diffusion-weighted image; Gaussian pyramid; Intravoxel incoherent motion imaging; iterative back-projection; super-resolution reconstruction; Diffusion weighted images; High resolution image; Intravoxel incoherent motions; Inverse affine transformations; Iterative back projections; Low resolution images; State-of-art methods; Super resolution reconstruction; Instrumentation; Electrical and Electronic Engineering
Abstract :
[en] Intravoxel incoherent motion imaging (IVIM) is a new magnetic resonance imaging (MRI) technique that can detect both diffusion and pseudo perfusion microscopic tissue information. However, it requires to acquire the diffusion-weighted (DW) images with multiple b-values, which leads to long acquisition time. To reduce the time, the spatial resolution of IVIM is usually not high enough to reflect the image details. To solve this problem, in this study, a multi-similarity block-based super-resolution reconstruction (SRR) method for IVIM was proposed. The fact that the image block similarity of DW images occurs not only across or within the images at different scales, but also the adjacent slices with the same b-values. The intra-slice and inter-slice similarity at multi-scales is defined as multi-similarity in this work. To fully explore the multi-similarity patches to assist SRR, a chain of Gaussian pyramids of a given image slice and the corresponding adjacent slices with the same b-value are established first, and then a random search strategy is used to find and identify the most similar blocks in low resolution (LR) image pyramids. Next, the matched blocks are mapped to high resolution (HR) image pyramids and then unwrapped by inverse affine transformation to replace original LR blocks. Finally, the super-resolution images are obtained through an iterative back-projection algorithm. To evaluate the performance of proposed method, we compared the brain and liver IVIM reconstruction results with several state-of-art methods. The results indicate that the multi-similarity-based method can achieve the best reconstruction results in both DW images and the corresponding IVIM parameter maps.
Disciplines :
Computer science
Author, co-author :
Huang, Jiqing ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) ; Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China
Wang, Lihui ; Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China
Qin, Jin; Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China
Chen, Yi; Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China
Cheng, Xinyu; Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China
Zhu, Yuemin; CNRS UMR 5220, Inserm U1206, INSA Lyon, Creatis, University of Lyon, Villeurbanne, France
Language :
English
Title :
Super-Resolution of Intravoxel Incoherent Motion Imaging Based on Multisimilarity
Publication date :
15 September 2020
Journal title :
IEEE Sensors Journal
ISSN :
1530-437X
eISSN :
1558-1748
Publisher :
Institute of Electrical and Electronics Engineers Inc.
Manuscript received February 18, 2020; revised May 6, 2020; accepted May 8, 2020. Date of publication May 11, 2020; date of current version August 14, 2020. This work was supported in part by the National Nature Science Foundations of China under Grant 61661010 and Grant 61562009, in part by the Program PHC Yuanpei 2018 under Grant 41400TC, in part by the Funds for Talents of Guizhou University under Grant 2013(33), and in part by the Guizhou Science and Technology Plan Project under Grant Qiankehe [2018]5301. The associate editor coordinating the review of this article and approving it for publication was Dr. Ing. Emiliano Schena. (Corresponding author: Lihui Wang.) Jiqing Huang, Lihui Wang, Jin Qin, Yi Chen, and Xinyu Cheng are with the Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang 550025, China (e-mail: jiqing-huang@outlook.com; wlh1984@gmail.com; qin_gs@163.com; yi-chen.gzu@outlook.com; xycheng@gzu.edu.cn).
Y. T. Li, J.-P. Cercueil, J. Yuan, W. Chen, R. Loffroy, Y. Xiáng, and J. Wáng, "Liver intravoxel incoherent motion (IVIM) magnetic resonance imaging: A comprehensive review of published data on normal values and applications for fibrosis and tumor evaluation, " Quantum Imag. Med. Surg., vol. 7, no. 1, p. 59, 2017.
B. Scherrer, A. Gholipour, and S. K. Warfield, "Super-resolution in diffusion-weighted imaging, " in Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervent., 2011, pp. 124-132.
N. M. H. Elsaid and Y.-C. Wu, "Super-resolution diffusion tensor imaging using SRCNN: A feasibility study, " in Proc. 41st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), Jul. 2019, pp. 2830-2834.
B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, "Enhanced deep residual networks for single image super-resolution, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), Jul. 2017, pp. 136-144.
L. M. Delfin, R. P. Elias, H. de Jesús Ochoa Domínguez, and O. O. O. Villegas, "Auto-regularized gradients of adaptive interpolation for MRI super-resolution, " J. Signal Process. Syst., vol. 91, no. 8, pp. 885-898, Aug. 2019.
J. V. Manjón, P. Coupé, A. Buades, V. Fonov, D. L. Collins, and M. Robles, "Non-local MRI upsampling, " Med. Image Anal., vol. 14, no. 6, pp. 784-792, Dec. 2010.
