WarpPINN: Cine-MR image registration with physics-informed neural networks

The diagnosis of heart failure usually includes a global functional assessment, such as ejection fraction measured by magnetic resonance imaging. However, these metrics have low discriminate power to distinguish different cardiomyopathies, which may not affect the global function of the heart. Quantifying local deformations in the form of cardiac strain can provide helpful information, but it remains a challenge. In this work, we introduce WarpPINN, a physics-informed neural network to perform image registration to obtain local metrics of heart deformation. We apply this method to cine magnetic resonance images to estimate the motion during the cardiac cycle. We inform our neural network of the near-incompressibility of cardiac tissue by penalizing the Jacobian of the deformation field. The loss function has two components: an intensity-based similarity term between the reference and the warped template images, and a regularizer that represents the hyperelastic behavior of the tissue. The architecture of the neural network allows us to easily compute the strain via automatic differentiation to assess cardiac activity. We use Fourier feature mappings to overcome the spectral bias of neural networks, allowing us to capture discontinuities in the strain field. The algorithm is tested on synthetic examples and on a cine SSFP MRI benchmark of 15 healthy volunteers, where it is trained to learn the deformation mapping of each case. We outperform current methodologies in landmark tracking and provide physiological strain estimations in the radial and circumferential directions. WarpPINN provides precise measurements of local cardiac deformations that can be used for a better diagnosis of heart failure and can be used for general image registration tasks. Source code is available at https://github.com/fsahli/WarpPINN.

Assessment of 4D flow MRI's quality by verifying its Navier-Stokes compatibility

4D Flow Magnetic Resonance Imaging (MRI) is the state-of-the-art technique to comprehensively measure the complex spatio-temporal and multidirectional patterns of blood flow. However, it is subject to artifacts such as noise and aliasing, which due to the 3D and dynamic structure is difficult to detect in clinical practice. In this work, a new mathematical and computational model to determine the quality of 4D Flow MRI is presented. The model is derived by assuming the true velocity satisfies the incompressible Navier-Stokes equations and that can be decomposed by the measurements u→meas plus an extra field w→ . Therefore, a non-linear problem with w→ as unknown arises, which serves as a measure of data quality. A stabilized finite element formulation tailored to this problem is proposed and analyzed. Then, extensive numerical examples-using synthetic 4D Flow MRI data as well as real measurements on experimental phantom and subjects-illustrate the ability to use w→ for assessing the quality of 4D Flow MRI measurements over space and time.

An off-resonance insensitive orthogonal CSPAMM sequence (ORI-O-CSPAMM) for the acquisition of CSPAMM and MICSR grids in half scan time

PURPOSE

To develop an Off-Resonance Insensitive Orthogonal CSPAMM sequence (ORI-O-CSPAMM) for the acquisition of CSPAMM and MICSR grids in half of the acquisition time.

METHODS

Phantom and mid-level left ventricle short-axis tagged images were acquired using CSPAMM, ORI-CSPAMM, O-CSPAMM, and the proposed ORI-O-CPAMM sequences to interrogate and compare its behavior under off-resonance effects produced by vegetable oil and subcutaneous and epicardial fat. The images were compared in terms of signal and the capacity to obtain complex difference and MICSR images.

RESULTS

Like ORI-CSPAMM, the proposed ORI-O-CSPAMM sequence removed almost completely the off-resonance artifacts produced during the tagging preparation. Tagging grids without DC components were obtained with ORI-O-CSPAMM using complex difference and MICSR from only two complementary images, which reduced the scan time to a half compared to CSPAMM and ORI-CSPAMM. The removal of off-resonance effects and the capacity to obtain MICSR images are advantages of ORI-O-CSPAMM over the O-CSPAMM sequence.

CONCLUSION

We developed a novel and fast tagging sequence designed to remove off-resonance effects during the tagging preparation, and to obtain complex difference and MICSR grids in half of the scan time compared to CSPAMM and ORI-CSPAMM sequences, which could allow its application to clinical protocols.

A comprehensive comparison between shortest-path HARP refinement, SinMod, and DENSEanalysis processing tools applied to CSPAMM and DENSE images

We addressed comprehensively the performance of Shortest-Path HARP Refinement (SP-HR), SinMod, and DENSEanalysis using 2D slices of synthetic CSPAMM and DENSE images with realistic contrasts obtained from 3D phantoms. The three motion estimation techniques were interrogated under ideal and no-ideal conditions (with MR induced artifacts, noise, and through-plane motion), considering several resolutions and noise levels. Under noisy conditions, and for isotropic pixel sizes of 1.5 mm and 3.0 mm in CSPAMM and DENSE images respectively, the nRMSE obtained for the circumferential and radial strain components were 10.7 ± 10.8% and 25.5 ± 14.8% using SP-HR, 11.9 ± 2.5% and 29.3 ± 6.5% using SinMod, and 6.4 ± 2.0% and 18.2 ± 4.6% using DENSEanalysis. Overall, the results showed that SP-HR tends to fail for large tissue motions, whereas SinMod and DENSEanalysis gave accurate displacement and strain field estimations, being the last which performed the best.