Yanqiu Feng

In vivo comparison of myocardial T1 with T2 and T2* in thalassaemia major

To compare myocardial T1 against T2 and T2* in patients with thalassemia major (TM) for myocardial iron characterization.

Improved MRI R2 * relaxometry of iron-loaded liver with noise correction.

Accurate and reproducible MRI R2* relaxometry for tissue iron quantification is important in managing transfusion‐dependent patients. MRI data are often acquired using array coils and reconstructed by the root‐sum‐square algorithm, and as such, measured signals follow the noncentral chi distribution. In this study, two noise‐corrected models were proposed for the liver R2* quantification: fitting the signal to the first moment and fitting the squared signal to the second moment in the presence of the noncentral chi noise. These two models were compared with the widely implemented offset and truncation models on both simulation and in vivo data. The results demonstrated that the “slow decay component” of the liver R2* was mainly caused by the noise. The offset model considerably overestimated R2* values by incorrectly adding a constant to account for the slow decay component. The truncation model generally produced accurate R2* measurements by only fitting the initial data well above the noise level to remove the major source of errors, but underestimated very high R2* values due to the sequence limit of obtaining very short echo time images. Both the first and second‐moment noise‐corrected models constantly produced accurate and precise R2* measurements by correctly addressing the noise problem. Magn Reson Med 70:1765–1774, 2013.

Sparse angular CT reconstruction using non-local means based iterative-correction POCS

In divergent-beam computed tomography (CT), sparse angular sampling frequently leads to conspicuous streak artifacts. In this paper, we propose a novel non-local means (NL-means) based iterative-correction projection onto convex sets (POCS) algorithm, named as NLMIC-POCS, for effective and robust sparse angular CT reconstruction. The motivation for using NLMIC-POCS is that NL-means filtered image can produce an acceptable priori solution for sequential POCS iterative reconstruction. The NLMIC-POCS algorithm has been tested on simulated and real phantom data. The experimental results show that the presented NLMIC-POCS algorithm can significantly improve the image quality of the sparse angular CT reconstruction in suppressing streak artifacts and preserving the edges of the image.

Generalized Gibbs priors based positron emission tomography reconstruction

Bayesian methods have been widely applied to the ill-posed problem of image reconstruction. Typically the prior information of the objective image is needed to produce reasonable reconstructions. In this paper, we propose a novel generalized Gibbs prior (GG-Prior), which exploits the basic affinity structure information in an image. The motivation for using the GG-Prior is that it has been shown to be effective noise suppression, while also maintaining sharp edges without oscillations. This feature makes it particularly attractive for the reconstruction of positron emission tomography (PET) where the aim is to identify the shape of objects from the background by sharp edges. We show that the standard paraboloidal surrogate coordinate ascent (PSCA) algorithm can be modified to incorporate the GG-Prior using a local linearized scheme in each iteration process. The proposed GG-Prior MAP reconstruction algorithm based on PSCA has been tested on simulated, real phantom data. Comparison studies with conventional filtered backprojection (FBP) method and Huber prior clearly demonstrate that the proposed GG-Prior performs better in lowering the noise, preserving the image edge and in higher signal noise ratio (SNR) condition.

Denoising of 3D magnetic resonance images by using higher-order singular value decomposition

The denoising of magnetic resonance (MR) images is important to improve the inspection quality and reliability of quantitative image analysis. Nonlocal filters by exploiting similarity and/or sparseness among patches or cubes achieve excellent performance in denoising MR images. Recently, higher-order singular value decomposition (HOSVD) has been demonstrated to be a simple and effective method for exploiting redundancy in the 3D stack of similar patches during denoising 2D natural image. This work aims to investigate the application and improvement of HOSVD to denoising MR volume data. The wiener-augmented HOSVD method achieves comparable performance to that of BM4D. For further improvement, we propose to augment the standard HOSVD stage by a second recursive stage, which is a repeated HOSVD filtering of the weighted summation of the residual and denoised image in the first stage. The appropriate weights have been investigated by experiments with different image types and noise levels. Experimental results over synthetic and real 3D MR data demonstrate that the proposed method outperforms current state-of-the-art denoising methods.

Automated truncation method for myocardial T2* measurement in thalassemia.

To propose an automated truncation method for myocardial T2* measurement and evaluate this method on a large population of patients with iron loading in the heart and scanned at multiple magnetic resonance imaging (MRI) centers.

Improved pixel-by-pixel MRI R2* relaxometry by nonlocal means.

To investigate the feasibility of improving MRI R2* mapping by filtering the images before curve‐fitting.

Denoising MR Images Using Non-Local Means Filter with Combined Patch and Pixel Similarity

Denoising is critical for improving visual quality and reliability of associative quantitative analysis when magnetic resonance (MR) images are acquired with low signal-to-noise ratios. The classical non-local means (NLM) filter, which averages pixels weighted by the similarity of their neighborhoods, is adapted and demonstrated to effectively reduce Rician noise without affecting edge details in MR magnitude images. However, the Rician NLM (RNLM) filter usually blurs small high-contrast particle details which might be clinically relevant information. In this paper, we investigated the reason of this particle blurring problem and proposed a novel particle-preserving RNLM filter with combined patch and pixel (RNLM-CPP) similarity. The results of experiments on both synthetic and real MR data demonstrate that the proposed RNLM-CPP filter can preserve small high-contrast particle details better than the original RNLM filter while denoising MR images.

Automated interventricular septum segmentation for black-blood myocardial T2* measurement in thalassemia.

To develop and validate an automated segmentation method that extracts the interventricular septum (IS) from myocardial black‐blood images for the T2* measurement in thalassemia patients.

Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data.

Image reconstruction from undersampled k-space data accelerates magnetic resonance imaging (MRI) by exploiting image sparseness in certain transform domains. Employing image patch representation over a learned dictionary has the advantage of being adaptive to local image structures and thus can better sparsify images than using fixed transforms (e.g. wavelets and total variations). Dictionary learning methods have recently been introduced to MRI reconstruction, and these methods demonstrate significantly reduced reconstruction errors compared to sparse MRI reconstruction using fixed transforms. However, the synthesis sparse coding problem in dictionary learning is NP-hard and computationally expensive. In this paper, we present a novel sparsity-promoting orthogonal dictionary updating method for efficient image reconstruction from highly undersampled MRI data. The orthogonality imposed on the learned dictionary enables the minimization problem in the reconstruction to be solved by an efficient optimization algorithm which alternately updates representation coefficients, orthogonal dictionary, and missing k-space data. Moreover, both sparsity level and sparse representation contribution using updated dictionaries gradually increase during iterations to recover more details, assuming the progressively improved quality of the dictionary. Simulation and real data experimental results both demonstrate that the proposed method is approximately 10 to 100 times faster than the K-SVD-based dictionary learning MRI method and simultaneously improves reconstruction accuracy.