Lijun Bao

An efficient de-convolution reconstruction method for spatiotemporal- encoding single-scan 2D MRI

Spatiotemporal-encoding single-scan MRI method is relatively insensitive to field inhomogeneity compared to EPI method. Conjugate gradient (CG) method has been used to reconstruct super-resolved images from the original blurred ones based on coarse magnitude-calculation. In this article, a new de-convolution reconstruction method is proposed. Through removing the quadratic phase modulation from the signal acquired with spatiotemporal-encoding MRI, the signal can be described as a convolution of desired super-resolved image and a point spread function. The de-convolution method proposed herein not only is simpler than the CG method, but also provides super-resolved images with better quality. This new reconstruction method may make the spatiotemporal-encoding 2D MRI technique more valuable for clinic applications.

Structure-adaptive sparse denoising for diffusion-tensor MRI

Diffusion tensor magnetic resonance imaging (DT-MRI) is becoming a prospective imaging technique in clinical applications because of its potential for in vivo and non-invasive characterization of tissue organization. However, the acquisition of diffusion-weighted images (DWIs) is often corrupted by noise and artifacts, and the intensity of diffusion-weighted signals is weaker than that of classical magnetic resonance signals. In this paper, we propose a new denoising method for DT-MRI, called structure-adaptive sparse denoising (SASD), which exploits self-similarity in DWIs. We define a similarity measure based on the local mean and on a modified structure-similarity index to find sets of similar patches that are arranged into three-dimensional arrays, and we propose a simple and efficient structure-adaptive window pursuit method to achieve sparse representation of these arrays. The noise component of the resulting structure-adaptive arrays is attenuated by Wiener shrinkage in a transform domain defined by two-dimensional principal component decomposition and Haar transformation. Experiments on both synthetic and real cardiac DT-MRI data show that the proposed SASD algorithm outperforms state-of-the-art methods for denoising images with structural redundancy. Moreover, SASD achieves a good trade-off between image contrast and image smoothness, and our experiments on synthetic data demonstrate that it produces more accurate tensor fields from which biologically relevant metrics can then be computed.

An aliasing artifacts reducing approach with random undersampling for spatiotemporally encoded single-shot MRI

Compared to the echo planar imaging (EPI), spatiotemporally encoded (SPEN) single-shot MRI holds better immunity to the field inhomogeneity, while retaining comparable spatial and temporal resolutions after the super-resolved reconstruction. Though various reconstruction methods have been proposed, the reconstructed SPEN images usually contain aliasing artifacts because of vast undersampling. A hybrid scheme based on random sampling, singular value decomposition (SVD) and compressed sensing (CS) was introduced to reduce these aliasing artifacts and improve the image quality. The efficiency of this hybrid scheme was demonstrated by numerical simulations and experiments on water phantom and in vivo rat brain. The hybrid scheme provided herein would benefit the SPEN approach in vast undersampling situation.

A comparative study of apparent diffusion coefficient and intravoxel incoherent motion-derived parameters for the characterization of common solid hepatic tumors

Background The performance of diffusion-weighted imaging parameters for characterizing hepatic tumors is controversial. Purpose To compare the performances of apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM)-derived parameters, including the pure diffusion coefficient (D), perfusion coefficient (D*), and perfusion fraction (f), in the characterization of common solid hepatic tumors. Material and Methods Twelve healthy volunteers and 43 patients underwent free-breath diffusion-weighted magnetic resonance imaging (DW-MRI) of the liver using eight b values (10–800u2009s/mm2). Twelve regions of interest (ROIs) of normal liver tissue in healthy volunteers and 49 hepatic lesions (23 hepatocellular carcinomas [HCCs], 16 hemangiomas, and 10 metastases) were measured. Conventional ADC(0,500) and ADCtotal obtained by the mono-exponential model, as well as D, D*, and f were calculated. Student t-tests and receiver operating characteristic (ROC) analysis were also performed. Results ADC(0,500), ADCtotal, and D were significantly lower in the malignant group ([1.48u2009±u20090.35]u2009×u200910−3u2009mm2/s; [1.35u2009±u20090.30]u2009×u200910−3u2009mm2/s; [1.18u2009±u20090.33]u2009×u200910−3u2009mm2/s) compared to the hemangioma group ([2.74u2009±u20091.03]u2009× 10−3u2009mm2/s; [2.61u2009±u20090.81]u2009×u200910−3u2009mm2/s; [1.97u2009±u20090.79]u2009×u200910−3u2009mm2/s]. D* did not differ among multiple comparisons. For the area under the ROC curve (AUC-ROC), the maximum value was attained with ADCtotal (0.983) and was closely followed by ADC(0,500) (0.967), with lower values obtained for D (0.837), f (0.649), and D* (0.599). Statistically significant differences were found between the AUC-ROC of both ADCs (ADCtotal and ADC(0,500)) and D. There was no statistically significant difference between the AUC-ROC of ADCtotal and ADC(0,500). Conclusion ADCs showed superior diagnostic performance compared to IVIM-derived parameters in detecting differences between the malignant group and hemangioma group.

