Download presentation

Presentation is loading. Please wait.

1
**Evaluation of Reconstruction Techniques**

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 Coil Sensitivity Function: Artifact Power [2]: 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* 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 [1], PILS [2], SMASH [3], GRAPPA [4], SPACE RIP [5], SEA [6] and their variations, utilizing complimentary information from all the channels. 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 ‘g’ factor for SENSE: Need of a Toolbox for Parallel MRI .x = point by point multiplication S = sensitivity encoding matrix Ψ = noise correlation matrix Quality of the reconstructed image by depends on: Receiver coil array configuration and coil localization k-space coverage Parallel Imaging technique used for reconstruction Resolution: Filtering for noise reduction by Polynomial filtering Windowing Median filtering Wavelet denoising Use of different phantoms to check degradation 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 Image Reconstruction: SENSE: 1D SENSE, Regularized SENSE, 2D SENSE* PILS: Fig.3: Resolution phantoms SMASH: Basic SMASH, AUTO-SMASH 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 Conclusion SPACE RIP: Variable density sampling and reconstruction GRAPPA: Multiple block implementation A software tool has been developed in MATLAB to analyze parallel imaging methods on the basis of SNR, resolution, artifact power and computational complexity. * Yet to be done Evaluation of Reconstruction Techniques The MATLAB Toolbox The toolbox can be used as a learning or testing tool and as a platform for developing new imaging methods. Signal-to-Noise Ratio (SNR): 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 SENSE Harmonics- fitting Gaussian fitting SMASH SPACE RIP GRAPPA PILS References Method 1: [1] Pruessmann K., et al., MRM, 42: , Nov.1999. [2] Grisworld M., et al., MRM, 44: , Oct ROI [3] Sodickson D., et al., MRM, 38: , 1997. Fig.2: SNR Calculation: Selection of region of interest (ROI) and noise(RON) [4] Grisworld M., et al., MRM, 47: , June 2002. [5] Kyriakos W., et al., MRM, 44: , Aug RON [6] Wright S., et al., Proc. Of 2nd Joint EMBS/BEMS Conference, Oct Method 2: [7] Kellman P., et al., IEEE Proc., Intl. Symposium On Biomedical Imaging, July 2002. [8] Walsh D., et al., MRM, 43: , Sept Method 3 ( with two acquisitions): Fig.1: Block Diagram of the developed toolbox [9] Hsuan-Lin F., et al., MRM, 51: , 2004. [10] Jakob P., et al., MAGMA, 7:42:54, 1998. Data Input: Simulated coil sensitivities and k-space data Acquired/ real data collected from the MR scanner [11] Firbank M., et al., Phys.Med.Biol, 44:N261-N264, 1999. S1 = mean signal intensity in the ROI of the one image SD1-2 = std. deviation in the ROI of the subtraction image [12] Weiger M., et al., MAGMA, 14:1-19, March 2002.

Similar presentations

OK

Partial Parallel imaging (PPI) in MR for faster imaging IMA Compressed Sensing June, 2007 Acknowledgement: NIH Grants 5RO1CA092004 and 5P41RR008079, Pierre-Francois.

Partial Parallel imaging (PPI) in MR for faster imaging IMA Compressed Sensing June, 2007 Acknowledgement: NIH Grants 5RO1CA092004 and 5P41RR008079, Pierre-Francois.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Free ppt on science and technology Ppt on work and energy class 11 Presentations ppt online Ppt on dual power supply Ppt on power crisis in india Ppt on bodybuilding Ppt on library management system project in java Ppt on carl friedrich gauss facts Ppt on network switching devices Ppt on summary writing rubric