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1
Dumbryte, Irma, Donatas Narbutis, Maria Androulidaki, Arturas Vailionis, Saulius Juodkazis, and Mangirdas Malinauskas. "Teeth Microcracks Research: Towards Multi-Modal Imaging." Bioengineering 10, no. 12 (November 25, 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, no. 1s (December 28, 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, and David Piwnica-Worms. "Multi-Modal Multi-Spectral Intravital Macroscopic Imaging of Signaling Dynamics in Real Time during Tumor–Immune Interactions." Cells 10, no. 3 (February 25, 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, no. 5 (October 30, 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, no. 5 (May 22, 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, and Matthias Raedle. "Innovative Imaging Techniques: A Conceptual Exploration of Multi-Modal Raman Light Sheet Microscopy." Micromachines 14, no. 9 (September 5, 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, no. 11 (November 30, 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, and Roshan D’Souza. "Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach." Journal of Imaging 5, no. 1 (December 30, 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, and Zaid Saad Madhi. "Multilevel Features Fusion of Intelligent Techniques for Brain Imaging Analysis." Fusion: Practice and Applications 11, no. 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 and Deepti Kakkar. "Diagnostic Assessment Techniques and Non-Invasive Biomarkers for Autism Spectrum Disorder." International Journal of E-Health and Medical Communications 10, no. 3 (July 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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Santosh Kumar. "Optimized Multi-Modal Healthcare Data Integration: Harnessing HPC and GPU-Accelerated CNNs for Enhanced CDSS." Journal of Information Systems Engineering and Management 10, no. 22s (March 15, 2025): 766–81. https://doi.org/10.52783/jisem.v10i22s.3619.
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The mixing of multi-modal healthcare information is critical for enhancing clinical decision support systems (CDSS) by means of leveraging various data assets, consisting of electronic health information (EHRs), medical imaging, and wearable sensor information. however, traditional device studying fashions hostilities to efficiently method and examine such heterogeneous datasets because of their complexity, excessive dimensionality, and interoperability challenges. To address those boundaries, we advocate the automatic Multi-Modal records Integration (AMMI-CDSS) framework, a High-performance computing (HPC)-based totally technique that makes use of GPU-improved deep learning models for actual-time, large-scale healthcare facts analysis. The AMMI-CDSS framework implements a multi-stage pipeline encompassing facts pre-processing, characteristic extraction, multi-modal information fusion, and deep learning-based predictive modelling. The proposed machine employs Convolutional Neural Networks (CNNs) for clinical image feature extraction, long brief-term memory (LSTM) networks for time-collection wearable sensor records, and multi-modal transformers for move-modal getting to know, all optimized thru HPC and parallel GPU computing. Comparative experiments demonstrate that GPU-based hybrid deep learning fashions drastically outperform traditional CPU-based totally techniques, reaching better accuracy, precision, recall, and computational performance in tasks which include ECG type and pores and skin cancer detection. The AMMI-CDSS device no longer only complements real-time scientific selection-making however also improves ailment analysis, risk prediction, and affected person monitoring. by way of integrating multi-supply healthcare records within a unified framework, AMMI-CDSS facilitates personalized medicine, reducing diagnostic mistakes and optimizing remedy techniques. This studies highlights the crucial function of excessive-performance computing, deep mastering, and multi-modal records fusion in reworking current healthcare analytics. future studies will awareness on improving model interpretability, integrating federated studying for privacy-retaining AI, and increasing actual-time selection assist capabilities in CDSS programs.
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Zhang, Zheyu, Gang Yang, Yueyi Zhang, Huanjing Yue, Aiping Liu, Yunwei Ou, Jian Gong, and Xiaoyan Sun. "TMFormer: Token Merging Transformer for Brain Tumor Segmentation with Missing Modalities." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (March 24, 2024): 7414–22. http://dx.doi.org/10.1609/aaai.v38i7.28572.
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Numerous techniques excel in brain tumor segmentation using multi-modal magnetic resonance imaging (MRI) sequences, delivering exceptional results. However, the prevalent absence of modalities in clinical scenarios hampers performance. Current approaches frequently resort to zero maps as substitutes for missing modalities, inadvertently introducing feature bias and redundant computations. To address these issues, we present the Token Merging transFormer (TMFormer) for robust brain tumor segmentation with missing modalities. TMFormer tackles these challenges by extracting and merging accessible modalities into more compact token sequences. The architecture comprises two core components: the Uni-modal Token Merging Block (UMB) and the Multi-modal Token Merging Block (MMB). The UMB enhances individual modality representation by adaptively consolidating spatially redundant tokens within and outside tumor-related regions, thereby refining token sequences for augmented representational capacity. Meanwhile, the MMB mitigates multi-modal feature fusion bias, exclusively leveraging tokens from present modalities and merging them into a unified multi-modal representation to accommodate varying modality combinations. Extensive experimental results on the BraTS 2018 and 2020 datasets demonstrate the superiority and efficacy of TMFormer compared to state-of-the-art methods when dealing with missing modalities.
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Cao, Bing, Junliang Guo, Pengfei Zhu, and Qinghua Hu. "Bi-directional Adapter for Multimodal Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 2 (March 24, 2024): 927–35. http://dx.doi.org/10.1609/aaai.v38i2.27852.
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Due to the rapid development of computer vision, single-modal (RGB) object tracking has made significant progress in recent years. Considering the limitation of single imaging sensor, multi-modal images (RGB, infrared, etc.) are introduced to compensate for this deficiency for all-weather object tracking in complex environments. However, as acquiring sufficient multi-modal tracking data is hard while the dominant modality changes with the open environment, most existing techniques fail to extract multi-modal complementary information dynamically, yielding unsatisfactory tracking performance. To handle this problem, we propose a novel multi-modal visual prompt tracking model based on a universal bi-directional adapter, cross-prompting multiple modalities mutually. Our model consists of a universal bi-directional adapter and multiple modality-specific transformer encoder branches with sharing parameters. The encoders extract features of each modality separately by using a frozen, pre-trained foundation model. We develop a simple but effective light feature adapter to transfer modality-specific information from one modality to another, performing visual feature prompt fusion in an adaptive manner. With adding fewer (0.32M) trainable parameters, our model achieves superior tracking performance in comparison with both the full fine-tuning methods and the prompt learning-based methods. Our code is available: https://github.com/SparkTempest/BAT.
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Sharma, Manoj Kumar, M. Shamim Kaiser, and Kanad Ray. "Deep convolutional neural network framework with multi-modal fusion for Alzheimer’s detection." International Journal of Reconfigurable and Embedded Systems (IJRES) 13, no. 1 (March 1, 2024): 179. http://dx.doi.org/10.11591/ijres.v13.i1.pp179-191.
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The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools. The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients. In this study, we integrated a lightweight custom convolutional neural network (CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology, which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by 2-5%. In conclusion, a customized lightweight CNN model and nature-inspired optimization techniques can significantly enhance progress detection, leading to better biomedical research and patient care.
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Mirzaei, Golrokh, Anahita Adeli, and Hojjat Adeli. "Imaging and machine learning techniques for diagnosis of Alzheimer’s disease." Reviews in the Neurosciences 27, no. 8 (December 1, 2016): 857–70. http://dx.doi.org/10.1515/revneuro-2016-0029.
