Relevant bibliographies by topics / PET/CT image processing

Contents

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

Academic literature on the topic 'PET/CT image processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "PET/CT image processing"

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Rossi, Farli, and Ashrani Aizzuddin Abd Rahni. "Joint Segmentation Methods of Tumor Delineation in PET – CT Images: A Review." International Journal of Engineering & Technology 7, no. 3.32 (2018): 137. http://dx.doi.org/10.14419/ijet.v7i3.32.18414.

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Segmentation is one of the crucial steps in applications of medical diagnosis. The accurate image segmentation method plays an important role in proper detection of disease, staging, diagnosis, radiotherapy treatment planning and monitoring. In the advances of image segmentation techniques, joint segmentation of PET-CT images has increasingly received much attention in the field of both clinic and image processing. PET - CT images have become a standard method for tumor delineation and cancer assessment. Due to low spatial resolution in PET and low contrast in CT images, automated segmentation
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Malczewski, Krzysztof. "Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals." Algorithms 13, no. 5 (2020): 129. http://dx.doi.org/10.3390/a13050129.

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One of the most challenging aspects of medical modalities such as Computed Tomography (CT) as well hybrid techniques such as CT/PET (Computed Tomography/Positron emission tomography) and PET/MRI is finding a balance between examination time, radiation dose, and image quality. The need for a dense sampling grid is associated with two major factors: image resolution enhancement, which leads to a strengthening of human perception, and image features interpretation. All these aspects make an unsupervised image processing much easier. The presented algorithm employs super-resolution-reconstruction
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Pietrzyk, U. "Does PET/CT render software registration obsolete?" Nuklearmedizin 44, S 01 (2005): S13—S17. http://dx.doi.org/10.1055/s-0038-1625209.

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Summary:It was the success of software-based image registration that eventually led to the introduction of hardware-based concepts for image fusion, such as combined PET/CT tomographs. A prototype PET/CT was first presented in 1998, with various commercial designs to follow since 2000. PET/ CT is used primarily as a diagnostic modality in the field of extra-cerebral oncology imaging. The major advantage of combined imaging over retrospective software registration is the nearly identical position of the patient during both complementary examination, and therefore tomograms of identical parts of
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Marinelli, Martina, Vincenzo Positano, Francesco Tucci, Danilo Neglia, and Luigi Landini. "Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms." Scientific World Journal 2012 (2012): 1–12. http://dx.doi.org/10.1100/2012/567067.

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Hybrid PET/CT scanners can simultaneously visualize coronary artery disease as revealed by computed tomography (CT) and myocardial perfusion as measured by positron emission tomography (PET). Manual registration is usually required in clinical practice to compensate spatial mismatch between datasets. In this paper, we present a registration algorithm that is able to automatically align PET/CT cardiac images. The algorithm bases on mutual information (MI) as registration metric and on genetic algorithm as optimization method. A multiresolution approach was used to optimize the processing time.
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Liu, Jiahui, Xiangjun Zhang, Tana Bai, and Yiming Liu. "Analysis on Brain Image Characteristics of Patients with Parkinson's Disease Under Multimodal Magnetic Resonance Technology." Journal of Medical Imaging and Health Informatics 11, no. 2 (2021): 606–11. http://dx.doi.org/10.1166/jmihi.2021.3371.

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In order to improve the early diagnosis rate of Parkinson's disease (PD), reduce the complications of PD in the later stage, and make the clinical intervention to alleviate the pain of Parkinson's patients early, Magnetic resonance imaging (MRI), positron emission tomography (PET), and computerized tomography (CT) were used to evaluate the characteristics of PD. A total of 34 patients diagnosed with PD admitted to Qilu Hospital of Shandong University from January 2017 to December 2018 were included in the research. According to the severity of the disease, the patients were divided into the in
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Deleu, Anne-Leen, Machaba Junior Sathekge, Alex Maes, Bart De Spiegeleer, Mike Sathekge, and Christophe Van de Wiele. "Characterization of FDG PET Images Using Texture Analysis in Tumors of the Gastro-Intestinal Tract: A Review." Biomedicines 8, no. 9 (2020): 304. http://dx.doi.org/10.3390/biomedicines8090304.

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Radiomics or textural feature extraction obtained from positron emission tomography (PET) images through complex mathematical models of the spatial relationship between multiple image voxels is currently emerging as a new tool for assessing intra-tumoral heterogeneity in medical imaging. In this paper, available literature on texture analysis using FDG PET imaging in patients suffering from tumors of the gastro-intestinal tract is reviewed. While texture analysis of FDG PET images appears clinically promising, due to the lack of technical specifications, a large variability in the implemented
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Elaiyaraja, K., and M. Senthil Kumar. "Fusion Imaging in Pixel Level Image Processing Technique – A Literature Review." International Journal of Engineering & Technology 7, no. 3.12 (2018): 175. http://dx.doi.org/10.14419/ijet.v7i3.12.15913.

