We propose a fast B₁⁺ mapping method (FAFI) based on actual flip-angle imaging (AFI), in which the single-echo gradient echo (GRE) acquisition in the conventional AFI sequence is replaced by a multi-shot echo planar imaging (Multi-shot EPI) acquisition. FAFI makes full use of the waiting time, and thus has greatly increased imaging speed. We evaluated the performance of FAFI on phantoms and human subjects (n=16), and compared the results with those obtained using the conventional AFI method. It was demonstrated that it was possible to achieve high acceleration factor with FAFI, while preserving good consistency with the conventional AFI results. FAFI provides a powerful tool for dynamic B₁⁺ mapping, and may find applications in dynamic B₁⁺ shimming and rapid spatially selective radio frequency (RF) excitation.

Occlusion of lenticulostriate arteries (LSAs) often leads to lacunar infarction. Digital subtraction angiography (DSA) has high spatial resolution and superior definition of small vessels, and is the most widely used clinical routine to image LSAs. One drawback of DSA, however, is its invasiveness. Time-of-flight (TOF) magnetic resonance angiography (MRA) was recently shown to be able to image LSAs at ultra-high fields (i.e., 7 T) with specially-designed receiving coils. However, image LSAs with TOF-MRA on clinical MRI systems can be difficult due to the facts that blood flow in LSAs is slow and the luminal diameters in these vessels are small (i.e., 0.3~0.7 mm) relative to the dimension of imaging voxels. In this study, the flow-sensitive black-blood (FSBB) technique was optimized to image LSAs at 3 T to achieve a proper balance among spatial resolution, signal-to-noise ratio and scan time.

Adaptive reconstruction (AR) has been widely used to combine multi-channel MRI images. AR can estimate coil sensitivity through signal and noise correlation matrices between different channels, and therefore achieve optimized signal-to-noise ratio (SNR) for the reconstructed images. However, AR is usually not optimized for phase images, which may lead to uncertainties when used to reconstruct phase images. In addition, the reconstructed phase images may contain artifacts when there are coil-dependent phase offsets and noise-corrupted phase images. In this study, an improved adaptive reconstruction (iAR) method was developed, which can eliminate coil-dependent phase offsets, and rearrange multi-channel data after a data quality judgment. Phantom and in vivo experiments demonstrated that the proposed method had improved robustness over the original AR method, and it can eliminate the artifacts contained in the reconstructed phase images while maintaining high SNR.

Spiral magnetic resonance imaging (MRI) plays an important role in functional imaging, parallel imaging and dynamic imaging. Most of the traditional image reconstruction algorithms for spiral imaging are based on kernel functions which interpolate the k-space data acquired with spiral trajectories onto a uniform grid in Cartesian space. Non-uniform fast Fourier transform (NUFFT) and least square method could then be applied after gridding to reconstruct the images. With these algorithms, the results are dependent on the choice of kernel function, and reconstruction errors are unavoidable during the gridding process. In this study, a spatio-temporal transform (STT) matrix, representing the relation between the image and sampled k-space data, is introduced in l₁ norm to optimize the problem based on spatio-temporal transform and compressed sensing (CS). The k-space data, rather than the after-gridding data, are used as the fidelity term, such that the gridding errors can be avoided. In addition, parallel computing on GPU can be applied to reduce the computation time for the STT matrix, making the algorithm more efficient.

Dictionary learning (DL) builds a set of basis functions from the input data, such that the data can be represented more sparsely. Based on the fact that certain magnetic resonance (MR) images can be easily segmented, we propose an algorithm named dictionary learning with segmentation (DLS). The algorithm achieves better image reconstruction quality by optimizing construction of the dictionary and to making representation of the MR images sparser though incorporating image segmentation into dictionary learning. The experimental results on simulated datasets and in vivo images demonstrated that the proposed algorithm can yield better reconstruction relative to the traditional dictionary learning algorithm.

Clinical magnetic resonance angiography (MRA) often involves extraction of images, which is often done manually by radiologists. The process can be tedious and time-consuming. In this study, we propose a new parallel vessel segmentation/tracking algorithm, utilizing large-scale parallel computing provided by graphics processing unit (GPU). The whole three-dimensional image volumes are first divided into small cubes, which share surface with their neighbors. Each cube is then processed separately to determine whether there are vessels passing through its surface. These results are then used for global segmentation and vessel tracking. Application of the algorithm to real MRA data showed that segmentation of a whole-brain MRA dataset could be achieved in less than 1 s.

A receiver for magnetic resonance imaging (MRI) was developed based on data acquisition card and graphic processing unit (GPU). The data acquisition card is controlled by a triggering signal generated from the spectrometer, and samples the output from the pre-amplifier directly. Digital mixing, multi-stage filter decimation and image reconstruction are performed by GPU subsequently. GPU is specialized for high speed computation. When processing signals it can achieve a combination of filters flexibly based on the needs. In addition, double precision floating point format is used in the whole process to ensure a high computation precision. The feasibility of the receiver was demonstrated experimentally.

