Yihang Zhou

Undersampled dynamic magnetic resonance imaging using patch-based spatiotemporal dictionaries

Dynamic magnetic resonance imaging (dMRI) requires high spatial and temporal resolutions, which is challenging due to the low imaging speed. To reduce the imaging time, a patch-based spatiotemporal dictionary learning (DL) model is proposed for compressed-sensing reconstruction of dynamic images from undersampled data. Specifically, the dynamic image sequence is divided into overlapping patches along both the spatial and temporal directions. These patches are expected to be sparsely represented over a set of temporal-dependent spatiotemporal dictionaries. The images are then reconstructed from the undersampled data in (k,t) space under such sparseness constraints, where the dictionaries are learned iteratively. Alternating optimization is applied to solve the problem. Simulation results show that the proposed method is capable of preserving details in both spatial and temporal directions.

A kernel-based compressed sensing approach to dynamic MRI from highly undersampled data

Compressed sensing (CS) has been used in dynamic MRI to reduce the data acquisition time. Several sparsifying transforms have been investigated to sparsify the dynamic image sequence. Most existing works have studied linear transformations only. In this paper, we proposed a novel kernel-based compressed sensing approach to dynamic MRI. The method represents the image sequence sparsely and adaptively using nonlinear transformations. Such nonlinearity is implemented using the kernel method, which maps the acquired undersampled k-space data onto a high dimensional feature space, then reconstructs the image sequence in the corresponding feature space using the conventional compressed sensing, and finally convert the image sequence back into the original space. Experimental results demonstrate that the proposed method improves the reconstruction quality of dynamic ASL-based perfusion MRI over the state-of-the-art method where linear transform is used.

Accelerating t1ρ cartilage imaging using compressed sensing with iterative locally adapted support detection and JSENSE

To accelerate T1ρ quantification in cartilage imaging using combined compressed sensing with iterative locally adaptive support detection and JSENSE.

Recovery of parametric manifold from reduced measurements: Application to magnetic resonance parameter mapping

Magnetic resonance (MR) quantitative imaging such as parameter mapping has great potential in clinical applications. It requires acquisition of a sequence of images at different time points to extract the quantitative parameters. A practical challenge is the tradeoff between spatial resolution and acquisition speed. A number of methods have been proposed to address the challenge. In this paper, a novel manifold recovery approach is proposed to obtain the quantitative map from highly reduced measurements. The performance of the proposed method is demonstrated using simulated and real datasets on parameter mapping.

k-t CSPI: A dynamic MRI reconstruction framework for combining compressed sensing and parallel imaging

In this paper, we propose a new dynamic MR image reconstruction technique that combines the compressed sensing-based dynamic methods with parallel imaging techniques to achieve high accelerations. The method decouples the reconstruction process into two sequential steps. In the first step, a series of aliased dynamic images is reconstructed using a CS method from the highly undersampled £-space data. In the second step, the missing £-space data for the original image are reconstructed by the nonlinear GRAPPA technique. The sampling strategy for each step is thereby designed independently such that the incoherent undersampling requirement for CS and structured undersampling requirement for parallel imaging can be satisfied simultaneously. Experimental results demonstrate that the proposed method improves the reconstruction quality of dynamic cardiac cine MRI over the state-of-the-art method.

Enabling high-speed wide-field dynamic imaging in multifocal photoacoustic computed microscopy: a simulation study.

Photoacoustic-computed microscopy (PACM) is an emerging technology that employs thousands of optical foci to provide wide-field high-resolution images of tissue optical absorption. A major limitation of PACM is the slow imaging speed, limiting its usage in dynamic imaging. In this study, we improved the speed through a two-step approach. First, we employed compressed sensing with partially known support to reduce the transducer element number, which subsequently improved the imaging speed at each optical scanning step. Second, we use the high-speed low-resolution image acquired without microlens array to inform dynamic changes in the high-resolution PACM image. Combining both approaches, we achieved high-resolution dynamic imaging over a wide field.

Accelerating MR parameter mapping using nonlinear manifold learning and supervised pre-imaging

In this paper, we propose a new reconstruction framework that utilizes nonlinear models to sparsely represent the MR parameter-weighted image in a high dimensional feature space. Different from the prior work with nonlinear models where the image series is reconstructed simultaneously, each image at a specific time point is assumed to lie in a low-dimensional manifold and is reconstructed individually. The low-dimensional manifold is learned from the training images generated by the parametric model. To reconstruct each image, among infinite number of solutions that satisfy the data consistent constraint, the one that is closest to the manifold is selected as the desired solution. The underlying optimization problem is solved using kernel trick and split Bregman iteration algorithm. The proposed method was evaluated on a set of in-vivo brain T2 mapping data set and shown to be superior to the conventional compressed sensing methods.

Accelerating dynamic magnetic resonance imaging by nonlinear sparse coding

Although being high-dimensional, dynamic magnetic resonance images usually lie on low-dimensional manifolds. Nonlinear models have been shown to capture well that latent low-dimensional nature of data, and can thus lead to improvements in the quality of constrained recovery algorithms. This paper advocates a novel reconstruction algorithm for dynamic magnetic resonance imaging (dMRI) based on nonlinear dictionary learned from low-spatial but high-temporal resolution images. The nonlinear dictionary is initially learned using kernel dictionary learning, and the proposed algorithm subsequently alternates between sparsity enforcement in the feature space and the data-consistency constraint in the original input space. Extensive numerical tests demonstrate that the proposed scheme is superior to popular methods that use linear dictionaries learned from the same set of training data.

Conflict-Cost Based Random Sampling Design for Parallel MRI with Low Rank Constraints

In compressed sensing MRI, it is very important to design sampling pattern for random sampling. For example, SAKE (simultaneous auto-calibrating and k-space estimation) is a parallel MRI reconstruction method using random undersampling. It formulates image reconstruction as a structured low-rank matrix completion problem. Variable density (VD) Poisson discs are typically adopted for 2D random sampling. The basic concept of Poisson disc generation is to guarantee samples are neither too close to nor too far away from each other. However, it is difficult to meet such a condition especially in the high density region. Therefore the sampling becomes inefficient. In this paper, we present an improved random sampling pattern for SAKE reconstruction. The pattern is generated based on a conflict cost with a probability model. The conflict cost measures how many dense samples already assigned are around a target location, while the probability model adopts the generalized Gaussian distribution which includes uniform and Gaussian-like distributions as special cases. Our method preferentially assigns a sample to a k-space location with the least conflict cost on the circle of the highest probability. To evaluate the effectiveness of the proposed random pattern, we compare the performance of SAKEs using both VD Poisson discs and the proposed pattern. Experimental results for brain data show that the proposed pattern yields lower normalized mean square error (NMSE) than VD Poisson discs.

Highly accelerated 3D dynamic contrast enhanced MRI from sparse spiral sampling using integrated partial separability model andJSENSE

Dynamic contrast enhanced MRI requires high spatial resolution for morphological information and high temporal resolution for contrast pharmacokinetics. The current techniques usually have to compromise the spatial information for the required temporal resolution. This paper presents a novel method that effectively integrates sparse sampling, parallel imaging, partial separable (PS) model, and sparsity constraints for highly accelerated DCE-MRI. Phased array coils were used to continuously acquire data from a stack of variable-density spiral trajectory with a golden angle. In reconstruction, the sparsity constraints, the coil sensitivities, spatial and temporal bases of the PS model are jointly estimated through alternating optimization. Experimental results from in vivo DCE liver imaging data show that the proposed method is able to achieve high spatial and temporal resolutions at the same time.