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AbstractAlzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.
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Fisher, Patrick M., and Ahmad R. Hariri. "Identifying serotonergic mechanisms underlying the corticolimbic response to threat in humans." Philosophical Transactions of the Royal Society B: Biological Sciences 368, no. 1615 (April 5, 2013): 20120192. http://dx.doi.org/10.1098/rstb.2012.0192.
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A corticolimbic circuit including the amygdala and medial prefrontal cortex (mPFC) plays an important role in regulating sensitivity to threat, which is heightened in mood and anxiety disorders. Serotonin is a potent neuromodulator of this circuit; however, specific serotonergic mechanisms mediating these effects are not fully understood. Recent studies have evaluated molecular mechanisms mediating the effects of serotonin signalling on corticolimbic circuit function using a multi-modal neuroimaging strategy incorporating positron emission tomography and blood oxygen level-dependent functional magnetic resonance imaging. This multi-modal neuroimaging strategy can be integrated with additional techniques including imaging genetics and pharmacological challenge paradigms to more clearly understand how serotonin signalling modulates neural pathways underlying sensitivity to threat. Integrating these methodological approaches offers novel opportunities to identify mechanisms through which serotonin signalling contributes to differences in brain function and behaviour, which in turn can illuminate factors that confer risk for illness and inform the development of more effective treatment strategies.
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Kumari, Pooja, Johann Kern, and Matthias Raedle. "Self-Supervised and Zero-Shot Learning in Multi-Modal Raman Light Sheet Microscopy." Sensors 24, no. 24 (December 20, 2024): 8143. https://doi.org/10.3390/s24248143.
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Advancements in Raman light sheet microscopy have provided a powerful, non-invasive, marker-free method for imaging complex 3D biological structures, such as cell cultures and spheroids. By combining 3D tomograms made by Rayleigh scattering, Raman scattering, and fluorescence detection, this modality captures complementary spatial and molecular data, critical for biomedical research, histology, and drug discovery. Despite its capabilities, Raman light sheet microscopy faces inherent limitations, including low signal intensity, high noise levels, and restricted spatial resolution, which impede the visualization of fine subcellular structures. Traditional enhancement techniques like Fourier transform filtering and spectral unmixing require extensive preprocessing and often introduce artifacts. More recently, deep learning techniques, which have shown great promise in enhancing image quality, face their own limitations. Specifically, conventional deep learning models require large quantities of high-quality, manually labeled training data for effective denoising and super-resolution tasks, which is challenging to obtain in multi-modal microscopy. In this study, we address these limitations by exploring advanced zero-shot and self-supervised learning approaches, such as ZS-DeconvNet, Noise2Noise, Noise2Void, Deep Image Prior (DIP), and Self2Self, which enhance image quality without the need for labeled and large datasets. This study offers a comparative evaluation of zero-shot and self-supervised learning methods, evaluating their effectiveness in denoising, resolution enhancement, and preserving biological structures in multi-modal Raman light sheet microscopic images. Our results demonstrate significant improvements in image clarity, offering a reliable solution for visualizing complex biological systems. These methods establish the way for future advancements in high-resolution imaging, with broad potential for enhancing biomedical research and discovery.
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S. Sagar Imambi, Santosh Kumar,. "Advanced Framework for Multi-Modal Healthcare Data Integration: Leveraging HPC with GPU Computing and CNN Architecture in CDSS." Journal of Electrical Systems 20, no. 1s (March 28, 2024): 1061–74. http://dx.doi.org/10.52783/jes.874.
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In this study, we shall be looking at the challenges involved in integrating multi-modal healthcare data in the clinical decision support systems (CDSS). We propose the Automated Multi-Modal Data Integration (AMMI-CDSS) algorithm, which will utilize the latest high-performance computing (HPC) techniques such as the Convolutional Neural Network (CNN) architecture and the Graphics Processing Unit (GPU) computing to provide precise and rapid analysis. Which features will be extracted, multi-modal data will be merged, data will be prepared and algorithms developed in a distributed computing environment. We illustrate how AMMI-CDSS through the use of real world datasets such as wearable sensors data, medical imaging, genetic data, and electronic health records (EHRs), can improve the clinical decision support. By performing harmonization of the diverse data sources into a unique dataset after thorough data preprocessing and complex calculations, AMMI-CDSS provides the analysis with better quality and coherence.
Our study allow us to make conclusion about how HPC-based CDSS models can be compared to conventional machine learning ones using their scalability and performance as key metrics. We enrich CDSS with the methodical framework for one-by-one testing and evaluation of proposed models and multi-modal healthcare data analysis. Future research might explore novel methods for integrating diverse types of healthcare data, as well as enhancing the HPC-based CDSS models by keeping them up-to-date.
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Ahmed, S., M. Hackett, S. Caine, N. Sylvain, H. Hou, S. Weese Maley, and ME Kelly. "P.061 Multi-modal synchrotron imaging techniques to quantify elemental and molecular changes after stroke in an animal model." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 43, S2 (June 2016): S35. http://dx.doi.org/10.1017/cjn.2016.165.
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Background: Effects of stroke at the cellular and sub-cellular level remain poorly understood by conventional techniques. We use synchrotron-based imaging techniques to study elemental and biochemical changes in the infarct and penumbra after stroke in an experimental model. Methods: Ischemic stroke is induced in mice using the previously validated photothrombotic model. Animals are sacrificed at various time-points after stroke. Fourier transform infrared spectroscopic imaging (FTIRI) is used to gather sub-cellular (<1 µm spatial resolution) imaging data of lipid oxidation and protein aggregation in the areas of interest. X-ray fluorescence (XRF) imaging is used to image the distribution of bio-important elements at the cellular and sub-cellular spatial resolutions. Routine histology and immunohistochemistry are used to co-localize cell-types to areas of interest. Results: Preliminary XRF results indicate significant reduction in the concentration of multiple elements in the infarct, compared to the penumbra, at day 1 post-stroke. Some elements begin to return to normal concentration in the penumbra at day 3. FTIRI data shows that lipid and total protein levels decrease, while aggregate protein levels increase in the penumbra. Conclusions: Multi-modality synchrotron imaging can be used to map elements as well as bio-molecules in a stroke model. A better understanding of these changes can guide therapeutic interventions after stroke.
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Masoudi, Babak, Sabalan Daneshvar, and Seyed Naser Razavi. "Multi-modal neuroimaging feature fusion via 3D Convolutional Neural Network architecture for schizophrenia diagnosis." Intelligent Data Analysis 25, no. 3 (April 20, 2021): 527–40. http://dx.doi.org/10.3233/ida-205113.
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Early and precise diagnosis of schizophrenia disorder (SZ) has an essential role in the quality of a patient’s life and future treatments. Structural and functional neuroimaging provides robust biomarkers for understanding the anatomical and functional changes associated with SZ. Each of the neuroimaging techniques shows only a different perspective on the functional or structural of the brain, while multi-modal fusion can reveal latent connections in the brain. In this paper, we propose an approach for the fusion of structural and functional brain data with a deep learning-based model to take advantage of data fusion and increase the accuracy of schizophrenia disorder diagnosis. The proposed method consists of an architecture of 3D convolutional neural networks (CNNs) that applied to magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) extracted features. We use 3D MRI patches, fMRI spatial independent component analysis (ICA) map, and DTI fractional anisotropy (FA) as model inputs. Our method is validated on the COBRE dataset, and an average accuracy of 99.35% is obtained. The proposed method demonstrates promising classification performance and can be applied to real data.
