Caiyun Shi

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.

Accelerated susceptibility-based positive contrast imaging of MR compatible metallic devices based on modified fast spin echo sequences

This study aims to develop an accelerated susceptibility-based positive contrast MR imaging method for visualizing MR compatible metallic devices. A modified fast spin echo sequence is used to accelerate data acquisition. Each readout gradient in the modified fast spin echo is slightly shifted by a short distance T shift. Phase changes accumulated within T shift are then used to calculate the susceptibility map by using a kernel deconvolution algorithm with a regularized ℓ1 minimization. To evaluate the proposed fast spin echo method, three phantom experiments were conducted and compared to a spin echo based technique and the gold standard CT for visualizing biopsy needles and brachytherapy seeds. Compared to the spin echo based technique, the data sampling speed of the proposed method was faster by 2-4 times while still being able to accurately visualize and identify the location of the biopsy needle and brachytherapy seeds. These results were confirmed by CT images of the same devices. Results also demonstrated that the proposed fast spin echo method can achieve good visualization of the brachytherapy seeds in positive contrast and in different orientations. It is also capable of correctly differentiating brachytherapy seeds from other similar structures on conventional magnitude images.

Learning block-structured incoherent dictionaries for sparse representation

Dictionary learning is still a challenging problem in signal and image processing. In this paper, we propose an efficient block-structured incoherent dictionary learning algorithm for sparse representations of image signals. The constrained minimization of dictionary learning is achieved by iteratively alternating between sparse coding and dictionary update. Without relying on any prior knowledge of the group structure for the input data, we develop a two-stage clustering method that identifies the underlying block structure of the dictionary under certain restricted constraints. The two-stage clustering method mainly consists of affinity propagation and agglomerative hierarchical clustering. To meet the conditions of both the upper bound and the lower bound of the mutual coherence of dictionary atoms, we introduce a regularization term for the objective function to adjust the block coherence of the overcomplete dictionary. The experiments on synthetic data and real images demonstrate that the proposed dictionary learning algorithm has lower representation error, higher visual quality and better reconstructed results than most of the state-of-the-art methods.中文摘要字典学习是信号和图像处理领域中的一个挑战性问题。本文提出一种有效的块结构化非相干性的字典学习算法来提高图像信号的稀疏表示效果, 采用交替迭代最小化方法来求解字典学习优化的目标函数。通过稀疏编码和字典更新两个步骤的交替迭代来实现约束字典学习的优化设计。不依赖于输入图像信号的先验知识, 我们设计一种两阶段聚类分析方法来识别学习字典的潜在块结构。为了满足字典原子相干性的约束条件, 我们对字典学习优化的目标函数, 引入一种正则项用于调节超完备字典的块相干性。模拟数据和真实图像的实验结果表明, 与现有的绝大数最先进的方法相比, 本文所提的字典学习算法能够获得较小的表示误差、较高的视觉质量和较好的重建效果。

Three-dimensional self-gated cardiac MR imaging for the evaluation of myocardial infarction in mouse model on a 3T clinical MR system

Purpose To develop and assess a three-dimensional (3D) self-gated technique for the evaluation of myocardial infarction (MI) in mouse model without the use of external electrocardiogram (ECG) trigger and respiratory motion sensor on a 3T clinical MR system. Methods A 3D T1-weighted GRE sequence with stack-of-stars sampling trajectories was developed and performed on six mice with MIs that were injected with a gadolinium-based contrast agent at a 3T clinical MR system. Respiratory and cardiac self-gating signals were derived from the Cartesian mapping of the k-space center along the partition encoding direction by bandpass filtering in image domain. The data were then realigned according to the predetermined self-gating signals for the following image reconstruction. In order to accelerate the data acquisition, image reconstruction was based on compressed sensing (CS) theory by exploiting temporal sparsity of the reconstructed images. In addition, images were also reconstructed from the same realigned data by conventional regridding method for demonstrating the advantageous of the proposed reconstruction method. Furthermore, the accuracy of detecting MI by the proposed method was assessed using histological analysis as the standard reference. Linear regression and Bland-Altman analysis were used to assess the agreement between the proposed method and the histological analysis. Results Compared to the conventional regridding method, the proposed CS method reconstructed images with much less streaking artifact, as well as a better contrast-to-noise ratio (CNR) between the blood and myocardium (4.1 ± 2.1 vs. 2.9 ± 1.1, p = 0.031). Linear regression and Bland-Altman analysis demonstrated that excellent correlation was obtained between infarct sizes derived from the proposed method and histology analysis. Conclusion A 3D T1-weighted self-gating technique for mouse cardiac imaging was developed, which has potential for accurately evaluating MIs in mice at 3T clinical MR system without the use of external ECG trigger and respiratory motion sensor.

