Space-time variant weighted regularization in compressed sensing cardiac cine MRI
| Autor: | Alejandro Godino-Moya, Claudia Prieto, Rosa-María Menchón-Lara, Javier Royuela-del-Val, Muhammad Usman, Carlos Alberola-López, Marcos Martín-Fernández |
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| Rok vydání: | 2019 |
| Předmět: |
Image quality
Computer science media_common.quotation_subject Fast Fourier transform Normal Distribution ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Biomedical Engineering Biophysics Magnetic Resonance Imaging Cine Fidelity Regularization (mathematics) Time 030218 nuclear medicine & medical imaging Motion 03 medical and health sciences 0302 clinical medicine Reference Values Image Processing Computer-Assisted Humans Leverage (statistics) Radiology Nuclear Medicine and imaging Computer vision media_common Fourier Analysis business.industry Space time Heart Reconstruction algorithm Data Compression Compressed sensing Artificial intelligence business Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Magnetic Resonance Imaging. 58:44-55 |
| ISSN: | 0730-725X |
| DOI: | 10.1016/j.mri.2019.01.005 |
| Popis: | Purpose To analyze the impact on image quality and motion fidelity of a motion-weighted space-time variant regularization term in compressed sensing cardiac cine MRI. Methods k-t SPARSE-SENSE with temporal total variation (tTV) is used as the base reconstruction algorithm. Motion in the dynamic image is estimated by means of a robust registration technique for non-rigid motion. The resulting deformation fields are used to leverage the regularization term. The results are compared with standard k-t SPARSE-SENSE with tTV regularization as well as with an improved version of this algorithm that makes use of tTV and temporal Fast Fourier Transform regularization in x-f domain. Results The proposed method with space-time variant regularization provides higher motion fidelity and image quality than the two previously reported methods. Difference images between undersampled reconstruction and fully sampled reference images show less systematic errors with the proposed approach. Conclusions Usage of a space-time variant regularization offers reconstructions with better image quality than the state of the art approaches used for comparison. |
| Databáze: | OpenAIRE |
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