Littérature scientifique sur le sujet « MRI IMAGE »
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Articles de revues sur le sujet "MRI IMAGE"
Zhang, Huixian, Hailong Li, Jonathan R. Dillman, Nehal A. Parikh et Lili He. « Multi-Contrast MRI Image Synthesis Using Switchable Cycle-Consistent Generative Adversarial Networks ». Diagnostics 12, no 4 (26 mars 2022) : 816. http://dx.doi.org/10.3390/diagnostics12040816.
Texte intégralYang, Huan, Pengjiang Qian et Chao Fan. « An Indirect Multimodal Image Registration and Completion Method Guided by Image Synthesis ». Computational and Mathematical Methods in Medicine 2020 (30 juin 2020) : 1–10. http://dx.doi.org/10.1155/2020/2684851.
Texte intégralDestyningtias, Budiani, Andi Kurniawan Nugroho et Sri Heranurweni. « Analisa Citra Medis Pada Pasien Stroke dengan Metoda Peregangan Kontras Berbasis ImageJ ». eLEKTRIKA 10, no 1 (19 juin 2019) : 15. http://dx.doi.org/10.26623/elektrika.v10i1.1105.
Texte intégralBellam, Kiranmai, N. Krishnaraj, T. Jayasankar, N. B. Prakash et G. R. Hemalakshmi. « Adaptive Multimodal Image Fusion with a Deep Pyramidal Residual Learning Network ». Journal of Medical Imaging and Health Informatics 11, no 8 (1 août 2021) : 2135–43. http://dx.doi.org/10.1166/jmihi.2021.3763.
Texte intégralSchramm, Georg, et Claes Nøhr Ladefoged. « Metal artifact correction strategies in MRI-based attenuation correction in PET/MRI ». BJR|Open 1, no 1 (novembre 2019) : 20190033. http://dx.doi.org/10.1259/bjro.20190033.
Texte intégral., Swapnali Matkar. « IMAGE SEGMENTATION METHODS FOR BRAIN MRI IMAGES ». International Journal of Research in Engineering and Technology 04, no 03 (25 mars 2015) : 263–66. http://dx.doi.org/10.15623/ijret.2015.0403045.
Texte intégralSingh, Ram, et Lakhwinder Kaur. « Noise-residue learning convolutional network model for magnetic resonance image enhancement ». Journal of Physics : Conference Series 2089, no 1 (1 novembre 2021) : 012029. http://dx.doi.org/10.1088/1742-6596/2089/1/012029.
Texte intégralYan, Rong. « The Value of Convolutional-Neural-Network-Algorithm-Based Magnetic Resonance Imaging in the Diagnosis of Sports Knee Osteoarthropathy ». Scientific Programming 2021 (2 juillet 2021) : 1–11. http://dx.doi.org/10.1155/2021/2803857.
Texte intégralOdusami, Modupe, Rytis Maskeliūnas et Robertas Damaševičius. « Pareto Optimized Adaptive Learning with Transposed Convolution for Image Fusion Alzheimer’s Disease Classification ». Brain Sciences 13, no 7 (8 juillet 2023) : 1045. http://dx.doi.org/10.3390/brainsci13071045.
Texte intégralWu, Hongliang, Guocheng Chen, Guibao Zhang et Minghua Dai. « Application of Multimodal Fusion Technology in Image Analysis of Pretreatment Examination of Patients with Spinal Injury ». Journal of Healthcare Engineering 2022 (12 avril 2022) : 1–10. http://dx.doi.org/10.1155/2022/4326638.
Texte intégralThèses sur le sujet "MRI IMAGE"
Al-Abdul, Salam Amal. « Image quality in MRI ». Thesis, University of Exeter, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288250.
Texte intégralCui, Xuelin. « Joint CT-MRI Image Reconstruction ». Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/86177.
Texte intégralPh. D.