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Image Processing is an art to get an enriched image or it can be used to retrieve information. This image processing methods are used in medical field also. Numerous modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) etc. are used to analyze and diagnose diseases.Pixel-level image fusion is a combination of several images collected from various inputs and gives more information than any other input messages. Pixel-level image fusion shows a vital role in medical imaging. In this paper, pixel-level image fusionsmethods are survived
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Bercier, Y., M. Schwaiger, S. I. Ziegler, and M. J. Martínez. "PET/CT BiographTM Sensation 16." Nuklearmedizin 45, no. 03 (2006): 126–33. http://dx.doi.org/10.1055/s-0038-1625926.

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SummaryAim: The new PET/CT Biograph Sensation 16 (BS16) tomographs have faster detector electronics which allow a reduced timing coincidence window and an increased lower energy threshold (from 350 to 400 keV). This paper evaluates the performance of the BS16 PET scanner before and after the Pico-3D electronics upgrade. Methods: Four NEMA NU 2–2001 protocols, (i) spatial resolution, (ii) scatter fraction, count losses and random measurement, (iii) sensitivity, and (iv) image quality, have been performed. Results: A considerable change in both PET count-rate performance and image quality is obs
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Syed Inthiyaz, Hasane Ahammad Sk, Praveen SR Konduri, et al. "A novel approach of MRI-CT Image fusion using CWT for finding Disease location." International Journal of Research in Pharmaceutical Sciences 11, no. 1 (2020): 497–506. http://dx.doi.org/10.26452/ijrps.v11i1.1850.

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Medical Image processing has tremendous applications in medical diagnosis. This broadsheet offerings the idea of a fusion of MRI(Magnetic Resource Imaging)-CT (Computed tomography) using Coverlet wavelet transform(CWT), which is used to find the disease location in an image. In the Medical field, CT provides maximum information on denser tissue with less amount of distortion and higher resolution images. Whereas, on the other hand, MRI provides information on softer tissue with much distortion. However, both are similar; the main difference lies where CT uses X-rays to produce images while MRI
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Rossi, Farli. "APPLICATION OF A SEMI-AUTOMATED TECHNIQUE IN LUNG LESION SEGMENTATION." Jurnal Teknoinfo 15, no. 1 (2021): 56. http://dx.doi.org/10.33365/jti.v15i1.945.

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Segmentation is one of the most important steps in automated medical diagnosis applications, which affects the accuracy of the overall system. In this study, we apply a semi-automated technique that combines an active contour and low-level processing techniques in lung lesion segmentation by extracting lung lesions from thoracic Positron Emission Tomography (PET)/Computed Tomography (CT) images. The lesions were first segmented in Positron Emission Tomography (PET) images which have been converted previously to Standardised Uptake Values (SUVs). The segmented PET images then serve as an initia
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Dissertations / Theses on the topic "PET/CT image processing"

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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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Desseroit, Marie-Charlotte. "Caractérisation et exploitation de l'hétérogénéité intra-tumorale des images multimodales TDM et TEP." Thesis, Brest, 2016. http://www.theses.fr/2016BRES0129/document.

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L’imagerie multi-modale Tomographie par émission de positons (TEP)/ Tomodensitométrie(TDM) est la modalité d’imagerie la plus utilisée pour le diagnostic et le suivi des patients en oncologie. Les images obtenues par cette méthode offrent une cartographie à la fois de la densité des tissus (modalité TDM) mais également une information sur l’activité métabolique des lésions tumorales (modalité TEP). L’analyse plus approfondie de ces images acquises en routine clinique a permis d’extraire des informations supplémentaires quant à la survie du patient ou à la réponse au(x) traitement(s). Toutes ce
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Eklund, Anders, Paul Dufort, Daniel Forsberg, and Stephen LaConte. "Medical Image Processing on the GPU : Past, Present and Future." Linköpings universitet, Medicinsk informatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93673.

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, hist
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Pacheco, Edward Florez. "Análise da dinâmica e quantificação metabólica de imagens de medicina nuclear na modalidade PET/CT." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-24062016-141858/.

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A presença da Medicina Nuclear como modalidade de obtenção de imagens médicas é um dos principais procedimentos utilizados hoje nos centros de saúde, tendo como grande vantagem a capacidade de analisar o comportamento metabólico do paciente, traduzindo-se em diagnósticos precoces. Entretanto, sabe-se que a quantificação em Medicina Nuclear é dificultada por diversos fatores, entre os quais estão a correção de atenuação, espalhamento, algoritmos de reconstrução e modelos assumidos. Neste contexto, o principal objetivo deste projeto foi melhorar a acurácia e a precisão na análise de imagens de P
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Zheng, Yiran. "CT-PET Image Fusion and PET Image Segmentation for Radiation Therapy." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1283542509.