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Shilaskar, Prof Swati. "Multi Modal AI-Driven System for Enhanced Neurological Detection Using EEG, Retinal, and Brain Imaging Data." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (November 30, 2024): 876–82. http://dx.doi.org/10.22214/ijraset.2024.65229.
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This study presents a novel approach to detecting neurological disorders by integrating electroencephalogram (EEG) signal analysis with retinal scans, brain imaging techniques (MRI and CT), and patient-reported data. The primary objective is to develop an AI-driven diagnostic tool that utilizes multi-modal data fusion and deep learning for feature extraction, significantly enhancing diagnostic accuracy and supporting personalized patient profiles. Advanced deep learning architectures are employed to identify meaningful patterns from each data source, while innovative fusion techniques synthesize these patterns to deliver comprehensive assessments. Key outcomes demonstrate substantial improvements in diagnostic precision, early detection capabilities, and real-time monitoring, facilitating timely clinical decision support. Validation through extensive testing on diverse datasets confirms a marked increase in diagnostic accuracy. The practical implications of this system include the early and accurate detection of neurodegenerative diseases, epilepsy, and psychiatric disorders, ultimately contributing to improved patient outcomes and tailored healthcare solutions.
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Priya, A. Saravana, and Dr Rajeswari Mukesh. "GA based Feature Selection for Multimodal Biometric Authentication." Indian Journal of Computer Science and Engineering 12, no. 2 (April 20, 2021): 526–38. http://dx.doi.org/10.21817/indjcse/2021/v12i2/211202163.
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Multi-modal biometric authentication effectively replaces uni-modal biometric authentication system towards addressing a wide range of technical glitches in identity management and authentication. Legitimacy is playing a vital role in banking, military, and healthcare sectors where highly secure, strategic and confidential data transmission is involved. By integrating many independent biometric systems, one can overcome the problems of spoofing. However, there is lack of a simple, efficient and sufficient biometric authentication. Hence, the present study focuses on designing and implementing a multi-modal biometric authentication using a Genetic Algorithm (GA) based feature extraction method. The proposed research focuses on extracting human Skeleton and Human face feature using 3D Imaging technology. This modelling technique is used to capture human joints including the depth data to improve the efficiency of the system. The proposed research is subdivided into three phases. These are, image preprocessing (MinMax method), feature extraction using Heuristic Optimization Techniques (HOT), and Personnel recognition via the Artificial Neural Network (ANN). The Performance of the proposed method is evaluated based on the measure of FAR, FRR and accuracy. Finally, the performance of proposed approach is compared with existing techniques like GA, Neural network, etc. Combined Biometric is done in an unobtrusive way whereas other human recognition needs physical contact.
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Lyoo, In Kyoon, Jaeuk Hwang, Minyoung Sim, Brian J. Dunn, and Perry F. Renshaw. "Advances in Magnetic Resonance Imaging Methods for the Evaluation of Bipolar Disorder." CNS Spectrums 11, no. 4 (April 2006): 269–86. http://dx.doi.org/10.1017/s1092852900020770.
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ABSTRACTThis article reviews the current state of magnetic resonance imaging techniques as applied to bipolar disorder. Addressed are conventional methods of structural neuroimaging and recently developed techniques. This latter group comprises volumetric analysis, voxel-based morphometry, the assessment of T2 white matter hyperintensities, shape analysis, cortical surface-based analysis, and diffusion tensor imaging. Structural analysis methods used in magnetic resonance imaging develop exponentially, and now present opportunities to identify disease-specific neuroanatomic alterations. Greater acuity and complementarity in measuring these alterations has led to the generation of further hypotheses regarding the pathophysiology of bipolar disorder. Included in the summary of findings is consideration of a resulting neuroanatomic model. Integrative issues and future directions in this relatively young field, including multi-modal approaches enabling us to produce more comprehensive results, are discussed.
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Shaikh, Torres, and Takeoka. "Neuroimaging in Pediatric Epilepsy." Brain Sciences 9, no. 8 (August 7, 2019): 190. http://dx.doi.org/10.3390/brainsci9080190.
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Pediatric epilepsy presents with various diagnostic challenges. Recent advances in neuroimaging play an important role in the diagnosis, management and in guiding the treatment of pediatric epilepsy. Structural neuroimaging techniques such as CT and MRI can identify underlying structural abnormalities associated with epileptic focus. Functional neuroimaging provides further information and may show abnormalities even in cases where MRI was normal, thus further helping in the localization of the epileptogenic foci and guiding the possible surgical management of intractable/refractory epilepsy when indicated. A multi-modal imaging approach helps in the diagnosis of refractory epilepsy. In this review, we will discuss various imaging techniques, as well as aspects of structural and functional neuroimaging and their application in the management of pediatric epilepsy.
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Mishra, Annu, Pankaj Gupta, and Peeyush Tewari. "Improved Global U-Net applied for multi-modal brain tumor fuzzy segmentation." Journal of Interdisciplinary Mathematics 27, no. 3 (2024): 547–61. http://dx.doi.org/10.47974/jim-1767.
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In this paper, we extended our work from Global U-Net combined with fuzzy amalgamation of Inception Model and Improved Kernel Variation for MRI Brain Image Segmentation [1] which was meant for single modality MRI images only to a brain tumor fuzzy segmentation. Many CNNs gives state of art results for a particular type of images. However, they cannot achieve the same result for the images captured from different imaging techniques. We experimented the Global U-Net model for MRI images earlier and this time we intended to make it applicable for other type of images too using the concept of fuzzy segmentation. The major concern was to overcome the limitations of single modality system that is not all the kernels of U-Net are capable of generating clear feature vectors for different image modalities. The result generated was satisfactory and we would further extend it for colored images.
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Rawas, Soha, and Agariadne Dwinggo Samala. "Revolutionizing Brain Tumor Analysis: A Fusion of ChatGPT and Multi-Modal CNN for Unprecedented Precision." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 08 (May 21, 2024): 37–48. http://dx.doi.org/10.3991/ijoe.v20i08.47347.
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In this study, we introduce an innovative approach to significantly enhance the precision and interpretability of brain tumor detection and segmentation. Our method ingeniously integrates the cutting-edge capabilities of the ChatGPT chatbot interface with a state-of-the-art multi-modal convolutional neural network (CNN). Tested rigorously on the BraTS dataset, our method showcases unprecedented performance, outperforming existing techniques in terms of both accuracy and efficiency, with an impressive Dice score of 0.89 for tumor segmentation. By seamlessly integrating ChatGPT, our model unveils deep-seated insights into the intricate decision-making processes, providing researchers and physicians with invaluable understanding and confidence in the results. This groundbreaking fusion holds immense promise, poised to revolutionize the landscape of medical imaging, with far-reaching implications for clinical practice and research. Our study exemplifies the transformative potential achieved through the synergistic combination of multi-modal CNNs and natural language processing, paving the way for remarkable advancements in brain tumor detection and segmentation.
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Changirwa, Diana, Jared Schlechte, and Braedon McDonald. "A Multi-Modal Toolkit for Studying Neutrophils in Cancer and Beyond." Cancers 13, no. 21 (October 23, 2021): 5331. http://dx.doi.org/10.3390/cancers13215331.