Medical imaging techniques play a central role in modern clinical diagnoses and treatments. Consequently, there is a constant demand to increase the overall quality of medical images. Since their inception, multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. Multimodal imaging techniques can provide more detailed diagnostic information by fusing medical images from different imaging modalities, thereby allowing clinical results to be more comprehensive to improve clinical interpretation. A new form of multimodal imaging technique, which combines the imaging procedures of computed tomography (CT) and magnetic resonance imaging (MRI), is known as the “omnitomography.” Both computed tomography and magnetic resonance imaging are the most commonly used medical imaging techniques today and their intrinsic properties are complementary. For example, computed tomography performs well for bones whereas the magnetic resonance imaging excels at contrasting soft tissues. Therefore, a multimodal imaging system built upon the fusion of these two modalities can potentially bring much more information to improve clinical diagnoses. However, the planned omni-tomography systems face enormous challenges, such as the limited ability to perform image reconstruction due to mechanical and hardware restrictions that result in significant undersampling of the raw data. Image reconstruction is a procedure required by both computed tomography and magnetic resonance imaging to convert raw data into final images. A general condition required to produce a decent quality of an image is that the number of samples of raw data must be sufficient and abundant. Therefore, undersampling on the omni-tomography system can cause significant degradation of the image quality or artifacts after image reconstruction. To overcome this drawback, we resort to compressed sensing techniques, which exploit the sparsity of the medical images, to perform iterative based image reconstruction for both computed tomography and magnetic resonance imaging. The sparsity of the images is found by applying sparse transform such as discrete gradient transform or wavelet transform in the image domain. With the sparsity and undersampled raw data, an iterative algorithm can largely compensate for the data inadequacy problem and it can reconstruct the final images from the undersampled raw data with minimal loss of quality. In addition, a novel “projection distance” is created to perform a joint reconstruction which further promotes the quality of the reconstructed images. Specifically, the projection distance exploits the structural similarities shared between the image of computed tomography and magnetic resonance imaging such that the insufficiency of raw data caused by undersampling is further accounted for. The improved performance of the proposed approach is demonstrated using a pair of undersampled body images and a pair of undersampled head images, each of which consists of an image of computed tomography and its magnetic resonance imaging counterpart. These images are tested using the proposed joint reconstruction method in this work, the conventional reconstructions such as filtered backprojection and Fourier transform, and reconstruction strategy without using multimodal imaging information (independent reconstruction). The results from this work show that the proposed method addressed these challenges by significantly improving the image quality from highly undersampled raw data. In particular, it improves about 5dB in signal-to-noise ratio and nearly 10% in structural similarity measurements compared to other methods. It achieves similar image quality by using less than 5% of the X-ray dose for computed tomography and 30% sampling rate for magnetic resonance imaging. It is concluded that, by using compressed sensing techniques and exploiting structural similarities, the planned joint computed tomography and magnetic resonance imaging system can perform imaging outstanding tasks with highly undersampled raw data.
Carmo, Bernardo S. « Image processing in echography and MRI ». Thesis, University of Southampton, 2005. https://eprints.soton.ac.uk/194557/.
Texte intégralGu, Wei Q. « Automated tracer-independent MRI/PET image registration ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29596.pdf.
Texte intégralIvarsson, Magnus. « Evaluation of 3D MRI Image Registration Methods ». Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139075.
Texte intégralLin, Xiangbo. « Knowledge-based image segmentation using deformable registration : application to brain MRI images ». Reims, 2009. http://theses.univ-reims.fr/exl-doc/GED00001121.pdf.
Texte intégralThe research goal of this thesis is a contribution to the intra-modality inter-subject non-rigid medical image registration and the segmentation of 3D brain MRI images in normal case. The well-known Demons non-rigid algorithm is studied, where the image intensities are used as matching features. A new force computation equation is proposed to solve the mismatch problem in some regions. The efficiency is shown through numerous evaluations on simulated and real data. For intensity based inter-subject registration, normalizing the image intensities is important for satisfying the intensity correspondence requirements. A non-rigid registration method combining both intensity and spatial normalizations is proposed. Topology constraints are introduced in the deformable model to preserve an expected property in homeomorphic targets registration. The solution comes from the correction of displacement points with negative Jacobian determinants. Based on the registration, a segmentation method of the internal brain structures is studied. The basic principle is represented by ontology of prior shape knowledge of target internal structure. The shapes are represented by a unified distance map computed from the atlas and the deformed atlas, and then integrated into the similarity metric of the cost function. A balance parameter is used to adjust the contributions of the intensity and shape measures. The influence of different parameters of the method and comparisons with other registration methods were performed. Very good results are obtained on the segmentation of different internal structures of the brain such as central nuclei and hippocampus
Soltaninejad, Mohammadreza. « Supervised learning-based multimodal MRI brain image analysis ». Thesis, University of Lincoln, 2017. http://eprints.lincoln.ac.uk/30883/.
Texte intégralDaga, P. « Towards efficient neurosurgery : image analysis for interventional MRI ». Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1449559/.
Texte intégralChi, Wenjun. « MRI image analysis for abdominal and pelvic endometriosis ». Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:27efaa89-85cd-4f8b-ab67-b786986c42e3.
Texte intégralHagio, Tomoe, et Tomoe Hagio. « Parametric Mapping and Image Analysis in Breast MRI ». Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/621809.
Texte intégralLivres sur le sujet "MRI IMAGE"
Brant, William E. Body MRI cases. New York : Oxford University Press, 2013.
Trouver le texte intégralSong, In-chʻan. MRI ŭi hwajil pʻyŏngka kisul kaebal = : Technology development of MRI image quality evaluation. [Seoul] : Sikpʻum Ŭiyakpʻum Anjŏnchʻŏng, 2007.
