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MRI Cross-Modality NeuroImage-to-NeuroImage Translation [article]

Qianye Yang, Nannan Li, Zixu Zhao, Xingyu Fan, Eric I-Chao Chang, Yan Xu
2018 arXiv   pre-print
Keywords: image-to-image, cross-modality, registration, segmentation, brain MRI  ...  Based on our proposed framework, we first propose a method for cross-modality registration by fusing the deformation fields to adopt the cross-modality information from translated modalities.  ...  Cross-Modality Registration The first application of our cross-modality generation framework is to use the translated modality for cross-modality image registration.  ... 
arXiv:1801.06940v2 fatcat:bmsizxutvffh5dgk47mxnmsb24

MRI Cross-Modality Image-to-Image Translation

Qianye Yang, Nannan Li, Zixu Zhao, Xingyu Fan, Eric I-Chao Chang, Yan Xu
2020 Scientific Reports  
Based on our proposed framework, we first propose a method for cross-modality registration by fusing the deformation fields to adopt the cross-modality information from translated modalities.  ...  We present a cross-modality generation framework that learns to generate translated modalities from given modalities in MR images.  ...  Contributions: (1) We introduce the end-to-end Image Modality Translation (IMT) network for cross-modality MRI generation to synthesize translated modalities from given modalities.  ... 
doi:10.1038/s41598-020-60520-6 pmid:32111966 pmcid:PMC7048849 fatcat:gimqzl7w2vfnjf4iisypuig2kq

Cross-modal Attention for MRI and Ultrasound Volume Registration [article]

Xinrui Song, Hengtao Guo, Xuanang Xu, Hanqing Chao, Sheng Xu, Baris Turkbey, Bradford J. Wood, Ge Wang, Pingkun Yan
2021 arXiv   pre-print
This paper aims to develop a self-attention mechanism specifically for cross-modal image registration.  ...  In the past few years, convolutional neural networks (CNNs) have been proved powerful in extracting image features crucial for image registration.  ...  Cross-modal Attention The proposed cross-modal attention block takes image features extracted from MRI and TRUS volumes by the preceding convolutional layers.  ... 
arXiv:2107.04548v2 fatcat:tnop2te3ivcunnzvydoetkvj4e

Standardized platform for coregistration of nonconcurrent diffuse optical and magnetic resonance breast images obtained in different geometries

Fred S. Azar, Kijoon Lee, Ali Khamene, Regine Choe, Alper Corlu, Soren D. Konecky, Frank Sauer, Arjun G. Yodh
2007 Journal of Biomedical Optics  
We combine data based on diffuse optical tomography ͑DOT͒ and magnetic resonance imaging ͑MRI͒.  ...  We demonstrate the multimodal registration method using a simulated phantom, and we present initial patient studies that confirm that tumorous regions in a patient breast found by both imaging modalities  ...  We acknowledge support from the NIH/NCI program U54 CA105480, "A Network for Translational Research in Optical Imaging: Multi-Dimensional Diffuse Optical Imaging in Breast Cancer," and also from the NIH  ... 
doi:10.1117/1.2798630 pmid:17994885 fatcat:avz5itruffh4zne47atky3f4u4

Self-Supervised Multi-Modal Alignment for Whole Body Medical Imaging [article]

Rhydian Windsor, Amir Jamaludin, Timor Kadir, Andrew Zisserman
2021 arXiv   pre-print
This paper explores the use of self-supervised deep learning in medical imaging in cases where two scan modalities are available for the same subject.  ...  (iii) Finally, we use these registrations to transfer segmentation maps from the DXA scans to the MR scans where they are used to train a network to segment anatomical regions without requiring ground-truth  ...  We are also grateful for support from a Royal Society Research Professorship and EPSRC Programme Grant Visual AI (EP/T028572/1).  ... 
arXiv:2107.06652v2 fatcat:tbxcki6gq5enlgiklish5zezfq

Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation [article]

Jue Jiang, Jason Hu, Neelam Tyagi, Andreas Rimner, Sean L. Berry, Joseph O. Deasy, Harini Veeraraghavan
2019 arXiv   pre-print
Therefore, we developed a cross-modality educed learning approach where MR information that is educed from CT is used to hallucinate MRI and improve CT segmentation.  ...  Our approach, called cross-modality educed deep learning segmentation (CMEDL) combines CT and pseudo MR produced from CT by aligning their features to obtain segmentation on CT.  ...  is the MRI image;GC→M and GM→C are the CT and MRI transfer networks; x m is the translated MRI image from xc; x c is the translated MRI image from xm. 1 A) generates a pMR image given a CT image.  ... 
arXiv:1909.04542v1 fatcat:minn5r2v7vfhnhylx5nhji5heq

Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images [article]

Adrià Casamitjana, Matteo Mancini, Juan Eugenio Iglesias
2021 arXiv   pre-print
We introduce a registration loss for weakly supervised image translation between domains that does not require perfectly aligned training data.  ...  Nonlinear inter-modality registration is often challenging due to the lack of objective functions that are good proxies for alignment.  ...  Image-to-Image translation using a registration loss The modality translation is performed by a generator network, G, with a similar architecture to [26] and trained using a combination of two losses  ... 
arXiv:2107.14449v2 fatcat:hfrh3itv2fcbfgaycmwa22ensu

Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy

Gayatri Kompella, Jeevakala Singarayan, Maria Antico, Fumio Sasazawa, Takeda Yu, Keerthi Ram, Ajay K. Pandey, Davide Fontanarosa, Mohanasankar Sivaprakasam
2022 Applied Sciences  
We have shown that the proposed algorithm is simple, robust and allows for the automatic global registration of 3D US and MRI that can enable US based image guidance in minimally invasive procedures.  ...  Registration of partial view intra-operative ultrasound (US) to pre-operative MRI is an essential step in image-guided minimally invasive surgery.  ...  This choice addresses the dual needs of working with cross-modal registration, and partial views.  ... 
doi:10.3390/app12115488 fatcat:4vjginjkkbg6vimi4w4loh7gwe

Comparative Study of Relevant Methods for MRI/X Brain Image Registration [chapter]

Marwa Abderrahim, Abir Baâzaoui, Walid Barhoumi
2020 Lecture Notes in Computer Science  
MRI and CT images.  ...  Most traditional registration tools use different methods for mono-and multi-modal registration, whereas the hybrid registration method is providing both mono and multi-modal brain registration of PET,  ...  Insight Segmentation and Registration Toolkit (ITK-Snap) is a popular tool for segmenting and registering medical images such as MRI, PET and CT [7] .  ... 
doi:10.1007/978-3-030-51517-1_30 fatcat:yna3jmmavvgt7apvcyy4yrbxqq

A Survey of Cross-Modality Brain Image Synthesis [article]

Guoyang Xie, Jinbao Wang, Yawen Huang, Yefeng Zheng, Feng Zheng, Yaochu Jin
2022 arXiv   pre-print
In this paper, we tend to approach multi-modality brain image synthesis task from different perspectives, which include the level of supervision, the range of modality synthesis, and the synthesis-based  ...  Particularly, we provide in-depth analysis on how cross-modality brain image synthesis can improve the performance of different downstream tasks.  ...  Our work aims to motivate the medical GANs to focus on how to make an appropriate cross-modality brain image synthesis to correctly improve their downstream tasks, such as image segmentation, registration  ... 
arXiv:2202.06997v2 fatcat:kqxte2xrcrcpjfkkhwrcxdjqsu

Multimodal Image Registration for Efficient Multi-resolution Visualization [chapter]

Joerg Meyer
2008 Mathematics and Visualization  
Arising from the clinical need for multimodal imaging, an integrated system for automated multimodal image registration and multi-source volume rendering has been developed, enabling simultaneous processing  ...  Efficient storage and processing of multimodal images as well as histogram transformation and registration will be discussed.  ...  Section 3 describes the pre-processing step of segmenting the images in each modality and correlating the scalar values for each cluster.  ... 
doi:10.1007/978-3-540-72630-2_8 fatcat:6u2mldbqmjevzav6rdupi5uxfy

Projection Profile Matching for Intraoperative MRI Registration Embedded in MR Imaging Sequence [chapter]

Nobuhiko Hata, Junichi Tokuda, Shigeo Morikawa, Takeyoshi Dohi
2002 Lecture Notes in Computer Science  
Fast image registration for magnetic resonance image (MRI)-guided surgery using projection profile matching embedded in MR pulse sequence is proposed.  ...  The paper also includes in-vivo experiment to registration MRI arm in motion.  ...  One of the solutions to overcome these limitations in intraoperative MRI is to fuse pre-operative MRI and/or other multi-modality images to provide complementary information [4] [5] [6] [7] [8] .  ... 
doi:10.1007/3-540-45787-9_21 fatcat:gahdvrh6onbcpo6oeznerfcrou

A statistical model-based technique for accounting for prostate gland deformation in endorectal coil-based MR imaging

A. M. Tahmasebi, R. Sharifi, H. K. Agarwal, B. Turkbey, M. Bernardo, P. Choyke, P. Pinto, B. Wood, J. Kruecker
2012 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
Using a leave-one-out cross-validation, our results demonstrated a mean estimation error of 1mm for a MR-to-MR registration.  ...  Despite such advantage, there exists a major complication in fusion of the two imaging modalities due to the deformation of the prostate shape in ERC-MRI.  ...  In order to use the proposed framework for a nonlinear registration between ERC-MRI and any other modality such as CT images, a set of landmarks that are identifiable in both modalities are extracted and  ... 
doi:10.1109/embc.2012.6347218 pmid:23367153 pmcid:PMC6663485 fatcat:n7bajfxdhnf43djzmyksyrctzy

Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging [article]

Yexing Hu and Berkan Lafci and Artur Luzgin and Hao Wang and Jan Klohs and Xose Luis Dean-Ben and Ruiqing Ni and Daniel Razansky and Wuwei Ren
2021 arXiv   pre-print
Nevertheless, registration of multi-modal images remains challenging, chiefly due to the entirely different image contrast rendered by these modalities.  ...  Here we propose a fully automated registration method for MSOT-MRI multimodal imaging empowered by deep learning.  ...  Table 3 . 3 1: Cross validation results for MRI and MSOT segmentation network Fold number Dice coefficients for MRI segmentation Dice coefficients for MSOT segmentation 1 0.991 0.982 2 0.990  ... 
arXiv:2109.01880v1 fatcat:4j7i2drterfpldeb3xaeu6ti64

Opportunities for image analysis in radiation oncology

Jason A. Dowling
2014 Australasian physical & engineering sciences in medicine  
Different methods may be required for mono-modal (e.g. between CT scans) or multimodality registration (e.g. MRI to CT).  ...  cone beam CT (CBCT), magnetic resonance imaging (MRI) and positron emission tomography (PET). Different image modalities are often combined.  ... 
doi:10.1007/s13246-014-0278-5 pmid:24859771 fatcat:qa2bcdhbxngw5mtzqxnlh3jrvi
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