Motion correction based reconstruction method for compressively sampled cardiac MR imaging |
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| Affiliation: | 1. Department of Electrical Engineering, Air University Islamabad, Pakistan;2. Department of Electronic Engineering, Faculty of Engineering & Technology International Islamic University, Islamabad, Pakistan;1. Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medical College, New York, NY, USA;2. Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea;3. Departments of Imaging and Medicine, Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA;4. CT and Nuclear Medicine Departments, Diagnóstico Maipú, Buenos Aires, Argentina;1. Department of Radiology, Osaka University Graduate School of Medicine;2. Global MR Applications and Workflow, GE Healthcare;3. Department of Radiology, Osaka Medical College;4. Department of Radiology, Osaka University Hospital;1. Imaging Research Laboratories, Robarts Research Institute, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada;2. Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada;3. Graduate Program in Biomedical Engineering, The University of Western Ontario, London, Ontario, Canada;1. Department of Radiology, Memorial Sloan Kettering Cancer Center;2. Department of Medical Physics, Memorial Sloan Kettering Cancer Center;3. Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center;4. Brain Tumor Center, Memorial Sloan Kettering Cancer Center;5. Department of Diagnostics, Imaging and Biomedical Technologies, GE Global Research |
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| Abstract: | Respiratory motion during Magnetic Resonance (MR) acquisition causes strong blurring artifacts in the reconstructed images. These artifacts become more pronounced when used with the fast imaging reconstruction techniques like compressed sensing (CS). Recently, an MR reconstruction technique has been done with the help of compressed sensing (CS), to provide good quality sparse images from the highly under-sampled k-space data. In order to maximize the benefits of CS, it is obvious to use CS with the motion corrected samples. In this paper, we propose a novel CS based motion corrected image reconstruction technique. First, k-space data have been assigned to different respiratory state with the help of frequency domain phase correlation method. Then, multiple sparsity constraints has been used to provide good quality reconstructed cardiac cine images with the highly under-sampled k-space data. The proposed method exploits the multiple sparsity constraints, in combination with demon based registration technique and a novel reconstruction technique to provide the final motion free images. The proposed method is very simple to implement in clinical settings as compared to existing motion corrected methods. The performance of the proposed method is examined using simulated data and clinical data. Results show that this method performs better than the reconstruction of CS based method of cardiac cine images. Different acceleration rates have been used to show the performance of the proposed method. |
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