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As key effector cells of the innate immune response, neutrophils are rapidly deployed to sites of inflammation where they deliver a payload of potent effector mechanisms that are essential for host defense against pathogens as well as tissue homeostasis. In addition, neutrophils are central contributors to the pathogenesis of a vast spectrum of inflammatory, degenerative, and neoplastic diseases. As our understanding of neutrophils in health and disease continually expands, so too does our appreciation of their complex and dynamic nature in vivo; from development, maturation, and trafficking to cellular heterogeneity and functional plasticity. Therefore, contemporary neutrophil research relies on multiple complementary methodologies to perform integrated analysis of neutrophil phenotypic heterogeneity, organ- and stimulus-specific trafficking mechanisms, as well as tailored effector functions in vivo. This review discusses established and emerging technologies used to study neutrophils, with a focus on in vivo imaging in animal models, as well as next-generation ex vivo model systems to study mechanisms of neutrophil function. Furthermore, we discuss how high-dimensional single-cell analysis technologies are driving a renaissance in neutrophil biology by redefining our understanding of neutrophil development, heterogeneity, and functional plasticity. Finally, we discuss innovative applications and emerging opportunities to integrate these high-dimensional, multi-modal techniques to deepen our understanding of neutrophils in cancer research and beyond.
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Chawla, Sanjeev, Vanessa Shehu, Pradeep K. Gupta, Kavindra Nath, and Harish Poptani. "Physiological Imaging Methods for Evaluating Response to Immunotherapies in Glioblastomas." International Journal of Molecular Sciences 22, no. 8 (April 8, 2021): 3867. http://dx.doi.org/10.3390/ijms22083867.
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Glioblastoma (GBM) is the most malignant brain tumor in adults, with a dismal prognosis despite aggressive multi-modal therapy. Immunotherapy is currently being evaluated as an alternate treatment modality for recurrent GBMs in clinical trials. These immunotherapeutic approaches harness the patient’s immune response to fight and eliminate tumor cells. Standard MR imaging is not adequate for response assessment to immunotherapy in GBM patients even after using refined response assessment criteria secondary to amplified immune response. Thus, there is an urgent need for the development of effective and alternative neuroimaging techniques for accurate response assessment. To this end, some groups have reported the potential of diffusion and perfusion MR imaging and amino acid-based positron emission tomography techniques in evaluating treatment response to different immunotherapeutic regimens in GBMs. The main goal of these techniques is to provide definitive metrics of treatment response at earlier time points for making informed decisions on future therapeutic interventions. This review provides an overview of available immunotherapeutic approaches used to treat GBMs. It discusses the limitations of conventional imaging and potential utilities of physiologic imaging techniques in the response assessment to immunotherapies. It also describes challenges associated with these imaging methods and potential solutions to avoid them.
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Gueninchault, N., H. Proudhon, and W. Ludwig. "Nanox: a miniature mechanical stress rig designed for near-field X-ray diffraction imaging techniques." Journal of Synchrotron Radiation 23, no. 6 (October 18, 2016): 1474–83. http://dx.doi.org/10.1107/s1600577516013850.
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Multi-modal characterization of polycrystalline materials by combined use of three-dimensional (3D) X-ray diffraction and imaging techniques may be considered as the 3D equivalent of surface studies in the electron microscope combining diffraction and other imaging modalities. Since acquisition times at synchrotron sources are nowadays compatible with four-dimensional (time lapse) studies, suitable mechanical testing devices are needed which enable switching between these different imaging modalities over the course of a mechanical test. Here a specifically designed tensile device, fulfilling severe space constraints and permitting to switch between X-ray (holo)tomography, diffraction contrast tomography and topotomography, is presented. As a proof of concept the 3D characterization of an Al–Li alloy multicrystal by means of diffraction contrast tomography is presented, followed by repeated topotomography characterization of one selected grain at increasing levels of deformation. Signatures of slip bands and sudden lattice rotations inside the grain have been shown by means ofin situtopography carried out during the load ramps, and diffraction spot peak broadening has been monitored throughout the experiment.
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Zhang, Xiong, Yao Zhang, and Hao Xiong. "Application Progress of MRI-IVIM, T1mapping and T2mapping in Diagnosis and Treatment of Breast Diseases." International Journal of Biology and Life Sciences 8, no. 1 (November 22, 2024): 10–13. http://dx.doi.org/10.54097/pkwysa27.
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This article summarizes the application progress of MRI-IVIM, T1mapping, and T2mapping techniques in the diagnosis and treatment of breast diseases. These techniques, leveraging their advantages of being non-radiative, high soft tissue resolution, and multi-functional imaging, provide significant support for the diagnosis of breast diseases. IVIM technology measures the microscopic motion of water molecules to reflect the microcirculatory perfusion of lesions; T1mapping technology measures T1 relaxation time to reveal the physical properties of tissues, aiding in distinguishing different types of breast tissue; T2mapping technology measures T2 relaxation time to reflect changes in the microscopic structure of tissues, providing sensitive indicators for early detection of breast diseases. These techniques can distinguish between benign and malignant lesions, assess the tumor microenvironment, predict breast cancer subtypes, and evaluate treatment efficacy, thus providing a basis for the selection and adjustment of treatment plans. However, these techniques also face challenges such as high costs and limited accessibility, lack of standardized data interpretation, and prolonged examination times that affect patient comfort. Future development directions will focus on the integration of technologies and multi-modal imaging, artificial intelligence and automation, and technological optimization and cost reduction, aiming to achieve personalized diagnosis and precise treatment of breast diseases, thereby improving treatment outcomes and patient quality of life.
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Arpit Mohankar, Aishwarya Nagpure, Sania Shaikh, Khushi Singh, and Firdous Jahan Shaikh. "Medical Image Segmentation." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 11 (November 15, 2024): 2569–74. http://dx.doi.org/10.47392/irjaeh.2024.0353.
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Medical image segmentation is a critical component in the development of computer-aided diagnosis and treatment planning systems. This paper provides a comprehensive survey of recent advances in segmentation techniques applied to various imaging modalities, including Magnetic Resonance Imaging (MRI). Traditional methods such as thresholding, region-growing, and active contours are reviewed alongside contemporary machine learning-based approaches, particularly deep learning models. The survey emphasizes the growing dominance of convolutional neural networks (CNNs) and their variants, including U-Net and Fully Convolutional Networks (FCNs), which have shown remarkable success in handling complex medical imaging challenges. Additionally, the paper discusses hybrid methods that combine classical techniques with artificial intelligence to improve accuracy and robustness in segmentation tasks. Key challenges such as class imbalance, boundary delineation, and computational efficiency are also highlighted. Future directions, including the integration of multi-modal data and advancements in self-supervised learning, are explored as potential solutions to overcome current limitations in medical image segmentation.
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Malek, Md, Takashi Nakazawa, Hyun-Woo Kang, Kouichi Tsuji, and Chul-Un Ro. "Multi-Modal Compositional Analysis of Layered Paint Chips of Automobiles by the Combined Application of ATR-FTIR Imaging, Raman Microspectrometry, and SEM/EDX." Molecules 24, no. 7 (April 8, 2019): 1381. http://dx.doi.org/10.3390/molecules24071381.