Black-blood T2* mapping with delay alternating with nutation for tailored excitation

PURPOSE To develop a black-blood T2* mapping method using a Delay Alternating with Nutation for Tailored Excitation (DANTE) preparation combined with a multi-echo gradient echo (GRE) readout (DANTE-GRE). MATERIALS AND METHODS Simulations of the Bloch equation for DANTE-GRE were performed to optimize sequence parameters. After optimization, the sequence was applied to a phantom scan and to neck and lower extremity scans conducted on 12 volunteers at 3T using DANTE-GRE, Motion-Sensitized Driven Equilibrium (MSDE)-GRE, and multi-echo GRE. T2* values were measured using an offset model. Statistical analyses were conducted to compare the T2* values between the three sequences. RESULTS Simulation results showed that blood suppression can be achieved with various DANTE parameter adjustments. T2* maps acquired by DANTE-GRE were consistent and comparable to those acquired with multi-echo GRE in phantom experiments. In the in vivo experiments, DANTE-GRE was more comparable to multi-echo GRE than MSDE-GRE regarding the measurement of muscle T2* values. CONCLUSION Due to its high signal intensity retention and effective blood signal suppression, DANTE-GRE allows for robust and accurate T2* quantification, superior to that of MSDE-GRE, while overcoming blood flow artifacts associated with traditional multi-echo GRE.

Accelerating PS model-based dynamic cardiac MRI using compressed sensing.

High spatiotemporal resolution MRI is a challenging topic in dynamic MRI field. Partial separability (PS) model has been successfully applied to dynamic cardiac MRI by exploiting data redundancy. However, the model requires substantial preprocessing data to accurately estimate the model parameters before image reconstruction. Since compressed sensing (CS) is a potential technique to accelerate MRI by reducing the number of acquired data, the combination of PS and CS, named as Stepped-SparsePS, was introduced to accelerate the preprocessing data acquisition of PS in this work. The proposed Stepped-SparsePS method sequentially reconstructs a set of aliased dynamic images in each channel based on PS model and then the final dynamic images from the aliased images using CS. The results from numerical simulations and in vivo experiments demonstrate that Stepped-SparsePS could significantly reduce data acquisition time while preserving high spatiotemporal resolution.

High spatiotemporal resolution fMRI using partial separability model

Blood oxygenation level dependent functional MRI (BOLD fMRI) requires repeatedly scanning the same region to capture neuronal activities, so the sampling data is very sparse along the temporal direction, which offers an opportunity to accelerate the fMRI. In this paper, (k-t) space data is sparsely acquired and reconstructed for BOLD fMRI using a partial separability (PS) model with a ℓ2-norm constraint. The proposed approach achieves a high temporal resolution of 200 ms without compromising spatial resolution (3.5 × 3.5 × 4.0 mm(3)). A simulation based on the EPI data with the right finger tapping experiment demonstrates that the proposed method can realize high spatiotemporal fMRI with accurate reconstruction of the activation regions from highly undersampled data. Meanwhile, preliminary in vivo experiment results also demonstrate the potential application of the proposed method in fMRI.

High resolution magnetic resonance thermometry based on the partial separable function model

A long-standing practical problem lies in achieving magnetic resonance thermometry (MRT) with high spatiotemporal resolution because the amount of data required increases exponentially as the physical dimension increases. To solve this problem, a novel method based on a partial separable function (PSF) model was proposed by exploiting the data redundancy. In this PSF model, two datasets (image data and navigating data) are applied for image reconstruction, which determine the spatial and temporal resolution respectively. After the phase information was extracted from the images reconstructed by the PS model, high spatial and temporal resolution MRT was realized by using the reference (proton resonance frequency) PRF shift technique. The simulation and experiment results of this novel method show that the spatial and dynamic characteristics of MRT images were accurately realized by use of PSF model in MRT. This method also has a smaller distortion of the temperature measurement than the conventional MRT. The proposed data acquisition and reconstruction method may facilitate the use of MR-monitored thermal ablations as an effective treatment option especially in moving tissues, such as liver and kidney.

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.