Relevant bibliographies by topics / Multi-Modal Medical Imaging

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Multi-Modal Medical Imaging'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Multi-Modal Medical Imaging"

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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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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: Clinical Applications of Diagnosis and Therapeutics 19, no. 1 (2011): 111–26. http://dx.doi.org/10.3233/xst-2010-028100281.

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Various types of advanced imaging technologies have significantly improved the quality of medical care available to patients. Corresponding medical image reconstruction algorithms, especially 3D reconstruction, play an important role in disease diagnosis and treatment assessment. However, these increasing reconstruction methods are not implemented in a unified software framework, which brings along lots of disadvantages such as breaking connection of different modalities, lack of module reuse and inconvenience to method comparison. This paper discusses reconstruction process from the viewpoint
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Zhang, Yilin. "Multi-Modal Medical Image Matching Based on Multi-Task Learning and Semantic-Enhanced Cross-Modal Retrieval." Traitement du Signal 40, no. 5 (2023): 2041–49. http://dx.doi.org/10.18280/ts.400522.

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With the continuous advancement of medical imaging technology, a vast amount of multi-modal medical image data has been extensively utilized for disease diagnosis, treatment, and research. Effective management and utilization of these data becomes a pivotal challenge, particularly when undertaking image matching and retrieval. Although numerous methods for medical image matching and retrieval exist, they primarily rely on traditional image processing techniques, often limited to manual feature extraction and singular modality handling. To address these limitations, this study introduces an alg
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T, Dr Kusuma. "Survey on Multi-Modal Medical Image Fusion." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 1126–31. http://dx.doi.org/10.22214/ijraset.2023.56694.

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Abstract: Multi-modality medical or clinical image fusion is a field of study aimed at enhancing diagnostic accuracy and aid in decisions to be taken by medical professional. Various fusion techniques such as pixel-based, region-based, and transformbased approaches are applied in image fusion to provide accurate fusion. Different devices which take scans of body such as MRI, CT, PET, SPECT, Ultrasound hold and carry different features, and different medical sensors obtain different information of the particular part of the body. Each of these imaging modalities offer only specific information
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Adil Ibrahim Khalil. "Multi-Modal Fusion Techniques for Improved Diagnosis in Medical Imaging." Journal of Information Systems Engineering and Management 10, no. 1s (2024): 47–56. https://doi.org/10.52783/jisem.v10i1s.100.

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Identifying diverse disease states is crucial for prompt and efficient clinical management. Complementary data from many medical imaging modalities, including MRI, CT, and PET, can be integrated to improve diagnostic performance. This work aims to assess how well multi-modal fusion methods work to enhance medical picture diagnosis. A multicenter study was conducted with 150 patients with different clinical conditions (mean age 58.2 ± 12.4 years, 52% female). After gathering data from MRI, CT, and PET scans, structural, functional, and textural characteristics were removed from each modality. T
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Dehghani, Farzaneh, Reihaneh Derafshi, Joanna Lin, Sayeh Bayat, and Mariana Bento. "Alzheimer Disease Detection Studies: Perspective on Multi-Modal Data." Yearbook of Medical Informatics 33, no. 01 (2024): 266–76. https://doi.org/10.1055/s-0044-1800756.

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Summary Objectives: Alzheimer's Disease (AD) is one of the most common neurodegenerative diseases, resulting in progressive cognitive decline, and so accurate and timely AD diagnosis is of critical importance. To this end, various medical technologies and computer-aided diagnosis (CAD), ranging from biosensors and raw signals to medical imaging, have been used to provide information about the state of AD. In this survey, we aim to provide a review on CAD systems for automated AD detection, focusing on different data types: namely, signals and sensors, medical imaging, and electronic medical re
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Pasupuleti, Murali Krishna. "AI-Driven Radiology: Multi-Modal Imaging Diagnosis Using Ensemble Models." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 05 (2025): 620–30. https://doi.org/10.62311/nesx/rphcr22.

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Abstract: The integration of artificial intelligence (AI) in radiology has significantly improved diagnostic workflows, particularly with the advent of multi-modal imaging systems. This study proposes an ensemble deep learning framework for radiological diagnosis by combining complementary information from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Modality-specific convolutional neural networks—ResNet50 for CT, DenseNet121 for MRI, and EfficientNet for PET—were independently trained and their outputs aggregated via a fusion layer for fi
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V, Bhavana, and Krishnappa H. K. "Multi-modal image fusion using contourlet and wavelet transforms: a multi-resolution approach." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (2022): 762. http://dx.doi.org/10.11591/ijeecs.v28.i2.pp762-768.