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For the forensic analysis of multi-layered paint chips of hit-and-run cars, detailed compositional analysis, including minor/trace chemical components in the multi-layered paint chips, is crucial for the potential credentials of the run-away car as the number of layers, painting process, and used paints are quite specific to the types of cars, color of cars, and their surface protection depending on the car manufacturer and the year of manufacture, and yet overall characteristics of some paints used by car manufacturers might be quite similar. In the present study, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) imaging, Raman microspectrometry (RMS), and scanning electron microscopy/energy-dispersive X-ray spectrometric (SEM/EDX) techniques were performed in combination for the detailed characterization of three car paint chip samples, which provided complementary and comprehensive information on the multi-layered paint chips. That is, optical microscopy, SEM, and ATR-FTIR imaging techniques provided information on the number of layers, physical heterogeneity of the layers, and layer thicknesses; EDX on the elemental chemical profiles and compositions; ATR-FTIR imaging on the molecular species of polymer resins, such as alkyd, alkyd-melamine, acrylic, epoxy, and butadiene resins, and some inorganics; and RMS on the molecular species of inorganic pigments (TiO2, ZnO, Fe3O4), mineral fillers (kaolinite, talc, pyrophyllite), and inorganic fillers (BaSO4, Al2(SO4)3, Zn3(PO4)2, CaCO3). This study demonstrates that the new multi-modal approach has powerful potential to elucidate chemical and physical characteristics of multi-layered car paint chips, which could be useful for determining the potential credentials of run-away cars.
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Khan, Hammaad. "“NECROTISING OTITIS EXTERNA: AN OVERVIEW OF IMAGING MODALITIES“." Journal of Ayub Medical College Abbottabad 34, no. 4 (September 28, 2022): 858–61. http://dx.doi.org/10.55519/jamc-04-8899.
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Necrotising Otitis Externa (NOE) has often posed some challenges in view of diagnosis and management by clinicians. One such challenge is the appropriate and timely use of imaging techniques, since its use is critical not only in diagnosis but also determining the extent and resolution of the disease. Hence, doctors in both primary and secondary health care need to be familiar with presenting symptoms, while specialists need to be appraised of advances in imagining techniques in management of NOE. Whilst there is a general consensus amongst clinicians on some aspects of management of NOE, there is very limited consensus on the use of imaging modalities. There is no single modality of imaging that can provide a complete picture of diagnosis, disease progression and resolution. There are some advantages and limitation of each methodology, which indicate that a multi-modal imaging technique at particular stages of the disease may provide better management outcomes. However, further research in this area is required, as there is not yet an established ‘gold standard’ for imaging in NOE. Keywords: Necrotising Otitis Externa; Malignant Otitis Externa; Skull base Osteomyelitis; Tc-99m bone scan; Ga-67 bone scan; Indium 111 labelled leukocyte scanning; SPECT
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Prathipa, R., and R. Ramadevi. "Medical Diagnosis with Multimodal Image Fusion Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8s (August 18, 2023): 108–21. http://dx.doi.org/10.17762/ijritcc.v11i8s.7180.
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Image Fusion is an effective approach utilized to draw out all the significant information from the source images, which supports experts in evaluation and quick decision making. Multi modal medical image fusion produces a composite fused image utilizing various sources to improve quality and extract complementary information. It is extremely challenging to gather every piece of information needed using just one imaging method. Therefore, images obtained from different modalities are fused Additional clinical information can be gleaned through the fusion of several types of medical image pairings. This study's main aim is to present a thorough review of medical image fusion techniques which also covers steps in fusion process, levels of fusion, various imaging modalities with their pros and cons, and the major scientific difficulties encountered in the area of medical image fusion. This paper also summarizes the quality assessments fusion metrics. The various approaches used by image fusion algorithms that are presently available in the literature are classified into four broad categories i) Spatial fusion methods ii) Multiscale Decomposition based methods iii) Neural Network based methods and iv) Fuzzy Logic based methods. the benefits and pitfalls of the existing literature are explored and Future insights are suggested. Moreover, this study is anticipated to create a solid platform for the development of better fusion techniques in medical applications.
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Lacroix, Lise-Marie, Fabien Delpech, Céline Nayral, Sébastien Lachaize, and Bruno Chaudret. "New generation of magnetic and luminescent nanoparticles for in vivo real-time imaging." Interface Focus 3, no. 3 (June 6, 2013): 20120103. http://dx.doi.org/10.1098/rsfs.2012.0103.
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A new generation of optimized contrast agents is emerging, based on metallic nanoparticles (NPs) and semiconductor nanocrystals for, respectively, magnetic resonance imaging (MRI) and near-infrared (NIR) fluorescent imaging techniques. Compared with established contrast agents, such as iron oxide NPs or organic dyes, these NPs benefit from several advantages: their magnetic and optical properties can be tuned through size, shape and composition engineering, their efficiency can exceed by several orders of magnitude that of contrast agents clinically used, their surface can be modified to incorporate specific targeting agents and antifolding polymers to increase blood circulation time and tumour recognition, and they can possibly be integrated in complex architecture to yield multi-modal imaging agents. In this review, we will report the materials of choice based on the understanding of the basic physics of NIR and MRI techniques and their corresponding syntheses as NPs. Surface engineering, water transfer and specific targeting will be highlighted prior to their first use for in vivo real-time imaging. Highly efficient NPs that are safer and target specific are likely to enter clinical application in a near future.
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Case, Michelle, Clara Huishi Zhang, Yvonne H. Datta, Stephen C. Nelson, Kalpna Gupta, and Bin He. "Non-Invasive Multi-Modal Imaging to Evaluate Disease Severity in Sickle Cell Disease." Blood 128, no. 22 (December 2, 2016): 1315. http://dx.doi.org/10.1182/blood.v128.22.1315.1315.
