Vectorial total generalized variation for accelerated multi-channel multi-contrast MRI

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• 作者：Itthi Chatnuntawech^a ; ¹ ; ^{ichatnun@mit.edu" class="auth_mail" title="E-mail the corresponding author} ; Adrian Martin^a ; ^b ; ¹ ; ^{adrian.martin@urjc.es" class="auth_mail" title="E-mail the corresponding author} ; Berkin Bilgic^c ; ^d ; ^{berkin@nmr.mgh.harvard.edu" class="auth_mail" title="E-mail the corresponding author} ; Kawin Setsompop^c ; ^d ; ^{kawin@nmr.mgh.harvard.edu" class="auth_mail" title="E-mail the corresponding author} ; Elfar Adalsteinsson^a ; ^e ; ^f ; ^{elfar@mit.edu" class="auth_mail" title="E-mail the corresponding author} ; Emanuele Schiavi^b ; ^{emanuele.schiavi@urjc.es" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Multi-contrast magnetic resonance imaging ; Compressed sensing ; Parallel imaging ; Total generalized variation ; Alternating direction method of multipliers (ADMM)
• 刊名：Magnetic Resonance Imaging
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：34
• 期：8
• 页码：1161-1170
• 全文大小：1756 K

文摘

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).