Purpose. To introduce a methodology for the reconstruction of multi-shot, multi-slice magnetic resonance imaging able to cope with both within-plane and through-plane rigid motion and to describe its application in structural brain imaging. Theory and Methods. The method alternates between motion estimation and reconstruction using a common objective function for both. Estimates of three-dimensional motion states for each shot and slice are gradually refined by improving on the fit of current reconstructions to the partial k-space information from multiple coils. Overlapped slices and super-resolution allow recovery of through-plane motion and outlier rejection discards artifacted shots. The method is applied to T2 and T1 brain scans acquired in different views. Results. The procedure has greatly diminished artifacts in a database of 1883 neonatal image volumes, as assessed by image quality metrics and visual inspection. Examples showing the ability to correct for motion and robustness against damaged shots are provided. Combination of motion corrected reconstructions for different views has shown further artifact suppression and resolution recovery. Conclusion. The proposed method addresses the problem of rigid motion in multi-shot multi-slice anatomical brain scans. Tests on a large collection of potentially corrupted datasets have shown a remarkable image quality improvement.


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    Title :

    Three-dimensional motion corrected sensitivity encoding reconstruction for multi-shot multi-slice MRI: Application to neonatal brain imaging


    Contributors:

    Publication date :

    2018-03-01


    Remarks:

    Cordero Grande , L , Hughes , E J , Hutter , J , Price , A N & Hajnal , J V 2018 , ' Three-dimensional motion corrected sensitivity encoding reconstruction for multi-shot multi-slice MRI : Application to neonatal brain imaging ' , Magnetic Resonance in Medicine , vol. 79 , no. 3 , pp. 1365-1376 . https://doi.org/10.1002/mrm.26796



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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