Compressed sensing (CS) has been demonstrated to accelerate MRI acquisitions by reconstructing sparse images of good quality from highly undersampled data. Motion during MR scans can cause inconsistencies in k‐space data, resulting in strong motion artifacts in the reconstructed images. For CS to be useful in these applications, motion correction techniques need to be combined with the undersampled reconstruction. Recently, joint motion correction and CS approaches have been proposed to partially correct for effects of motion. However, the main limitation of these approaches is that they can only correct for affine deformations. In this work, we propose a novel motion corrected CS framework for free‐breathing dynamic cardiac MRI that incorporates a general motion correction formulation directly into the CS reconstruction. This framework can correct for arbitrary affine or nonrigid motion in the CS reconstructed cardiac images, while simultaneously benefiting from highly accelerated MR acquisition. The application of this approach is demonstrated both in simulations and in vivo data for 2D respiratory self‐gated free‐breathing cardiac CINE MRI, using a golden angle radial acquisition. Results show that this approach allows for the reconstruction of respiratory motion corrected cardiac CINE images with similar quality to breath‐held acquisitions. Magn Reson Med 70:504–516, 2013.

A reconstruction strategy is proposed for physiological motion correction, which overcomes many limitations of existing techniques. The method is based on a general framework allowing correction for arbitrary motion–nonrigid or affine, making it suitable for cardiac or abdominal imaging, in the context of multiple coil, arbitrarily sampled acquisition. A model is required to predict motion in the field of view at each sample time point, based on prior knowledge provided by external sensors. A theoretical study is carried out to analyze the influence of motion prediction errors. Small errors are shown to propagate linearly in that reconstruction algorithm, and thus induce a reconstruction residue that is bounded (stability). Furthermore, optimization of the motion model is proposed in order to minimize this residue. This leads to reformulating reconstruction as two inverse problems which are coupled: motion‐compensated reconstruction (known motion) and model optimization (known image). A fixed‐point multiresolution scheme is described for inverting these two coupled systems. This framework is shown to allow fully autocalibrated reconstructions, as coil sensitivities and motion model coefficients are determined directly from the corrupted raw data. The theory is validated with real cardiac and abdominal data from healthy volunteers, acquired in free‐breathing. Magn Reson Med 60:146–157, 2008.

Evaluation of small bowel motility from two‐dimensional dynamic MRI is of increasing interest for the assessment of segmental diseases such as Crohn's disease. We propose to use nonrigid image registration for automated motility analysis. Registration is particularly difficult when localized intensity changes occur (e.g., due to intraluminal flow or through‐plane motion). Therefore, displacements were extracted using a joint registration and modeling of intensity changes. Results were analyzed in 10 patients with known or suspected Crohn's disease. Bowel wall displacements were assessed by tracking bowel cross‐sectional markers over time. Automated tracking with the proposed technique was in good agreement with manual tracking (similar bias, improved standard deviations). The ability to quantify different grades of bowel motility was investigated by comparing direct motion measures (i.e., changes in bowel cross‐sectional diameter or area) and various parametric maps, e.g., based on the Jacobian of the measured displacements with expert grading. Among these measures, the standard deviation over time of the Jacobian was the best at predicting grades from 1 (moving normally) to 4 (not moving at all) in 50 bowel regions with normal and pathologically reduced motility, yielding 93% correct classification with a 1‐class tolerance (i.e., same score as the expert ± 1). Magn Reson Med, 2012.

A system was developed for real-time electrocardiogram (ECG) analysis and artifact correction during magnetic resonance (MR) scanning, to improve patient monitoring and triggering of MR data acquisitions. Based on the assumption that artifact production by magnetic field gradient switching represents a linear time invariant process, a noise cancellation (NC) method is applied to ECG artifact linear prediction. This linear prediction is performed using a digital finite impulse response (FIR) matrix, that is computed employing ECG and gradient waveforms recorded during a training scan. The FIR filters are used during further scanning to predict artifacts by convolution of the gradient waveforms. Subtracting the artifacts from the raw ECG signal produces the correction with minimal delay. Validation of the system was performed both off-line, using prerecorded signals, and under actual examination conditions. The method is implemented using a specially designed Signal Analyzer and Event Controller (SAEC) computer and electronics. Real-time operation was demonstrated at 1 kHz with a delay of only 1 ms introduced by the processing. The system opens the possibility of automatic monitoring algorithms for electrophysiological signals in the MR environment