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Åkesson, Lars. "Partial Volume Correction in PET/CT." Thesis, Stockholm University, Medical Radiation Physics (together with KI), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-8322.

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<p>In this thesis, a two-dimensional pixel-wise deconvolution method for partial volume correction (PVC) for combined Positron Emission Tomography and Computer Tomography (PET/CT) imaging has been developed. The method is based on Van Cittert's deconvolution algorithm and includes a noise reduction method based on adaptive smoothing and median filters. Furthermore, a technique to take into account the position dependent PET point spread function (PSF) and to reduce ringing artifacts is also described. The quantitative and qualitative performance of the proposed PVC algorithm was evaluated usin
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Baluwala, Habib. "Physically motivated registration of diagnostic CT and PET/CT of lung volumes." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:797c00c0-1efa-43e2-8268-e3d09ced0e06.

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Lung cancer is a disease affecting millions of people every year and poses a serious threat to global public health. Accurate lung cancer staging is crucial to choose an appropriate treatment protocol and to determine prognosis, this requires the acquisition of contrast-enhanced diagnostic CT (d-CT) that is usually followed by a PET/CT scan. Information from both d-CT and PET scan is used by the clinician in the staging process; however, these images are not intrinsically aligned because they are acquired on different days and on different scanners. Establishing anatomical correspondence, i.e.
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D’Antò, Michela <1984&gt. "CT perfusion image processing: analysis of liver tumors." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5499/.

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Perfusion CT imaging of the liver has potential to improve evaluation of tumour angiogenesis. Quantitative parameters can be obtained applying mathematical models to Time Attenuation Curve (TAC). However, there are still some difficulties for an accurate quantification of perfusion parameters due, for example, to algorithms employed, to mathematical model, to patient’s weight and cardiac output and to the acquisition system. In this thesis, new parameters and alternative methodologies about liver perfusion CT are presented in order to investigate the cause of variability of this technique.
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Kotasidis, Fotis. "Spatiotemporal image reconstruction with resolution recovery for dynamic PET/CT in oncology." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/spatiotemporal-image-reconstruction-with-resolution-recovery-for-dynamic-petct-in-oncology(d3f936ed-f917-42a2-8842-e7f50b244035).html.

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Positron emission tomography (PET) is a powerful and highly specialised imaging modality that has the inherent ability to detect and quantify changes in the bio-distribution of an intravenously administered radio-labelled tracer, through dynamic image acquisition of the system under study. By modelling the temporal distribution of the tracer, parameters of interest regarding specific biological processes can be derived. Traditionally parameter estimation is done by first reconstructing a set of dynamic images independently, followed by kinetic modelling, leading to parameters of reduced accura
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Lee, Ki Sung. "Pragmatic image reconstruction for high resolution PET scanners /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/5967.

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Books on the topic "PET/CT image processing"

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Wahl, Richard L. Atlas of PET/CT: With SPECT/CT. Saunders, 2008.

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Panetta, Daniele, and Niccoló Camarlinghi. 3D Image Reconstruction for CT and PET. CRC Press, 2020. http://dx.doi.org/10.1201/9780429270239.

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Cardiac CT, PET, and MR. 2nd ed. Wiley-Blackwell, 2010.

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Mohsen, Farsad, and Mansi Luigi, eds. PET-CT beyond FDG: A quick guide to image interpretation. Springer, 2010.

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Fanti, Stefano, Mohsen Farsad, and Luigi Mansi, eds. PET-CT Beyond FDG A Quick Guide to Image Interpretation. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-93909-2.

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Atlas of PET/CT with SPECT / CT with DVD. Saunders, 2007.

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Dilsizian, Vasken, and Gerald M. Pohost. Cardiac CT, PET and MR. Wiley & Sons, Incorporated, John, 2010.

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Dilsizian, Vasken, Robert O. Bonow, and Gerald M. Pohost. Cardiac CT, PET and MR. Wiley & Sons, Incorporated, John, 2008.

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Dilsizian, Vasken, and Gerald M. Pohost. Cardiac CT, PET and MR. Wiley & Sons, Incorporated, John, 2019.

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Dilsizian, Vasken, Robert O. Bonow, and Gerald M. Pohost. Cardiac CT, PET and MR. Wiley & Sons, Incorporated, John, 2008.

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Book chapters on the topic "PET/CT image processing"

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Belcari, Nicola, Ronald Boellaard, and Matteo Morrocchi. "PET/CT and PET/MR Tomographs: Image Acquisition and Processing." In Nuclear Medicine Textbook. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-95564-3_9.

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Chen, Chin-Tu, Charles A. Pelizzari, George T. Y. Chen, Malcolm D. Cooper, and David N. Levin. "Image Analysis of PET Data with the aid of CT and MR Images." In Information Processing in Medical Imaging. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4615-7263-3_41.