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Abstract Background: Pain is one of the major symptoms of sickle cell disease (SCD). Sickle pain can include both nociceptive and neuropathic components and can start from infancy. Unfortunately, pain management remains challenging in these patients partly due to the lack of understanding and tools to non-invasively map brain function. Pain can alter neural networks and in turn neuropathic changes can augment pain leading to chronic pain. To analyze brain function we have developed a non-invasive electroencephalography (EEG) coupled to functional magnetic resonance imaging (fMRI) to map how chronic pain affects neurological changes in the brain of SCD patients. Methods: Simultaneously recorded EEG and fMRI during resting state were recorded in patients with SCD (N = 11) and healthy controls (N = 13). MR-compatible amplifiers and a 64-channel EEG cap were used to record the EEG data inside a 3T Siemens Trio MR scanner. The fMRI data were recorded with a 16 channel head coil and an echo-planar imaging sequence. The fMRI data were preprocessed using SPM8 software, while EEGLAB was used to preprocess the EEG data. A group independent component analysis (ICA) was performed on the fMRI data using GIFT software. The ICA revealed 9 resting state networks (RSN) observed in both patients and controls. A spectrogram was used on the EEG data to obtain power time courses for different frequency bands (including delta, theta, alpha, beta1, and beta2). EEG-fMRI analysis was performed using the power time courses and comparing them across all voxel time courses using a general linear model. Group level results were generated from single subject maps using height and extent thresholds of p < 0.001. Any regions of interest (ROI) for the EEG-fMRI power analysis were identified using contrast image maps between the control and patient groups. The z-score of the ROI was obtained from back-projected RSN maps. Z-scores were then correlated to clinical data to identify any relation between neurological measures and disease severity. Results: The RSN identified by the group ICA included the default mode network, salience network, sensory motor network, and others. The EEG-fMRI power analysis showed that patients have greater activation in the insula and rolandic operculum compared to controls in the beta1 frequency band. This result was confirmed when a contrast image of "patients > controls" was obtained and a ROI was identified in the left insula. The primary peak location of this ROI was (x = -36, y = 2, z = 13) and the z-score of the primary peak was 2.99. Due to the overall layout of the group activation map for the beta1 frequency and the location of the ROI, the salience network was selected to be the RSN studied. Back-projected salience network maps were used to obtain individual z-scores from the left insula ROI from controls and patients. The individual z-scores were compared to clinical data and a significant correlation was found between the mean z-score and age for both patients (p < 0.005) and controls (p < 0.005). However, while controls showed a strong negative correlation between z-score and age, patients showed a positive correlation between z-score and age. Conclusions: These results indicate that the beta1 frequency activation observed in patients is most likely due to the salience network, which is theorized to be responsible for processing external input, including pain. We found that patients have different neurological activation compared to controls within common EEG bands. Furthermore, the left insula ROI z-scores increased with age in patients, indicating that the left insula's role in the salience network may be stronger as patients age. This may be due to disease severity, and hence pain, increasing with age in SCD. The opposite trend was observed in controls, where the role of the left insula in the salience network seems to decrease in strength with age. This most likely reflects altered connectivity of RSN due to normal aging. Our results suggest that altered behavior in beta1 can be used as a biomarker of disease severity, and that non-invasive imaging techniques can be used to identify biomarkers of disease severity. This work was supported in part by NIH grant U01-HL117664 and NSF IGERT grant DGE-1069104. Disclosures No relevant conflicts of interest to declare.
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Li, Xiong, Yangping Qiu, Juan Zhou, and Ziruo Xie. "Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis." Current Genomics 22, no. 8 (December 2021): 564–82. http://dx.doi.org/10.2174/1389202923666211216163049.
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Background: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD) in vivo. Mining potential molecular markers of AD from high-dimensional, multi-modal neuroimaging and omics data will provide a new basis for early diagnosis and intervention in AD. In order to discover the real pathogenic mutation and even understand the pathogenic mechanism of AD, lots of machine learning methods have been designed and successfully applied to the analysis and processing of large-scale AD biomedical data. Objective: To introduce and summarize the applications and challenges of machine learning methods in Alzheimer's disease multi-source data analysis. Methods: The literature selected in the review is obtained from Google Scholar, PubMed, and Web of Science. The keywords of literature retrieval include Alzheimer's disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, multi-omics data integration, and so on. Conclusion: This study comprehensively introduces machine learning-based processing techniques for AD neuroimaging data and then shows the progress of computational analysis methods in omics data, such as the genome, proteome, and so on. Subsequently, machine learning methods for AD imaging analysis are also summarized. Finally, we elaborate on the current emerging technology of multi-modal neuroimaging, multi-omics data joint analysis, and present some outstanding issues and future research directions.
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Seymour, Linda M., Marco Nicola, Max I. Kessler, Claire L. Yost, Alessandro Bazzacco, Alessandro Marello, Enrico Ferraris, Roberto Gobetto, and Admir Masic. "On the production of ancient Egyptian blue: Multi-modal characterization and micron-scale luminescence mapping." PLOS ONE 15, no. 11 (November 24, 2020): e0242549. http://dx.doi.org/10.1371/journal.pone.0242549.
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The ancient pigment Egyptian blue has long been studied for its historical significance; however, recent work has shown that its unique visible induced luminescent property can be used both to identify the pigment and to inspire new materials with this characteristic. In this study, a multi-modal characterization approach is used to explore variations in ancient production of Egyptian blue from shabti statuettes found in the village of Deir el-Medina in Egypt (Luxor, West Bank) dating back to the New Kingdom (18th-20th Dynasties; about 1550–1077 BCE). Using quantitative SEM-EDS analysis, we identify two possible production groups of the Egyptian blue and demonstrate the presence of multiple phases within samples using cluster analysis and ternary diagram representations. Using both macro-scale non-invasive (X-rays fluorescence and multi-spectral imaging) and micro-sampling (SEM-EDS and Raman confocal microspectroscopy) techniques, we correlate photoluminescence and chemical composition of the ancient samples. We introduce Raman spectroscopic imaging as a means to capture simultaneously visible-induced luminesce and crystal structure and utilize it to identify two classes of luminescing and non-luminescing silicate phases in the pigment that may be connected to production technologies. The results presented here provide a new framework through which Egyptian blue can be studied and inform the design of new materials based on its luminescent property.
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Santosh Kumar. "Impact of HPC on Multi-Modal Data Integration for Heart Failure Prediction in Clinical Decision Support Systems." Advances in Nonlinear Variational Inequalities 28, no. 5s (February 3, 2025): 175–93. https://doi.org/10.52783/anvi.v28.3658.
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Introduction: High-performance computing (HPC) integrated CDSS has played a vital role in making healthcare decisions using the latest computational techniques. This study emphasizes the impact of HPC-enhanced methods on decision support systems (DSS) in healthcare informatics. It focuses on improvements in accuracy, reliability, and usability, and their effects on patient outcomes and healthcare delivery efficiency. Objectives: Our study aims at designing and implementing an HPC-integrated CDSS framework for the prediction of heart failure. Thus, it can be used for concatenating multimodal data and examining the impact of HPC-integrated CDSS on various types of healthcare informatics, such as EHR, ECG, and medical imaging. In order to improve predictive accuracy, reliability, and operational efficiency with ensemble learning methods. Methods: The study integrates multimodal data, including Electronic Health Records (EHR), ECG signals, and medical imaging, with advanced machine learning techniques. The proposed ensemble model is benchmarked against traditional approaches like 3D-CNN, RNN, VGGNet, and YOLOv5. The performance of the model was evaluated on several criteria, such as accuracy, precision, sensitivity, and specificity. ROC curve analyses were conducted to examine the performance improvements of the model-based fusion approach over concatenation fusion. Results: The proposed ensemble model demonstrated excellent outcomes over traditional approaches. Also, it shows superior accuracy, precision, sensitivity, FPR, FNR, and F1 measure.ROC curve analyses demonstrated that the model-based fusion approach significantly enhanced predictive precision as well as clinical decision-making. Conclusions: This study provides empirical evidence on the benefits of HPC in healthcare decision-making. HPC-integrated CDSS impacts the quality of patient care outcomes and enhances the efficiency of healthcare delivery.
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A.V. Krishnarao P. "Advancements in Disease Detection and Volume Reduction: A Review on Medical Imaging and Healthcare Innovations." Journal of Information Systems Engineering and Management 10, no. 19s (March 12, 2025): 10–15. https://doi.org/10.52783/jisem.v10i19s.2969.
