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Chu CY, Sun CY, Kuai ZX, Yang F, Zhu YM. Structure Prior Constrained Estimation of Human Cardiac Diffusion Tensors. IEEE Trans Biomed Eng 2019; 66:3220-3230. [PMID: 30843792 DOI: 10.1109/tbme.2019.2902381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE The purpose of this paper is to increase the accuracy of human cardiac diffusion tensor (DT) estimation in diffusion magnetic resonance imaging (dMRI) with a few diffusion gradient directions. METHODS A structure prior constrained (SPC) method is proposed. The method consists in introducing two regularizers in the conventional nonlinear least squares estimator. The two regularizers penalize the dissimilarity between neighboring DTs and the difference between estimated and prior fiber orientations, respectively. A novel numerical solution is presented to ensure the positive definite estimation. RESULTS Experiments on ex vivo human cardiac data show that the SPC method is able to well estimate DTs at most voxels, and is superior to state-of-the-art methods in terms of the mean errors of principal eigenvector, second eigenvector, helix angle, transverse angle, fractional anisotropy, and mean diffusivity. CONCLUSION The SPC method is a practical and reliable alternative to current denoising- or regularization-based methods for the estimation of human cardiac DT. SIGNIFICANCE The SPC method is able to accurately estimate human cardiac DTs in dMRI with a few diffusion gradient directions.
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Lin KH, Chen YL, Lin PY, Hsieh CE, Ko CJ, Lin CC, Ming YZ. A Follow-Up Study on the Renal Protective Efficacy of Telbivudine for Hepatitis B Virus-Infected Taiwanese Patients After Living Donor Liver Transplant. EXP CLIN TRANSPLANT 2016; 15:65-68. [PMID: 28004999 DOI: 10.6002/ect.2015.0362] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVES Preservation of renal function is an important issue after living donor liver transplant. We aimed to examine the renal protective efficacy of telbivudine in hepatitis B virus-infected patients after living donor liver transplant. MATERIALS AND METHODS In this retrospective study, we compared 18 patients who received telbivudine 600 mg once per day and 23 patients who received entecavir 1 mg once per day after living donor liver transplant. Clinical data were obtained through chart review and included Model for End-Stage Liver Disease score and pre- and postoperative aspartate aminotransferase, alanine aminotransferase, and creatinine levels and estimated glomerular filtration rate. RESULTS Posttransplant estimated glomerular filtration rates and creatinine levels were calculated, and improvement of renal function was found in the group of patients who received telbivudine. Significant improvements were shown in estimated glomerular filtration rates started after 9 months of administration and creatinine levels after 12 months compared with patients who received entecavir. CONCLUSIONS In our study, long-term telbivudine therapy is associated with a sustained improvement of renal function in patients with hepatitis B virus infection after living donor liver transplant.
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Affiliation(s)
- Kuo-Hua Lin
- From the Department of General Surgery, Changhua Christian Hospital, Changhua, Taiwan
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Giannakidis A, Melkus G, Yang G, Gullberg GT. On the averaging of cardiac diffusion tensor MRI data: the effect of distance function selection. Phys Med Biol 2016; 61:7765-7786. [PMID: 27754986 DOI: 10.1088/0031-9155/61/21/7765] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Diffusion tensor magnetic resonance imaging (DT-MRI) allows a unique insight into the microstructure of highly-directional tissues. The selection of the most proper distance function for the space of diffusion tensors is crucial in enhancing the clinical application of this imaging modality. Both linear and nonlinear metrics have been proposed in the literature over the years. The debate on the most appropriate DT-MRI distance function is still ongoing. In this paper, we presented a framework to compare the Euclidean, affine-invariant Riemannian and log-Euclidean metrics using actual high-resolution DT-MRI rat heart data. We employed temporal averaging at the diffusion tensor level of three consecutive and identically-acquired DT-MRI datasets from each of five rat hearts as a means to rectify the background noise-induced loss of myocyte directional regularity. This procedure is applied here for the first time in the context of tensor distance function selection. When compared with previous studies that used a different concrete application to juxtapose the various DT-MRI distance functions, this work is unique in that it combined the following: (i) metrics were judged by quantitative-rather than qualitative-criteria, (ii) the comparison tools were non-biased, (iii) a longitudinal comparison operation was used on a same-voxel basis. The statistical analyses of the comparison showed that the three DT-MRI distance functions tend to provide equivalent results. Hence, we came to the conclusion that the tensor manifold for cardiac DT-MRI studies is a curved space of almost zero curvature. The signal to noise ratio dependence of the operations was investigated through simulations. Finally, the 'swelling effect' occurrence following Euclidean averaging was found to be too unimportant to be worth consideration.
