Partial fourier shells trajectory for non-cartesian MRI.

Non-Cartesian MRI acquisition has demonstrated various advantages in many clinical applications. The shells trajectory is a 3D non-Cartesian MRI acquisition technique that samples the k-space using a series of concentric shells to achieve efficient 3D isotropic acquisition. Partial Fourier acquisition is an acceleration technique that is widely used in Cartesian MRI. It exploits the conjugate symmetry of k-space measurement to reduce the number of k-space samples compared to full-k-space acquisition, without loss of spatial resolution. For a Cartesian MRI acquisition, the direction of partial Fourier acceleration is aligned either with the phase encoded or frequency encoded direction. In those cases, the underlying image matrix can be reconstructed from the undersampled k-space data using a non-iterative, homodyne reconstruction framework. However, designing a non-Cartesian acquisition trajectory that is compatible with non-iterative homodyne reconstruction is not nearly as straightforward as in the Cartesian case. One reason is the non-iterative homodyne reconstruction requires (slightly over) half of the k-space to be fully sampled. Since the direction of partial Fourier acceleration varies throughout the acquisition in the non-Cartesian trajectory, directly applying the same partial Fourier acquisition pattern (as in Cartesian acquisitions) to a non-Cartesian trajectory does not necessarily yield a continuous, physically-achievable trajectory. In this work, we develop an asymmetric shells trajectory with fully-automated trajectory and gradient waveform design to achieve partial Fourier acquisition for the shells trajectory. We then demonstrate a non-iterative image reconstruction framework for the proposed trajectory. Phantom and in vivo brain scans based on spoiled gradient echo (SPGR) shells and magnetization-prepared shells (MP-shells) were performed to test the proposed trajectory design and reconstruction method. Our phantom and in vivo results demonstrate that the proposed partial Fourier shells trajectory maintains the desirable image contrast and high sampling efficiency from the fully sampled shells, while further reducing data acquisition time.

[1]  Yunhong Shu,et al.  NonCartesian MR image reconstruction with integrated gradient nonlinearity correction. , 2015, Medical physics.

[2]  G. Zaharchuk,et al.  Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. , 2015, Magnetic resonance in medicine.

[3]  Jens Frahm,et al.  Spiral imaging: A critical appraisal , 2005, Journal of magnetic resonance imaging : JMRI.

[4]  D. Nishimura,et al.  Fast Three Dimensional Magnetic Resonance Imaging , 1995, Magnetic resonance in medicine.

[5]  Jeffrey A. Fessler,et al.  Regularized fieldmap estimation in MRI , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[6]  Yunhong Shu,et al.  Three‐dimensional MRI with an undersampled spherical shells trajectory , 2006, Magnetic resonance in medicine.

[7]  P. Boesiger,et al.  Advances in sensitivity encoding with arbitrary k‐space trajectories , 2001, Magnetic resonance in medicine.

[8]  Jeffrey A. Fessler,et al.  Model-Based Image Reconstruction for MRI , 2010, IEEE Signal Processing Magazine.

[9]  Matt A. Bernstein,et al.  Contrast‐enhanced intracranial magnetic resonance angiography with a spherical shells trajectory and online gridding reconstruction , 2009, Journal of magnetic resonance imaging : JMRI.

[10]  J. Pipe,et al.  Sampling density compensation in MRI: Rationale and an iterative numerical solution , 1999, Magnetic resonance in medicine.

[11]  Yunhong Shu,et al.  Integrated image reconstruction and gradient nonlinearity correction , 2015, Magnetic resonance in medicine.

[12]  P. Kellman,et al.  Virtual coil concept for improved parallel MRI employing conjugate symmetric signals , 2009, Magnetic resonance in medicine.

[13]  J. Mugler,et al.  Three‐dimensional magnetization‐prepared rapid gradient‐echo imaging (3D MP RAGE) , 1990, Magnetic resonance in medicine.

[14]  Jeffrey A. Fessler,et al.  Nonuniform fast Fourier transforms using min-max interpolation , 2003, IEEE Trans. Signal Process..

[15]  Maxim Zaitsev,et al.  Single shot concentric shells trajectories for ultra fast fMRI , 2012, Magnetic resonance in medicine.

[16]  Yunhong Shu,et al.  Partial fourier and parallel MR image reconstruction with integrated gradient nonlinearity correction , 2016, Magnetic resonance in medicine.

[17]  Seung-Jean Kim,et al.  A fast method for designing time-optimal gradient waveforms for arbitrary k-space trajectories , 2008, IEEE Transactions on Medical Imaging.

[18]  J. Pauly,et al.  A homogeneity correction method for magnetic resonance imaging with time-varying gradients. , 1991, IEEE transactions on medical imaging.

[19]  S J Riederer,et al.  Theoretical limits of spatial resolution in elliptical‐centric contrast‐enhanced 3D‐MRA , 1999, Magnetic resonance in medicine.

[20]  Yunhong Shu,et al.  Magnetization‐prepared shells trajectory with automated gradient waveform design , 2018, Magnetic resonance in medicine.

[21]  G. Glover,et al.  Spiral‐in/out BOLD fMRI for increased SNR and reduced susceptibility artifacts , 2001, Magnetic resonance in medicine.

[22]  Dwight G. Nishimura,et al.  Rapid gridding reconstruction with a minimal oversampling ratio , 2005, IEEE Transactions on Medical Imaging.

[23]  S. Riederer,et al.  Improved venous suppression and spatial resolution with SENSE in elliptical centric 3D contrast‐enhanced MR angiography , 2004, Magnetic resonance in medicine.

[24]  J. Cuppen,et al.  Reducing MR imaging time by one-sided reconstruction , 1987 .

[25]  D. Noll,et al.  Homodyne detection in magnetic resonance imaging. , 1991, IEEE transactions on medical imaging.

[26]  Martin Blaimer,et al.  Non‐Cartesian data reconstruction using GRAPPA operator gridding (GROG) , 2007, Magnetic resonance in medicine.