F. Yang, Y.-M. Zhu, I. E. Magnin, J.-H. Luo, P. Croisille, and P. B. Kingsley, "Feature-based interpolation of diffusion tensor fields and application to human cardiac DT-MRI, " Med. Image Anal., vol. 16, no. 2, pp. 459-481, Feb. 2012.
S. Yu, R. Zhang, S. Wu, J. Hu, and Y. Xie, "An edge-directed interpolation method for fetal spine MR images, " Biomed. Eng. OnLine, vol. 12, no. 1, p. 102, 2013.
F. Shi, J. Cheng, L. Wang, P.-T. Yap, and D. Shen, "LRTV: MR image super-resolution with low-rank and total variation regularizations, " IEEE Trans. Med. Imag., vol. 34, no. 12, pp. 2459-2466, Dec. 2015.
J. T. Hsu, C. C. Yen, C. C. Li, M. Sun, B. Tian, and M. Kaygusuz, "Application of wavelet-based POCS super-resolution for cardiovascular MRI image enhancement, " in Proc. 3rd Int. Conf. Image Graph. (ICIG), 2004, pp. 572-575.
S. Tourbier, X. Bresson, P. Hagmann, J.-P. Thiran, R. Meuli, and M. B. Cuadra, "An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization, " NeuroImage, vol. 118, pp. 584-597, Sep. 2015.
X. Zhang, E. Y. Lam, E. X. Wu, and K. K. Y. Wong, "Application of Tikhonov regularization to super-resolution reconstruction of brain MRI images, " in Medical Imaging and Informatics. Berlin, Germany: Springer, 2008, pp. 51-56.
J. Yang, J. Wright, T. S. Huang, and Y. Ma, "Image super-resolution via sparse representation, " IEEE Trans. Image Process., vol. 19, no. 11, pp. 2861-2873, Nov. 2010.
W. Dong, L. Zhang, G. Shi, and X. Wu, "Image deblurring and superresolution by adaptive sparse domain selection and adaptive regularization, " IEEE Trans. Image Process., vol. 20, no. 7, pp. 1838-1857, 2011.
R. Timofte, V. De Smet, and L. Van Gool, "A+: Adjusted anchored neighborhood regression for fast super-resolution, " in Proc. Asian Conf. Comput. Vis., 2014, pp. 111-126.
Y.-H. Wang, J. Qiao, J.-B. Li, P. Fu, S.-C. Chu, and J. F. Roddick, "Sparse representation-based MRI super-resolution reconstruction, " Measurement, vol. 47, pp. 946-953, Jan. 2014.
K. Srinivasan, A. Sharma, and A. Ankur, "Group sparse based superresolution of magnetic resonance images for superior lesion diagnosis, " in Proc. 1st Int. Conf. Med. Health Informat. (ICMHI), 2017, pp. 95-100.
J. Ota, K. Umehara, N. Ishimaru, and T. Ishida, "Application of sparsecoding super-resolution to 16-bit DICOM images for improving the image resolution in MRI, " Open J. Med. Imag., vol. 7, no. 4, p. 144, 2017.
J. Kim, J. K. Lee, and K. M. Lee, "Accurate image super-resolution using very deep convolutional networks, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, pp. 1646-1654.
W. Shi et al., "Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, pp. 1874-1883.
C. Ledig et al., "Photo-realistic single image super-resolution using a generative adversarial network, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 4681-4690.
C. Dong, C. C. Loy, and X. Tang, "Accelerating the super-resolution convolutional neural network, " in Proc. Eur. Conf. Comput. Vis., 2016, pp. 391-407.
M. Haris, G. Shakhnarovich, and N. Ukita, "Deep back-projection networks for super-resolution, " in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 1664-1673.
S. Jain et al., "Patch-based super-resolution of MR spectroscopic images: Application to multiple sclerosis, " Frontiers Neurosci., vol. 11, p. 13, Jan. 2017.
J.-B. Huang, A. Singh, and N. Ahuja, "Single image super-resolution from transformed self-exemplars, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2015, pp. 5197-5206.
C. Ye et al., "Estimation of intravoxel incoherent motion parameters using low b-values, " PLoS ONE, vol. 14, no. 2, Feb. 2019, Art. no. e0211911.
C. Dong, C. C. Loy, K. He, and X. Tang, "Image super-resolution using deep convolutional networks, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 295-307, Feb. 2016.
M. Delbany et al., "One-millimeter isotropic breast diffusion-weighted imaging: Evaluation of a superresolution strategy in terms of signal-tonoise ratio, sharpness and apparent diffusion coefficient, " Magn. Reson. Med., vol. 81, no. 4, pp. 2588-2599, Apr. 2019.
J. Salvador, Example-Based Super Resolution. New York, NY, USA: Academic, 2016.
H. Wang et al., "Deep learning enables cross-modality superresolution in fluorescence microscopy, " Nature Methods, vol. 16, no. 1, pp. 103-110, Jan. 2019.
W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang, "Deep Laplacian pyramid networks for fast and accurate super-resolution, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 624-632.
