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Literatura académica sobre el tema "Multi-Modal Imaging Techniques"

Autor: Grafiati

Publicado: 5 de abril de 2025

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Artículos de revistas sobre el tema "Multi-Modal Imaging Techniques"

Dumbryte, Irma, Donatas Narbutis, Maria Androulidaki, Arturas Vailionis, Saulius Juodkazis y Mangirdas Malinauskas. "Teeth Microcracks Research: Towards Multi-Modal Imaging". Bioengineering 10, n.º 12 (25 de noviembre de 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-Raman spectroscopy, optical properties and chemical and elemental composition of the material can be evaluated, thus helping to assess the structural integrity of the tooth at the MC site. Approaching tooth samples having cracks from different perspectives and using complementary laboratory techniques, there is a natural progression from 3D to multi-modal imaging, where the volumetric (passive: dimensions) information of the tooth sample can be supplemented by dynamic (active: composition, interaction) image data. Revelation of tooth cracks clearly shows the need to re-assess the role of these MCs and their effect on the structural integrity and longevity of the tooth. This provides insight into the nature of cracks in natural hard materials and contributes to a better understanding of how bio-inspired structures could be designed to foresee crack propagation in biosolids.

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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, n.º 1s (28 de diciembre de 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. The three fusion strategies studied were fusion through concatenation, fusion through kernels, and fusion through attention. The fused features were used to train classification models such as Convolutional Neural Networks (CNNs), ensemble techniques, and Support Vector Machines (SVMs). ROC analysis was utilized to assess the diagnostic performance. The multi-modal fusion techniques outperformed the single-modality methods in diagnosing performance. Attention-based fusion yielded the top AUCs of 0.92, 0.89, and 0.91 for brain tumors, neurodegenerative diseases, and cardiovascular conditions, respectively. This significantly improved (p<0.05) compared to the AUC of the best single-modality models. Multi-modal fusion methods are powerful for combining data from various imaging modalities to improve diagnostic accuracy for various medical conditions. These findings highlight the advantages of combining information sources to improve clinical judgment and patient care.

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Liu, Tracy W., Seth T. Gammon, David Fuentes y David Piwnica-Worms. "Multi-Modal Multi-Spectral Intravital Macroscopic Imaging of Signaling Dynamics in Real Time during Tumor–Immune Interactions". Cells 10, n.º 3 (25 de febrero de 2021): 489. http://dx.doi.org/10.3390/cells10030489.

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A major obstacle in studying the interplay between cancer cells and the immune system has been the examination of proposed biological pathways and cell interactions in a dynamic, physiologically relevant system in vivo. Intravital imaging strategies are one of the few molecular imaging techniques that can follow biological processes at cellular resolution over long periods of time in the same individual. Bioluminescence imaging has become a standard preclinical in vivo optical imaging technique with ever-expanding versatility as a result of the development of new emission bioluminescent reporters, advances in genomic techniques, and technical improvements in bioluminescence imaging and processing methods. Herein, we describe an advance of technology with a molecular imaging window chamber platform that combines bioluminescent and fluorescent reporters with intravital macro-imaging techniques and bioluminescence spectral unmixing in real time applied to heterogeneous living systems in vivo for evaluating tumor signaling dynamics and immune cell enzyme activities concurrently.

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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, n.º 5 (30 de octubre de 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 algorithm for medical image matching grounded in multi-task learning, further investigating a semantic-enhanced technique for cross-modal medical image retrieval. By deeply exploring complementary semantic information between different modality medical images, these methods offer novel perspectives and tools for the domain of medical image matching and retrieval.

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Kimm, Melanie A., Maxim Shevtsov, Caroline Werner, Wolfgang Sievert, Wu Zhiyuan, Oliver Schoppe, Bjoern H. Menze et al. "Gold Nanoparticle Mediated Multi-Modal CT Imaging of Hsp70 Membrane-Positive Tumors". Cancers 12, n.º 5 (22 de mayo de 2020): 1331. http://dx.doi.org/10.3390/cancers12051331.

