 国际磁共振学会。
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