Xiang Feng

Accelerated magnetic resonance imaging using the sparsity of multi-channel coil images.

Joint estimation of coil sensitivities and output image (JSENSE) is a promising approach that improves the reconstruction of parallel magnetic resonance imaging (pMRI). However, when acceleration factor increases, the signal to noise ratio (SNR) of JSENSE reconstruction decreases as quickly as that of the conventional pMRI. Although sparse constraints have been used to improve the JSENSE reconstruction in recent years, these constraints only use the sparsity of the output image, which cannot fully exploit the prior information of pMRI. In this paper, we use the sparsity of coil images, instead of the output image, to exploit more prior information for JSENSE. Numerical simulation, phantom and in vivo experiments demonstrate that the proposed method has better performance than the SparseSENSE method and the constrained JSENSE method using the sparsity of the output image only.

High resolution dynamic cardiac MRI using partial separability of spatiotemporal signals with a novel sampling scheme

The partial separability (PS) of spatiotemporal signals has been exploited to accelerate dynamic cardiac MRI by sampling two datasets (training and imaging datasets) without breath-holding or ECG triggering. According to the theory of partially separable functions, the wider the range of spatial frequency components covered by the training dataset, the more accurate the temporal constraint imposed by the PS model. Therefore, it is necessary to develop a new sampling scheme for the PS model in order to cover a wider range of spatial frequency components. In this paper, we propose the use of radial sampling trajectories for collecting the training dataset and Cartesian sampling trajectories for collecting the imaging dataset. In vivo high resolution cardiac MRI experiments demonstrate that the proposed data sampling scheme can significantly improve the image quality. The image quality using the PS model with the proposed sampling scheme is comparable to that of a commercial method using retrospective cardiac gating and breath-holding. The success of this study demonstrates great potential for high-quality, high resolution dynamic cardiac MRI without ECG gating or breath-holding through use of the PS model and the novel data sampling scheme.

A robust algorithm for high-resolution dynamic MRI based on the partially separable functions model

A recently developed partially separable functions (PSF) model can be used to generate high-resolution dynamic magnetic resonance imaging (MRI). However, this method could not robustly reconstruct high-quality MR images because the estimation of the PSF parameters is often interfered by the noise of the sampled MR data. To improve the robustness of MRI reconstruction using the PSF model, we proposed a new algorithm to estimate the PSF parameters by jointly using robust principal component analysis and modified truncated singular value decomposition regularization methods, instead of using the least square fitting method in the original PSF model. The experiment results of in vivo cardiac MRI demonstrated that the proposed algorithm can robustly reconstruct dynamic MR images with higher signal-to-noise ratio and clearer anatomical structures in comparison with the previous PSF model.

A simulation study of dynamic MRI based on partially separable functions and keyhole techniques

The partially separable function (PSF) model can significantly improve MRI spatial and temporal resolutions. However, the pre-data acquisition of PSF is very time-consuming in practical. To overcome this limitation, in this paper we proposed using keyhole for fast pre-data acquisition of PSF. The simulation results showed that the pre-data acquired by keyhole can be properly processed by partially separable functions, and the MR images can be reconstructed in high quality without any motion artifacts. This result demonstrated the potential of dynamic MRI by combine use of PSF and keyhole techniques.

PSF model simulation study using a cardiac phantom

Partially separable functions (PSF) model has been proposed for cardiac MRI due to its ability to get high spatial-temporal resolution dynamic images without the need of breath holding and triggering. During the PSF reconstruction, the choice of model order (L) is very important. It directly affects image quality and the models ability to capture the motion. However, how to choose the value of L accurately has never been investigated. In this work, we use a numerical cardiac phantom to study the effect of the model order, L, in image reconstruction, and try to find out the optimal model order by different approaches. Simulation result shows that the model order is determined by the number of object motional states and it can be directly calculated by using the ratios of the differences of singular values from the navigator dataset. This simulation results may give a guidance of PSF research in the future.

PS Model-Based Dynamic Cardiac MRI with Compressed Sensing

Real-time cardiac MRI is a challenging topic in MRI field. The partial separability (PS) model has been successfully applied to cardiac MR imaging. However, it is necessary to acquire lots of pre-scanned data to accurately estimate the model parameters before image reconstruction. In order to accelerate the speed of the pre-scanned data acquisition, a new method applying compressive sensing (CS) to the PS model is proposed in this paper, in which the lowrank and sparsity properties of dynamic images were used as priori information for MRI reconstruction. The experiment results show that the proposed method can achieve high resolution dynamic MR imaging and overcome the shortcoming of the conventional PS model.

High temporal resolution of dynamic MRI using the PSF model with robust reconstruction

A recently developed method known as partially separable functions (PSF) can be used to generate high temporal resolution images for dynamic imaging. And the accurate estimation of parameters is critical for the result in the PSF model. However, the parameters estimation is often interfered by noises during sampling. So the image reconstruction is fragile sometimes. To improve the robustness of reconstruction, this article proposes a new method jointly using robust Principal Component Analysis (RPCA) and modified truncated Singular Value Decomposition (MTSVD) regularization. The preliminary results of in-vivo Cardiac MR (CMR) experiment have shown that the proposed method can improve the robustness to noises when reconstructing dynamic images with high temporal resolution.

Combination of sensitivity encoding and Partial Fourier in fast thin-slab 3D MR imaging

Sensitivity Encoding (SENSE) has been widely used in fast imaging which removes the aliasing using coil sensitivities. However there are two problems applied in the second phase encoding (slice) direction in thin slab 3D imaging. First, the received B1 field knowledge may not be able to provide adequate sensitivity information due to the region of the coil cover is small. Second, the noise amplification of the reconstruction of SENSE will further expand the influence of RF leakage which degrades the quality of the image. In this work, we propose a 3D fast imaging method which combines Partial Fourier acquisition along second phase encoding (slice) direction with SENSE along first phase encoding (phase) direction. The method can efficiently reduces acquisition time with high image quality. The result of experiments using 3D GRE sequence in phantom at 3T Siemens system showed the performance of this approach.

The phase study of PSF model in MR

Partially separable functions (PSF) has been admitted highly sparse sampling in k-t space and proved an effective way to achieve high spatiotemporal resolution. However, it is still unsure whether the PSF reconstructed algorithm effects a change of the phase of magnetic resonance (MR) image. In view of this, we simulated a series of images in which the phases of designed area were exponential changed. After sparse data sampling and reconstruction by PSF, the phase variations of these images were compared before and after PSF processing. Simulation results showed that the phases of these images were preserved after the PSF reconstruction.