Free-Breathing & Ungated Cardiac MRI Using Iterative SToRM (i-SToRM)

Technical Note: Swing golden angle - A navigator-interleaved golden angle trajectory with eddy current suppression - Application in free-running cardiac MRI

BACKGROUND

Golden angle (GA) radial trajectory is advantageous for dynamic magnetic resonance imaging (MRI). Recently, several advanced algorithms have been developed based on navigator-interleaved GA trajectory to realize free-running cardiac MRI. However, navigator-interleaved GA trajectory suffers from the eddy-current effect, which reduces the image quality.

PURPOSE

This work aims to integrate the navigator-interleaved GA trajectory with clinical cardiac MRI acquisition, with the minimum eddy-current artifacts. The ultimate goal is to realize a high-quality free-running cardiac imaging technique.

METHODS

In this paper, we propose a new "swing golden angle" (swingGA) radial profile order. SwingGA samples the k-space by rotating back and forth at the generalized golden ratio interval, with smoothly interleaved navigator readouts. The sampling efficiency and angle increment distributions were investigated by numerical simulations. Static phantom imaging experiments were conducted to evaluate the eddy current effect, compared with cartesian, golden angle radial (GA), and tiny golden angle (tGA) trajectories. Furthermore, 12 heart-healthy subjects (aged 21-25 years) were recruited for free-running cardiac imaging with different sampling trajectories. Dynamic images were reconstructed by a low-rank subspace-constrained algorithm. The image quality was evaluated by signal-to-noise-ratio and spectrum analysis in the heart region, and compared with traditional clinical cardiac MRI images.

RESULTS

SwingGA pattern achieves the highest sampling efficiency (mSE > 0.925) and the minimum azimuthal angle increment (mAD < 1.05). SwingGA can effectively suppress eddy currents in static phantom images, with the lowest normalized root mean square error (nRMSE) values among radial trajectories. For the in-vivo cardiac images, swingGA enjoys the highest SNR both in the blood pool and myocardium, and contains the minimum level of high-frequency artifacts. The free-running cardiac images have good consistency with traditional clinical cardiac MRI, and the swingGA sampling pattern achieves the best image quality among all sampling patterns.

CONCLUSIONS

The proposed swingGA sampling pattern can effectively improve the sampling efficiency and reduce the eddy currents for the navigator-interleaved GA sequence. SwingGA is a promising sampling pattern for free-running cardiac MRI.

Motion Compensated Unsupervised Deep Learning for 5D MRI

We propose an unsupervised deep learning algorithm for the motion-compensated reconstruction of 5D cardiac MRI data from 3D radial acquisitions. Ungated free-breathing 5D MRI simplifies the scan planning, improves patient comfort, and offers several clinical benefits over breath-held 2D exams, including isotropic spatial resolution and the ability to reslice the data to arbitrary views. However, the current reconstruction algorithms for 5D MRI take very long computational time, and their outcome is greatly dependent on the uniformity of the binning of the acquired data into different physiological phases. The proposed algorithm is a more data-efficient alternative to current motion-resolved reconstructions. This motion-compensated approach models the data in each cardiac/respiratory bin as Fourier samples of the deformed version of a 3D image template. The deformation maps are modeled by a convolutional neural network driven by the physiological phase information. The deformation maps and the template are then jointly estimated from the measured data. The cardiac and respiratory phases are estimated from 1D navigators using an auto-encoder. The proposed algorithm is validated on 5D bSSFP datasets acquired from two subjects.

Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling

The spatial resolution and temporal frame-rate of dynamic magnetic resonance imaging (MRI) can be improved by reconstructing images from sparsely sampled k -space data with mathematical modeling of the underlying spatiotemporal signals. These models include sparsity models, linear subspace models, and non-linear manifold models. This work presents a novel linear tangent space alignment (LTSA) model-based framework that exploits the intrinsic low-dimensional manifold structure of dynamic images for accelerated dynamic MRI. The performance of the proposed method was evaluated and compared to state-of-the-art methods using numerical simulation studies as well as 2D and 3D in vivo cardiac imaging experiments. The proposed method achieved the best performance in image reconstruction among all the compared methods. The proposed method could prove useful for accelerating many MRI applications, including dynamic MRI, multi-parametric MRI, and MR spectroscopic imaging.