Trouver le texte intégralauthor, Shah Lubdha M., et Nielsen Jared A. author, dir. Specialty imaging : Functional MRI. Salt Lake City, Utah : Amirsys, 2014.
Trouver le texte intégralBrain imaging with MRI and CT : An image pattern approach. Cambridge : Cambridge University Press, 2012.
Trouver le texte intégralContrast-enhanced MRI of the breast. Basel : Karger, 1990.
Trouver le texte intégralR, Beck, dir. Contrast-enhanced MRI of the breast. 2e éd. Berlin : Springer, 1996.
Trouver le texte intégralMRI of the lumbar spine : A practical approach to image interpretation. Thorofare, N.J : Slack, 1987.
Trouver le texte intégralW, Bancroft Laura, et Bridges, Mellena D., M.D., dir. MRI normal variants and pitfalls. Philadelphia, PA : Lippincott Williams and Wilkins, 2009.
Trouver le texte intégralPoldrack, Russell A. Handbook of functional MRI data analysis. Cambridge : Cambridge University Press, 2011.
Trouver le texte intégralCiulla, Carlo. Improved signal and image interpolation in biomedical applications : The case of magnetic resonance imaging (MRI). Hershey PA : Medical Information Science Reference, 2009.
Trouver le texte intégralChapitres de livres sur le sujet "MRI IMAGE"
Ashburner, J., et K. J. Friston. « Image Registration ». Dans Functional MRI, 285–99. Berlin, Heidelberg : Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58716-0_26.
Texte intégralZeng, Gengsheng Lawrence. « MRI Reconstruction ». Dans Medical Image Reconstruction, 175–92. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05368-9_7.
Texte intégralRajan, Sunder S. « Image Contrast and Pulse Sequences ». Dans MRI, 40–65. New York, NY : Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1632-2_4.
Texte intégralEnglish, Philip T., et Christine Moore. « Image Production ». Dans MRI for Radiographers, 37–43. London : Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3403-9_4.
Texte intégralEnglish, Philip T., et Christine Moore. « Image Quality ». Dans MRI for Radiographers, 45–50. London : Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3403-9_5.
Texte intégralEnglish, Philip T., et Christine Moore. « Image Artifacts ». Dans MRI for Radiographers, 51–70. London : Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3403-9_6.
Texte intégralMurray, Rachel, et Natasha Werpy. « Image interpretation and artefacts ». Dans Equine MRI, 101–45. Chichester, UK : John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118786574.ch4.
Texte intégralQu, Liangqiong, Yongqin Zhang, Zhiming Cheng, Shuang Zeng, Xiaodan Zhang et Yuyin Zhou. « Multimodality MRI Synthesis ». Dans Medical Image Synthesis, 163–87. Boca Raton : CRC Press, 2023. http://dx.doi.org/10.1201/9781003243458-14.
Texte intégralWeishaupt, Dominik, Victor D. Köchli et Borut Marincek. « Image Contrast ». Dans How does MRI work ?, 11–20. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-07805-1_3.
Texte intégralAhrar, Kamran, et R. Jason Stafford. « MRI-Guided Biopsy ». Dans Percutaneous Image-Guided Biopsy, 49–63. New York, NY : Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8217-8_5.
Texte intégralActes de conférences sur le sujet "MRI IMAGE"
Singh, Upasana, et Manoj Kumar Choubey. « A Review : Image Enhancement on MRI Images ». Dans 2021 5th International Conference on Information Systems and Computer Networks (ISCON). IEEE, 2021. http://dx.doi.org/10.1109/iscon52037.2021.9702464.
Texte intégralOtazo, Ricardo, Ramiro Jordan, Fa-Hsuan Lin et Stefan Posse. « Superresolution Parallel MRI ». Dans 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4379269.
Texte intégralFaghihpirayesh, Razieh, Davood Karimi, Deniz Erdogmus et Ali Gholipour. « Automatic brain pose estimation in fetal MRI ». Dans Image Processing, sous la direction de Ivana Išgum et Olivier Colliot. SPIE, 2023. http://dx.doi.org/10.1117/12.2647613.
Texte intégralCarlos, Justin Bernard A., Francisco Emmanuel T. Munsayac, Nilo T. Bugtai et Renann G. Baldovino. « MRI Knee Image Enhancement using Image Processing ». Dans 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). IEEE, 2021. http://dx.doi.org/10.1109/hnicem54116.2021.9732053.
Texte intégralWang, Jiacheng, Hao Li, Han Liu, Dewei Hu, Daiwei Lu, Keejin Yoon, Kelsey Barter, Francesca Bagnato et Ipek Oguz. « SSL2 Self-Supervised Learning meets semi-supervised learning : multiple clerosis segmentation in 7T-MRI from large-scale 3T-MRI ». Dans Image Processing, sous la direction de Ivana Išgum et Olivier Colliot. SPIE, 2023. http://dx.doi.org/10.1117/12.2654522.