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Affiliation(s)
- Archontis Giannakidis
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, SW3 6NP, UK. National Heart & Lung Institute, Imperial College London, London, SW3 6NP, UK
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Baust M, Weinmann A, Wieczorek M, Lasser T, Storath M, Navab N. Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging Based on a Riemannian Manifold Approach. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1972-1989. [PMID: 27168594 DOI: 10.1109/tmi.2016.2528820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this paper, we consider combined TV denoising and diffusion tensor fitting in DTI using the affine-invariant Riemannian metric on the space of diffusion tensors. Instead of first fitting the diffusion tensors, and then denoising them, we define a suitable TV type energy functional which incorporates the measured DWIs (using an inverse problem setup) and which measures the nearness of neighboring tensors in the manifold. To approach this functional, we propose generalized forward- backward splitting algorithms which combine an explicit and several implicit steps performed on a decomposition of the functional. We validate the performance of the derived algorithms on synthetic and real DTI data. In particular, we work on real 3D data. To our knowledge, the present paper describes the first approach to TV regularization in a combined manifold and inverse problem setup.
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Kong Y, Li Y, Wu J, Shu H. Noise reduction of diffusion tensor images by sparse representation and dictionary learning. Biomed Eng Online 2016; 15:5. [PMID: 26758740 PMCID: PMC4710997 DOI: 10.1186/s12938-015-0116-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2015] [Accepted: 12/11/2015] [Indexed: 11/10/2022] Open
Abstract
Background The low quality of diffusion tensor image (DTI) could affect the accuracy of oncology diagnosis. Methods We present a novel sparse representation based denoising method for three dimensional DTI by learning adaptive dictionary with the context redundancy between neighbor slices. In this study, the context redundancy among the adjacent slices of the diffusion weighted imaging volumes is utilized to train sparsifying dictionaries. Therefore, higher redundancy could be achieved for better description of image with lower computation complexity. The optimization problem is solved efficiently using an iterative block-coordinate relaxation method. Results The effectiveness of our proposed method has been assessed on both simulated and real experimental DTI datasets. Qualitative and quantitative evaluations demonstrate the performance of the proposed method on the simulated data. The experiments on real datasets with different b-values also show the effectiveness of the proposed method for noise reduction of DTI. Conclusions The proposed approach well removes the noise in the DTI, which has high potential to be applied for clinical oncology applications.
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Affiliation(s)
- Youyong Kong
- Lab of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China. .,Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China.
| | - Yuanjin Li
- Lab of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China. .,Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China. .,Department of Computer, Chuzhou University, Chuzhou, China.
| | - Jiasong Wu
- Lab of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China. .,Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China.
| | - Huazhong Shu
- Lab of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China. .,Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China.
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Wu Y, Liu G, Huang M, Guo J, Jiang J, Yang W, Chen W, Feng Q. Prostate segmentation based on variant scale patch and local independent projection. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1290-1303. [PMID: 24893258 DOI: 10.1109/tmi.2014.2308901] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Accurate segmentation of the prostate in computed tomography (CT) images is important in image-guided radiotherapy; however, difficulties remain associated with this task. In this study, an automatic framework is designed for prostate segmentation in CT images. We propose a novel image feature extraction method, namely, variant scale patch, which can provide rich image information in a low dimensional feature space. We assume that the samples from different classes lie on different nonlinear submanifolds and design a new segmentation criterion called local independent projection (LIP). In our method, a dictionary containing training samples is constructed. To utilize the latest image information, we use an online updated strategy to construct this dictionary. In the proposed LIP, locality is emphasized rather than sparsity; local anchor embedding is performed to determine the dictionary coefficients. Several morphological operations are performed to improve the achieved results. The proposed method has been evaluated based on 330 3-D images of 24 patients. Results show that the proposed method is robust and effective in segmenting prostate in CT images.