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Imaging techniques such as computed tomographies (CT) play a major role in clinical imaging and diagnosis of malignant lesions. In recent years, metal nanoparticle platforms enabled effective payload delivery for several imaging techniques. Due to the possibility of surface modification, metal nanoparticles are predestined to facilitate molecular tumor targeting. In this work, we demonstrate the feasibility of anti-plasma membrane Heat shock protein 70 (Hsp70) antibody functionalized gold nanoparticles (cmHsp70.1-AuNPs) for tumor-specific multimodal imaging. Membrane-associated Hsp70 is exclusively presented on the plasma membrane of malignant cells of multiple tumor entities but not on corresponding normal cells, predestining this target for a tumor-selective in vivo imaging. In vitro microscopic analysis revealed the presence of cmHsp70.1-AuNPs in the cytosol of tumor cell lines after internalization via the endo-lysosomal pathway. In preclinical models, the biodistribution as well as the intratumoral enrichment of AuNPs were examined 24 h after i.v. injection in tumor-bearing mice. In parallel to spectral CT analysis, histological analysis confirmed the presence of AuNPs within tumor cells. In contrast to control AuNPs, a significant enrichment of cmHsp70.1-AuNPs has been detected selectively inside tumor cells in different tumor mouse models. Furthermore, a machine-learning approach was developed to analyze AuNP accumulations in tumor tissues and organs. In summary, utilizing mHsp70 on tumor cells as a target for the guidance of cmHsp70.1-AuNPs facilitates an enrichment and uniform distribution of nanoparticles in mHsp70-expressing tumor cells that enables various microscopic imaging techniques and spectral-CT-based tumor delineation in vivo.

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Manser, Steffen, Shaun Keck, Mario Vitacolonna, Felix Wuehler, Ruediger Rudolf y Matthias Raedle. "Innovative Imaging Techniques: A Conceptual Exploration of Multi-Modal Raman Light Sheet Microscopy". Micromachines 14, n.º 9 (5 de septiembre de 2023): 1739. http://dx.doi.org/10.3390/mi14091739.

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Advances in imaging of microscopic structures are supported and complemented by adaptive visualization tools. These tools enable researchers to precisely capture and analyze complex three-dimensional structures of different kinds such as crystals, microchannels and electronic or biological material. In this contribution, we focus on 3D cell cultures. The new possibilities can play a particularly important role in biomedical research, especially here in the study of 3D cell cultures such as spheroids in the field of histology. By applying advanced imaging techniques, detailed information about the spatial arrangement and interactions between cells can be obtained. These insights help to gain a better understanding of cellular organization and function and have potential implications for the development of new therapies and drugs. In this context, this study presents a multi-modal light sheet microscope designed for the detection of elastic and inelastic light scattering, particularly Rayleigh scattering as well as the Stokes Raman effect and fluorescence for imaging purposes. By combining multiple modalities and stitching their individual results, three-dimensional objects are created combining complementary information for greater insight into spatial and molecular information. The individual components of the microscope are specifically selected to this end. Both Rayleigh and Stokes Raman scattering are inherent molecule properties and accordingly facilitate marker-free imaging. Consequently, altering influences on the sample by external factors are minimized. Furthermore, this article will give an outlook on possible future applications of the prototype microscope.

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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, n.º 11 (30 de noviembre de 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 that is used for the detection and analysis of specific problem. The idea behind fusion is to achieve and get better contrast and better fused image. The algorithm is making use of the common pyramid type and similarity type fusion algorithm with the neural networks model to achieve a better and more flexible fusion method. The advantages of image fusion medically are widespread. It plays a pivotal role in tumour localization, surgical planning and in treatment assessment.

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Bashiri, Fereshteh, Ahmadreza Baghaie, Reihaneh Rostami, Zeyun Yu y Roshan D’Souza. "Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach". Journal of Imaging 5, n.º 1 (30 de diciembre de 2018): 5. http://dx.doi.org/10.3390/jimaging5010005.

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Multi-modal image registration is the primary step in integrating information stored in two or more images, which are captured using multiple imaging modalities. In addition to intensity variations and structural differences between images, they may have partial or full overlap, which adds an extra hurdle to the success of registration process. In this contribution, we propose a multi-modal to mono-modal transformation method that facilitates direct application of well-founded mono-modal registration methods in order to obtain accurate alignment of multi-modal images in both cases, with complete (full) and incomplete (partial) overlap. The proposed transformation facilitates recovering strong scales, rotations, and translations. We explain the method thoroughly and discuss the choice of parameters. For evaluation purposes, the effectiveness of the proposed method is examined and compared with widely used information theory-based techniques using simulated and clinical human brain images with full data. Using RIRE dataset, mean absolute error of 1.37, 1.00, and 1.41 mm are obtained for registering CT images with PD-, T1-, and T2-MRIs, respectively. In the end, we empirically investigate the efficacy of the proposed transformation in registering multi-modal partially overlapped images.

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Al-Sharify, Talib A. Al, Mohammed Hussein .., Aqeel Hussen y Zaid Saad Madhi. "Multilevel Features Fusion of Intelligent Techniques for Brain Imaging Analysis". Fusion: Practice and Applications 11, n.º 1 (2023): 100–113. http://dx.doi.org/10.54216/fpa.110108.

