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Dynamic MR image reconstruction from incomplete k‐space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction problem is still challenging due to its ill‐posed nature. Most existing methods either suffer from long iterative reconstruction time or explore limited prior knowledge. This paper proposes a dynamic MR imaging method with both k‐space and spatial prior knowledge integrated via multi‐supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k‐space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multi‐supervised network training technique is developed to constrain the frequency domain information and the spatial domain information. The comparisons with classical k‐t FOCUSS, k‐t SLR, L+S and the state‐of‐the‐art CNN‐based method on in vivo datasets show our method can achieve improved reconstruction results in shorter time. [ABSTRACT FROM AUTHOR]

Purpose: To reconstruct aliasing‐free full field‐of‐view (FOV) images for reduced FOV (rFOV) parallel imaging (PI) with Cartesian and Wave sampling, which suffers from aliasing artifacts using existing PI methods. Theory and Methods: The sensitivity encoding method (SENSE) was extended to the Soft‐SENSE models supporting multiple‐set coil sensitivity maps (CSM) and point spread functions (PSF) for Cartesian and Wave sampled rFOV PI, respectively. The multiple‐set CSM and PSF were created from full FOV CSM and PSF according to the image folding process induced by rFOV sampling. The Soft‐SENSE reconstructions could be solved by the same algorithms for the conventional full FOV SENSE reconstruction. Results: Soft‐SENSE using multiple‐set full FOV CSM and PSF successfully reconstruct aliasing‐free full FOV image from rFOV PI data with Cartesian and Wave sampling. The proposed rFOV PI enables flexible control of the aliasing and achieves comparable geometry factors as the standard full FOV PI with the same net acceleration factor. Reduced FOV PI improves the computational efficiency of iterative compressed sensing (CS) and PI reconstruction, especially for high‐resolution volumetric imaging, thanks to the reduced fast Fourier transform (FFT) size. Moreover, rFOV PI reconstruction provides a potential alternative to the phase oversampling for the FOV aliasing problem. Conclusion: The proposed Soft‐SENSE using full FOV CSM and PSF could reconstruct aliasing‐free full FOV image for rFOV PI, and make it a viable solution enabling more flexible PI acceleration and effectively improving the computational efficiency of iterative CSPI reconstruction. [ABSTRACT FROM AUTHOR]

Deep learning methods have achieved attractive performance in dynamic MR cine imaging. However, most of these methods are driven only by the sparse prior of MR images, while the important low-rank (LR) prior of dynamic MR cine images is not explored, which may limit further improvements in dynamic MR reconstruction. In this paper, a learned singular value thresholding (Learned-SVT) operator is proposed to explore low-rank priors in dynamic MR imaging to obtain improved reconstruction results. In particular, we put forward a model-based unrolling sparse and low-rank network for dynamic MR imaging, dubbed as SLR-Net. SLR-Net is defined over a deep network flow graph, which is unrolled from the iterative procedures in the iterative shrinkage-thresholding algorithm (ISTA) for optimizing a sparse and LR-based dynamic MRI model. Experimental results on a single-coil scenario show that the proposed SLR-Net can further improve the state-of-the-art compressed sensing (CS) methods and sparsity-driven deep learning-based methods with strong robustness to different undersampling patterns, both qualitatively and quantitatively. Besides, SLR-Net has been extended to a multi-coil scenario, and achieved excellent reconstruction results compared with a sparsity-driven multi-coil deep learning-based method under a high acceleration. Prospective reconstruction results on an open real-time dataset further demonstrate the capability and flexibility of the proposed method on real-time scenarios. [ABSTRACT FROM AUTHOR]

Purpose: To develop a robust, accurate, and accelerated T1ρ quantification solution for submillimeter in vivo whole‐brain imaging. Methods: A multislice T1ρ mapping solution (MS‐T1ρ) was developed based on a two‐acquisition scheme using turbo spin echo with RF cycling to allow for whole‐brain coverage with 0.8‐mm in‐plane resolution. A compressed sensing–based fast imaging method, SCOPE, was used to accelerate the MS‐T1ρ acquisition time to a total scan time of 3 minutes 31 seconds. A phantom experiment was conducted to assess the accuracy of MS‐T1ρ by comparing the T1ρ value obtained using MS‐T1ρ with the reference value obtained using the standard single‐slice T1ρ mapping method. In vivo scans of 13 volunteers were acquired prospectively to validate the robustness of MS‐T1ρ. Results: In the phantom study, the T1ρ values obtained with MS‐T1ρ were in good agreement with the reference T1ρ values (R2 = 0.9991) and showed high consistency throughout all slices (coefficient of variation = 2.2 ± 2.43%). In the in vivo experiments, T1ρ maps were successfully acquired for all volunteers with no visually noticeable artifacts. There was no significant difference in T1ρ values between MS‐T1ρ acquisitions and fully sampled acquisitions for all brain tissues (p‐value >.05). In the intraclass correlation coefficient and Bland‐Altman analyses, the accelerated T1ρ measurements show moderate to good agreement to the fully sampled reference values. Conclusion: The proposed MS‐T1ρ solution allows for high‐resolution whole‐brain T1ρ mapping within 4 minutes and may provide a potential tool for investigating neural diseases. [ABSTRACT FROM AUTHOR]

