Compressed sensing in hyperpolarized3He Lung MRI
| Autor: | S. R. Parnell, Jim M. Wild, Martin H. Deppe, K.J. Lee, Juan Parra-Robles, Salma Ajraoui |
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| Rok vydání: | 2010 |
| Předmět: |
Adult
Male Image processing Middle Aged Helium Magnetic Resonance Imaging Imaging Three-Dimensional Wavelet Nuclear magnetic resonance Compressed sensing Pulmonary Emphysema Signal-to-noise ratio (imaging) Temporal resolution Healthy volunteers Image Processing Computer-Assisted Humans Effective diffusion coefficient Female Radiology Nuclear Medicine and imaging Prospective Studies Lung Lung ventilation Algorithms Mathematics Biomedical engineering |
| Zdroj: | Magnetic Resonance in Medicine. 63:1059-1069 |
| ISSN: | 1522-2594 0740-3194 |
| DOI: | 10.1002/mrm.22302 |
| Popis: | In this work, the application of compressed sensing techniques to the acquisition and reconstruction of hyperpolarized (3)He lung MR images was investigated. The sparsity of (3)He lung images in the wavelet domain was investigated through simulations based on fully sampled Cartesian two-dimensional and three-dimensional (3)He lung ventilation images, and the k-spaces of 2D and 3D images were undersampled randomly and reconstructed by minimizing the L1 norm. The simulation results show that temporal resolution can be readily improved by a factor of 2 for two-dimensional and 4 to 5 for three-dimensional ventilation imaging with (3)He with the levels of signal to noise ratio (SNR) (approximately 19) typically obtained. The feasibility of producing accurate functional apparent diffusion coefficient (ADC) maps from undersampled data acquired with fewer radiofrequency pulses was also demonstrated, with the preservation of quantitative information (mean ADC(cs) approximately mean ADC(full) approximately 0.16 cm(2) sec(-1)). Prospective acquisition of 2-fold undersampled two-dimensional (3)He images with a compressed sensing k-space pattern was then demonstrated in a healthy volunteer, and the results were compared to the equivalent fully sampled images (SNR(cs) = 34, SNR(full) = 19). |
| Databáze: | OpenAIRE |
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