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With the use of multi-level features fusion, this work provides a new method for recognizing cognitive brain activity, which we term the Improved Multi-modal cognitive brain-imaging method (IMCBI). Identifying brain areas and basing judgments on insights into intelligent cognitive behavior for babies and adolescents presents a number of methodological issues that the suggested approach seeks to address. In order to understand how the brain functions during various motor, perceptual, and cognitive tasks, IMCBI employs smart methods for fusing data at several levels. This technique employs functional magnetic resonance imaging (fMRI) data to assess human behavioral activity in the brain while engaging in a variety of activities. It does so by combining an inter-subject retrieval strategy with deep neural networks (DNN). The research shows that the suggested method, which uses multi-level fusion of features, greatly raises the accuracy ratio to 95.63 percent, the sensitivity to 95.42 percent, and the specificity to 94.3 three point three percent. The findings demonstrate the method's efficacy in recognizing brain activity based on high-level cognitive ability, making it a useful tool for predicting clinical and behavioral responses.

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Tanu y Deepti Kakkar. "Diagnostic Assessment Techniques and Non-Invasive Biomarkers for Autism Spectrum Disorder". International Journal of E-Health and Medical Communications 10, n.º 3 (julio de 2019): 79–95. http://dx.doi.org/10.4018/ijehmc.2019070105.

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Autism spectrum disorder (ASD) is a complex heterogeneous neurological disorder that has led to a spectrum of diagnosis techniques. The screening instruments, medical and technological tools initiate the diagnosis process. Clinicians and psychologists propose therapies depending on the examination done by these methodologies. The literature has accounted dozens of diagnostic methods and alternative and complementary therapies but still lack in highlighting the proper biomarker for early detection and intervention. The emerging multi-modal neuro-imaging techniques have correlated the brain's functional and structural measures and diagnosed ASD with more sensitivity than individual approaches. The purpose of this review article is: (i) to provide an overview of the emerging ASD diagnosis methods and different markers and; (ii) to present the idea of integrating all the individual methods in to a multi-modal diagnostic system to enhance detection sensitivity. This system possesses the potential to diagnose and predict ASD clinically, neurologically & objectively with high detection sensitivity.

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Más fuentes

Tesis sobre el tema "Multi-Modal Imaging Techniques"

Namati, Jacqueline Thiesse. "Phenotype characterization of lung structure in inbred mouse strains using multi modal imaging techniques". Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/256.

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Research involved in modeling human lung disease conditions has provided insight into disease development, progression, and treatment. In particular, mouse models of human pulmonary disease are increasingly utilized to characterize lung disease conditions. With advancements in small animal imaging it is now possible to investigate the phenotypic differences expressed in inbred mouse strains in vivo to investigate specific disease conditions that affect the lung. In this thesis our aim was to generate a comprehensive characterization of the normative mouse lung phenotypes in three of the most utilized strains of mice, C57BL/6, A/J, and BALB/c, through imaging techniques. The imaging techniques that we utilized in this research included micro-CT, a custom Large Image Microscope Array (LIMA) system for 3D microscopy, and classical histology. Micro-CT provided a non-destructive technique for acquiring in vivo and fixed lung images. The LIMA 3D microscopy system was utilized for direct correspondence of the gold standard histology images as well as to validate the anatomical structures and measurements that were extracted from the micro-CT images. Finally, complete lung histology slices were utilized for assessment of the peripheral airspace structures that were not resolvable using the micro-CT imaging system. Through our developed imaging acquisition and processing strategies we have been able to successful characterize important phenotypes in the mouse lung that have not previously been known as well as identify strain variations. These findings will provide the scientific community with valuable information to be better equipped and capable of pursuing new avenues of research in investigating pulmonary disease conditions that can be modeled in the mouse.

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Sidlipura, Ravi Kumar Sujith Kumar. "Multi-modal and multiscale image analysis work flows for characterizing through-thickness impregnation of fiber reinforced composites manufactured by simplified CRTM process". Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Lille Douai, 2024. http://www.theses.fr/2024MTLD0010.

