- Poster presentation
- Open Access
Real-time low-latency self-calibrating grog for interventional mri
© Saybasili et al; licensee BioMed Central Ltd. 2010
- Published: 21 January 2010
- Weight Calculation
- Reconstruction Performance
- Coil Sensitivity
- Density Compensation
- Radial Acquisition
Self-Calibrating GROG (SC-GROG) is a GRAPPA Operator based gridding algorithm. When compared to existing non-Cartesian imaging methods, e.g. convolution gridding, SC-GROG is advantageous in many respects. First, the gridding kernel is derived from the non-Cartesian data itself, and there are no parameters to deal with (e.g. gridding kernel size, gridding kernel choice). Second, density compensation is straightforward (simple averaging). Third, it can successfully grid both undersampled and fully sampled datasets. Auto-calibrated, parameter-free imaging with SC-GROG is well suited to MRI guided interventions.
We present the first real-time low-latency implementation of the SC-GROG algorithm, RT-GROG, for multi-slice radial acquisitions to guide cardiovascular interventions.
SC-GROG's weights calculation and image reconstruction steps are decoupled to run asynchronously, and parallelized in C++ using OpenMP and Pthreads libraries. Per sample 2D weights that are normally calculated during gridding are pre-calculated and stored in a lookup table (LUT) to increase reconstruction performance.
Single frame reconstruction performance comparison between RT-GROG and SC-GROG (32 coil acquisition)
128 × 64
128 × 96
Weights calculation performance comparison between RT-GROG and SC-GROG (32 coil acquisition)
128 × 64
128 × 96
We present the first real-time SC-GROG implementation. Weights calculation and reconstruction processes are decoupled to run asynchronously and parallelized. An LUT improves reconstruction performance by avoiding the need for the time-consuming 2D gridding weights calculation. RT-GROG reconstruction was always faster than the data acquisition even for 32 channel data sets. Additionally, weights update performance is fast enough to track changes in coil sensitivity profiles for better adaptability. Our implementation can be easily adapted to spiral imaging.
This article is published under license to BioMed Central Ltd.