A radio frequency (RF) pulse generator for magnetic resonance imaging (MRI) was designed based on field programmable gate array (FPGA) and direct digital synthesis (DDS). The generator utilizes FPGA to achieve DDS and embed soft pulse waveform (RAM), multiplier and related control logic. The RF generator showed high technical specifications, with frequency, phase and amplitude resolution up to 32 bits, 16 bits and 16 bits, respectively. Time precision of the soft pulse waveform reached 0.1 μs. FPGA provided a programmable interface, facilitating the control from sequence controller and generation of RF pulses. The feasibility of the design was demonstrated with imaging experiments.

Ultra-short echo-time (UTE) imaging has many potential applications in clinical diagnosis and scientific researches. Image quality in UTE can be degraded by imperfections in k-space trajectory resulting from eddy currents, gradient delay and so on. In this work, an improved method for k-space trajectory correction in UTE was proposed. Experimental results showed that this method could correct back effects of the trajectory errors, and improve the quality of UTE images significantly. In addition, with the use of the method, UTE would have lower performance requirement for magnetic resonance imaging (MRI) hardware system, which is beneficial for development of routine UTE applications.

A weighted linear least-square (WLS) algorithm is generally applied for multi-echo phase data to estimate the voxel-by-voxel field shift, which is further used for inversion of susceptibility map. The fitting error on the estimated field map, induced from conventional WLS method, may lead to artifacts and low signal-to-noise ratio on the susceptibility map, especially in the regions with inhomogeneous distribution of magnetic susceptibility. To improve the accuracy of the estimated field map, a truncated WLS approach was used to truncate the signal of low signal-to-noise ratio and capture the field information before the signal in a voxel decays to the noise level, which can enhance the contrast of structures in the bottom of the brain on the susceptibility map. Experimental studies demonstrated that susceptibility noise was dramatically reduced by method of truncated WLS.

The MEGA-PRESS sequence has been widely used to measure the δ_H 3.02 γ-amino butyric acid (GABA) signal by J-difference editing. However, the sequence cannot eliminate the macromolecule signals at the same chemical shift completely. Symmetrical editing can be applied to suppress the macromolecule signals. However, the approach is rarely applied at field strength of 3 T due to insufficient frequency selectivity of the editing pulse. In this study, GABA+(i.e., GABA+macromolecules) and macromolecule-suppressed GABA signals in the occipital lobe of human subjects were measured with symmetrical editing at 3 T. The duration of the editing pulse was increased from 14 ms to 20 ms to improve frequency selectivity, and echo time (TE) from 68 ms to 80 ms. It was found that the fraction of the total signal retained following macromolecule suppression ([GABA]/[GABA+]) was 0.73. It is concluded that symmetric macromoleculesuppressed editing can be used to acquire macromolecule-suppressed GABA signals, and may serve as a better tool to assess inter-individual differences in cerebral GABA level.

A T₁ magnetic resonance imaging (MRI) contrast agent specific to lung cancer stem cells Gd-DOTA-HCBP-1 was synthesized by solid-phase peptide synthesis technology. Its longitudinal relaxation rate (r₁) was 6.15 mmol^-1·L·s^-1, approximately 0.6 times higher than that of the commercial T₁ contrast agents Dotarem at 11.7 T. In vitro MRI experiments indicated that the detectability of the cancer stem cells could be significantly improved to about 2 000~4 000 cells per sphere by the use of the probe.

A macromolecular gadolinium complex was prepared as a magnetic resonance imaging (MRI) contrast agent. An ethlendiamine-modified aspartic acid-valine copolymer was synthesized, conjugated with DOTA, and then chelated with Gd³⁺(AI-EDA-DOTA-Gd). T₁-relaxivity of the agent (i.e., 12.6 mmol^-1·L·s^-1) was 2.2 times that of Gd-DOTA (i.e., 5.8 mmol^-1·L·s^-1). Hemolytic tests showed that the macromolecular contrast agent had good blood compatibility. Histological results demonstrated low toxicity of the agent in vivo. MRI experiments on rats demonstrated a prominent enhancement in liver after AI-EDA-DOTA-Gd injection, which persisted longer than that obtained by Gd-DOTA injection. The mean percentage enhancement in liver parenchyma was (55.1±5.7)% at 30~70 min after injection.

Accurate manipulation of spin systems can be achieved by radio frequency (RF) pulses, which play a key role in nuclear magnetic resonance (NMR). This paper analyses the time-frequency characteristics of RF pulses, such as the optimal shaped pulse, using different time-frequency analysis methods such as short-time Fourier transform (STFT), continuous wavelet transform (CWT) and Wigner-Ville distribution (WVD). The performances of these methods were then compared. The results indicated that combination of the above methods may achieve a better performance in analyzing the amplitude and phase distribution of complex RF pulse in joint time-frequency domain. The research provides a reference for understanding of the effects of complex RF pulses on specific spin systems.