24 December 2013 Using motion correction to improve real-time cardiac MRI reconstruction
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Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90671I (2013) https://doi.org/10.1117/12.2051669
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom
Cardiac gating or breath-hold MRI acquisition is challenging. In particular, data collected in a short amount of time might be insufficient for the diagnosis of patients with impaired breath-holding capabilities and/or arrhythmia. A major challenge in cardiac MRI is the motion of the heart itself, the pulsate blood flow, and the respiratory motion. Furthermore, the motion of the diaphragm in the chest moving up and down gets translated to the heart when a patient breathes. Therefore, artifacts arise due to the changes in signal intensity or phase as a function of time, resulting in blurry images. This paper describes a novel reconstruction strategy for real time cardiac MRI without requiring the use of an electro-cardiogram or of breath holding. In this research we focused on automation and evaluation of the performance of our proposed method in real time MRI data to ensure a good basis for the signal extraction. Hence, it assists in the reconstruction. The proposed method enables one to extract cardiac beating waveforms directly from real-time cardiac MRI series collected from freely breathing patients and without cardiac gating. Our method only requires minimal user involvement as initialization step. Thereafter, the method follows the registered area in every frame and updates itself.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
E. Bilgazyev, E. Bilgazyev, I. Uyanik, I. Uyanik, M. Unan, M. Unan, Dipan Shah, Dipan Shah, Nikolaos V. Tsekos, Nikolaos V. Tsekos, E. L. Leiss, E. L. Leiss, } "Using motion correction to improve real-time cardiac MRI reconstruction ", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90671I (24 December 2013); doi: 10.1117/12.2051669; https://doi.org/10.1117/12.2051669

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