Gotowa bibliografia na temat „Multi-modal imaging”

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Artykuły w czasopismach na temat "Multi-modal imaging"

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Mohankumar, Arthi, and Roshni Mohan. "Multi-Modal imaging of torpedo maculopathy." TNOA Journal of Ophthalmic Science and Research 61, no. 1 (2023): 143. http://dx.doi.org/10.4103/tjosr.tjosr_9_22.

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Zhang, Haotian, Qiaoyu Ma, Yiran Qiu, and Zongying Lai. "A Multi-Hierarchical Complementary Feature Interaction Network for Accelerated Multi-Modal MR Imaging." Applied Sciences 14, no. 21 (2024): 9764. http://dx.doi.org/10.3390/app14219764.

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Magnetic resonance (MR) imaging is widely used in the clinical field due to its non-invasiveness, but the long scanning time is still a bottleneck for its popularization. Using the complementary information between multi-modal imaging to accelerate imaging provides a novel and effective MR fast imaging solution. However, previous technologies mostly use simple fusion methods and fail to fully utilize their potential sharable knowledge. In this study, we introduced a novel multi-hierarchical complementary feature interaction network (MHCFIN) to realize joint reconstruction of multi-modal MR ima
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Alilet, Mona, Julien Behr, Jean-Philippe Nueffer, Benoit Barbier-Brion, and Sébastien Aubry. "Multi-modal imaging of the subscapularis muscle." Insights into Imaging 7, no. 6 (2016): 779–91. http://dx.doi.org/10.1007/s13244-016-0526-1.

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Dumbryte, Irma, Donatas Narbutis, Maria Androulidaki, Arturas Vailionis, Saulius Juodkazis, and Mangirdas Malinauskas. "Teeth Microcracks Research: Towards Multi-Modal Imaging." Bioengineering 10, no. 12 (2023): 1354. http://dx.doi.org/10.3390/bioengineering10121354.

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This perspective is an overview of the recent advances in teeth microcrack (MC) research, where there is a clear tendency towards a shift from two-dimensional (2D) to three-dimensional (3D) examination techniques, enhanced with artificial intelligence models for data processing and image acquisition. X-ray micro-computed tomography combined with machine learning allows 3D characterization of all spatially resolved cracks, despite the locations within the tooth in which they begin and extend, and the arrangement of MCs and their structural properties. With photoluminescence and micro-/nano-Rama
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Hallinan, Robert, Brendan M. Connolly, and David C. Mackenzie. "Renal vein thrombosis: Multi-modal imaging findings." Visual Journal of Emergency Medicine 38 (January 2025): 102194. https://doi.org/10.1016/j.visj.2025.102194.

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Blinowska, Katarzyna, Gernot Müller-Putz, Vera Kaiser, et al. "Multimodal Imaging of Human Brain Activity: Rational, Biophysical Aspects and Modes of Integration." Computational Intelligence and Neuroscience 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/813607.

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Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging
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Watkin, Kenneth L., and Michael A. McDonald. "Multi-Modal Contrast Agents." Academic Radiology 9, no. 2 (2002): S285—S289. http://dx.doi.org/10.1016/s1076-6332(03)80205-2.

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Merkle, Arno, Leah L. Lavery, Jeff Gelb, and Nicholas Piché. "Fusing Multi-scale and Multi-modal 3D Imaging and Characterization." Microscopy and Microanalysis 20, S3 (2014): 820–21. http://dx.doi.org/10.1017/s1431927614005820.

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Dong, Di, Jie Tian, Yakang Dai, Guorui Yan, Fei Yang, and Ping Wu. "Unified reconstruction framework for multi-modal medical imaging." Journal of X-Ray Science and Technology 19, no. 1 (2011): 111–26. http://dx.doi.org/10.3233/xst-2010-0281.

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Bansal, Reema, Nitin Kumar, and Monika Balyan. "Multi-modal imaging in benign familial fleck retina." Indian Journal of Ophthalmology 69, no. 6 (2021): 1641. http://dx.doi.org/10.4103/ijo.ijo_633_21.

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Rozprawy doktorskie na temat "Multi-modal imaging"

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Kachatkou, Anton S. "Instrumentation for multi-dimensional multi-modal imaging in microscopy." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509391.

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Blattmann, Marc [Verfasser], Hans [Akademischer Betreuer] Zappe, Çağlar [Akademischer Betreuer] Ataman, and Andreas [Akademischer Betreuer] Seifert. "Concept for a multi-modal endoscopic imaging system." Freiburg : Universität, 2017. http://d-nb.info/1148929363/34.

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Hoffman, David. "Hybrid PET/MRI Nanoparticle Development and Multi-Modal Imaging." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3253.