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Cette thèse présente une étude expérimentale pour améliorer le moulage par compression et transfert de résine thermoplastique (CRTM), axée sur l'efficacité industrielle, la durabilité et la recyclabilité, conformément aux objectifs de développement durable pour l’industrie, l’innovation et l’action climatique. En abordant la complexité de l'écoulement de la résine à plusieurs échelles dans le CRTM, cette recherche étudie l'écoulement transversal (à travers l’épaisseur) et la porosité induite par le processus à l'échelle méso des faisceaux de fibres de verre afin d'améliorer l'uniformité de l'imprégnation et le contrôle du compactage, en faisant le lien entre les cadres théoriques et les applications évolutives. L’étude est conduite sur une préforme, constituées de 6 couches de fibres de verre UD ([0/90]3) et d’une matrice thermoplastique en polypropylene (PP) mise en forme par un procédé CRTM . Un procédé « CRTM simplifié » permettant de contrôler la direction du front de matière est développé sur une presse industrielle, pilotée en déplacement. Trois configurations de procédé sont analysées : Configuration 1 (Référence) : configuration de type « film stacking » comme base de comparaison de la distribution de la résine et de la structure des fibres. Configuration 2 (CRTM simplifié) : Compression contrôlée par déplacement, les films de polymères formant initialement une couche unique en surface de la préforme. Configuration 3 (CRTM simplifié avec scellement des bords) : Compression améliorée avec un dispositif d’étanchéité limitant les fuites de résine en périphérie de la préforme et assurant un écoulement transversal. Un protocole d’analyse d'imagerie 2D est proposé, incluant l’analyse en lumière polarisée, la microscopie à fluorescence et la microscopie électronique à balayage pour caractériser qualitativement et quantitativement les taux de porosités au niveau des mèches et des plis de tissus. Un processus original de polissage en deux étapes permet de préserver l'intégrité de la surface. L'étude est complétée par une évaluation fine des mécanismes d'imprégnation à l'aide de la technique d'inspection hélicoïdale en microtomographie à rayon-X (micro-CT). Les résultats démontrent que les paramètres de compaction influencent directement le niveau d'imprégnation, atteignant une limite d'imprégnation. Cette thèse établit une démarche d’analyse du procédé CRTM pour des composites thermoplastiques haute performance, en vue d’une maitrise et d’une optimisation du procédé. Elle offre des perspectives sur des protocoles d’analyse précis basés sur l’étude à différentes échelles, améliorant la compréhension de l'interaction entre l'imprégnation et la perméabilité. Ces résultats répondent aux exigences de précision dans des secteurs tels que l'automobile et l'aérospatiale, où les composites CRTM sont essentiels pour les applications structurelles
This thesis presents an experimental study to advance thermoplastic Compression Resin Transfer Molding (CRTM), focusing on industrial efficiency, sustainability, and recyclability goals aligned with the Sustainable Development Goals for Industry, Innovation, and Climate Action. By addressing multi-scale resin flow complexity in CRTM, this research investigates transverse flow and process-induced porosity at the meso scale of glass fiber bundles to improve impregnation uniformity and compaction control, bridging theoretical frameworks with scalable applications. The study focuses on a thermoplastic polypropylene matrix reinforced with six layers of bidirectional UD woven glass fibers ([0/90]3) consolidated on a CRTM setup. The “Simplified CRTM” method is developed on an industrial press, using displacement-controlled compaction ratios. This method omits active resin injection, relying on a uniformly distributed viscous polymer pool beneath the unsaturated preform to drive resin flow uniformly with a unidirectional flow path. Controlled displacement and pressure optimize resin paths, manage fiber volume fraction, and reduce porosity. Three multi-step compaction configurations are evaluated: Configuration 1 (Reference): Uses force compaction as a baseline for comparing resin distribution and fiber structure. Configuration 2 (simplified CRTM): Displacement-controlled compaction enhances resin infiltration but faces challenges like edge race-tracking and fiber volume fraction (Vf) variability, affecting impregnation. Configuration 3 (simplified CRTM with Edge Sealing): Introduces high-temperature sealant tape at mold edges, limiting resin escape, maintaining transverse flow, and reducing porosity and race-tracking. Configuration 3 edge-sealing technique establishes a reproducible process for high quality CRTM composites. An advanced 2D multi-modal imaging protocol, tailored for partially impregnated samples produced via simplified CRTM with unfilled spaces and fragile microstructures, includes polarized light microscopy, fluorescence microscopy, and scanning electron microscopy for qualitative and quantitative characterization. An original two-step polishing process preserves surface integrity, and image post-processing workflows quantify impregnation quality and void distribution. The study is completed with a fine evaluation of the impregnation mechanisms using X-ray micro computed tomography technique (micro-CT) relying on helicoidal inspection method. Results demonstrate that compaction parameters directly impact impregnation level, reaching an impregnation limit. This thesis establishes a scalable, data-driven CRTM framework bridging laboratory experimentation with industrial requirements for high-performance thermoplastic composites. It offers insights into streamlined protocols and microstructure-based analysis, enhancing understanding of the interplay between impregnation and permeability in CRTM. These findings align with precision demands in sectors like automotive and aerospace, where CRTM composites are crucial for structural applications

