Recent theoretical advances in the field of compressive sampling-also referred to as compressed sensing (CS)-hold considerable promise for practical applications in MRI, but the fundamental condition of sparsity required in the CS framework is usually not fulfilled in MR images. However, in dynamic imaging, data sparsity can readily be introduced by applying the Fourier transformation along the temporal dimension assuming that only parts of the field-of-view (FOV) change at a high temporal rate while other parts remain stationary or change slowly. The second condition for CS, random sampling, can easily be realized by randomly skipping phase-encoding lines in each dynamic frame. In this work, the feasibility of the CS framework for accelerated dynamic MRI is assessed. Simulated datasets are used to compare the reconstruction results for different reduction factors, noise, and sparsity levels. In vivo cardiac cine data and Fourier-encoded velocity data of the carotid artery are used to test the reconstruction performance relative to k-t broad-use linear acquisition speed-up technique (k-t BLAST) reconstructions. Given sufficient data sparsity and base signal-to-noise ratio (SNR), CS is demonstrated to result in improved temporal fidelity compared to k-t BLAST reconstructions for the example data sets used in this work. Magn Reson Med 59:365-373, 2008.

This paper describes a method for 4D imaging, which is used to study respiratory organ motion, a key problem in various treatments. Whilst the commonly used imaging methods rely on simplified breathing patterns to acquire one breathing cycle, the proposed method was developed to study irregularities in organ motion during free breathing over tens of minutes. The method does not assume a constant breathing depth or even strict periodicity and does not depend on an external respiratory signal. Time-resolved 3D image sequences were reconstructed by retrospective stacking of dynamic 2D images using internal image-based sorting. The generic method is demonstrated for the liver and for the lung. Quantitative evaluations of the volume consistency show the advantages over one-dimensional measurements for image sorting. Dense deformation fields describing the respiratory motion were estimated from the reconstructed volumes using non-rigid 3D registration. All obtained motion fields showed variations in the range of minutes such as drifts and deformations, which changed both the exhalation position of the liver and the breathing pattern. The obtained motion data are used in proton therapy planning to evaluate dose delivery methodologies with respect to their motion sensitivity. Besides this application, the new possibilities of studying respiratory motion are valuable for other applications such as the evaluation of gating techniques with respect to residual motion.

Cardiac diffusion MRI based on stimulated-echo acquisition mode (STEAM) techniques is hampered by its inherent low signal-to-noise ratio (SNR) efficiency. Diffusion imaging using standard spin-echo (SE) techniques, on the other hand, offers higher SNRs but has been considered impractical for the beating heart due to excessive signal attenuation from cardiac bulk motion. In this work the effect of systolic cardiac motion on different diffusion-encoding schemes was studied in detail. Numerical simulations based on in vivo motion data (acquired by MRI tagging techniques) demonstrate an up to 10-fold decrease in bulk motion sensitivity of the diffusion encoding if the firstorder moment of the diffusion-encoding gradients is nullified. It is shown that the remaining systolic phase pattern on the myocardium does not influence the magnitude images if the spatial resolution is chosen to be higher than 4 mm. Given these relatively low resolution requirements, we obtained in vivo diffusion-weighted (DW) short-axis images from four healthy volunteers using an SE-based diffusion-encoding sequence with excitation and refocusing in orthogonal planes for field of view Key words: diffusion imaging; heart; spin echo; myocardial structure; motion sensitivity Diffusion tensor imaging (DTI) (1) can provide unique biologically and clinically relevant information that is not available from other imaging modalities. In neuroimaging, for example, paths of large water diffusion can be tracked to reveal the microstructure of the examined tissue (2,3). Several studies (4,5) have investigated the orientation of the white matter tracts of the brain in tumor patients by measuring the degree of diffusion anisotropy. Current myocardial imaging methods provide information about cardiac anatomy and perfusion, but the structural information about the myocardium remains to be assessed by diffusion imaging. Ex vivo studies have shown that DTI can reveal the 3D architecture of the myocardial fibers (6,7) without destruction of the sample. Furthermore, ischemic (8) and infarcted (9) myocardial regions can be quantified, and the diffusion information may complement data from perfusion studies. Nevertheless, special acquisition methods are necessary because the displacement due to diffusion is about three orders of magnitude smaller than cardiac bulk motion resulting from respiration and cardiac pulsation.Initially, the stimulated-echo acquisition mode (STEAM) (10) technique was proposed for cardiac diffusion imaging (11) in vivo. The STEAM technique encodes diffusion over an exact cardiac cycle to obtain insensitivity to bulk cardiac motion; however, in subsequent investigations it was found that such diffusion encoding interacts with myocardial strain (12). Therefore, methods to overcome this effect have been proposed (13-15). These methods require an additional strain measurement to either deconvolve the strain effect by computation (13,14) or to define a "sweet spot" in the cardiac cycle where strain effects cancel out (15). Recently, another stimu...