Nonlinear Gradient Field Mapping Using a Spherical Grid Phantom for 3 and 7 Tesla MR Imaging Systems Equipped with High-performance Gradient Coils.

Recent high-performance gradient coils are fabricated mainly at the expense of spatial linearity. In this study, we measured the spatial nonlinearity of the magnetic field generated by the gradient coils of two MRI systems with high-performance gradient coils. The nonlinearity of the gradient fields was measured using 3D gradient echo sequences and a spherical phantom with a built-in lattice structure. The spatial variation of the gradient field was approximated to the 3rd order polynomials. The coefficients of the polynomials were calculated using the steepest descent method. The geometric distortion of the acquired 3D MR images was corrected using the polynomials and compared with the 3D images corrected using the harmonic functions provided by the MRI venders. As a result, it was found that the nonlinearity correction formulae provided by the vendors were insufficient and needed to be verified or corrected using a geometric phantom such as used in this study.

Implementation of the QRAPMASTER data analysis using dictionary matching and quantitative evaluation of the magnetization transfer effect.

PURPOSE: To clarify the magnetization transfer (MT) effect on T1 and T2 values obtained with the QRAPMASTER sequence. METHODS: A phantom consisting of MnCl2 aqueous solution with various proton relaxation times and a chicken breast sample was imaged with the QRAPMASTER sequence and a multislice multiple spin-echo (MSMSE) sequence that was the basis of the QRAPMASTER sequence using a 1.5 T MRI system. T1 values were calculated by data matching using the dictionary dataset created by a Bloch image simulation of the QRAPMASTER sequence. T2 values were calculated by data matching using the dictionary dataset created by a Bloch image simulation of the MSMSE sequence. The MT effect on the images acquired with the QRAPMASTER and MSMSE sequences was calculated by numerically solving Bloch equations using a two-pool model. RESULTS: The linearity and accuracy of the regression lines between the T1 values measured by the QRAPMASTER sequence and those measured by the standard method excluding the T1 values of the chicken breast sample was excellent (R = 0.9969-0.9986, slope = 1.0065-1.016) for consecutive four slices including the central slice. The linearity of the regression lines for the T2 values of all samples was good (R = 0.963-0.985) for the four slices. The accuracy of the regression line was not good (slope = 0.674-0.758), which was mainly due to the effect of eddy currents. The large deviation of the T1 values of the chicken breast sample from the regression line was semi-quantitatively reproduced by the Bloch simulation for the two-pool model. CONCLUSION: This study demonstrated that the T1 value of a biological sample obtained by the QRAPMASTER sequence was shortened by the MT effect.

Bloch Simulation of a Three-point Dixon Experiment Using a Four-dimensional Numerical Phantom.

A 4D numerical phantom, which is defined in the 3D spatial axes and the resonance frequency axis, is indispensable for Bloch simulations of biological tissues with complex distribution of materials. In this study, a 4D numerical phantom was created using MR image datasets of a biological sample containing water and fat, and the Bloch simulations were performed using the 4D numerical phantom. As a result, 3D images of the sample containing water and fat were successfully reproduced, which demonstrated the usefulness of the concept of the 4D numerical phantom.

Development of a method for the Bloch image simulation of biological tissues.

PURPOSE: The purpose of this study is to develop a method for the Bloch image simulation of biological tissues including various chemical components and T2* distribution. METHODS: The nuclear spins in the object material were modeled as a spectral intensity function Sr→ω defined by superposition of Lorentz functions with various central precession frequencies and the half width of 1/(πT2'), where 1/T2' is a relaxation rate attributable to microscopic field inhomogeneity in a voxel. Four-dimensional numerical phantoms were created to simulate Sr→ω and used for MRI simulations of the phantoms containing water and fat protons. Single slice multiple (16) gradient-echo sequences (ΔTE = 2.2 and 1.384 ms) were used for experiments at 1.5 T and 3 T and MRI simulations to evaluate the validity of the approach. RESULTS: Experimentally measured image intensities of the multiple gradient-echo imaging sequences were well reproduced by the MRI simulations. The correlation coefficients between the experimentally measured image intensities and those numerically simulated were 0.9895 to 0.9992 for the 4-component phantom at 1.5 T and 0.9580 to 0.9996 for the 7-component phantom at 3 T. CONCLUSION: T2* and chemical shift effects were successfully implemented in the MRI simulator (BlochSolver). Because this approach can be applied to other MRI simulators, the method developed in this study is useful for MRI simulation of biological tissues containing water and fat protons.

