Retracted: Reducing motion artifacts in 4D MR images using principal component analysis (PCA) combined with linear polynomial fitting model.

ABSTRACT: We have previously developed a retrospective 4D-MRI technique using body area as the respiratory surrogate, but generally, the reconstructed 4D MR images suffer from severe or mild artifacts mainly caused by irregular motion during image acquisition. Those image artifacts may potentially affect the accuracy of tumor target delineation or the shape representation of surrounding nontarget tissues and organs. So the purpose of this study is to propose an approach employing principal component analysis (PCA), combined with a linear polynomial fitting model, to remodel the displacement vector fields (DVFs) obtained from deformable image registration (DIR), with the main goal of reducing the motion artifacts in 4D MR images. Seven patients with hepatocellular carcinoma (2/7) or liver metastases

SUBMITTER: Yang J 

PROVIDER: S-EPMC5690092 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature