Combination of Compressed Sensing and Parallel Imaging for Highly-Accelerated 3 D First-Pass Cardiac Perfusion MRI

INTRODUCTION: Whole-heart coverage per heartbeat is desirable for first-pass cardiac perfusion MRI studies. 3D imaging offers a particularly appealing alternative to the SNR and volumetric coverage limitations of multi-slice 2D techniques [1-2]. However, 3D approaches are more susceptible to cardiac motion artifacts due to their longer acquisition time. Parallel imaging using many-element coil arrays can be used to substantially accelerate 3D acquisitions. At last year’s ISMRM meeting, whole-heart coverage per heartbeat was reported using a 32-element array with 8-fold acceleration [3]. However, further acceleration is required to achieve an adequate spatial and temporal resolution for clinical studies. An alternative acceleration technique for perfusion MRI is compressed sensing (CS) [4]. CS exploits spatial and temporal correlations, resulting in sparsity of image series content, to achieve high levels of undersampling without loss of image information. We have recently presented a combination of CS and parallel imaging (PI) to increase the acceleration rate for 2D perfusion MRI by exploiting the properties of joint multicoil sparsity [5]. In this work, we extend our previously developed combination of CS and PI to 3D perfusion MRI to achieve whole-heart coverage per heartbeat with increased spatial and temporal resolution using a 32-element coil array with a net acceleration factor of 16. METHODS: Fist-pass 3D cardiac perfusion MRI was performed on two healthy volunteers with 0.1 mmol/kg of Gd-DTPA (Magnevist). A 3D saturation-recovery TurboFLASH pulse sequence was modifed to include user defined phase-encoding, partition-encoding and time (ky-kz-t) sampling pattern (Fig. 1), and this pulse sequence was implemented on a 1.5T scanner (Siemens, Avanto) equipped with a 32element cardiac coil array (In Vivo). An axial acquisition was performed in mid-diastole to reduce sensitivity to cardiac motion. The relevant imaging parameters include: FOV = 340×340×100 mm, image matrix = 128×128×16, spatial resolution = 2.65×2.65×6.25 mm, flip angle = 10, TE/TR = 0.9/2.3 ms, temporal resolution = 294 ms (complete volume), repetitions = 40. Low spatial resolution coil sensitivity data were acquired during the first heartbeat of the dynamic imaging, with flip angle = 5 and without the saturation pulse. Acceleration was accomplished using ky-kz-t random undersampling in which a different variable density undersampling pattern along ky-kz was used for each temporal volume to produce the required incoherent artifacts in the sparse x-y-z-f domain. Image reconstruction was performed offline using a combination of compressed sensing and SENSE [5], where joint sparsity is enforced on the multicoil combination rather than on each coil separately, in order to exploit oversampling and incoherence along the coil dimension. The acquisition model for each coil is given by , , i x y z i = y F S d , where yi is the undersampled data, , , x y z F is the Fourier transform along x, y and z, Si represents the coil sensitivities and d is the dynamic image to be reconstructed. The multicoil acquisition model is formulated by concatenating the individual models into = y Ed . A Fourier transform