Denoising human cardiac diffusion tensor magnetic resonance images using sparse representation combined with segmentation

Cardiac diffusion tensor magnetic resonance imaging (DT-MRI) is noise sensitive, and the noise can induce numerous systematic errors in subsequent parameter calculations. This paper proposes a sparse representation-based method for denoising cardiac DT-MRI images. The method first generates a dictionary of multiple bases according to the features of the observed image. A segmentation algorithm based on nonstationary degree detector is then introduced to make the selection of atoms in the dictionary adapted to the images features. The denoising is achieved by gradually approximating the underlying image using the atoms selected from the generated dictionary. The results on both simulated image and real cardiac DT-MRI images from ex vivo human hearts show that the proposed denoising method performs better than conventional denoising techniques by preserving image contrast and fine structures.

Quantitative Susceptibility Mapping Using Structural Feature Based Collaborative Reconstruction (SFCR) in the Human Brain

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 l 1 norm constraint and a voxel fidelity based l 2 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.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 l 1 norm constraint and a voxel fidelity based l 2 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.

Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI

Undersampling k-space is an effective way to decrease acquisition time for MRI. However, aliasing artifacts introduced by undersampling may blur the edges of magnetic resonance images, which often contain important information for clinical diagnosis. Moreover, k-space data is often contaminated by the noise signals of unknown intensity. To better preserve the edge features while suppressing the aliasing artifacts and noises, we present a new wavelet-based algorithm for undersampled MRI reconstruction. The algorithm solves the image reconstruction as a standard optimization problem including a ℓ(2) data fidelity term and ℓ(1) sparsity regularization term. Rather than manually setting the regularization parameter for the ℓ(1) term, which is directly related to the threshold, an automatic estimated threshold adaptive to noise intensity is introduced in our proposed algorithm. In addition, a prior matrix based on edge correlation in wavelet domain is incorporated into the regularization term. Compared with nonlinear conjugate gradient descent algorithm, iterative shrinkage/thresholding algorithm, fast iterative soft-thresholding algorithm and the iterative thresholding algorithm using exponentially decreasing threshold, the proposed algorithm yields reconstructions with better edge recovery and noise suppression.

Sparse representation based MRI denoising with total variation

Diffusion tensor magnetic resonance imaging is a newly developed imaging technique; however, this technique is noise sensitive. This paper presents a novel method for sparse representation denoising of MR images that propose sparse representation of the corrupted images with the knowledge of the Rician noise model. The proposed model inferring the prior that MR images are composed of several separated regions with uniform intensity, therefore, total variation can be combined to further smooth every region. Since sparse representation performs well in extracting features from images, coupled with the total variation regularization, the method offers excellent combination of noise removal and edge preservation. The experiment results demonstrate that the proposed method preserves most of the fine structure in cardiac diffusion weighted images.

Background field removal using a region adaptive kernel for quantitative susceptibility mapping of human brain

Background field removal is an important MR phase preprocessing step for quantitative susceptibility mapping (QSM). It separates the local field induced by tissue magnetic susceptibility sources from the background field generated by sources outside a region of interest, e.g. brain, such as air-tissue interface. In the vicinity of air-tissue boundary, e.g. skull and paranasal sinuses, where large susceptibility variations exist, present background field removal methods are usually insufficient and these regions often need to be excluded by brain mask erosion at the expense of losing information of local field and thus susceptibility measures in these regions. In this paper, we propose an extension to the variable-kernel sophisticated harmonic artifact reduction for phase data (V-SHARP) background field removal method using a region adaptive kernel (R-SHARP), in which a scalable spherical Gaussian kernel (SGK) is employed with its kernel radius and weights adjustable according to an energy functional reflecting the magnitude of field variation. Such an energy functional is defined in terms of a contour and two fitting functions incorporating regularization terms, from which a curve evolution model in level set formation is derived for energy minimization. We utilize it to detect regions of with a large field gradient caused by strong susceptibility variation. In such regions, the SGK will have a small radius and high weight at the sphere center in a manner adaptive to the voxel energy of the field perturbation. Using the proposed method, the background field generated from external sources can be effectively removed to get a more accurate estimation of the local field and thus of the QSM dipole inversion to map local tissue susceptibility sources. Numerical simulation, phantom and in vivo human brain data demonstrate improved performance of R-SHARP compared to V-SHARP and RESHARP (regularization enabled SHARP) methods, even when the whole paranasal sinus regions are preserved in the brain mask. Shadow artifacts due to strong susceptibility variations in the derived QSM maps could also be largely eliminated using the R-SHARP method, leading to more accurate QSM reconstruction.

Single-Shot

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