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The development of hybrid PET/MRI imaging systems needs to be paralleled with the development of a hybrid intrinsic PET/MRI probes. The aim of this work was to develop and validate a novel radio-superparamagnetic nanoparticle (r-SPNP) for hybrid PET/MRI imaging. This was achieved with the synthesis of superparamagnetic iron oxide nanoparticles (SPIONs) that intrinsically incorporated 59Fe and manganese iron oxide nanoparticles (MIONs) that intrinsically incorporated 52Mn. Both [59Fe]-SPIONs and [52Mn]-MIONs were produced through thermal decomposition synthesis. The physiochemical characteristi
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Halai, Ajay Devshi. "Multi-modal imaging of brain networks subserving speech comprehension." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/multimodal-imaging-of-brain-networks-subserving-speech-comprehension(8f1b55b1-6d06-452e-8efc-8f1bb89fd481).html.

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Neurocognitive models of speech comprehension generally outline either the spatial or temporal organisation of speech processing and rarely consider combining the two to provide a more complete model. Simultaneous EEG-fMRI recordings have the potential to link these domains, due to the complementary high spatial (fMRI) and temporal (EEG) sensitivities. Although the neural basis of speech comprehension has been investigated intensively during the past few decades there are still some important outstanding questions. For instance, there is considerable evidence from neuropsychology and other con
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Wang, Xue. "An Integrated Multi-modal Registration Technique for Medical Imaging." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3512.

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Registration of medical imaging is essential for aligning in time and space different modalities and hence consolidating their strengths for enhanced diagnosis and for the effective planning of treatment or therapeutic interventions. The primary objective of this study is to develop an integrated registration method that is effective for registering both brain and whole-body images. We seek in the proposed method to combine in one setting the excellent registration results that FMRIB Software Library (FSL) produces with brain images and the excellent results of Statistical Parametric Mapping (
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Li, Lin. "Multi-scale spectral embedding representation registration (MSERg) for multi-modal imaging registration." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1467902012.

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Mera-Pirttijarvi, Ross Jalmari. "Targeted multi-modal imaging : using the Ugi reaction with metals." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/targeted-multimodal-imaging-using-the-ugi-reaction-with-metals(00ca616e-b8bd-466a-86dc-d1799851fbd1).html.

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The current 'gold standard method' of detecting cancer relies on microscopic examination by specialised pathologists. However, there are risks associated with surgery and biopsies and so the ability to diagnose cancer and other diseases in a non-invasive manner is highly attractive. There are many imaging techniques suitable for this, each with their own advantages and disadvantages, which can be improved by the use of contrast agents. The incorporation of targeting vectors allows for the specific imaging of desired tissues. Further to this, the incorporation of more than one contrast agent in
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Chan, Ho-Ming. "A supervised learning framework for multi-modal rigid registration with applications to angiographic images /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20CHAN.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 60-62). Also available in electronic version. Access restricted to campus users.
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Yao, Nailin, and 姚乃琳. "Visual hallucinations in Parkinson's disease : a multi-modal MRI investigation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/196477.

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Background Visual hallucinations (VH) are an important non-motor complication of Parkinson’s disease (PD) which carries a negative prognosis, but their biological basis is unclear. Multi-modal magnetic resonance imaging (MRI) can be used to evaluate structural and functional brain mechanisms underpinning VH in PD. Methods To assess cerebral microstructure and resting functional activities in patients with idiopathic PD and VH, I compared PD patients with VH (PDVH) and PD patients without VH (PDnonVH), while healthy controls (HC) were also recruited for comparison. Diffusion tensor imagi
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Petersen, Steffen E. "Insights into cardiac remodelling by multi-modal magnetic resonance imaging and spectroscopy." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419318.

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Książki na temat "Multi-modal imaging"

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Dynamic brain imaging: Multi-modal methods and in vivo applications. Humana, 2009.

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Hyder, Fahmeed. Dynamic Brain Imaging: Multi-Modal Methods and in Vivo Applications. Humana Press, 2014.

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Dynamic Brain Imaging: Multi-Modal Methods and in Vivo Applications. Humana Press, 2009.

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Wu, Min, Lu Yang, Dong-Hyun Kim, Changqiang Wu, and Peng Mi, eds. Bottom-Up Approach: a Route for Effective Multi-modal Imaging of Tumors. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88974-453-4.

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Planning Coverage of Points of Interest via Multiple Imaging Surveillance Assets: A Multi-Modal Approach. Storming Media, 2003.

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Części książek na temat "Multi-modal imaging"

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Langmann, Benjamin. "Multi-Modal Background Subtraction." In Wide Area 2D/3D Imaging. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06457-0_5.

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Sossi, Vesna. "Multi-modal Imaging and Image Fusion." In Small Animal Imaging. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-12945-2_22.

