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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 kspaces 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) (~19) typically obtained. Hyperpolarized (HP) helium-3 gas MRI takes advantage of the nonequilibrium polarization achieved by optical pumping to provide high-resolution images of lung ventilation and function (1-3). The non-renewable longitudinal magnetization is depleted with application of radiofrequency (RF) excitations, and the relationship between the SNR, k-space sampling pattern, and the number of RF pulses is highly flip-angle dependent. Moreover, lung imaging requires rapid sequences to capture dynamic gas flow in the airways and ventilation volume during a breath hold (20 sec). Hence, HP 3 He MRI is a good candidate for undersampling schemes. In previous work, parallel RF encoding (4), radial (5), and spiral (6) methods have been used in human HP gas MRI of the lungs to accelerate the acquisition. These methods can be demanding on the hardware in that they require multiple receivers and implementation of robust non-Cartesian sequences. Recently, compressed sensing (CS) techniques have been applied to MRI acquisition and reconstruction. These methods were developed in the field of information theory by Donoho (7) and Candes et al. (8) and were recently applied to proton MRI (9) and spectroscopic imaging of HP 13 C (10) by Lustig and co-workers. The idea behind the theory is to reconstruct a subset of linear measurements, much smaller than the actual full data set, using a nonlinear method. With CS algorithms, the sparsity of MR images in a specific transform domain can be exploited in order to reconstruct images from undersampled k-space (9). The reconstruction relies on L1-norm minimization in a sparse transform space and the quality of reconstruction relies on the sparsity of the data.In this work, CS theory was applied to the reconstruction of subsampled Cartesian-encoded HP 3 He gas images of the lungs. The potential advantages and limitations of the method were investigated in the context of a potentially faster acquisition time with fewer RF pulses. First, the sparsity of lung images in the wavelet transform domain was investigated in order to validate the possibility of applying the CS method as a means of subsampling. Then, simulations were performed to investigate the feasibility of two-dimensional (2D) and three-dimensional (3D) Cartesian CS undersampling for 3 He lung MRI. The effect of reduction factor upon the quality of...

Transmit gain (B 1+) calibration is necessary for the adjustment of radiofrequency (RF) power levels to the desired flip angles. In proton MRI, this is generally an automated process before the actual scan without any user interaction. For other nuclei, it is usually time consuming and difficult, especially in the case of hyperpolarised MR. In the current work, transmit gain calibration was implemented on the basis of the Bloch-Siegert phase shift. From the same data, the centre frequency, line broadening and SNR could also be determined. The T(1) and B(0) insensitivity, and the wide range of B 1+ over which this technique is effective, make it well suited for nonproton applications. Examples are shown for hyperpolarised (13)C and (3)He applications.

Multiple-breath washout hyperpolarized (3)He MRI was used to calculate regional parametric images of fractional ventilation (r) as the ratio of fresh gas entering a volume unit to the total end inspiratory volume of the unit. Using a single dose of inhaled hyperpolarized gas and a total acquisition time of under 1 min, gas washout was measured by dynamic acquisitions during successive breaths with a fixed delay. A two-dimensional (2D) imaging protocol was investigated in four healthy subjects in the supine position, and in a second protocol the capability of extending the washout imaging to a three-dimensional (3D) acquisition covering the whole lungs was tested. During both protocols, subjects were breathing comfortably, only restricted by synchronization of breathing to the sequence timings. The 3D protocol was also successfully tested on one patient with cystic fibrosis. Mean r values from each volunteer were compared with global gas volume turnover, as calculated from flow measurement at the mouth divided by total lung volume (from MRI images), and a significant correlation (r = 0.74, P < 0.05) was found. The effects of gravity on R were investigated, and an average decrease in r of 5.5%/cm (Δr = 0.016 ± 0.006 cm(-1)) from posterior to anterior was found in the right lung. Intersubject reproducibility of r imaging with the 2D and 3D protocol was tested, and a significant correlation between repeated experiments was found in a pixel-by-pixel comparison. The proposed methods can be used to measure r on a regional basis.