Saybasili Haris, Kellman Peter, Griswold Mark A, Derbyshire J Andrew, Guttman Michael A
National Institutes of Health, National Heart, Lung, and Blood Institute, DHHS, Bethesda, Maryland 20892-1061, USA.
Magn Reson Med. 2009 Jun;61(6):1425-33. doi: 10.1002/mrm.21922.
The temporal generalized autocalibrating partially parallel acquisitions (TGRAPPA) algorithm for parallel MRI was modified for real-time low latency imaging in interventional procedures using image domain, B(1)-weighted reconstruction. GRAPPA coefficients were calculated in k-space, but applied in the image domain after appropriate transformation. Convolution-like operations in k-space were thus avoided, resulting in improved reconstruction speed. Image domain GRAPPA weights were combined into composite unmixing coefficients using adaptive B(1)-map estimates and optimal noise weighting. Images were reconstructed by pixel-by-pixel multiplication in the image domain, rather than time-consuming convolution operations in k-space. Reconstruction and weight-set calculation computations were parallelized and implemented on a general-purpose multicore architecture. The weight calculation was performed asynchronously to the real-time image reconstruction using a dedicated parallel processing thread. The weight-set coefficients were computed in an adaptive manner with updates linked to changes in the imaging scan plane. In this implementation, reconstruction speed is not dependent on acceleration rate or GRAPPA kernel size.
用于并行磁共振成像的时间广义自校准部分并行采集(TGRAPPA)算法,通过使用图像域、B(1)加权重建进行了改进,以用于介入手术中的实时低延迟成像。GRAPPA系数在k空间中计算,但在经过适当变换后应用于图像域。这样就避免了k空间中的类卷积操作,从而提高了重建速度。利用自适应B(1)映射估计和最佳噪声加权,将图像域GRAPPA权重组合成复合解混系数。图像在图像域中通过逐像素乘法进行重建,而不是在k空间中进行耗时的卷积操作。重建和权重集计算在通用多核架构上并行实现。权重计算使用专用并行处理线程与实时图像重建异步执行。权重集系数以自适应方式计算,更新与成像扫描平面的变化相关联。在这种实现方式中,重建速度不依赖于加速率或GRAPPA内核大小。