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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 (SPM) when registering whole-body images. To assess attainment of these objectives, the following registration tasks were performed: (1) FDG_CT with FLT_CT images, (2) pre-operation MRI with intra-operation CT images, (3) brain only MRI with corresponding PET images, and (4) MRI T1 with T2, T1 with FLAIR, and T1 with GE images. Then, the results of the proposed method will be compared to those obtained using existing state-of-the-art registration methods such as SPM and FSL. Initially, three slices were chosen from the reference image, and the normalized mutual information (NMI) was calculated between each of them for every slice in the moving image. The three pairs with the highest NMI values were chosen. The wavelet decomposition method is applied to minimize the computational requirements. An initial search applying a genetic algorithm is conducted on the three pairs to obtain three sets of registration parameters. The Powell method is applied to reference and moving images to validate the three sets of registration parameters. A linear interpolation method is then used to obtain the registration parameters for all remaining slices. Finally, the aligned registered image with the reference image were displayed to show the different performances of the 3 methods, namely the proposed method, SPM and FSL by gauging the average NMI values obtained in the registration results. Visual observations are also provided in support of these NMI values. For comparative purposes, tests using different multi-modal imaging platforms are performed.

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Bedard, Noah. "Multi-Modal Imaging Techniques for Early Cancer Diagnostics". Thesis, 2012. http://hdl.handle.net/1911/64685.

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Cancer kills more Americans under the age of 75 than any other disease. Although most cancers occur in epithelial surfaces that can be directly visualized, the majority of cases are detected at an advanced stage. Optical imaging and spectroscopy may provide a solution to the need for non-invasive and effective early detection tools. These technologies are capable of examining tissue over a wide range of spatial scales, with widefield macroscopic imaging typically spanning several square-centimeters, and high resolution in vivo microscopy techniques enabling cellular and subcellular features to be visualized. This work presents novel technologies in two important areas of optical imaging: high resolution imaging and widefield imaging. For subcellular imaging applications, new high resolution endomicroscope techniques are presented with improved lateral resolution, larger field-of-view, increased contrast, decreased background signal, and reduced cost compared to existing devices. A new widefield optical technology called multi-modal spectral imaging is also developed. This technique provides real-time in vivo spectral data over a large field-of-view, which is useful for detecting biochemical alterations associated with neoplasia. The described devices are compared to existing technologies, tested using ex vivo tissue specimens, and evaluated for diagnostic potential in a multi-patient oral cancer clinical trial.

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Po, Ming Jack. "Multi-scale Representations for Classification of Protein Crystal Images and Multi-Modal Registration of the Lung". Thesis, 2015. https://doi.org/10.7916/D87M06MZ.

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In recent years, multi-resolution techniques have become increasingly popular in the image processing community. New techniques have been developed with applications ranging from edge detection, texture recognition, image registration, multi-resolution features for image classification and more. The central focus of this two-part thesis is the multi-resolution analysis of images. In the first part, we used multi-resolution approaches to help with the classification of a set of protein crystal images. In the second, similar approaches were used to help register a set of 3D image volumes that would otherwise be computationally prohibitive without leveraging multi-resolution techniques. Specifically, the first part of this work proposes a classification framework that is being developed in collaboration with NorthEast Structural Genomics Consoritum (NESG) to assist in the automated screening of protein crystal images. Several groups have previously proposed automated algorithms to expedite such analysis. However, none of the classifiers described in the literature are sufficiently accurate or fast enough to be practical in a structural genomics production pipeline. The second part of this work proposes a 3D image registration algorithm to register regions of emphysema as quantified by densitometry on lung CT with MR lung volumes. The ability to register quantitatively-determined regions of emphysema with perfusion MRI will allow for further exploration of the pathophysiology of Chronic Obstructive Pulmonary Disorder (COPD). The registration method involves the registration of CT volumes at different levels of inspiration (total lung capacity to functional residual capacity [FRC]) followed by another registration between FRC-CT and FRC-MR volume pairs.

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Namati, Jacqueline Thiesse McLennan Geoffrey. "Phenotype characterization of lung structure in inbred mouse strains using multi modal imaging techniques y Jacqueline Thiesse Namati". 2009. http://ir.uiowa.edu/etd/256/.

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Capítulos de libros sobre el tema "Multi-Modal Imaging Techniques"

Dong, Pei, Yanrong Guo, Dinggang Shen y Guorong Wu. "Multi-atlas and Multi-modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph". En Patch-Based Techniques in Medical Imaging, 188–96. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28194-0_23.

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Sharma, Deepshikha, Ulrike Rothenhaeusler, Katharina Schmidt-Ott, Marvin Nurit, Yuly Castro Cartagena, Gaetan Le-Goic, Edith Joseph, Sony George y Tiziana Lombardo. "Monitoring and Understanding VOC Induced Glass Corrosion Using Multi-modal Imaging Techniques". En Lecture Notes in Mechanical Engineering, 359–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17594-7_27.

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Geremia, Ezequiel, Bjoern H. Menze, Marcel Prastawa, M. A. Weber, Antonio Criminisi y Nicholas Ayache. "Brain Tumor Cell Density Estimation from Multi-modal MR Images Based on a Synthetic Tumor Growth Model". En Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging, 273–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36620-8_27.

