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The proposed method will facilitate real-time dynamic and quantitative MR imaging via OLED sequence, and deep convolutional neural network has the potential to reconstruct maps from complex MRI sequences efficiently.

A novel image encoding approach based on linear frequency-swept excitation has been recently proposed to overcome artifacts induced by various field perturbations in single-shot echo planar imaging. In this article, we develop a new super-resolved reconstruction method for it using the concepts of local k-space and partial Fourier transform. This method is superior to the originally developed conjugate gradient algorithm in convenience, image quality, and stability of solution. Reduced field-of-view is applied to the phase encoding direction to further enhance the spatial resolution and field perturbation immunity of the image obtained. Effectiveness of this new combined reconstruction method is demonstrated with a series of experiments on biological samples. Two single-shot sequences with different encoding features are tested. The results show that this reconstruction method maintains excellent field perturbation immunity and improves fidelity of the images. In vivo experiments on rat indicate that this solution is favorable for ultrafast imaging applications in which severe susceptibility heterogeneities around the tissue-air or tissue-bone interfaces, motion and oblique plane effects usually compromise the echo planar imaging image quality.

A new augmenting path based algorithm called draining algorithm is proposed for the maximum flow problem in this letter. Unlike other augmenting path based algorithms which augment gradually the flow from zero-flow to the maximum flow, the proposed algorithm drains the redundant capacities out of the network to achieve the maximum flow. Experimental results shown the high efficiency of the proposed algorithm in near saturated network, thought it has a same computational complex with the traditional augmenting path approach for regular flow networks.

Purpose To investigate the characteristics of nuclear Overhauser enhancement (NOE) imaging signals in the brain at 7T. Methods Fresh hen eggs, as well as six healthy, and six C6 glioma-bearing Wistar rats were scanned using CEST-MRI and CEST-MRS sequences (saturation duration 3 s, power 1.47 μT) with and without lipid suppression. CEST data were acquired over an offset range of −6 to +6 ppm relative to the water resonance in 0.5 ppm steps. Results The water signals were not disrupted by other protons during the CEST-MRS sequences, and true NOE signals could be observed. Using the CEST-MRI sequence without lipid suppression, pseudo NOE imaging signals were observed in the lipid-containing regions (egg yolk, scalp, and even white matter). These pseudo NOE signals were almost (but incompletely) removed with the lipid suppression. Egg yolk results indicated the presence of the NOE to olefinic protons overlapping with the water signal. In vivo experiments showed that the amide proton transfer signal was larger in the tumor, while the NOE signal was larger in the normal white matter. Conclusions True NOE signals can be detected using MRS sequences, and considerable pseudo NOE imaging signals may be observed using MRI sequences.

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The reconstruction of MR quantitative susceptibility mapping (QSM) from local phase measurements is an ill posed inverse problem and different regularization strategies incorporating a priori information extracted from magnitude and phase images have been proposed. However, the anatomy observed in magnitude and phase images does not always coincide spatially with that in susceptibility maps, which could give erroneous estimation in the reconstructed susceptibility map. In this paper, we develop a structural feature based collaborative reconstruction (SFCR) method for QSM including both magnitude and susceptibility based information. The SFCR algorithm is composed of two consecutive steps corresponding to complementary reconstruction models, each with a structural feature based l1 norm constraint and a voxel fidelity based l2 norm constraint, which allows both the structure edges and tiny features to be recovered, whereas the noise and artifacts could be reduced. In the M-step, the initial susceptibility map is reconstructed by employing a k-space based compressed sensing model incorporating magnitude prior. In the S-step, the susceptibility map is fitted in spatial domain using weighted constraints derived from the initial susceptibility map from the M-step. Simulations and in vivo human experiments at 7T MRI show that the SFCR method provides high quality susceptibility maps with improved RMSE and MSSIM. Finally, the susceptibility values of deep gray matter are analyzed in multiple head positions, with the supine position most approximate to the gold standard COSMOS result.