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Medical imaging remains a cornerstone of modern healthcare, essential for accurate disease detection and optimized treatment planning. This review examines advanced imaging technologies such as X-ray, CT, MRI, and ultrasound, alongside emerging methodologies incorporating machine learning (ML) and artificial intelligence (AI). Techniques for disease detection focus on identifying abnormalities, lesions, or pathological transformations, while strategies for volumetric reduction address minimizing affected tissues or organs. The integration of these approaches facilitates timely interventions and aids in evaluating treatment efficacy with precision. Despite significant advancements, challenges persist, including enhancing detection sensitivity, improving volumetric accuracy, and effectively integrating multi-modal imaging datasets. This discussion emphasizes current innovations, barriers to progress, and future directions, advocating for solutions that advance personalized healthcare. Furthermore, the role of mobile applications for efficient processing and analysis, combined with the scalability of cloud storage solutions, underscores the importance of leveraging technology to address contemporary medical imaging demands.
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Atak, Mehmet Fatih, Banu Farabi, Cristian Navarrete-Dechent, Gennady Rubinstein, Milind Rajadhyaksha, and Manu Jain. "Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation." Diagnostics 13, no. 5 (February 23, 2023): 854. http://dx.doi.org/10.3390/diagnostics13050854.
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Cutaneous malignancies are common malignancies worldwide, with rising incidence. Most skin cancers, including melanoma, can be cured if diagnosed correctly at an early stage. Thus, millions of biopsies are performed annually, posing a major economic burden. Non-invasive skin imaging techniques can aid in early diagnosis and save unnecessary benign biopsies. In this review article, we will discuss in vivo and ex vivo confocal microscopy (CM) techniques that are currently being utilized in dermatology clinics for skin cancer diagnosis. We will discuss their current applications and clinical impact. Additionally, we will provide a comprehensive review of the advances in the field of CM, including multi-modal approaches, the integration of fluorescent targeted dyes, and the role of artificial intelligence for improved diagnosis and management.
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Tröger, Jessica, Christian Hoischen, Birgit Perner, Shamci Monajembashi, Aurélien Barbotin, Anna Löschberger, Christian Eggeling, Michael M. Kessels, Britta Qualmann, and Peter Hemmerich. "Comparison of Multiscale Imaging Methods for Brain Research." Cells 9, no. 6 (June 1, 2020): 1377. http://dx.doi.org/10.3390/cells9061377.
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A major challenge in neuroscience is how to study structural alterations in the brain. Even small changes in synaptic composition could have severe outcomes for body functions. Many neuropathological diseases are attributable to disorganization of particular synaptic proteins. Yet, to detect and comprehensively describe and evaluate such often rather subtle deviations from the normal physiological status in a detailed and quantitative manner is very challenging. Here, we have compared side-by-side several commercially available light microscopes for their suitability in visualizing synaptic components in larger parts of the brain at low resolution, at extended resolution as well as at super-resolution. Microscopic technologies included stereo, widefield, deconvolution, confocal, and super-resolution set-ups. We also analyzed the impact of adaptive optics, a motorized objective correction collar and CUDA graphics card technology on imaging quality and acquisition speed. Our observations evaluate a basic set of techniques, which allow for multi-color brain imaging from centimeter to nanometer scales. The comparative multi-modal strategy we established can be used as a guide for researchers to select the most appropriate light microscopy method in addressing specific questions in brain research, and we also give insights into recent developments such as optical aberration corrections.
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Shaw, Jeremy A., Alastair Boyd, Michael House, Gary Cowin, and Boris Baer. "Multi-modal imaging and analysis in the search for iron-based magnetoreceptors in the honeybee Apis mellifera." Royal Society Open Science 5, no. 9 (September 2018): 181163. http://dx.doi.org/10.1098/rsos.181163.
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The honeybee Apis mellifera is one of many animal species for which empirical evidence of a magnetic sense has been provided. The underlying mechanisms postulated for magnetoreception in bees are varied, but most point towards the abdomen as the most likely anatomical region for its location, partly owing to the large accumulation of iron in trophocyte cells that comprise the honeybee fat body. Using a multi-modal imaging and analysis approach, we have investigated iron in the honeybee, with a particular focus on the abdomen and the utility of such techniques as applied to magnetoreception. Abdominal iron is shown to accumulate rapidly, reaching near maximum levels only 5 days after emerging from the comb and is associated with the accumulation of iron within the fat body. While fat body iron could be visualized, no regions of interest, other than perhaps the fat body itself, were identified as potential sites for magnetoreceptive cells. If an iron-based magnetoreceptor exists within the honeybee abdomen the large accumulation of iron in the fat body is likely to impede its discovery.
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Ng, Woei Shyuan, Hendrik Sielaff, and Ziqing Winston Zhao. "Phase Separation-Mediated Chromatin Organization and Dynamics: From Imaging-Based Quantitative Characterizations to Functional Implications." International Journal of Molecular Sciences 23, no. 14 (July 21, 2022): 8039. http://dx.doi.org/10.3390/ijms23148039.
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As an effective and versatile strategy to compartmentalize cellular components without the need for lipid membranes, phase separation has been found to underpin a wide range of intranuclear processes, particularly those involving chromatin. Many of the unique physico-chemical properties of chromatin-based phase condensates are harnessed by the cell to accomplish complex regulatory functions in a spatially and temporally controlled manner. Here, we survey key recent findings on the mechanistic roles of phase separation in regulating the organization and dynamics of chromatin-based molecular processes across length scales, packing states and intranuclear functions, with a particular emphasis on quantitative characterizations of these condensates enabled by advanced imaging-based approaches. By illuminating the complex interplay between chromatin and various chromatin-interacting molecular species mediated by phase separation, this review sheds light on an emerging multi-scale, multi-modal and multi-faceted landscape that hierarchically regulates the genome within the highly crowded and dynamic nuclear space. Moreover, deficiencies in existing studies also highlight the need for mechanism-specific criteria and multi-parametric approaches for the characterization of chromatin-based phase separation using complementary techniques and call for greater efforts to correlate the quantitative features of these condensates with their functional consequences in close-to-native cellular contexts.
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Md Tuhin Mia. "Enhancing Lung and Breast Cancer Screening with Advanced AI and Image Processing Techniques." Journal of Medical and Health Studies 5, no. 4 (November 12, 2024): 81–96. http://dx.doi.org/10.32996/jmhs.2024.5.4.11.
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This research investigates the application of CNNs for diagnostics improvements in lung and breast cancers based on AI image classification approaches. Using the datasets with 15,000 images describing lung cancer and 10,000 images describing cases of breast cancer, the models showed high performance: 90% for lung cancer and 99% for breast cancer classification. The descriptive analysis pointed out different features in imaging, such as dense tissue structure and irregular cell patterns; the models successfully identified these. The findings underlined the vital role that AI could play in assisting radiologists by delivering preliminary analysis, triaging high-risk cases, and leading to early cancer detection. Essential challenges were highlighted: ethical considerations concerning patients' privacy and AI algorithms' transparency. The limitation of the dataset diversity resulted in the conclusion that only broader data can ensure good generalization in various clinical settings. They recommended integrating the AI tool with clinical workflow and also called for training radiologists for effectiveness. Future research directions include real-time imaging and patient data integration for comprehensive diagnostic support and multi-modal approaches that combine imaging with genomics for more precise predictions. This leads to a more personalized cancer diagnosis and treatment plan, thus ultimately improving the results of the patients. This research, therefore, underlines the transformative capabilities of AI and image processing in modernizing cancer screening and diagnostics toward more accurate and efficient healthcare practices.