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Mani, V. R. S. "Deep Learning Models for Semantic Multi-Modal Medical Image Segmentation". En Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, 107–25. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7544-7.ch007.

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In this chapter, the author paints a comprehensive picture of different deep learning models used in different multi-modal image segmentation tasks. This chapter is an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different types of multi-modal images and the corresponding types of convolution neural networks used in the segmentation task. The chapter starts with an introduction to CNN topology and describes various models like Hyper Dense Net, Organ Attention Net, UNet, VNet, Dilated Fully Convolutional Network, Transfer Learning, etc.

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Shkel Anton, Natarajan Shyam, Schimpf Stefan, Culjat Martin O., Brose Andreas, Boese Axel, Schmidt Bertram et al. "A Transurethral Catheter-Based Ultrasound System for Multi-Modal Fusion". En Studies in Health Technology and Informatics. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-022-2-463.

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Current methods of prostate cancer diagnosis and therapy rely on accurate imaging of the prostate using real-time ultrasound. Transurethral ultrasound (TUUS) may improve upon the current gold standard through improved 3D visualization and co-registration (fusion) with CT and MRI. A prototype transurethral ultrasound (TUUS) catheter-based transducer array and system was developed, featuring 32 elements with a diameter of 18F (6mm). A robust, multi-channel ultrasound transceiver was also developed to enable TUUS imaging using pulse-echo and frequency-based signal processing methods. The feasibility of a TUUS imaging system suitable for multi-modal image fusion and novel ultrasound signaling techniques was demonstrated.

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Udendhran, R. y Balamurugan M. "Demystification of Deep Learning-Driven Medical Image Processing and Its Impact on Future Biomedical Applications". En Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, 844–60. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7544-7.ch043.

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The recent growth of big data has ushered in a new era of deep learning algorithms in every sphere of technological advance, including medicine, as well as in medical imaging, particularly radiology. However, the recent achievements of deep learning, in particular biomedical applications, have, to some extent, masked decades-long developments in computational technology for medical image analysis. The methods of multi-modality medical imaging have been implemented in clinical as well as research studies. Due to the reason that multi-modal image analysis and deep learning algorithms have seen fast development and provide certain benefits to biomedical applications, this chapter presents the importance of deep learning-driven medical imaging applications, future advancements, and techniques to enhance biomedical applications by employing deep learning.

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Udendhran, R. y Balamurugan M. "Demystification of Deep Learning-Driven Medical Image Processing and Its Impact on Future Biomedical Applications". En Deep Neural Networks for Multimodal Imaging and Biomedical Applications, 155–71. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3591-2.ch010.

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Lawrie, Stephen M., Eve C. Johnston y Daniel R. Weinberger. "Towards an integrated imaging of schizophrenia". En Schizophrenia: From neuroimaging to neuroscience, 363–96. Oxford University PressOxford, 2004. http://dx.doi.org/10.1093/oso/9780198525967.003.0013.

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Abstract The preceding chapters in this book demonstrate both the great advances in technology applied to brain imaging and knowledge of schizophrenia gained from these techniques over the past 25 years or so. In this chapter, we aim to synthesize the findings from these studies, in the light of other improvements in our understanding of schizophrenia and related neuropsychiatric conditions—particularly those deriving from genetics, epidemiology, and cognitive neuropsychology. We consider the conceptual and methodological problems of the field, and indicate where we think definite progress is likely in the foreseeable future (e.g. scanning both special and representative populations, multi-modal imaging, employing complementary approaches to study disconnectivity, and using neuroimaging databases).

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Kilindris, Thomas V. y Kiki Theodorou. "Combining Geometry and Image in Biomedical Systems". En Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications, 197–212. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-314-2.ch013.

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Patient anatomy, biochemical response, as well functional evaluation at organ level, are key fields that produce a significant amount of multi modal information during medical diagnosis. Visualization, processing, and storage of the acquired data sets are essential tasks in everyday medical practice. In order to perform complex processing that involves or rely on image data a robust as well versatile data structure was used as extension of the Visualization Toolkit (VTK). The proposed structure serves as a universal registration container for acquired information and post processed resulted data. The structure is a dynamic multidimensional data holder to host several modalities and/or Meta data like fused image sets, extracted features (volumetric, surfaces, edges) providing a universal coordinate system used for calculations and geometric processes. A case study of Treatment Planning System (TPS) in the stereotactic radiotherapy (RT) based on the proposed structure is discussed as an efficient medical application.

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Talwar, Rajneesh, Manvinder Sharma y Sonia. "A Comprehensive Review on Artificial Intelligence-Driven Radiomics for Early Cancer Detection and Intelligent Medical Supply Chain". En Advances in Logistics, Operations, and Management Science, 226–54. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1347-3.ch015.