Objectives: To compare visualization of joint intracranial and carotid vessel walls between 5× compressed sensing accelerated three-dimensional DANTE-SPACE sequence (CS VWI) acquired in 5 min and the same sequence accelerated by 2.7× parallel imaging (PI VWI) which takes 9–10 min currently. Methods: Following institutional review board approval and informed consent, 28 subjects including 20 stroke patients underwent PI and CS VWI examinations with an acquired spatial resolution of isotropic 0.55 mm and joint coverage of intracranial and carotid arteries. Quantitative wall thickness measurements of CS VWI and PI VWI were compared on healthy volunteers and patients with wall thickening respectively. Subjective wall visualizations of the two VWI methods on patients were scored by two radiologists blindly and independently using a 4-point scale followed by inter-rater reproducibility analysis. Results: Linear regression analysis of wall thickness measurements showed excellent agreement between CS VWI and PI VWI in both healthy volunteers (r = 0.99) and stroke patients with wall thickening (r = 0.99). Subjective wall visualization score of CS VWI was slightly lower than PI VWI (3.13 ± 0.41 vs. 3.31 ± 0.79) but still had good diagnostic quality (> 3 based on a 4-point scale). The two radiologists' scores agreed excellently, evidenced by the intraclass correlation coefficient (ICC) values being higher than 0.75 (p < 0.001). Conclusions: Compressed sensing expedients joint intracranial and carotid VWI acquired at an isotropic resolution of 0.55 mm in 5 min without compromising quantitative vessel wall thickness measurement or diagnostic wall visualization. Key Points: • CS VWI facilitates comprehensive visualization of intracranial and carotid vessel walls at an acquired isotropic resolution of 0.55 mm in a single 5-min scan. • CS VWI affords comparable vessel wall visualization and morphology measurement as PI VWI with a shortened acquisition time by 45%. • CS VWI alleviates the intensive trade-off between imaging resolution and scan time, and benefits the scan efficiency, motion robustness, and patient tolerance of high-resolution joint intracranial and carotid VWI. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to develop a novel feature refinement MR reconstruction method from highly undersampled multichannel acquisitions for improving the image quality and preserve more detail information. Theory and Methods: The feature refinement technique, which uses a feature descriptor to pick up useful features from residual image discarded by sparsity constrains, is applied to preserve the details of the image in compressed sensing and parallel imaging in MRI (CS‐pMRI). The texture descriptor and structure descriptor recognizing different types of features are required for forming the feature descriptor. Feasibility of the feature refinement was validated using three different multicoil reconstruction methods on in vivo data. Results: Experimental results show that reconstruction methods with feature refinement improve the quality of reconstructed image and restore the image details more accurately than the original methods, which is also verified by the lower values of the root mean square error and high frequency error norm. Conclusion: A simple and effective way to preserve more useful detailed information in CS‐pMRI is proposed. This technique can effectively improve the reconstruction quality and has superior performance in terms of detail preservation compared with the original version without feature refinement. Magn Reson Med 80:211–223, 2018. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

Due to the 3-D nature of hyperspectral images, as well as the spatial properties (such as regularity and continuity) of land covers, many 3-D feature extraction operators have been designed to fully exploit the joint spatial–spectral information. However, the large amount of obtained features can suffer from the “curse of dimensionality” problem, especially for the small training sample set. Moreover, various spatial–spectral features can represent the characteristics of the hyperspectral image from different aspects. In this paper, a multiple 3-D feature fusion framework (M3DF3) has been proposed for hyperspectral image classification. First, we extend the 2-D Gabor surface feature into 3-D (3DSF) domains to comply with the spatial–spectral structure of the hyperspectral image, which is directly applied on the original hyperspectral image instead of the Gabor features. Second, three 3-D feature extraction methods, including the 3-D morphological profile, the 3-D local binary pattern, and the proposed 3DSF, that, respectively, characterize the hyperspectral image from three different angles, i.e., morphology, local dependence, and shape smoothness, are fused under a multitask sparse representation framework to take full advantage of the multiple 3-D features together. The proposed M3DF3 approach was fully tested on three real-world hyperspectral image data, i.e., the widely used Indian Pines, Pavia University, and Houston University. The results show that our method can achieve as high as 68.22%, 79.44%, and 72.84% accuracies, respectively, even when only few samples, i.e., three samples per class, are used for training. [ABSTRACT FROM AUTHOR]