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In recent years, vast improvement and progress has been observed in the field of medical research, especially in digital medical imaging technology. Medical image fusion has been widely used in clinical diagnosis to get valuable information from different modalities of medical images to enhance its quality by fusing images like computed tomography (CT), and magnetic resonance imaging (MRI). MRI gives clear information on delicate tissue while CT gives details about denser tissues. A multi-resolution approach is proposed in this work for fusing medical images using non-sub-sampled contourlet tr
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V., Bhavana, and Krishnappa H. K. "Multi-modal image fusion using contourlet and wavelet transforms: a multi-resolution approach." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (2022): 762–68. https://doi.org/10.11591/ijeecs.v28.i2.pp762-768.

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In recent years, vast improvement and progress has been observed in the field of medical research, especially in digital medical imaging technology. Medical image fusion has been widely used in clinical diagnosis to get valuable information from different modalities of medical images to enhance its quality by fusing images like computed tomography (CT), and magnetic resonance imaging (MRI). MRI gives clear information on delicate tissue while CT gives details about denser tissues. A multi-resolution approach is proposed in this work for fusing medical images using non-sub-sampled contourlet tr
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A, Sathya. "Multi-Modal Image Fusion for Early Disease Diagnosis: AI in Medical Imaging." Multidisciplinary Journal for Applied Research in Engineering and Technology 4, no. 2 (2024): 16–20. https://doi.org/10.54228/mjaret0624010.

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This study focuses on a novel multi-modal image fusion algorithm with artificial intelligence in medical imaging for early-stage disease diagnosis. We introduced Deep Multi-Cascade Fusion (DMC-Fusion) algorithm, which fuses classifier-based features from MRI, CT and PET with self-supervised learning techniques. By leveraging a unique dataset of 10,000 multi-modal medical images of five different types of diseases, including brain tumors and lung cancer, the proposed method outperformed existing single-modality imaging systems by improving accuracy in the detection of early-stage diseases to 25
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Dissertations / Theses on the topic "Multi-Modal Medical Imaging"

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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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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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Mali, Shruti Atul. "Multi-Modal Learning for Abdominal Organ Segmentation." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285866.

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Deep Learning techniques are widely used across various medical imaging applications. However, they are often fine-tuned for a specific modality and are not generalizable when it comes to new modalities or datasets. One of the main reasons for this is large data variations for e.g., the dynamic range of intensity values is large across multi-modal images. The goal of the project is to develop a method to address multi-modal learning that aims at segmenting liver from Computed Tomography (CT) images and abdominal organs from Magnetic Resonance (MR) images using deep learning techniques. In this
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Stimpel, Bernhard [Verfasser], Andreas [Akademischer Betreuer] Maier, Andreas [Gutachter] Maier, and Ge [Gutachter] Wang. "Multi-modal Medical Image Processing with Applications in Hybrid X-ray/Magnetic Resonance Imaging / Bernhard Stimpel ; Gutachter: Andreas Maier, Ge Wang ; Betreuer: Andreas Maier." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2021. http://d-nb.info/1227040881/34.

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Al-Taie, Ahmed A. Abdulredha [Verfasser], Lars [Akademischer Betreuer] Linsen, Horst [Akademischer Betreuer] Hahn, and Timo [Akademischer Betreuer] Ropinski. "Uncertainty Estimation and Visualization in Segmenting Uni- and Multi-modal Medical Imaging Data / Ahmed A. Abdulredha Al-Taie. Betreuer: Lars Linsen. Gutachter: Lars Linsen ; Horst Hahn ; Timo Ropinski." Bremen : IRC-Library, Information Resource Center der Jacobs University Bremen, 2016. http://d-nb.info/1081256249/34.

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"Variational and spline based multi-modal non-rigid medical image registration and applications." Thesis, 2005. http://library.cuhk.edu.hk/record=b6074158.

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In the brain mapping case, the geodesic closest points are used as the anatomical constraints for the inter-subject non-rigid registration. The method uses the anatomical constraint in the non-rigid registration which is much more reasonable for the anatomical correspondence. The registration result shows significant improvement comparing with the iterative closest points based method.<br>In the third application, we use the non-rigid registration method to register the different sweeps of freehand ultrasound images. We setup a 3D freehand ultrasound imaging system to capture images of a human
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Sanny, Dween Rabius. "Development of advanced regularization methods to improve photoacoustic tomography." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/5333.

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Photoacoustic tomography (PAT) is a scalable imaging modality having huge potential for imaging biological samples at very high depth to resolution ratio, thereby playing pivotal role in the areas of neuroscience, cardiovascular research, tumor biology and evolution research. The crucial step in PAT is the image reconstruction or the solving the inverse problem. The reconstruction can be performed by using analytical and model-based methods. The reconstruction schemes like backprojection, filtered backprojection, time reversal, delay and sum, or Fourier-based inversion have shown potenti
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Shaw, Calbvin B. "Development of Novel Reconstruction Methods Based on l1--Minimization for Near Infrared Diffuse Optical Tomography." Thesis, 2012. http://etd.iisc.ac.in/handle/2005/3229.