An Accurate Dictionary Creation Method for MR Fingerprinting Using a Fast Bloch Simulator.

This study proposes an accurate method for creating a dictionary for magnetic resonance fingerprinting (MRF) using a fast Bloch image simulator. An MRF sequence based on a fast imaging with steady precession sequence and a numerical phantom were used for dictionary generation. Cartesian and spiral readout gradients were used for the Bloch image simulation. The validity and usefulness of the method for accurate dictionary creation were demonstrated by MRF parameter maps obtained by pattern matching with the dictionaries generated by the proposed method.

A Fast GPU-optimized 3D MRI Simulator for Arbitrary k-space Sampling.

PURPOSE: To develop a fast 3D MRI simulator for arbitrary k-space sampling using a graphical processing unit (GPU) and demonstrate its performance by comparing simulation and experimental results in a real MRI system. MATERIALS AND METHODS: A fast 3D MRI simulator using a GeForce GTX 1080 GPU (NVIDIA Corporation, Santa Clara, CA, USA) was developed using C++ and the CUDA 8.0 platform (NVIDIA Corporation). The unique advantage of this simulator was that it could use the same pulse sequence as used in the experiment. The performance of the MRI simulator was measured using two GTX 1080 GPUs and 3D Cones sequences. The MRI simulation results for 3D non-Cartesian sampling trajectories like 3D Cones sequences using a numerical 3D phantom were compared with the experimental results obtained with a real MRI system and a real 3D phantom. RESULTS: The performance of the MRI simulator was about 3800-4900 gigaflops for 128- to 4-shot 3D Cones sequences with 2563 voxels, which was about 60% of the performance of the previous MRI simulator optimized for Cartesian sampling calculated for a Cartesian sampling gradient-echo sequence with 2563 voxels. The effects of the static magnetic field inhomogeneity, radio-frequency field inhomogeneity, gradient field nonlinearity, and fast repetition times on the MR images were reproduced in the simulated images as observed in the experimental images. CONCLUSION: The 3D MRI simulator developed for arbitrary k-space sampling optimized using GPUs is a powerful tool for the development and evaluation of advanced imaging sequences including both Cartesian and non-Cartesian k-space sampling.

BlochSolver: A GPU-optimized fast 3D MRI simulator for experimentally compatible pulse sequences.

A magnetic resonance imaging (MRI) simulator, which reproduces MRI experiments using computers, has been developed using two graphic-processor-unit (GPU) boards (GTX 1080). The MRI simulator was developed to run according to pulse sequences used in experiments. Experiments and simulations were performed to demonstrate the usefulness of the MRI simulator for three types of pulse sequences, namely, three-dimensional (3D) gradient-echo, 3D radio-frequency spoiled gradient-echo, and gradient-echo multislice with practical matrix sizes. The results demonstrated that the calculation speed using two GPU boards was typically about 7 TFLOPS and about 14 times faster than the calculation speed using CPUs (two 18-core Xeons). We also found that MR images acquired by experiment could be reproduced using an appropriate number of subvoxels, and that 3D isotropic and two-dimensional multislice imaging experiments for practical matrix sizes could be simulated using the MRI simulator. Therefore, we concluded that such powerful MRI simulators are expected to become an indispensable tool for MRI research and development.