Blind retrospective motion correction of MR images

2013 ◽  
Vol 70 (6) ◽  
pp. 1608-1618 ◽  
Author(s):  
Alexander Loktyushin ◽  
Hannes Nickisch ◽  
Rolf Pohmann ◽  
Bernhard Schölkopf
Keyword(s):  
Motion Correction ◽  
Mr Images

Automatic motion correction of clinical shoulder MR images

1999 ◽  
Author(s):  
Armando Manduca ◽  
Kiaran P. McGee ◽  
Edward B. Welch ◽  
Joel P. Felmlee ◽  
Richard L. Ehman
Keyword(s):  
Motion Correction ◽  
Mr Images

scholarly journals MR-PET head motion correction based on co-registration of multi-contrast MR images

2018 ◽  
Author(s):  
Zhaolin Chen ◽  
Francesco Sforazzini ◽  
Jakub Baran ◽  
Thomas Close ◽  
N. Jon Shah ◽  
...  
Keyword(s):  
Motion Correction ◽  
Estimation Method ◽  
Correction Method ◽  
Head Motion ◽  
Mr Images ◽  
Image Sharpness ◽  
Positron Emission ◽  
Quantitative Accuracy ◽  
Slow Infusion ◽  
Pet Image Reconstruction

AbstractHead motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous Magnetic Resonance-Positron Emission Tomography (MR-PET) makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images. In this paper, we introduce a fully automated PET motion correction method, MR-guided MAF, based on the co-registration of multi-contrast MR images. The performance of the MR-guided MAF method was evaluated using MR-PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18-F]FDG). Compared with conventional methods, MR guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR-PET scanners. The fully automated motion estimation method has been implemented as a publicly available web-service.

The shoulder: adaptive motion correction of MR images.

Radiology ◽  
1997 ◽  
Vol 205 (2) ◽  
pp. 541-545 ◽  
Author(s):  
K P McGee ◽  
R C Grimm ◽  
J P Felmlee ◽  
J R Rydberg ◽  
S J Riederer ◽  
...  
Keyword(s):  
Motion Correction ◽  
Mr Images ◽  
Adaptive Motion

Retrospective Motion Correction of Magnitude-Input MR Images

2015 ◽  
pp. 3-12 ◽  
Author(s):  
Alexander Loktyushin ◽  
Christian Schuler ◽  
Klaus Scheffler ◽  
Bernhard Schölkopf
Keyword(s):  
Motion Correction ◽  
Mr Images

scholarly journals MR-PET head motion correction based on co-registration of multicontrast MR images

2019 ◽  
Author(s):  
Zhaolin Chen ◽  
Francesco Sforazzini ◽  
Jakub Baran ◽  
Thomas Close ◽  
Nadim Jon Shah ◽  
...  
Keyword(s):  
Motion Correction ◽  
Head Motion ◽  
Mr Images

Blind multirigid retrospective motion correction of MR images

2014 ◽  
Vol 73 (4) ◽  
pp. 1457-1468 ◽  
Author(s):  
Alexander Loktyushin ◽  
Hannes Nickisch ◽  
Rolf Pohmann ◽  
Bernhard Schölkopf
Keyword(s):  
Motion Correction ◽  
Mr Images

The impact of motion correction on lesion characterization in DCE breast MR images

2011 ◽  
Author(s):  
Martin Bergtholdt ◽  
Sven Kabus ◽  
Rafael Wiemker ◽  
Thomas Buelow
Keyword(s):  
Motion Correction ◽  
Mr Images ◽  
Breast Mr ◽  
Lesion Characterization ◽  
The Impact

scholarly journals Brain Tumor IDH, 1p/19q, and MGMT Molecular Classification Using MRI-based Deep Learning: Effect of Motion and Motion Correction

2020 ◽  
Author(s):  
Sahil S. Nalawade ◽  
Fang F. Yu ◽  
Chandan Ganesh Bangalore Yogananda ◽  
Gowtham K. Murugesan ◽  
Bhavya R. Shah ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motion Correction ◽  
Molecular Classification ◽  
Motion Artifact ◽  
Learning Networks ◽  
Clinical Implementation ◽  
Mr Images ◽  
Patient Motion ◽  
Classifier Performance ◽  
Molecular Profiles

AbstractDeep learning has shown promise for predicting glioma molecular profiles using MR images. Before clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. We sought to evaluate the effects of motion artifact on glioma marker classifier performance and develop a deep learning motion correction network to restore classification accuracies. T2w images and molecular information were retrieved from the TCIA and TCGA databases. Three-fold cross-validation was used to train and test the motion correction network on artifact-corrupted images. We then compared the performance of three glioma marker classifiers (IDH mutation, 1p/19q codeletion, and MGMT methylation) using motion-corrupted and motion-corrected images. Glioma marker classifier performance decreased markedly with increasing motion corruption. Applying motion correction effectively restored classification accuracy for even the most motion-corrupted images. For IDH classification, an accuracy of 99% was achieved, representing a new benchmark in non-invasive image-based IDH classification and exceeding the original performance of the network. Robust motion correction can enable high accuracy in deep learning MRI-based molecular marker classification rivaling tissue-based characterization.STATEMENT OF SIGNIFICANCEDeep learning networks have shown promise for predicting molecular profiles of gliomas using MR images. We demonstrate that patient motion artifact, which is frequently encountered in the clinic, can significantly impair the performance of these algorithms. The application of robust motion correction algorithms can restore the performance of these networks, rivaling tissue-based characterization.

scholarly journals Robust motion correction and outlier rejection of in vivo functional MR images of the fetal brain and placenta during maternal hyperoxia

2015 ◽  
Author(s):  
Wonsang You ◽  
Ahmed Serag ◽  
Iordanis E. Evangelou ◽  
Nickie Andescavage ◽  
Catherine Limperopoulos
Keyword(s):  
Motion Correction ◽  
Fetal Brain ◽  
Outlier Rejection ◽  
Mr Images
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