Texte intégralManduca, Armando, David S. Lake, Natalia Khaylova et Richard L. Ehman. « Image-space automatic motion correction for MRI images ». Dans Medical Imaging 2004, sous la direction de J. Michael Fitzpatrick et Milan Sonka. SPIE, 2004. http://dx.doi.org/10.1117/12.532952.
Texte intégralDevadas, Prathima, G. Kalaiarasi et M. Selvi. « Intensity based Image Registration on Brain MRI Images ». Dans 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2020. http://dx.doi.org/10.1109/icirca48905.2020.9183191.
Texte intégralDSouza, Adora M., Lele Chen, Yue Wu, Anas Z. Abidin, Chenliang Xu et Axel Wismüller. « MRI tumor segmentation with densely connected 3D CNN ». Dans Image Processing, sous la direction de Elsa D. Angelini et Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2293394.
Texte intégralPlassard, Andrew J., L. Taylor Davis, Allen T. Newton, Susan M. Resnick, Bennett A. Landman et Camilo Bermudez. « Learning implicit brain MRI manifolds with deep learning ». Dans Image Processing, sous la direction de Elsa D. Angelini et Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2293515.
Texte intégralNoothout, Julia, Elbrich Postma, Sanne Boesveldt, Bob D. de Vos, Paul Smeets et Ivana Išgum. « Automatic segmentation of the olfactory bulbs in MRI ». Dans Image Processing, sous la direction de Bennett A. Landman et Ivana Išgum. SPIE, 2021. http://dx.doi.org/10.1117/12.2580354.
Texte intégralRapports d'organisations sur le sujet "MRI IMAGE"
Yang, Xiaofeng, Tian Liu, Jani Ashesh, Hui Mao et Walter Curran. Fusion of Ultrasound Tissue-Typing Images with Multiparametric MRI for Image-guided Prostate Cancer Radiation Therapy. Fort Belvoir, VA : Defense Technical Information Center, octobre 2014. http://dx.doi.org/10.21236/ada622473.
Texte intégralBaumgaertel, Jessica A., Paul A. Bradley et Ian L. Tregillis. 65036 MMI data matched qualitatively by RAGE (with mix) synthetic MMI images. Office of Scientific and Technical Information (OSTI), février 2014. http://dx.doi.org/10.2172/1122056.
Texte intégralGrossberg, Stephen. A MURI Center for Intelligent Biomimetic Image Processing and Classification. Fort Belvoir, VA : Defense Technical Information Center, novembre 2007. http://dx.doi.org/10.21236/ada474727.
Texte intégralGarrett, A. J. Ground truth measurements plan for the Multispectral Thermal Imager (MTI) satellite. Office of Scientific and Technical Information (OSTI), janvier 2000. http://dx.doi.org/10.2172/752199.
Texte intégralKurdziel, Karen, Michael Hagan, Jeffrey Williamson, Donna McClish, Panos Fatouros, Jerry Hirsch, Rhonda Hoyle, Kristin Schmidt, Dorin Tudor et Jie Liu. Multimodality Image-Guided HDR/IMRT in Prostate Cancer : Combined Molecular Targeting Using Nanoparticle MR, 3D MRSI, and 11C Acetate PET Imaging. Fort Belvoir, VA : Defense Technical Information Center, août 2005. http://dx.doi.org/10.21236/ada446542.
Texte intégralMartin, Kathi, Nick Jushchyshyn et Claire King. James Galanos Evening Gown c. 1957. Drexel Digital Museum, 2018. http://dx.doi.org/10.17918/jkyh-1b56.
Texte intégralMartin, Kathi, Nick Jushchyshyn et Daniel Caulfield-Sriklad. 3D Interactive Panorama Jessie Franklin Turner Evening Gown c. 1932. Drexel Digital Museum, 2015. http://dx.doi.org/10.17918/9zd6-2x15.
Texte intégralMarcot, Bruce, M. Jorgenson, Thomas Douglas et Patricia Nelsen. Photographic aerial transects of Fort Wainwright, Alaska. Engineer Research and Development Center (U.S.), août 2022. http://dx.doi.org/10.21079/11681/45283.
Texte intégralLamontagne, M., K. B. S. Burke et L. Olson. Felt reports and impact of the November 25, 1988, magnitude 5.9 Saguenay, Quebec, earthquake sequence. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328194.
Texte intégralMartin, Kathi, Nick Jushchyshyn et Claire King. Christian Lacroix Evening gown c.1990. Drexel Digital Museum, 2017. http://dx.doi.org/10.17918/wq7d-mc48.
Texte intégral