Implementation of the proposed methods described in Vectorial total generalized variation for accelerated multi-channel multi-contrast MRI. For more complete technical details, please refer to my Ph.D. thesis entitled Acquisition and reconstruction methods for magnetic resonance imaging.
Purpose To develop and implement an efficient reconstruction technique to improve accelerated multi-channel multi-contrast MRI.
Theory and Methods The vectorial total generalized variation (TGV) operator is used as a regularizer for the sensitivity encoding (SENSE) technique to improve image quality of multi-channel multi-contrast MRI. The alternating direction method of multipliers (ADMM) is used to efficiently reconstruct the data. The performance of the proposed method (MC-TGV-SENSE) is assessed on two healthy volunteers at several acceleration factors.
Results As demonstrated on the in vivo results, MC-TGV-SENSE had the lowest root-mean-square error (RMSE), highest structural similarity index, and best visual quality at all acceleration factors, compared to other methods under consideration. MC-TGV-SENSE yielded up to 17.3% relative RMSE reduction compared to the widely used total variation regularized SENSE. Furthermore, we observed that the reconstruction time of MC-TGV-SENSE is reduced by approximately a factor of two with comparable RMSEs by using the proposed ADMM-based algorithm as opposed to the more commonly used Chambolle–Pock primal-dual algorithm for the TGV-based reconstruction.
Conclusion MC-TGV-SENSE is a better alternative than the existing reconstruction methods for accelerated multi-channel multi-contrast MRI. The proposed method exploits shared information among the images (MC), mitigates staircasing artifacts (TGV), and uses the encoding power of multiple receiver coils (SENSE).
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script_TSE3Contrasts.m is the main file.
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MC_TGV_SENSE_SB.m contains the ADMM implementation of multi-channel MRI with multi-contrast TGV regularization.
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MC_TV_SENSE_SB.m contains the ADMM implementation of multi-channel MRI with multi-contrast TV regularization.
The data files are too big to be included in this repository. Please contact me directly for example data.