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Diffuse optical tomography uses near infrared (NIR) light as the probing media to recover the distributions of tissue optical properties. It has a potential to become an adjunct imaging modality for breast and brain imaging, that is capable of providing functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) tends to be non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. Traditional image reconstruction methods in diffuse optical to
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Shaw, Calbvin B. "Development of Novel Reconstruction Methods Based on l1--Minimization for Near Infrared Diffuse Optical Tomography." Thesis, 2012. http://hdl.handle.net/2005/3229.

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Diffuse optical tomography uses near infrared (NIR) light as the probing media to recover the distributions of tissue optical properties. It has a potential to become an adjunct imaging modality for breast and brain imaging, that is capable of providing functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) tends to be non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. Traditional image reconstruction methods in diffuse optical to
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Jaya, Prakash *. "Development of Next Generation Image Reconstruction Algorithms for Diffuse Optical and Photoacoustic Tomography." Thesis, 2014. http://etd.iisc.ac.in/handle/2005/3112.

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Biomedical optical imaging is capable of providing functional information of the soft bi-ological tissues, whose applications include imaging large tissues, such breastand brain in-vivo. Biomedical optical imaging uses near infrared light (600nm-900nm) as the probing media, givin ganaddedadvantageofbeingnon-ionizingimagingmodality. The tomographic technologies for imaging large tissues encompasses diffuse optical tomogra-phyandphotoacoustictomography. Traditional image reconstruction methods indiffuse optical tomographyemploysa �2-norm based regularization, which is known to remove high frequenc
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Book chapters on the topic "Multi-Modal Medical Imaging"

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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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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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Chen, Yu, Jiawei Chen, Dong Wei, Yuexiang Li, and Yefeng Zheng. "OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images." In Multiscale Multimodal Medical Imaging. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37969-8_3.

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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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Ge, Hongkun, Guorong Wu, Li Wang, Yaozong Gao, and Dinggang Shen. "Hierarchical Multi-modal Image Registration by Learning Common Feature Representations." In Machine Learning in Medical Imaging. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24888-2_25.

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Zhang, Sen, Changzheng Zhang, Lanjun Wang, et al. "MSAFusionNet: Multiple Subspace Attention Based Deep Multi-modal Fusion Network." In Machine Learning in Medical Imaging. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_7.

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Lu, Zilin, Mengkang Lu, and Yong Xia. "$$\mathrm {M^{2}F}$$: A Multi-modal and Multi-task Fusion Network for Glioma Diagnosis and Prognosis." In Multiscale Multimodal Medical Imaging. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18814-5_1.

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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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Sikka, Apoorva, Skand Vishwanath Peri, and Deepti R. Bathula. "MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net for Multi-modal Alzheimer’s Classification." In Simulation and Synthesis in Medical Imaging. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00536-8_9.

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Conference papers on the topic "Multi-Modal Medical 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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R, Senthil Murugan K., Harini S, Jayabharathi P, Shekkina Paulin J, and Vishal Oviya S. "Multi-Modal Medical Image Fusion Based on cGAN and the Curvelet Transform with 3D Imaging." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10860158.

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Chen, J., Y. Liu, S. Wei, A. Carass, and Y. Du. "Unsupervised Learning of Multi-modal Affine Registration for PET/CT." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10655957.

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Xu, Wenqi, Hong-Seng Gan, Shengen Wu, Zimu Wang, Muhammad Hanif Ramlee, and Wan Mahani Hafizah. "MMKNet: A Multi-Modal Knowledge Network for Predicting Both Seen and Unseen Classes in Medical Imaging." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033473.

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Nivetha, B., P. S. Gomathi, T. Abirami, Menakadevi Nanjundan, G. Boopathi Raja, and B. Gowsika. "Transformer-Based Multi-Modal Deep Learning Framework for Early Disease Prognosis Using EHR and Medical Imaging Data." In 2025 6th International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2025. https://doi.org/10.1109/icirca65293.2025.11089837.

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Kalluri, K. S., P. W. Segars, M. Saranathan, et al. "Addition of detailed structures to the XCAT brain phantom for in-silico high-resolution multi-modal imaging system usage." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10656810.

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Da-Ano, R., O. Tankyevych, C. Cheze Le Rest, and D. Visvikis. "Semi-Supervised and Unsupervised Deep Learning Combination for Automated PDL-1 Status Prediction in Lung Cancer with Multi-modal PET/CT Fusion." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10658178.

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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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Vetter, Christoph, Christoph Guetter, Chenyang Xu, and Rüdiger Westermann. "Non-rigid multi-modal registration on the GPU." In Medical Imaging, edited by Josien P. W. Pluim and Joseph M. Reinhardt. SPIE, 2007. http://dx.doi.org/10.1117/12.709629.

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