Claudia Prieto

Motion corrected compressed sensing for free-breathing dynamic cardiac MRI

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.

Whole-heart coronary MR angiography with 2D self-navigated image reconstruction

Several self‐navigation techniques have been proposed to improve respiratory motion compensation in coronary MR angiography. In this work, we implemented a 2D self‐navigation method by using the startup profiles of a whole‐heart balanced Steady‐state free precession sequence, which are primarily used to catalyze the magnetization towards the steady‐state. To create 2D self‐navigation images (2DSN), we added phase encoding gradients to the startup profiles. With this approach we calculated foot–head and left–right motion and performed retrospective translational motion correction. The 2DSN images were reconstructed from 10 startup profiles acquired at the beginning of each shot. Nine healthy subjects were scanned, and the proposed method was compared to a 1D self‐navigation (1DSN) method with foot–head correction only. Foot–head correction was also performed with the diaphragmatic 1D pencil beam navigator (1Dnav) using a tracking factor of 0.6. 2DSN shows improved motion correction compared to 1DSN and 1Dnav for all coronary arteries and all subjects for the investigated diaphragmatic gating window of 10 mm. The visualized vessel length of the right coronary artery could be significantly improved with a multiple targeted 2D self‐navigation approach, compared to 2DSN method. Magn Reson Med, 2012.

k-t group sparse: A method for accelerating dynamic MRI

Compressed sensing (CS) is a data‐reduction technique that has been applied to speed up the acquisition in MRI. However, the use of this technique in dynamic MR applications has been limited in terms of the maximum achievable reduction factor. In general, noise‐like artefacts and bad temporal fidelity are visible in standard CS MRI reconstructions when high reduction factors are used. To increase the maximum achievable reduction factor, additional or prior information can be incorporated in the CS reconstruction. Here, a novel CS reconstruction method is proposed that exploits the structure within the sparse representation of a signal by enforcing the support components to be in the form of groups. These groups act like a constraint in the reconstruction. The information about the support region can be easily obtained from training data in dynamic MRI acquisitions. The proposed approach was tested in two‐dimensional cardiac cine MRI with both downsampled and undersampled data. Results show that higher acceleration factors (up to 9‐fold), with improved spatial and temporal quality, can be obtained with the proposed approach in comparison to the standard CS reconstructions. Magn Reson Med, 2011.

Highly efficient respiratory motion compensated free-breathing coronary mra using golden-step Cartesian acquisition

To develop an efficient 3D affine respiratory motion compensation framework for Cartesian whole‐heart coronary magnetic resonance angiography (MRA).

Nonrigid Motion Modeling of the Liver From 3-D Undersampled Self-Gated Golden-Radial Phase Encoded MRI

Magnetic resonance imaging (MRI) has been commonly used for guiding and planning image guided interventions since it provides excellent soft tissue visualization of anatomy and allows motion modeling to predict the position of target tissues during the procedure. However, MRI-based motion modeling remains challenging due to the difficulty of acquiring multiple motion-free 3-D respiratory phases with adequate contrast and spatial resolution. Here, we propose a novel retrospective respiratory gating scheme from a 3-D undersampled high-resolution MRI acquisition combined with fast and robust image registrations to model the nonrigid deformation of the liver. The acquisition takes advantage of the recently introduced golden-radial phase encoding (G-RPE) trajectory. G-RPE is self-gated, i.e., the respiratory signal can be derived from the acquired data itself, and allows retrospective reconstructions of multiple respiratory phases at any arbitrary respiratory position. Nonrigid motion modeling is applied to predict the liver deformation of an average breathing cycle. The proposed approach was validated on 10 healthy volunteers. Motion model accuracy was assessed using similarity-, surface-, and landmark-based validation methods, demonstrating precise model predictions with an overall target registration error of TRE = 1.70 ± 0.94 mm which is within the range of the acquired resolution.

3D undersampled golden-radial phase encoding for DCE-MRA using inherently regularized iterative SENSE.

One of the current limitations of dynamic contrast‐enhanced MR angiography is the requirement of both high spatial and high temporal resolution. Several undersampling techniques have been proposed to overcome this problem. However, in most of these methods the tradeoff between spatial and temporal resolution is constant for all the time frames and needs to be specified prior to data collection. This is not optimal for dynamic contrast‐enhanced MR angiography where the dynamics of the process are difficult to predict and the image quality requirements are changing during the bolus passage. Here, we propose a new highly undersampled approach that allows the retrospective adaptation of the spatial and temporal resolution. The method combines a three‐dimensional radial phase encoding trajectory with the golden angle profile order and non‐Cartesian Sensitivity Encoding (SENSE) reconstruction. Different regularization images, obtained from the same acquired data, are used to stabilize the non‐Cartesian SENSE reconstruction for the different phases of the bolus passage. The feasibility of the proposed method was demonstrated on a numerical phantom and in three‐dimensional intracranial dynamic contrast‐enhanced MR angiography of healthy volunteers. The acquired data were reconstructed retrospectively with temporal resolutions from 1.2 sec to 8.1 sec, providing a good depiction of small vessels, as well as distinction of different temporal phases. Magn Reson Med, 2010.

