Compressed sensing plus motion (CS + M): A new perspective for improving undersampled MR image reconstruction

Avilés-Rivero, Angelica I.; Debroux, Noémie; Williams, Guy B.; Graves, Martin J.; Schönlieb, Carola-Bibiane

doi:10.1016/j.media.2020.101933

Cited by 16 publications

(20 citation statements)

References 50 publications

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“…Therefore, in MRE, the raw signal measurements are captured in the time-spatial frequency domain, that is, k-t space. 13 Here, k is the 2D spatial frequency variable and t is the temporal coordinate representing the phase offset.…”

Section: Mre Reconstruction Using Compressed Sensing (Cs)mentioning

confidence: 99%

Phase‐regularized and displacement‐regularized compressed sensing for fast magnetic resonance elastography

2023

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Liver magnetic resonance elastography (MRE) is a noninvasive stiffness measurement technique that captures the tissue displacement in the phase of the signal. To limit the scanning time to a single breath‐hold, liver MRE usually involves advanced readout techniques such as simultaneous multislice (SMS) or multishot methods. Furthermore, all these readout techniques require additional in‐plane acceleration using either parallel imaging capabilities, such as sensitivity encoding (SENSE), or k‐space undersampling, such as compressed sensing (CS). However, these methods apply a single regularization function on the complex image. This study aims to design and evaluate methods that use separate regularization on the magnitude and phase of MRE to exploit their distinct spatiotemporal characteristics. Specifically, we introduce two compressed sensing methods. The first method, termed phase‐regularized compressed sensing (PRCS), applies a two‐dimensional total variation (TV) prior to the magnitude and two‐dimensional wavelet regularization to the phase. The second method, termed displacement‐regularized compressed sensing (DRCS), exploits the spatiotemporal redundancy using 3D total variation on the magnitude. Additionally, DRCS includes a displacement fitting function to apply wavelet regularization to the displacement phasor. Both DRCS and PRCS were evaluated with different levels of compression factors in three datasets: an in silico abdomen dataset, an in vitro tissue‐mimicking phantom, and an in vivo liver dataset. The reconstructed images were compared with the full sampled reconstruction, zero‐filling reconstruction, wavelet‐regularized compressed sensing, and a low rank plus sparse reconstruction. The metrics used for quantitative evaluation were the structural similarity index (SSIM) of magnitude (M‐SSIM), displacement (D‐SSIM), and shear modulus (S‐SSIM), and mean shear modulus. Results from highly undersampled in silico and in vitro datasets demonstrate that the DRCS method provides higher reconstruction quality than the conventional compressed sensing method for a wide range of stiffness values. Notably, DRCS provides 24% and 22% increase in D‐SSIM compared with CS for the in silico and in vitro datasets, respectively. Comparison with liver stiffness measured from full sampled data and highly undersampled data (CR=4) demonstrates that the DRCS method provided the strongest correlation ( R2=0.95), second‐lowest mean bias (−0.18 kPa, lowest for CS with −0.16 kPa), and lowest coefficient of variation (CV=3.6%). Our results demonstrate the potential of using DRCS to improve the reconstruction quality of accelerated MRE.

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Section: Mre Reconstruction Using Compressed Sensing (Cs)mentioning

confidence: 99%

Phase‐regularized and displacement‐regularized compressed sensing for fast magnetic resonance elastography

2023

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“…One can use any optimisation algorithm that supports non-differentiable terms for computing solutions to each of the subproblems (39) and (40). In dimension d = 2 one could simply use a primal-dual hybrid gradient scheme [10] as outlined in [8], see also [3], here both applications use the optical flow constraint (35). In higher dimensions where the computational burden of the forward operator becomes more prevalent, it is advised to consider other schemes with fewer operator evaluations, we refer to [40] for an application to dynamic 3D photoacoustic tomography as well as [17] for dynamic 3D computed tomography.…”

Section: Implementation and Reconstructionmentioning

confidence: 99%

Image reconstruction in dynamic inverse problems with temporal models

Hauptmann¹,

Öktem²,

Schönlieb³

2020

Preprint

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The paper surveys variational approaches for image reconstruction in dynamic inverse problems. Emphasis is on methods that rely on parametrised temporal models. These are here encoded as diffeomorphic deformations with time dependent parameters, or as motion constrained reconstruction where the motion model is given by a partial differential equation. The survey also includes recent development in integrating deep learning for solving these computationally demanding variational methods. Examples are given for 2D dynamic tomography, but methods apply to general inverse problems.

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“…The need for external tracking hardware is relieved by adopting B-spline-based and optical flow-based motion estimation in this joint optimization context [30], [31]. More recently, variational methods [32] and dictionary learning [33] are also employed to solve this joint optimization problem for CMR reconstruction. However, all these methods demand a relatively long estimation time because of their iterative optimization nature.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction-driven motion estimation for motion-compensated MR CINE imaging

Pan¹,

Huang²,

Rueckert³

et al. 2023

Preprint

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In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions by incorporating motion information between frames. In this work, we propose a deep learning-based framework to address the MCMR problem efficiently. Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i.e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization. We discard the canonical motion-warping loss (similarity measurement between motion-warped images and target images) to estimate the motion, but drive the motion estimation process directly by the final reconstruction performance. The higher reconstruction quality is achieved without using any smoothness loss terms and without iterative processing between motion estimation and reconstruction. Therefore, we avoid non-trivial loss weighting factors tuning and time-consuming iterative processing. Experiments on 43 inhouse acquired 2D CINE datasets indicate that the proposed MCMR framework can deliver artifact-free motion estimation and high-quality MR images even for imaging accelerations up to 20x. The proposed framework is compared to SOTA non-MCMR and MCMR methods and outperforms these methods qualitatively and quantitatively in all applied metrics across all experiments with different acceleration rates.

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Compressed sensing plus motion (CS + M): A new perspective for improving undersampled MR image reconstruction

Cited by 16 publications

References 50 publications

Phase‐regularized and displacement‐regularized compressed sensing for fast magnetic resonance elastography

Phase‐regularized and displacement‐regularized compressed sensing for fast magnetic resonance elastography

Image reconstruction in dynamic inverse problems with temporal models

Reconstruction-driven motion estimation for motion-compensated MR CINE imaging

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