Development of Real-Time Magnetic Resonance Imaging of Mouse Hearts at 9.4 Tesla— Simulations and First Application

Abstract

A novel method for real-time magnetic resonance imaging for the assessment of cardiac function in mice at 9.4 T is proposed. The technique combines a highly undersampled radial gradient echo acquisition with an image reconstruction utilizing both parallel imaging and compressed sensing. Simulations on an in silico phantom were performed to determine the achievable acceleration factor and to optimize regularization parameters. Several parameters characterizing the quality of the reconstructed images (such as spatial and temporal image sharpness or compartment areas) were calculated for this purpose. Subsequently, double-gated segmented cine data as well as non-gated undersampled real-time data using only six projections per timeframe (temporal resolution ~ 10 ms) were acquired in a mid-ventricular slice of four normal mouse hearts in vivo. The highly accelerated data sets were then subjected to the introduced reconstruction technique and results were validated against the fully sampled references. Functional parameters obtained from real-time and fully sampled data agreed well with a comparable accuracy for left-ventricular volumes and a slightly larger scatter for mass. This study introduces and validates a real-time cine-MRI technique, which significantly reduces scan time in preclinical cardiac functional imaging and has the potential to investigate mouse models with abnormal heart rhythm.

Proximal Methods for Elliptic Optimal Control Problems with Sparsity Cost Functional

“…In particular, we prove an ( ) 2 1 k  convergence rate of the value of reduced cost functional, where k is the number of proximal iterations. This notion of convergence is used in l 1 -based optimization and in some application fields [14]. We remark that many arguments in our analysis are similar to those presented in the finite-dimensional case.…”

Feasibility of real‐time cardiac MRI in mice using tiny golden angle radial sparse

“…Haji‐Valiyadeh et al 27 applied a balanced steady‐state free precession sequence (b‐SSFP) with tyGRASP in RT imaging. For preclinical research, Wech et al 21 investigated the application of radial generalized autocalibrating partially parallel acquisitions (GRAPPA) with large GA (111.25°) in RT imaging. In our study, we applied the tyGRASP to nongated RT functional imaging and first‐pass perfusion imaging with single heartbeat temporal resolution in the Nexn‐induced heart failure mouse model.…”

“…However, the acquisition times in the minute range for a single slice still limit its application, eg for pharmacological stress or first‐pass perfusion imaging. To reduce acquisition times, the application of real‐time (RT) methods not demanding any gating (real time) for the rapid and continuous acquisition of image datasets, such as parallel imaging, 12–14 k‐t acceleration methods, 15–17 and compressed sensing (CS), 18–20 have been suggested and initially evaluated in mice 21 . Radial trajectories have shown favorable properties for RT imaging by their intrinsic low motion artifact level.…”

“…Further, due to the continuous recording of all spatial frequencies with every single spoke, undersampling results in almost incoherent artifacts, often showing no noticeable effect on the reconstructed images 22 . The concept of golden angle (GA) angular spacing 23 enabled data acquisition with optimal k‐space coverage that was almost independent of the number of projections (Fibonacci sequence) and ensured incoherent undersampling artifacts 21 . Its extension to tiny golden angle (tyGA) 24 enabled the translation of the GA principle to higher field strength and provided even larger flexibility in the selection of the number of projections used for reconstruction of a single frame (generalized Fibonacci sequence).…”

Optimization and validation of accelerated golden‐angle radial sparse MRI reconstruction with self‐calibrating GRAPPA operator gridding

“…The feasibility of combining GROG with iterative non-Cartesian image reconstruction has been investigated in several studies. For example, it has been applied to non-Cartesian MR parameter mapping (30), real-time mouse imaging (31), dynamic contrast-enhanced MRI (32,33), and cardiac perfusion imaging (34). These results showed that GROG enables remarkable reduction of reconstruction time without sacrificing image quality or temporal fidelity.…”