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Carnevale, Lorenzo, and Giuseppe Lembo. "Innovative MRI Techniques in Neuroimaging Approaches for Cerebrovascular Diseases and Vascular Cognitive Impairment." International Journal of Molecular Sciences 20, no. 11 (May 30, 2019): 2656. http://dx.doi.org/10.3390/ijms20112656.
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Cognitive impairment and dementia are recognized as major threats to public health. Many studies have shown the important role played by challenges to the cerebral vasculature and the neurovascular unit. To investigate the structural and functional characteristics of the brain, MRI has proven an invaluable tool for visualizing the internal organs of patients and analyzing the parameters related to neuronal activation and blood flow in vivo. Different strategies of imaging can be combined to obtain various parameters: (i) measures of cortical and subcortical structures (cortical thickness, subcortical structures volume); (ii) evaluation of microstructural characteristics of the white matter (fractional anisotropy, mean diffusivity); (iii) neuronal activation and synchronicity to identify functional networks across different regions (functional connectivity between specific regions, graph measures of specific nodes); and (iv) structure of the cerebral vasculature and its efficacy in irrorating the brain (main vessel diameter, cerebral perfusion). The high amount of data obtainable from multi-modal sources calls for methods of advanced analysis, like machine-learning algorithms that allow the discrimination of the most informative features, to comprehensively characterize the cerebrovascular network into specific and sensitive biomarkers. By using the same techniques of human imaging in pre-clinical research, we can also investigate the mechanisms underlying the pathophysiological alterations identified in patients by imaging, with the chance of looking for molecular mechanisms to recover the pathology or hamper its progression.
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Razo, Irma Berrueta, Kerry Shea, Tiffany-Jayne Allen, Hervé Boutin, Adam McMahon, Nicholas Lockyer, and Philippa J. Hart. "Accumulation of Bioactive Lipid Species in LPS-Induced Neuroinflammation Models Analysed with Multi-Modal Mass Spectrometry Imaging." International Journal of Molecular Sciences 25, no. 22 (November 8, 2024): 12032. http://dx.doi.org/10.3390/ijms252212032.
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Neuroinflammation is a complex biological process related to a variety of pathologies, often requiring better understanding in order to develop new, targeted therapeutic interventions. Within this context, multimodal Mass Spectrometry Imaging (MSI) has been used to characterise molecular changes in neuroinflammation for biomarker discovery not possible to other techniques. In this study, molecules including bioactive lipids were detected across inflamed regions of the brain in rats treated with lipopolysaccharide (LPS). The detected lipids may be acting as inflammatory mediators of the immune response. We identified that N-acyl-phosphatidylethanolamine (NAPE) species accumulated in the inflamed area. The presence of these lipids could be related to the endocannabinoid (eCB) signalling system, mediating an anti-inflammatory response from microglial cells at the site of injury to balance pro-inflammation and support neuronal protection. In addition, polyunsaturated fatty acids (PUFAs), specifically n-3 and n-6 species, were observed to accumulate in the area where LPS was injected. PUFAs are directly linked to anti-inflammatory mediators resolving inflammation. Finally, acylcarnitine species accumulated around the inflammation region. Accumulation of these molecules could be due to a deficient β-oxidation cycle.
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Sangare, Mama, C. Tekete, O. K. Bagui, A. Ba, and J. T. Zoueu. "Identification of Bacterial Diseases in Rice Plants Leaves by the Use of Spectroscopic Imaging." Applied Physics Research 7, no. 6 (October 24, 2015): 61. http://dx.doi.org/10.5539/apr.v7n6p61.
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<p class="1Body">Rice is staple in the African habitats menu. Bacterial wilt (BLB) and leaf streak (BLS) are some of the phytopathological diseases which restrain rice production around the world. In this paper, multi-spectral and multi-modal imaging techniques have been developed to characterize the rice leaves with symptoms of bacterial wilt (BLB) and leaf streak (BLS), and to provide information on their effects, in order to reduce their spread. First, we recorded microscopic and spectroscopic images of the samples using multimodal and multispectral microscope, with spectral region ranging from UV to NIR, for each mode. Then, we extracted the spectral footprints of the cells constituents, in transmission, reflection and scattering from the spectral images. Applying multivariate statistical analysis methods to this optical spectra allowed us to characterize the effect of bacterial rice leaves caused by <em>Xanthomonas oryzae</em> strains. The results of the proposed technique can be useful for easy identification of this type of infection, and can serve as routine approach in biochemical and agronomic laboratories.</p>
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Abuowaida, Suhaila, Yazan Alnsour, Zaher Salah, Raed Alazaidah, Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Nawaf Alshdaifat, and Bashar Al-haj Moh’d. "Hybrid Ensemble Architecture for Brain Tumor Segmentation Using EfficientNetB4-MobileNetV3 with Multi-Path Decoders." Data and Metadata 4 (February 26, 2025): 374. https://doi.org/10.56294/dm2025374.
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Brain tumor segmentation based on multi-modal magnetic resonance imaging is a challenging medical problem due to tumors heterogeneity, irregular boundaries, and inconsistent appearances. For this purpose, we propose a hybrid primal and dual ensemble architecture leveraging EfficientNetB4 and MobileNetV3 through a cross-network novel feature interaction mechanism and an adaptive ensemble learning approach. The proposed method enables segmentation by leveraging recent attention mechanisms, dedicated decoders, and uncertainty estimation techniques. The proposed model was extensively evaluated using the BraTS2019-2021 datasets, achieving an outstanding performance with mean Dice scores of 0.91, 0.87, and 0.83 on whole tumor, tumor core and enhancing tumor regions respectively. The proposed architecture achieves stable performance over a range of tumor types and sizes, with low relative computational cost.
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Cabral, Sophia, Mikita Klimenka, Fopefoluwa Bademosi, Damon Lau, Stefanie Pender, Lorenzo Villaggi, James Stoddart, James Donnelly, Peter Storey, and David Benjamin. "A Contactless Multi-Modal Sensing Approach for Material Assessment and Recovery in Building Deconstruction." Sustainability 17, no. 2 (January 14, 2025): 585. https://doi.org/10.3390/su17020585.
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As material scarcity and environmental concerns grow, material reuse and waste reduction are gaining attention based on their potential to reduce carbon emissions and promote net-zero buildings. This study develops an innovative approach that combines multi-modal sensing technologies with machine learning to enable contactless assessment of in situ building materials for reuse potential. By integrating thermal imaging, red, green, and blue (RGB) cameras, as well as depth sensors, the system analyzes material conditions and reveals hidden geometries within existing buildings. This approach enhances material understanding by analyzing existing materials, including their compositions, histories, and assemblies. A case study on drywall deconstruction demonstrates that these technologies can effectively guide the deconstruction process, potentially reducing material costs and carbon emissions significantly. The findings highlight feasible scenarios for drywall reuse and offer insights into improving existing deconstruction techniques through automated feedback and visualization of cut lines and fastener positions. This research indicates that contactless assessment and automated deconstruction methods are technically viable, economically advantageous, and environmentally beneficial. Serving as an initial step toward novel methods to view and classify existing building materials, this study lays a foundation for future research, promoting sustainable construction practices that optimize material reuse and reduce negative environmental impact.