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An intelligent supply chain is essential in the continuously changing environment of the healthcare industry because it combines modern technology, data analytics, and artificial intelligence. Artificial intelligence-driven radiomics enables the extraction of intricate details from medical images, allowing for the early detection and diagnosis of cancer. These algorithms can identify subtle patterns and features in imaging data that might go unnoticed by human observers. Early detection is critical for improving survival rates and treatment outcomes. In this chapter, a review is done on convolutional neural networks (CNNs), transfer learning, ensemble models, radiomics features and machine learning, deep learning for histopathology, multi-modal integration, risk assessment models, and real-time image analysis. The review compresses work on parameters like cancer type, dataset size, accuracy, complexity, and applications of these AI techniques.

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Actas de conferencias sobre el tema "Multi-Modal Imaging Techniques"

Song, Jun, Yusi Miao, Joanne A. Matsubara, Marinko V. Sarunic y Myeong Jin Ju. "Multi-modal functional sensorless adaptive optics for small animal retinal imaging". En Optical Coherence Imaging Techniques and Imaging in Scattering Media, editado por Maciej Wojtkowski, Yoshiaki Yasuno y Benjamin J. Vakoc. SPIE, 2023. http://dx.doi.org/10.1117/12.2670968.

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Izatt, Joseph A. "Novel Multi-Modal Sub-Diffraction Imaging Modalities Enabled by Structured Illumination Microscopy". En Novel Techniques in Microscopy. Washington, D.C.: OSA, 2017. http://dx.doi.org/10.1364/ntm.2017.nm2c.1.

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Cochran, Jeffrey M., David R. Busch, Han Y. Ban, Venkaiah C. Kavuri, Martin J. Schweiger, Simon R. Arridge y Arjun G. Yodh. "Multi-modal diffuse optical techniques for breast cancer neoadjuvant chemotherapy monitoring (Conference Presentation)". En Multimodal Biomedical Imaging XII, editado por Fred S. Azar y Xavier Intes. SPIE, 2017. http://dx.doi.org/10.1117/12.2251455.

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Spielman-Sun, Eleanor, Sharon Bone y Samuel Webb. "Integrating synchrotron x-ray fluorescence mapping with complementary imaging techniques to obtain multi-modal datasets for the earth and environmental sciences at SSRL". En Goldschmidt 2024. United States of America: Geochemical Society, 2024. https://doi.org/10.46427/gold2024.24226.

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Leung, Nathanael. "3D multi-modal imaging of demineralised dentine using combinedscanning transmission X-ray microscopy (STXM-CT) and micro-X-ray diffraction (µ-XRD-CT) tomography techniques". En Microscience Microscopy Congress 2021 incorporating EMAG 2021. Royal Microscopical Society, 2021. http://dx.doi.org/10.22443/rms.mmc2021.268.

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Yang, Zhuo, Jaehyuk Kim, Yan Lu, Ho Yeung, Brandon Lane, Albert Jones y Yande Ndiaye. "A Multi-Modal Data-Driven Decision Fusion Method for Process Monitoring in Metal Powder Bed Fusion Additive Manufacturing". En 2022 International Additive Manufacturing Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/iam2022-96740.

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Abstract Data fusion techniques aim to improve inference results or decision making by ‘combining’ multiple data sources. Additive manufacturing (AM) in-situ monitoring systems measure various physical phenomena and generate multiple types of data. Data types that occur at different scales and sampling rates during a build process. Data types that can be used to monitor the state of that process. Monitoring typically requires software tools to analyze multiple data sources. There are two reasons. First, data only from an individual data source may not be accurate enough or large enough to monitor the process stat. Second, a single source will be limited by the relevancy of the observations, signal-to-noise ratio, or other measurement uncertainties. This work proposes a decision-level, multimodal, data fusion method that combines multiple, in-situ, AM monitoring data sources to improve overall, process-monitoring performance. The work is based on a recent, laser powder bed fusion (LPBF) experiment that was conducted to create overhang surfaces throughout a 3D part. The data from that experiment is used to illustrate and validate the proposed method. The overhang features were designed with different shapes. angles, and build locations. The features are formed using constant laser power and scan speed. A high-frequency, coaxial, melt-pool, imaging system and a low-frequency layerwise staring camera are the two, in-situ, monitoring, data sources used in that experiment. The Naïve Bayes and the k-nearest-neighbors algorithms are first applied to each data set for overhang feature detection. Then both hard voting and soft voting are adopted in fusing the classification outcomes. The results show that while none of the individual classifiers are perfect in detecting overhang features, the fused decision of the 324 test samples achieved 100% detection accuracy.