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Kong Y, Wang D, Shi L, Hui SCN, Chu WCW. Adaptive distance metric learning for diffusion tensor image segmentation. PLoS One 2014; 9:e92069. [PMID: 24651858 PMCID: PMC3961296 DOI: 10.1371/journal.pone.0092069] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 02/17/2014] [Indexed: 11/23/2022] Open
Abstract
High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.
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Affiliation(s)
- Youyong Kong
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Defeng Wang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- The Chinese University of Hong Kong Shenzhen Research Institute, Shenzhen, China
- Department of Biomedical Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- * E-mail: (DW); (WCWC)
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Steve C. N. Hui
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Winnie C. W. Chu
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- The Chinese University of Hong Kong Shenzhen Research Institute, Shenzhen, China
- * E-mail: (DW); (WCWC)
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Luo J, Wang S, Li W, Zhu Y. Removal of truncation artefacts in magnetic resonance images by recovering missing spectral data. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2012; 224:82-93. [PMID: 23063801 DOI: 10.1016/j.jmr.2012.08.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2011] [Revised: 08/14/2012] [Accepted: 08/16/2012] [Indexed: 06/01/2023]
Abstract
Truncation artefacts are often present in many archived clinical magnetic resonance (MR) images due to the need of shortening the acquisition time by sampling a part of their k-space. This artificial information degrades the quality of the image and may hamper clinical diagnosis. In this paper, we propose a novel method to remove the artefacts by recovering the missing k-space or spectral data. The method consists of four steps: (a) estimating the truncated k-space from the images containing truncations artefacts, (b) computing the parameters of the sparse representation of the difference image of an image from the estimated truncated k-space, (c) recovering the missing spectral data using the parameters computed in (b), and (d) obtaining the artefact-removed image through inverse Fourier transform of the estimated and the recovered spectral data. Experiments on both simulated and real MR images have shown that the proposed method effectively removes truncation artefacts while preserving image quality and outperforms both the conventional Hamming window method and the popular TV method.
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Affiliation(s)
- Jianhua Luo
- School of Aeronautics and Astronautics, Shanghai Jiaotong University, Shanghai 200240, PR China.
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Hu C, Qu X, Guo D, Bao L, Chen Z. Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI. Magn Reson Imaging 2011; 29:907-15. [DOI: 10.1016/j.mri.2011.04.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2011] [Revised: 04/12/2011] [Accepted: 04/22/2011] [Indexed: 11/29/2022]
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Recent advances in diffusion MRI modeling: Angular and radial reconstruction. Med Image Anal 2011; 15:369-96. [PMID: 21397549 DOI: 10.1016/j.media.2011.02.002] [Citation(s) in RCA: 82] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2010] [Revised: 01/31/2011] [Accepted: 02/08/2011] [Indexed: 02/04/2023]
Abstract
Recent advances in diffusion magnetic resonance image (dMRI) modeling have led to the development of several state of the art methods for reconstructing the diffusion signal. These methods allow for distinct features to be computed, which in turn reflect properties of fibrous tissue in the brain and in other organs. A practical consideration is that to choose among these approaches requires very specialized knowledge. In order to bridge the gap between theory and practice in dMRI reconstruction and analysis we present a detailed review of the dMRI modeling literature. We place an emphasis on the mathematical and algorithmic underpinnings of the subject, categorizing existing methods according to how they treat the angular and radial sampling of the diffusion signal. We describe the features that can be computed with each method and discuss its advantages and limitations. We also provide a detailed bibliography to guide the reader.
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