Whole‐Heart Coronary MRA with 3D Affine Motion Correction Using 3D Image‐Based Navigation

Robust motion correction is necessary to minimize respiratory motion artefacts in coronary MR angiography (CMRA). The state‐of‐the‐art method uses a 1D feet‐head translational motion correction approach, and data acquisition is limited to a small window in the respiratory cycle, which prolongs the scan by a factor of 2–3. The purpose of this work was to implement 3D affine motion correction for Cartesian whole‐heart CMRA using a 3D navigator (3D‐NAV) to allow for data acquisition throughout the whole respiratory cycle.

Improved UTE-based attenuation correction for cranial PET-MR using dynamic magnetic field monitoring

PURPOSE Ultrashort echo time (UTE) MRI has been proposed as a way to produce segmented attenuation maps for PET, as it provides contrast between bone, air, and soft tissue. However, UTE sequences require samples to be acquired during rapidly changing gradient fields, which makes the resulting images prone to eddy current artifacts. In this work it is demonstrated that this can lead to misclassification of tissues in segmented attenuation maps (AC maps) and that these effects can be corrected for by measuring the true k-space trajectories using a magnetic field camera. METHODS The k-space trajectories during a dual echo UTE sequence were measured using a dynamic magnetic field camera. UTE images were reconstructed using nominal trajectories and again using the measured trajectories. A numerical phantom was used to demonstrate the effect of reconstructing with incorrect trajectories. Images of an ovine leg phantom were reconstructed and segmented and the resulting attenuation maps were compared to a segmented map derived from a CT scan of the same phantom, using the Dice similarity measure. The feasibility of the proposed method was demonstrated in in vivo cranial imaging in five healthy volunteers. Simulated PET data were generated for one volunteer to show the impact of misclassifications on the PET reconstruction. RESULTS Images of the numerical phantom exhibited blurring and edge artifacts on the bone-tissue and air-tissue interfaces when nominal k-space trajectories were used, leading to misclassification of soft tissue as bone and misclassification of bone as air. Images of the tissue phantom and the in vivo cranial images exhibited the same artifacts. The artifacts were greatly reduced when the measured trajectories were used. For the tissue phantom, the Dice coefficient for bone in MR relative to CT was 0.616 using the nominal trajectories and 0.814 using the measured trajectories. The Dice coefficients for soft tissue were 0.933 and 0.934 for the nominal and measured cases, respectively. For air the corresponding figures were 0.991 and 0.993. Compared to an unattenuated reference image, the mean error in simulated PET uptake in the brain was 9.16% when AC maps derived from nominal trajectories was used, with errors in the SUV max for simulated lesions in the range of 7.17%-12.19%. Corresponding figures when AC maps derived from measured trajectories were used were 0.34% (mean error) and -0.21% to +1.81% (lesions). CONCLUSIONS Eddy current artifacts in UTE imaging can be corrected for by measuring the true k-space trajectories during a calibration scan and using them in subsequent image reconstructions. This improves the accuracy of segmented PET attenuation maps derived from UTE sequences and subsequent PET reconstruction.

Model-based reconstruction for cardiac cine MRI without ECG or breath holding.

This paper describes an acquisition and reconstruction strategy for cardiac cine MRI that does not require the use of electrocardiogram or breath holding. The method has similarities with self‐gated techniques as information about cardiac and respiratory motion is derived from the imaging sequence itself; here, by acquiring the center k‐space line at the beginning of each segment of a balanced steady‐state free precession sequence. However, the reconstruction step is fundamentally different: a generalized reconstruction by inversion of coupled systems is used instead of conventional gating. By correcting for nonrigid cardiac and respiratory motion, generalized reconstruction by inversion of coupled systems (GRICS) uses all acquired data, whereas gating rejects data acquired in certain motion states. The method relies on the processing and analysis of the k‐space central line data: local information from a 32‐channel cardiac coil is used in order to automatically extract eigenmodes of both cardiac and respiratory motion. In the GRICS framework, these eigenmodes are used as driving signals of a motion model. The motion model is defined piecewise, so that each cardiac phase is reconstructed independently. Results from six healthy volunteers, with various slice orientations, show improved image quality compared to combined respiratory and cardiac gating. Magn Reson Med 63:1247–1257, 2010.

Retrospective Rigid Motion Correction in k-Space for Segmented Radial MRI

Motion occurring during magnetic resonance imaging acquisition is a major factor of image quality degradation. Self-navigation can help reduce artefacts by estimating motion from the acquired data to enable motion correction. Popular self-navigation techniques rely on the availability of a fully-sampled motion-free reference to register the motion corrupted data with. In the proposed technique, rigid motion parameters are derived using the inherent correlation between radial segments in k-space. The registration is performed exclusively in k-space using the Phase Correlation Method, a popular registration technique in computer vision. Robust and accurate registration has been carried out from radial segments composed of as few as 32 profiles. Successful self-navigation has been performed on 2-D dynamic brain scans corrupted with continuous motion for six volunteers. Retrospective motion correction using the derived self-navigation parameters resulted in significant improvement of image quality compared to the conventional sliding window. This work also demonstrates the benefits of using a bit-reversed ordering scheme to limit undesirable effects specific to retrospective motion correction on radial trajectories. This method provides a fast and efficient mean of measuring rigid motion directly in k-space from dynamic radial data under continuous motion.