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Bielecki, Michael A. y Paul A. Iaizzo. "The Use of a Pulsatile Perfusion Apparatus for the Assessment of Aortic Valve Function within Formalin Fixed Human Hearts: Pre- And Post-Tavr Implantation with Subsequent Micro-CT Analyses". En 2022 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/dmd2022-1059.

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Abstract Transcatheter aortic valve replacement (TAVR) is often the clinical choice for patients with severe aortic stenosis or as an alternative to surgical aortic valve replacement for high-risk patients. In these patients, the incidence of complications, including aortic annular rupture, coronary occlusion, and newonset atrial fibrillation is just under five percent. The Visible Heart® Laboratories have a library of over 500 perfusion-fixed human hearts preserved in formalin. These specimens can be utilized to better understand aortic valvular function associated with various diseased states with proper pulsatile profusion. This preclinical benchtop model could also be used for the testing of TAVR devices: e.g., to better understand proper placement techniques. Here we describe the continued development of a pulsatile perfusion apparatus constructed to assess the aortic valve function of these human heart specimens: i.e., pre- and post- TAVR deployment. Multi-modal imaging can be utilized, including videoscopes, fluoroscopy, and echocardiography. Resultant placements, the device-tissue interface within the valvular annulus can be subsequently assessed using micro-CT imaging. This pre-clinical approach also allows for this unique human heart. Specimens to be utilized numerous times, providing real anatomical scenarios for the testing of these devices.

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Princye, P. Hosanna y Suzain Mehak. "Brain Tumor Detection Using Image Processing". En International Conference on Recent Trends in Computing & Communication Technologies (ICRCCT’2K24). International Journal of Advanced Trends in Engineering and Management, 2024. http://dx.doi.org/10.59544/itqb1258/icrcct24p112.

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This proposed work presents an innovative artificial intelligence (AI) based algorithm for the accurate identification of brain tumors through medical imaging analysis. Brain tumors pose a significant health risk, and timely and precise diagnosis is crucial for effective treatment planning. Traditional diagnostic methods often require extensive manual analysis and are subject to human error. In this context, our proposed algorithm leverages the capabilities of deep learning and image processing to enhance the efficiency and accuracy of brain tumor identification. The algorithm utilizes a convolutional neural network (CNN) architecture trained on a diverse and extensive dataset of brain images, encompassing various tumor types, sizes, and locations. The CNN is designed to extract intricate features from medical imaging modalities such as magnetic resonance imaging (MRI) and computed tomography (CT) scans. Transfer learning techniques are employed to capitalize on pre trained models, allowing the algorithm to generalize effectively across different datasets and patient populations. To ensure clinical relevance and reliability, our algorithm incorporates a multi modal approach, combining information from various imaging sequences. The algorithm’s performance is validated through extensive experimentation on a large dataset of anonymized patient scans, demonstrating superior sensitivity and specificity compared to existing diagnostic methods. Furthermore, the algorithm’s interpretability is enhanced through the incorporation of attention mechanisms, providing insights into the regions influencing tumor identification. This proposed work contributes to the advancement of AI in healthcare by offering a robust and efficient solution for brain tumor identification. The proposed algorithm has the potential to significantly reduce the time required for diagnosis, enabling clinicians to make informed decisions promptly. As a result, patients can receive timely and tailored treatment plans, ultimately improving outcomes and prognosis for individuals affected by brain tumors. The performance of the algorithms are evaluated using MATLAB software.

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Ge, Xiaowei, Fátima C. Pereira, Yifan Zhu, Michael Wagner y Ji-Xin Cheng. "Unveiling the impact of drug on single cell metabolism in human gut microbiome by an SRS-FISH platform". En Frontiers in Optics. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/fio.2023.fm6e.3.

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This study presents a multi-modal chemical imaging technique that delineates single-bacteria metabolism activeness and drug accumulation with identities, enabling the study of the impact of two host-targeted drugs on particular bacteria within the gut microbiota.

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Fujii, Kengo, Nao Kurokawa, Kazuki Kawai, Shogo Morita, Kazuki Shimose, Ryosuke Kujime y Hirotsugu Yamamoto. "Generating Sound just Below an Aerial Image Formed with AIRR". En JSAP-OSA Joint Symposia. Washington, D.C.: Optica Publishing Group, 2017. http://dx.doi.org/10.1364/jsap.2017.6a_a409_3.

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AIRR (aerial imaging by retro-reflection) is an aerial-image-forming technique [1]. To give a sense to the aerial image formed with AIRR, we have developed an interactive system with the aerial image using Kinect [2]. Furthermore, multi-modal aerial display has been developed in combination of a warm aerial image formed with double-layered arrays of rectangular mirrors (WARM) and an aerial visual image with AIRR [3].

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