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Dafni Rose, J., K. Jaspin, and K. Vijayakumar. "Lung Cancer Diagnosis Based on Image Fusion and Prediction Using CT and PET Image." In Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6141-2_4.

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Fröhlich, Magali, Christophe Bolinhas, Adrien Depeursinge, et al. "Holographic Visualisation and Interaction of Fused CT, PET and MRI Volumetric Medical Imaging Data Using Dedicated Remote GPGPU Ray Casting." In Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01045-4_12.

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Pietrzyk, U. "Image Fusion." In PET and PET-CT in Oncology. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18803-9_5.

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Czernin, J., M. Dahlbom, O. Ratib, and C. Schiepers. "PET/CT Image Artifacts." In Atlas of PET/CT Imaging in Oncology. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18517-5_8.

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Kim, E. Edmund, and Franklin C. L. Wong. "Technical Principles, Radiation Safety, and Image Interpretation." In Clinical PET and PET/CT. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0802-5_7.

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Brouwers, Adrienne H., Klaas P. Koopmans, Rudi A. J. O. Dierckx, and Philip H. Elsinga. "Dopa PET-CT." In PET-CT Beyond FDG A Quick Guide to Image Interpretation. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-93909-2_10.

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Brouwers, Adrienne H., Klaas P. Koopmans, Rudi A. J. O. Dierckx, and Philip H. Elsinga. "HTP PET-CT." In PET-CT Beyond FDG A Quick Guide to Image Interpretation. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-93909-2_14.

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Farsad, Mohsen, Vincenzo Allegri, and Paolo Castellucci. "Choline PET-CT." In PET-CT Beyond FDG A Quick Guide to Image Interpretation. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-93909-2_3.

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Conference papers on the topic "PET/CT image processing"

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Wang, Tonghe, Yang Lei, Sibo Tian, et al. "Lung tumor segmentation of PET/CT using dual pyramid mask R-CNN." In Image Processing, edited by Bennett A. Landman and Ivana Išgum. SPIE, 2021. http://dx.doi.org/10.1117/12.2580987.

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Chen, Chin-Tu, Xiaolong Ouyang, Caesar Ordonez, Xiaoping Hu, Wing H. Wong, and Charles E. Metz. "Incorporation of structural CT and MR images in PET image reconstruction." In Medical Imaging V: Image Processing, edited by Murray H. Loew. SPIE, 1991. http://dx.doi.org/10.1117/12.45219.

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Dirks, Ine, Marleen Keyaerts, Bart Neyns, and Jef Vandemeulebroucke. "Automated threshold selection on whole-body 18F-FDG PET/CT for assessing tumor metabolic response." In Image Processing, edited by Bennett A. Landman and Ivana Išgum. SPIE, 2020. http://dx.doi.org/10.1117/12.2549796.

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Han, Fangfang, Jinzhu Yang, Yang Liu, and Hong Zhao. "Research on Preprocessing Algorithm for PET-CT Image Registration." In 2010 International Conference on Optoelectronics and Image Processing (ICOIP). IEEE, 2010. http://dx.doi.org/10.1109/icoip.2010.333.

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Nayyeri, Fereshteh, Ashrani Aizuddin Abd Rahni, and Aini Ab Aziz. "Modelling the GE discovery 690 PET/CT scanner." In 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2015. http://dx.doi.org/10.1109/icsipa.2015.7412182.

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Santini, Gianmarco, Constance Fourcade, Noémie Moreau, et al. "Unpaired PET/CT image synthesis of liver region using CycleGAN." In 16th International Symposium on Medical Information Processing and Analysis, edited by Jorge Brieva, Natasha Lepore, Eduardo Romero Castro, and Marius G. Linguraru. SPIE, 2020. http://dx.doi.org/10.1117/12.2576095.

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Sroubek, Filip, Michal Sorel, Jiri Boldys, and Jan Sroubek. "PET image reconstruction using prior information from CT or MRI." In 2009 16th IEEE International Conference on Image Processing (ICIP 2009). IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5413928.

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Nguyen, Chuong, Joseph Havlicek, Quyen Duong, et al. "An automatic 3D CT/PET segmentation framework for bone marrow proliferation assessment." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7533136.

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Chambon, S., A. Moreno, A. P. Santhanam, J. P. Rolland, E. Angelini, and I. Bloch. "CT-PET Landmark-based Lung Registration Using a Dynamic Breathing Model." In 14th International Conference on Image Analysis and Processing (ICIAP 2007). IEEE, 2007. http://dx.doi.org/10.1109/iciap.2007.4362857.

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Lian, Chunfeng, Su Ruan, Thierry Denoux, Yu Guo, and Pierre Vera. "Accurate tumor segmentation in FDG-PET images with guidance of complementary CT images." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8297123.

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