High-Resolution Free-Breathing Quantitative First-Pass Perfusion Cardiac MR Using Dual-Echo Dixon With Spatio-Temporal Acceleration

Abstract

IntroductionTo develop and test the feasibility of free-breathing (FB), high-resolution quantitative first-pass perfusion cardiac MR (FPP-CMR) using dual-echo Dixon (FOSTERS; Fat-water separation for mOtion-corrected Spatio-TEmporally accelerated myocardial peRfuSion).Materials and MethodsFOSTERS was performed in FB using a dual-saturation single-bolus acquisition with dual-echo Dixon and a dynamically variable Cartesian k-t undersampling (8-fold) approach, with low-rank and sparsity constrained reconstruction, to achieve high-resolution FPP-CMR images. FOSTERS also included automatic in-plane motion estimation and T2* correction to obtain quantitative myocardial blood flow (MBF) maps. High-resolution (1.6 x 1.6 mm2) FB FOSTERS was evaluated in eleven patients, during rest, against standard-resolution (2.6 x 2.6 mm2) 2-fold SENSE-accelerated breath-hold (BH) FPP-CMR. In addition, MBF was computed for FOSTERS and spatial wavelet-based compressed sensing (CS) reconstruction. Two cardiologists scored the image quality (IQ) of FOSTERS, CS, and standard BH FPP-CMR images using a 4-point scale (1–4, non-diagnostic – fully diagnostic).ResultsFOSTERS produced high-quality images without dark-rim and with reduced motion-related artifacts, using an 8x accelerated FB acquisition. FOSTERS and standard BH FPP-CMR exhibited excellent IQ with an average score of 3.5 ± 0.6 and 3.4 ± 0.6 (no statistical difference, p > 0.05), respectively. CS images exhibited severe artifacts and high levels of noise, resulting in an average IQ score of 2.9 ± 0.5. MBF values obtained with FOSTERS presented a lower variance than those obtained with CS.DiscussionFOSTERS enabled high-resolution FB FPP-CMR with MBF quantification. Combining motion correction with a low-rank and sparsity-constrained reconstruction results in excellent image quality.

Systematic review of reconstruction techniques for accelerated quantitative MRI

“…However, due to the rank constraint, the optimization problem described by Equation () is not convex. Therefore, the nuclear norm of the matrix of contrast images $$$ {\left\Vert \boldsymbol{I}\right\Vert}_{\ast } $$$, that is, the sum of its singular values, has been often used as convex relaxation of the rank function, 61,79,80,82,85‐99 giving the reconstruction problem: $$$$ \hat{\boldsymbol{I}}=\underset{\boldsymbol{I}}{\arg\ \min }{\left\Vert \boldsymbol{Y}-\mathcal{E}\boldsymbol{I}\right\Vert}_2^2+\sum \limits_i^{n^i}{\lambda}_i{\left\Vert {\phi}_i\left(\boldsymbol{I}\right)\right\Vert}_l+\gamma {\left\Vert \boldsymbol{I}\right\Vert}_{\ast }. $$$$ The nuclear norm regularization can also be applied to phase data in applications where phase correction needs to be integrated into the image reconstruction 100 .…”

High-resolution quantification of stress perfusion defects by cardiac magnetic resonance

“…These include work to improve the spatial resolution 7 and to increase the spatial coverage of the ventricle, 8–10 facilitated by motion-compensated compressed sensing reconstructions. 11 These developments allow the identification of transmural gradients in perfusion and ensure that all coronary territories are covered, as well as simplifying the acquisition and analysis. In particular, the high spatial resolution is a key advantage of CMR over positron emission tomography (PET) and single-photon emission computed tomography.…”