Reconstruction of 3D Cardiac MR Images from 2D Slices Using Directional Total Variation

被引:4
|
作者
Basty, Nicolas [1 ]
McClymont, Darryl [2 ]
Teh, Irvin [2 ,3 ]
Schneider, Juergen E. [2 ,3 ]
Grau, Vicente [1 ]
机构
[1] Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford, England
[2] Univ Oxford, Radcliffe Dept Med, Div Cardiovasc Med, Oxford, England
[3] Univ Leeds, Leeds Inst Cardiovasc & Metab Med, Leeds, W Yorkshire, England
基金
英国工程与自然科学研究理事会; 英国生物技术与生命科学研究理事会; 英国惠康基金;
关键词
3D image reconstruction; Super-resolution; Cardiac MRI; Regularisation; Directional total variation; SUPERRESOLUTION;
D O I
10.1007/978-3-319-67564-0_13
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Cardiac MRI allows for the acquisition of high resolution images of the heart. Long acquisition times of MRI make it impractical to image the full heart in 3D at high resolution. As a result, multiple 2D images are commonly acquired with a slice thickness greater than the in-plane resolution. One way of achieving isotropic high-resolution images is to apply post-processing techniques such as super-resolution to produce high resolution images from low resolution input. We use shortaxis stacks as well as orthogonal long-axis views in a super-resolution framework, constraining the reconstruction using the contrast independent directional total variation algorithm to produce a high resolution 3D reconstruction with isotropic resolution. The 3D reconstruction retains the contrast of the short-axis stack, but incorporates the edge information from both the short-axis and the long-axis stacks. Results show improved reconstructions, with a segmentation voxel misclassification rate of 3.51% as opposed to 4.27% using linear interpolation.
引用
收藏
页码:127 / 135
页数:9
相关论文
共 50 条
  • [31] Patient-specific 3D CT Images Reconstruction from 2D KV Images
    Ding, Y.
    Patel, S. H.
    Holmes, J.
    Feng, H.
    McGee, L. A.
    Rwigema, J. C.
    Vora, S. A.
    Wong, W. W.
    Ma, D. J.
    Foote, R. L.
    Li, B.
    Liu, W.
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2024, 118 (05): : E68 - E69
  • [32] Estimating 3D Objects from 2D Images using 3D Transformation Network
    Ul Islam, Naeem
    Park, Jaebyung
    2021 18TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS (UR), 2021, : 471 - 475
  • [33] Face recognition from 2D and 3D images using 3D Gabor filters
    Wang, YJ
    Chua, CS
    IMAGE AND VISION COMPUTING, 2005, 23 (11) : 1018 - 1028
  • [34] Fast 3D Volumetric Image Reconstruction from 2D MRI Slices by Parallel Processing
    Somoballi Ghoshal
    Shreemoyee Goswami
    Amlan Chakrabarti
    Susmita Sur-Kolay
    SN Computer Science, 5 (8)
  • [35] 3D reconstruction of spine image from 2D MRI slices along one axis
    Ghoshal, Somoballi
    Banu, Sourav
    Chakrabarti, Amlan
    Sur-Kolay, Susmita
    Pandit, Alok
    IET IMAGE PROCESSING, 2020, 14 (12) : 2746 - 2755
  • [36] Reconstruction of 3D deformation from 2D MR velocity mapping with incompressibility constraints
    Gao, JX
    Masood, S
    Deligianni, F
    Yang, GZ
    ITAB 2003: 4TH INTERNATIONAL IEEE EMBS SPECIAL TOPIC CONFERENCE ON INFORMATION TECHNOLOGY APPLICATIONS IN BIOMEDICINE, CONFERENCE PROCEEDINGS: NEW SOLUTIONS FOR NEW CHALLENGES, 2003, : 134 - 137
  • [37] Stochastic reconstruction of 3D porous media from 2D images using generative adversarial networks
    Valsecchi, Andrea
    Damas, Sergio
    Tubilleja, Cristina
    Arechalde, Javier
    NEUROCOMPUTING, 2020, 399 : 227 - 236
  • [38] 3D Model Reconstruction from Multi-views of 2D Images using Radon Transform
    Sobani, Siti Syazalina Mohd.
    Mahmood, Nasrul Humaimi
    Zakaria, Nor Aini
    Ariffin, Ismail
    JURNAL TEKNOLOGI, 2015, 74 (06): : 21 - 26
  • [39] Improved 3D human face reconstruction from 2D images using blended hard edges
    Ding Y.
    Mok P.Y.
    Neural Computing and Applications, 2024, 36 (24) : 14967 - 14987
  • [40] 3D reconstruction of 2D fluorescence histology images and registration with in vivo MR images: Application in a rodent stroke model
    Stille, Maik
    Smith, Edward J.
    Crum, William R.
    Modo, Michel
    JOURNAL OF NEUROSCIENCE METHODS, 2013, 219 (01) : 27 - 40