Presentation on theme: "A MATLAB Toolbox for Parallel Imaging using Multiple Phased Array Coils Swati D. Rane, Jim X. Ji Magnetic Resonance Systems Laboratory, Department of Electrical."— Presentation transcript:
A MATLAB Toolbox for Parallel Imaging using Multiple Phased Array Coils Swati D. Rane, Jim X. Ji Magnetic Resonance Systems Laboratory, Department of Electrical Engineering, Texas A&M University Parallel Magnetic Resonance Imaging (MRI) uses an array of receivers/ transceivers to accelerate imaging speed, by reducing the phase encodings. The image is reconstructed using different methods such as SENSE , PILS , SMASH , GRAPPA , SPACE RIP , SEA  and their variations, utilizing complimentary information from all the channels. Parallel Magnetic Resonance Imaging Need of a Toolbox for Parallel MRI Quality of the reconstructed image by depends on: Receiver coil array configuration and coil localization k-space coverage Parallel Imaging technique used for reconstruction Optimality of the reconstruction can be evaluated on the basis of: Signal-to-Noise Ratio (SNR) Artifact Power Resolution ‘g’ factor (for SENSE) or numerical conditions Computational complexity There is a need of a tool To help select the optimal method for a given imaging environment To provide a platform for developing new algorithms To facilitate the learning/ testing of parallel imaging methods Data Input: Simulated coil sensitivities and k-space data Acquired/ real data collected from the MR scanner Coil Sensitivity Function: In simulation, coil maps are generated with a linear array of receivers with Gaussian profiles or a non-linear array of receivers with 2D Gaussian profiles. Biot- Savart’s Law* Coil sensitivity is estimated by Use of reference scans and divide by a body coil image Use of extra calibration lines and Sum-Of-Squares technique Using singular value decomposition Image Reconstruction: Evaluation of Reconstruction Techniques Method 2: Method 3 ( with two acquisitions): S 1 = mean signal intensity in the ROI of the one image SD 1-2 = std. deviation in the ROI of the subtraction image Use of different phantoms to check degradation Fig.3: Resolution phantoms Fig.2: SNR Calculation: Selection of region of interest (ROI) and noise(RON) Conclusion A software tool has been developed in MATLAB to analyze parallel imaging methods on the basis of SNR, resolution, artifact power and computational complexity. The toolbox can be used as a learning or testing tool and as a platform for developing new imaging methods. References The MATLAB Toolbox Fig.1: Block Diagram of the developed toolbox Filtering for noise reduction by Polynomial filtering Windowing Median filtering Wavelet denoising SENSE: 1D SENSE, Regularized SENSE, 2D SENSE* PILS: SMASH: Basic SMASH, AUTO-SMASH GRAPPA: Multiple block implementation SPACE RIP: Variable density sampling and reconstruction Signal-to-Noise Ratio (SNR): Method 1: Artifact Power : ‘g’ factor for SENSE: Resolution:  Pruessmann K., et al., MRM, 42: , Nov  Grisworld M., et al., MRM, 44: , Oct  Sodickson D., et al., MRM, 38: ,  Grisworld M., et al., MRM, 47: , June  Kyriakos W., et al., MRM, 44: , Aug  Wright S., et al., Proc. Of 2 nd Joint EMBS/BEMS Conference, Oct  Kellman P., et al., IEEE Proc., Intl. Symposium On Biomedical Imaging, July  Walsh D., et al., MRM, 43: , Sept  Hsuan-Lin F., et al., MRM, 51: ,  Jakob P., et al., MAGMA, 7:42:54,  Firbank M., et al., Phys.Med.Biol, 44:N261-N264, ROI RON. x = point by point multiplication S = sensitivity encoding matrix Ψ = noise correlation matrix * Yet to be done Sensitivity Estimation Filtering Data Input - Simulated data - Acquired data Improved Reconstruction - Iterative SOS Reconstruction - Regularized SENSE - AUTO-SMASH Performance Analysis - SNR - Artifact Power - ‘g’ factor calculation - Resolution - Computations Reconstruction SENSE Harmonics- fitting Gaussian fitting SMASH SPACE RIP GRAPPA PILS  Weiger M., et al., MAGMA, 14:1-19, March 2002.