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Zhou, Ziqi, Xinna Guo, Wanqi Yang, et al. "Cross-Modal Attention-Guided Convolutional Network for Multi-modal Cardiac Segmentation." In Machine Learning in Medical Imaging. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_69.

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Zaman, Akib, Lu Zhang, Jingwen Yan, and Dajiang Zhu. "Multi-modal Image Prediction via Spatial Hybrid U-Net." In Multiscale Multimodal Medical Imaging. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37969-8_1.

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Krmicek, Vojtech, and Michèle Sebag. "Functional Brain Imaging with Multi-objective Multi-modal Evolutionary Optimization." In Parallel Problem Solving from Nature - PPSN IX. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11844297_39.

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Yu, Zheng, Yanyuan Qiao, Yutong Xie, and Qi Wu. "Multi-modal Adapter for Medical Vision-and-Language Learning." In Machine Learning in Medical Imaging. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45673-2_39.

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Pöschl, Christiane, and Otmar Scherzer. "Distance Measures and Applications to Multi-Modal Variational Imaging." In Handbook of Mathematical Methods in Imaging. Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-92920-0_4.

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Chartsias, Agisilaos, Thomas Joyce, Rohan Dharmakumar, and Sotirios A. Tsaftaris. "Adversarial Image Synthesis for Unpaired Multi-modal Cardiac Data." In Simulation and Synthesis in Medical Imaging. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68127-6_1.

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Mardal, Kent-André, Marie E. Rognes, Travis B. Thompson, and Lars Magnus Valnes. "Concluding Remarks and Outlook." In Mathematical Modeling of the Human Brain. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95136-8_7.

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Tong, Tong, Katherine Gray, Qinquan Gao, Liang Chen, and Daniel Rueckert. "Nonlinear Graph Fusion for Multi-modal Classification of Alzheimer’s Disease." In Machine Learning in Medical Imaging. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24888-2_10.

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Streszczenia konferencji na temat "Multi-modal imaging"

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N, Padmasini, Deeksha Lakshmi V, Gokul Nath M, and Harsha K. R. "Multi-Modal Medical Imaging Interface for Lung Disease Classification." In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2025. https://doi.org/10.1109/aide64228.2025.10987446.

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Nhat Nguyen, Quang Luong, Ruiming Cao, and Laura Waller. "Multi-Modal Deformable Image Registration Using Untrained Neural Networks." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980732.

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Sharshar, Ahmed, Yasser Attia, Mohammad Yaqub, and Mohsen Guizani. "Pulmofusion: Advancing Pulmonary Health with Efficient Multi-Modal Fusion." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981233.

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Fang, Hao, Ronald B. Liu, Jingyu Sun, Pierre O. Bagnaninchi, Zhe Liu, and Yunjie Yang. "Multi-Modal EIT Imaging Using Lensfree and Flexible Impedance Sensor." In 2024 IEEE SENSORS. IEEE, 2024. https://doi.org/10.1109/sensors60989.2024.10784633.

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Dudhabhate, Santosh, Nilanjan Chattopadhyay, Akash Parekh, and Nitin Singhal. "Multi-Modal Integration for Predicting Biochemical Recurrence in Prostate Cancer." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980823.

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Khoubafarin, Somaiyeh, Peuli Nath, Hannah Popofski, and Aniruddha Ray. "High resolution Multi-Modal Microscopy using Microlens Substrates." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.atu4b.1.

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The resolution of optical microscopes is limited by the numerical aperture of objective lens. We introduced a cost-effective microlens substrate for multimodal optical microscopy, that enables imaging beyond the diffraction limit, which was used to image mammalian cells and tissues.
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Sükei, Emese, Elisabeth Rumetshofer, Niklas Schmidinger, et al. "Improving Clinical Predictions with Multi-Modal Pre-training in Retinal Imaging." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635447.

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Li, Jianqiang, Jiatao Yang, Haitao Gan, and Zhongwei Huang. "Parkinson's Disease Diagnosis with Sparse Learning of Multi-Modal Adaptive Similarity." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635628.

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Prümmer, M., J. Hornegger, M. Pfister, and A. Dörfler. "Multi-modal 2D-3D non-rigid registration." In Medical Imaging, edited by Joseph M. Reinhardt and Josien P. W. Pluim. SPIE, 2006. http://dx.doi.org/10.1117/12.652321.

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Duric, Neb, Cuiping Li, Peter Littrup, et al. "Multi-modal breast imaging with ultrasound tomography." In Medical Imaging, edited by Stephen A. McAleavey and Jan D'hooge. SPIE, 2008. http://dx.doi.org/10.1117/12.772203.

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Raporty organizacyjne na temat "Multi-modal imaging"

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Wong, Stephen T., and Jared C. Gilliam. Mobile, Multi-modal, Label-Free Imaging Probe Analysis of Choroidal Oximetry and Retinal Hypoxia. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada624123.

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