References
1 Andre JB, et al. Towards quantifying the prevalence, severity, and cost associated with patient motion during clinical MR examinations. J Am Coll Radiol 2015; 12: 689-695.
2 Batchelor PG, et al. Matrix description of general motion correction applied to multishot images. Magn Reson Med. 2005; 54: 1273–80.
3 Atkinson D, et al. Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion. IEEE Trans Med Imag. 1997; 16: 903–10.
4 Manke D, et al. Novel prospective respiratory motion correction approach for free-breathing coronary MR angiography using a patient-adapted affine motion model. Magn Reson Med. 2003; 50: 122–131.
5 Montalt-Tordera J, et al. Machine learning in Magnetic Resonance Imaging: Image reconstruction. Physica Medica. 2003; 83: 79-87.
6 Chen Y, et al. AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis. Proc of the IEEE. 2022; 110(2): 224 – 245.
7 Zeng C, et al. Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI. Front Neuroinform 2020.
8 Küstner T, et al. Retrospective correction of motion-affected MR images using deep learning frameworks. Magn Reson Med. 2019; 82(4): 1527-1540.
9 Pawar K, et al. Suppressing motion artefacts in MRI using an Inception-ResNet network with motion simulation augmentation. NMR in Biomed. 2019; 35(4).
10 Tamada D, et al. Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver. Magn Reson Med Sci. 2020; 19(1): 64-76.
11 Qi H, et al. End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA. Magn Reson Med. 2021: 86(4); 1983-1996.
The information on this website is not intended for consumers. It is directed exclusively for healthcare professionals and persons who are engaging in purchasing or the business of wholesaling therapeutic goods (in accordance with the Therapeutic Goods Advertising Code Instrument 2021).
By selecting “Continue” you are indicating that you are the intended audience. Click “Cancel” to be redirected to the Canon Medical Systems global website.
© Canon Medical Systems ANZ Pty Limited.
© Canon Medical Systems ANZ Pty Limited.