Recently, sparse representation-based classification (SRC), which assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, has successfully been applied to hyperspectral imagery. Alternatively, spatial information, which means the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, we have presented a new spectral-spatial-combined SRC method, abbreviated as SSSRC or $$\mathrm{S}^{3}\mathrm{RC}$$ , to jointly consider the spectral and spatial neighborhood information of each pixel to explore the spectral and spatial coherence by the SRC method. Furthermore, a fast interference-cancelation operation is adopted to accelerate the classification procedure of $$\mathrm{S}^{3}\mathrm{RC}$$ , named $$\mathrm{FS}^{3}\mathrm{RC}$$ . Experimental results have shown that both the proposed SRC-based approaches, $$\mathrm{S}^{3}\mathrm{RC}$$ and $$\mathrm{FS}^{3}\mathrm{RC}$$ , could achieve better performance than the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Purpose: The application of compressed sensing (CS) technology in magnetic resonance imaging (MRI) is to accelerate the MRI scan speed by incoherent undersampling of k-space data and nonlinear iterative reconstruction of MRI images. This paper generalizes the existing rosette trajectories to configure the sampling patterns for undersampled k-space data acquisition in MRI scans. The arch and curvature characteristics of the generalized rosette trajectories are analyzed to explore their feasibility and advantages for CS reconstruction of MRI images. Methods: Two key properties crucial to the CS MRI application, the scan speed and sampling incoherence of the generalized rosette trajectories, are analyzed. The analysis on the scan speed of generalized rosette trajectories is based on the transversal time derived from the curvature of the trajectories, and the sampling incoherence is based on the evaluation of the point spread function for the measurement matrix. The results of analysis are supported by extensive simulations where the performances of rosette, spiral, and radial sampling patterns at different acceleration factors are compared. Results: It is shown that compared with spiral trajectories, the arch and curvature characteristics of the generalized rosette trajectories are more feasible to meet the physical requirements of undersampled k-space data acquisition in terms of time shortness and scan area. It is further shown that the sampling pattern of the rosette trajectory has higher incoherence than that of the other trajectories and can thus achieve higher reconstruction performance. Reconstruction performances illustrate that the rosette trajectory can achieve about 10% higher peak signal-to-noise ratio than radial and spiral trajectories under the high acceleration factor R = 10. Conclusions: The generalized rosette trajectories can be a desirable candidate for CS reconstruction of MRI. [ABSTRACT FROM AUTHOR]

• A model-based unrolled low-rank plus sparse network (L+S-Net) for dynamic MR reconstruction is proposed. • A learned singular value thresholding (LSVT) was introduced to exploit the deep low rank prior in dynamic MR. • The iterative steps are unrolled into a network whose regularization parameters are learned during training instead of the empirical selection. • The convergence analysis guarantees the performance of the reconstruction theoretically. • Retrospective and prospective study on single-coil/multi-coil datasets show that the L+S-Net can improve the reconstruction result both qualitatively and quantitatively. [Display omitted] In dynamic magnetic resonance (MR) imaging, low-rank plus sparse (L+S) decomposition, or robust principal component analysis (PCA), has achieved stunning performance. However, the selection of the parameters of L+S is empirical, and the acceleration rate is limited, which are common failings of iterative compressed sensing MR imaging (CS-MRI) reconstruction methods. Many deep learning approaches have been proposed to address these issues, but few of them use a low-rank prior. In this paper, a model-based low-rank plus sparse network, dubbed L+S-Net, is proposed for dynamic MR reconstruction. In particular, we use an alternating linearized minimization method to solve the optimization problem with low-rank and sparse regularization. Learned soft singular value thresholding is introduced to ensure the clear separation of the L component and S component. Then, the iterative steps are unrolled into a network in which the regularization parameters are learnable. We prove that the proposed L+S-Net achieves global convergence under two standard assumptions. Experiments on retrospective and prospective cardiac cine datasets show that the proposed model outperforms state-of-the-art CS and existing deep learning methods and has great potential for extremely high acceleration factors (up to 24 ×). [ABSTRACT FROM AUTHOR]