Aurélien Bustin

Motion-Corrected, Super-Resolution Reconstruction for High-Resolution 3D Cardiac Cine MRI

Cardiac cine MRI with 3D isotropic resolution is challenging as it requires efficient data acquisition and motion management. It is proposed to use a 2D balanced SSFP (steady-state free precession) sequence rather than its 3D version as it provides better contrast between blood and tissue. In order to obtain 3D isotropic images, 2D multi-slice datasets are acquired in different orientations (short axis, horizontal long axis and vertical long axis) while the patient is breathing freely. Image reconstruction is performed in two steps: (i) a motion-compensated reconstruction of each image stack corrects for nonrigid cardiac and respiratory motion; (ii) a super-resolution (SR) algorithm combines the three motion-corrected volumes (with low resolution in the slice direction) into a single volume with isotropic resolution. The SR reconstruction was implemented with two regularization schemes including a conventional one (Tikhonov) and a feature-preserving one (Beltrami). The method was validated in 8 volunteers and 10 patients with breathing difficulties. Image sharpness, as assessed by intensity profiles and by objective metrics based on the structure tensor, was improved with both SR techniques. The Beltrami constraint provided efficient denoising without altering the effective resolution.

Impact of denoising on precision and accuracy of saturation-recovery-based myocardial T1 mapping

To evaluate the impact of a novel postprocessing denoising technique on accuracy and precision in myocardial T1 mapping.

A vectorized Levenberg-Marquardt model fitting algorithm for efficient post-processing of cardiac T 1 mapping MRI

PURPOSE T1 mapping is an emerging MRI research tool to assess diseased myocardial tissue. Recent research has been focusing on the image acquisition protocol and motion correction, yet little attention has been paid to the curve fitting algorithm. METHODS After nonrigid registration of the image series, a vectorized Levenberg-Marquardt (LM) technique is proposed to improve the robustness of the curve fitting algorithm by allowing spatial regularization of the parametric maps. In addition, a region-based initialization is proposed to improve the initial guess of the T1 value. The algorithm was validated with cardiac T1 mapping data from 16 volunteers acquired with saturation-recovery (SR) and inversion-recovery (IR) techniques at 3T, both pre- and post-injection of a contrast agent. Signal models of T1 relaxation with 2 and 3 parameters were tested. RESULTS The vectorized LM fitting showed good agreement with its pixel-wise version but allowed reduced calculation time (60 s against 696 s on average in Matlab with 256 × 256 × 8(11) images). Increasing the spatial regularization parameter led to noise reduction and improved precision of T1 values in SR sequences. The region-based initialization was particularly useful in IR data to reduce the variability of the blood T1. CONCLUSIONS We have proposed a vectorized curve fitting algorithm allowing spatial regularization, which could improve the robustness of the curve fitting, especially for myocardial T1 mapping with SR sequences.

Joint denoising and motion correction: initial application in single-shot cardiac MRI

Background Single-shot (SSH) pulse sequences in CMR are beneficial for rapid image acquisition that is robust to motion, especially in arrhythmic patients or poor breath-holders. However, this fast scanning technique trades scan time for a lower signal-to-noise ratio compared to conventional multi-shot acquisitions. Here we propose a motion-compensated denoising technique that improves the image quality from multiple free-breathing singleshot acquisitions.

Isotropic 3D cardiac cine MRI allows efficient sparse segmentation strategies based on 3D surface reconstruction

Segmentation of cardiac cine MRI data is routinely used for the volumetric analysis of cardiac function. Conventionally, 2D contours are drawn on short‐axis (SAX) image stacks with relatively thick slices (typically 8 mm). Here, an acquisition/reconstruction strategy is used for obtaining isotropic 3D cine datasets; reformatted slices are then used to optimize the manual segmentation workflow.

Correction to: 3D SASHA myocardial T1 mapping with high accuracy and improved precision

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Motion Estimated-Compensated Reconstruction with Preserved-Features in Free-Breathing Cardiac MRI

To develop an efficient motion-compensated reconstruction technique for free-breathing cardiac magnetic resonance imaging (MRI) that allows high-quality images to be reconstructed from multiple undersampled single-shot acquisitions. The proposed method is a joint image reconstruction and motion correction method consisting of several steps, including a non-rigid motion extraction and a motion-compensated reconstruction. The reconstruction includes a denoising with the Beltrami regularization, which offers an ideal compromise between feature preservation and staircasing reduction. Results were assessed in simulation, phantom and volunteer experiments. The proposed joint image reconstruction and motion correction method exhibits visible quality improvement over previous methods while reconstructing sharper edges. Moreover, when the acceleration factor increases, standard methods show blurry results while the proposed method preserves image quality. The method was applied to free-breathing single-shot cardiac MRI, successfully achieving high image quality and higher spatial resolution than conventional segmented methods, with the potential to offer high-quality delayed enhancement scans in challenging patients.

A fully automated binning method for improved SHARP reconstruction of free-breathing cardiac images

Background Despite recent progress in fast cardiac imaging, respiratory motion remains a challenging problem, usually leading to poor image quality when scanning poor breath-holder patients or acquiring high spatial resolution images. Today respiratory motion is compensated using navigators or external physiological sensors and can result in decreased scan efficiency and increased setup complexity. We recently proposed a motion compensated reconstruction, Single-sHot Accelerated Reconstruction with Preserved-features, or SHARP, that enables high-resolution motion-corrected reconstruction of multiple single-shot images acquired in free-breathing, with respiratory motion derived directly from the single-shot images. In the present work, a fast and automatic self-navigated binning method is described, which aims to accelerate the SHARP reconstruction process while improving image quality. The rationale for accelerating SHARP is that raw data acquired in similar motion states can be clustered into a reduced number of motion states, thereby, improving the quality of images from which to extract motion.

Motion compensated reconstruction from free breathing 2D radial cardiac MRI data

Background Cardiac magnetic resonance imaging (CMR) is affected by both cardiac and respiratory motion. While ECG-gated imaging within a breath hold is often the method of choice to limit motion-related artifacts, free-breathing methods are favorable in patients with limited breath hold capability. Free-breathing approaches require either rapid singleshot scans to reduce respiratory motion artifacts at the expense of spatial resolution or higher resolution segmented respiratory-gated scans at the expense of scan time efficiency. Previous work has exploited the favorable properties (e.g., motion robustness, uniform sampling density) of Golden Angle [1] radial sampling including motion. Recently introduced motion compensated reconstructions [2,3] have been applied to various clinical applications. In this work, we propose to combine a 2D radial Golden Angle data acquisition scheme with a recently developed motion compensated reconstruction strategy [4] to obtain high-resolution motion compensated CMR data from time-efficient cardiac-gated free-breathing exams.