Keywords: Motion Correction, AI/ML Image Reconstruction
Motivation: The irregular fetal motion during MRI acquisition result in loss of structural continuity and corrupted 3D volumetric information.
Goal(s): Herein, we propose a hybrid convolutional neural network (CNN) and Transformer based method for fetal brain MRI 3D volume reconstruction.
Approach: Firstly, a coarse-to-fine CNN and Transformer based method is proposed to solve arbitrary fetal motion correction of 2D slices. Secondly, an initial 3D volume is estimated by point spread function. Finally, we propose an encoder-decoder model to reconstruct high-resolution fetal brain MRI 3D volume.
Results: The experimental results demonstrate that the proposed method is effective and efficient.
Impact: The proposed fetal brain MRI 3D volume reconstruction method based on CNN and Transformer can solve arbitrary motion correction of 2D slices and reconstruct high-resolution fetal brain MRI 3D volumes effectively and efficiently.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords