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Journal articles on the topic 'Imaging methodologies'

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Author: Grafiati

Published: 19 October 2024

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1

Smith, Sarah E., Brian D. Slaughter, and Jay R. Unruh. "Imaging methodologies for systems biology." Cell Adhesion & Migration 8, no. 5 (September 3, 2014): 468–77. http://dx.doi.org/10.4161/cam.29152.

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López, Claudia S., Kevin Loftis, Guillaume Thibault, Sunjong Kwon, Erin Stempinski, Jessica L. Riesterer, and Joe W. Gray. "Correlation Of Imaging Technologies: Methodologies." Microscopy and Microanalysis 25, S2 (August 2019): 2678–79. http://dx.doi.org/10.1017/s1431927619014120.

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3

Pogue, Brian W., Summer L. Gibbs-Strauss, Pablo A. Valdés, Kimberley S. Samkoe, David W. Roberts, and Keith D. Paulsen. "Review of Neurosurgical Fluorescence Imaging Methodologies." IEEE Journal of Selected Topics in Quantum Electronics 16, no. 3 (2010): 493–505. http://dx.doi.org/10.1109/jstqe.2009.2034541.

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4

MacDonald, A. B. "Borrelia burgdorferi tissue morphologies and imaging methodologies." European Journal of Clinical Microbiology & Infectious Diseases 32, no. 8 (March 12, 2013): 1077–82. http://dx.doi.org/10.1007/s10096-013-1853-5.

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5

Pogue, Brian W., Summer Gibbs-Strauss, Pablo A. Valdés, Kimberley Samkoe, David W. Roberts, and Keith D. Paulsen. "Correction to “Review of Neurosurgical Fluorescence Imaging Methodologies”." IEEE Journal of Selected Topics in Quantum Electronics 16, no. 6 (November 2010): 1847. http://dx.doi.org/10.1109/jstqe.2010.2084074.

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6

Takeda, Mitsuo, Wolfgang Osten, and Eriko Watanabe. "Holographic 3D Imaging through Random Media: Methodologies and Challenges." Light: Advanced Manufacturing 3 (2022): 1. http://dx.doi.org/10.37188/lam.2022.014.

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Magno, Cathryn, and Jackie Kirk. "Imaging girls: visual methodologies and messages for girls' education." Compare: A Journal of Comparative and International Education 38, no. 3 (June 2008): 349–62. http://dx.doi.org/10.1080/03057920802066667.

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8

Liu, Shu-Lin, Zhi-Gang Wang, Hai-Yan Xie, An-An Liu, Don C. Lamb, and Dai-Wen Pang. "Single-Virus Tracking: From Imaging Methodologies to Virological Applications." Chemical Reviews 120, no. 3 (January 17, 2020): 1936–79. http://dx.doi.org/10.1021/acs.chemrev.9b00692.

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9

Smith, Louise E., Rod Smallwood, and Sheila Macneil. "A comparison of imaging methodologies for 3D tissue engineering." Microscopy Research and Technique 73, no. 12 (October 27, 2010): 1123–33. http://dx.doi.org/10.1002/jemt.20859.

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10

Li, Gan, Michael S. Westphall, Austin Z. Salome, Kenneth W. Lee, Joshua J. Coon, and Timothy Grant. "New methodologies for preparing and imaging cryo-EM samples." Acta Crystallographica Section A Foundations and Advances 79, a1 (July 7, 2023): a159. http://dx.doi.org/10.1107/s2053273323098406.

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Tanaka, Hidekazu, and Zahra Sharif Khodaei. "Reliability Assessment of SHM Methodologies for Damage Detection." Key Engineering Materials 713 (September 2016): 244–47. http://dx.doi.org/10.4028/www.scientific.net/kem.713.244.

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Probability-based imaging which illustrates a distribution map of probability of damage presence in structures is a diagnostic method well established for damage detection in sensorized structures. Since the quality of the recorded signal is directly linked to the reliability of the diagnostic outcome, the assessment of robustness of the damage detection methodology is of high significance. In this paper, robustness and reliability of the current probability based imaging algorithms have been assessed for detecting BVID in a composite panel. Consequently, a proposed outlier analysis and DI probability distribution damage detection algorithm was shown to improve the reliability of the detection method.

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12

Waheed, Syed Hamza, Saeedeh Mirbagheri, Shruti Agarwal, Arash Kamali, Noushin Yahyavi-Firouz-Abadi, Ammar Chaudhry, Michael DiGianvittorio, Sachin K. Gujar, Jay J. Pillai, and Haris I. Sair. "Reporting of Resting-State Functional Magnetic Resonance Imaging Preprocessing Methodologies." Brain Connectivity 6, no. 9 (November 2016): 663–68. http://dx.doi.org/10.1089/brain.2016.0446.

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Russo, Valentina, and Luisa Di Paola. "Chemical reaction engineering methodologies for post-contrastographic biomedical imaging analysis." Asia-Pacific Journal of Chemical Engineering 9, no. 3 (April 30, 2014): 354–63. http://dx.doi.org/10.1002/apj.1803.

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Moran, Patrick, and Brittany Coats. "Biological Sample Preparation for SEM Imaging of Porcine Retina." Microscopy Today 20, no. 2 (February 28, 2012): 28–31. http://dx.doi.org/10.1017/s1551929511001374.

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Sample preparation is a critical step in scanning electron microscopy (SEM) imaging. This is especially true for biological samples because of charge build-up and sensitivity to vacuum and electron beam damage. In terms of ultrastructure imaging, a variety of advancements in detectors and approaches have improved biological imaging such that fewer steps are required for sample preparation. However, the conventional approach incorporating osmium tetroxide fixing, ethanol dehydrating, critical-point drying, and coating still finds useful application. This paper evaluates three biological sample-preparation methodologies for imaging the ultrastructure of immature porcine retina. The three preparation methods examined are critical-point drying (CPD), hexamethyldisilazane (HMDS) dehydration, and direct imaging by environmental scanning electron microscopy (ESEM). Preparation methodologies were evaluated based on resulting image quality and reduced potential for artifacts.

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Ogundairo, Oluwatosin, Oluwatoyin Ayo Farai, Chinedu Paschal Maduka, Chiamaka Chinaemelum Okongwu, Abdulraheem Olaide Babarinde, and Olamide Tolulope Sodamade. "Review on MALDI Imaging for Direct Tissue Imaging and its Application in Pharmaceutical Research." International Journal of Research and Scientific Innovation X, no. XII (2024): 130–41. http://dx.doi.org/10.51244/ijrsi.2023.1012011.

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Matrix-Assisted Laser Desorption/Ionization (MALDI) imaging has emerged as a powerful analytical technique, enabling direct tissue imaging in pharmaceutical research. This review provides an in-depth exploration of the principles, methodologies, and applications of MALDI imaging in the context of studying drug distribution and molecular changes within tissues. provides a structured approach to exploring the principles, methodologies, applications, challenges, and future directions of MALDI Imaging in pharmaceutical research. Each section aims to contribute to a comprehensive understanding of the technique’s significance and potential for transformative contributions to the field. The study begins by elucidating the underlying principles of MALDI imaging, highlighting the role of matrix-assisted laser desorption/ionization in generating spatially resolved mass spectra from biological samples. Special emphasis is placed on advancements in instrumentation and sample preparation techniques that have enhanced the spatial resolution and sensitivity of MALDI imaging. The application of MALDI imaging in pharmaceutical research is comprehensively explored, focusing on its pivotal role in drug development, pharmacokinetic studies, and toxicity assessments. Case studies and examples illustrate how MALDI imaging facilitates the visualization of drug distribution within tissues, offering valuable insights into drug metabolism, biodistribution, and pharmacodynamics.

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Erwin, Roland J., and Stephen M. Rao. "Convergence of Functional Magnetic Resonance Imaging and Event-Related Potential Methodologies." Brain and Cognition 42, no. 1 (February 2000): 53–55. http://dx.doi.org/10.1006/brcg.1999.1160.

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Abdulaziz, Marwa, Emily G. Deegan, Alex Kavanagh, Lynn Stothers, Denise Pugash, and Andrew Macnab. "Advances in basic science methodologies for clinical diagnosis in female stress urinary incontinence." Canadian Urological Association Journal 11, no. 6S2 (June 13, 2017): 117. http://dx.doi.org/10.5489/cuaj.4583.

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We provide an overview of advanced imaging techniques currently being explored to gain greater understanding of the complexity of stress urinary incontinence (SUI) through better definition of structural anatomic data. Two methods of imaging and analysis are detailed for SUI with or without prolapse: 1) open magnetic resonance imaging (MRI) with or without the use of reference lines; and 2) 3D reconstruction of the pelvis using MRI. An additional innovative method of assessment includes the use of near infrared spectroscopy (NIRS), which uses non-invasive photonics in a vaginal speculum to objectively evaluate pelvic floor muscle (PFM) function as it relates to SUI pathology. Advantages and disadvantages of these techniques are described. The recent innovation of open-configuration magnetic resonance imaging (MRO) allows images to be captured in sitting and standing positions, which better simulates states that correlate with urinary leakage and can be further enhanced with 3D reconstruction. By detecting direct changes in oxygenated muscle tissue, the NIRS vaginal speculum is able to provide insight into how the oxidative capacity of the PFM influences SUI. The small number of units able to provide patient evaluation using these techniques and their cost and relative complexity are major considerations, but if such imaging can optimize diagnosis, treatment allocation, and selection for surgery enhanced imaging techniques may prove to be a worthwhile and cost-effective strategy for assessing and treating SUI.

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Silva, Francisco, Maria Paula Cabral Campello, and António Paulo. "Radiolabeled Gold Nanoparticles for Imaging and Therapy of Cancer." Materials 14, no. 1 (December 22, 2020): 4. http://dx.doi.org/10.3390/ma14010004.

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In the Last decades, nanotechnology has provided novel and alternative methodologies and tools in the field of medical oncology, in order to tackle the issues regarding the control and treatment of cancer in modern society. In particular, the use of gold nanoparticles (AuNPs) in radiopharmaceutical development has provided various nanometric platforms for the delivery of medically relevant radioisotopes for SPECT/PET diagnosis and/or radionuclide therapy. In this review, we intend to provide insight on the methodologies used to obtain and characterize radiolabeled AuNPs while reporting relevant examples of AuNPs developed during the last decade for applications in nuclear imaging and/or radionuclide therapy, and highlighting the most significant preclinical studies and results.

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Chen, Zhaolin, Sharna D. Jamadar, Shenpeng Li, Francesco Sforazzini, Jakub Baran, Nicholas Ferris, Nadim Jon Shah, and Gary F. Egan. "From simultaneous to synergistic MR‐PET brain imaging: A review of hybrid MR‐PET imaging methodologies." Human Brain Mapping 39, no. 12 (August 4, 2018): 5126–44. http://dx.doi.org/10.1002/hbm.24314.

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20

Pereira, Ana Inês, Pedro Franco-Gonçalo, Pedro Leite, Alexandrine Ribeiro, Maria Sofia Alves-Pimenta, Bruno Colaço, Cátia Loureiro, Lio Gonçalves, Vítor Filipe, and Mário Ginja. "Artificial Intelligence in Veterinary Imaging: An Overview." Veterinary Sciences 10, no. 5 (April 28, 2023): 320. http://dx.doi.org/10.3390/vetsci10050320.

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Artificial intelligence and machine learning have been increasingly used in the medical imaging field in the past few years. The evaluation of medical images is very subjective and complex, and therefore the application of artificial intelligence and deep learning methods to automatize the analysis process would be very beneficial. A lot of researchers have been applying these methods to image analysis diagnosis, developing software capable of assisting veterinary doctors or radiologists in their daily practice. This article details the main methodologies used to develop software applications on machine learning and how veterinarians with an interest in this field can benefit from such methodologies. The main goal of this study is to offer veterinary professionals a simple guide to enable them to understand the basics of artificial intelligence and machine learning and the concepts such as deep learning, convolutional neural networks, transfer learning, and the performance evaluation method. The language is adapted for medical technicians, and the work already published in this field is reviewed for application in the imaging diagnosis of different animal body systems: musculoskeletal, thoracic, nervous, and abdominal.

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21

Weinstein, Milton C. "Methodologic Considerations in Planning Clinical Trials of Cost-Effectiveness of Magnetic Resonance Imaging With a Commentary on Guyatt and Drummond." International Journal of Technology Assessment in Health Care 1, no. 3 (July 1985): 567–81. http://dx.doi.org/10.1017/s0266462300001483.

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The purpose of this paper is to outline some of the key methodologic issues in designing clinical trials to assess the cost-effectiveness of clinical magnetic resonance imaging (MRI). Because the possible objectives of such trials are so diverse, and the appropriate methodologies correspondingly numerous, no attempt is made to identify a single objective or methodology as appropriate. Rather, this paper should be read as an “options paper,” in which alternative formulations of objectives, outcome measures, study design, and protocol instrumentation are reviewed, with critical discussion of their theoretical strengths and weaknesses, as well as their practical implementability.

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22

Karargyris, A., and N. Bourbakis. "Wireless Capsule Endoscopy and Endoscopic Imaging: A Survey on Various Methodologies Presented." IEEE Engineering in Medicine and Biology Magazine 29, no. 1 (January 2010): 72–83. http://dx.doi.org/10.1109/memb.2009.935466.

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23

Tsingas, Constantine, and Antonis Vafidis. "Workshop on Practical Methodologies for Seismic Imaging and Reservoir Characterization in review." First Break 15, no. 2 (February 1997): 57–58. http://dx.doi.org/10.1046/j.1365-2397.1997.d01-140.x.

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24

Morsch, Marco, Filip Braet, and Delfine Cheng. "Foreword to the special issue on zebrafish imaging: Emerging techniques and methodologies." Micron 136 (September 2020): 102877. http://dx.doi.org/10.1016/j.micron.2020.102877.

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25

Schlücker, Sebastian, Michael D. Schaeberle, Scott W. Huffman, and Ira W. Levin. "Raman Microspectroscopy: A Comparison of Point, Line, and Wide-Field Imaging Methodologies." Analytical Chemistry 75, no. 16 (August 2003): 4312–18. http://dx.doi.org/10.1021/ac034169h.

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26

Francis, Susan T., and Sally Eldeghaidy. "Imaging methodologies and applications for nutrition research: what can functional MRI offer?" Proceedings of the Nutrition Society 74, no. 2 (October 24, 2014): 89–98. http://dx.doi.org/10.1017/s0029665114001530.

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Food intake is influenced by a complex regulatory system involving the integration of a wide variety of sensory inputs across multiple brain areas. Over the past decade, advances in neuroimaging using functional MRI (fMRI) have provided valuable insight into these pathways in the human brain. This review provides an outline of the methodology of fMRI, introducing the widely used blood oxygenation level-dependent contrast for fMRI and direct measures of cerebral blood flow using arterial spin labelling. A review of fMRI studies of the brain's response to taste, aroma and oral somatosensation, and how fat is sensed and mapped in the brain in relation to the pleasantness of food, and appetite control is given. The influence of phenotype on individual variability in cortical responses is addressed, and an overview of fMRI studies investigating hormonal influences (e.g. peptide YY, cholecystokinin and ghrelin) on appetite-related brain processes provided. Finally, recent developments in MR technology at ultra-high field (7 T) are introduced, highlighting the advances this can provide for fMRI studies to investigate the neural underpinnings in nutrition research. In conclusion, neuroimaging methods provide valuable insight into the mechanisms of flavour perception and appetite behaviour.

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27

Babu, K. N., A. K. Mathur, David R. Thompson, Robert O. Green, Piyushkumar N. Patel, R. P. Prajapati, Brian D. Bue, Sven Geier, Michael L. Eastwood, and Mark C. Helmlinger. "An Empirical Comparison of Calibration and Validation Methodologies for Airborne Imaging Spectroscopy." Current Science 116, no. 7 (April 10, 2019): 1101. http://dx.doi.org/10.18520/cs/v116/i7/1101-1107.

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28

Arias, José L. "Advanced methodologies to formulate nanotheragnostic agents for combined drug delivery and imaging." Expert Opinion on Drug Delivery 8, no. 12 (November 21, 2011): 1589–608. http://dx.doi.org/10.1517/17425247.2012.634794.

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29

Hoa, Ombeline, Chrystel Lafont, Pierre Fontanaud, Anne Guillou, Yasmine Kemkem, Rhonda D. Kineman, Raul M. Luque, Tatiana Fiordelisio Coll, Paul Le Tissier, and Patrice Mollard. "Imaging and Manipulating Pituitary Function in the Awake Mouse." Endocrinology 160, no. 10 (July 22, 2019): 2271–81. http://dx.doi.org/10.1210/en.2019-00297.

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Abstract Extensive efforts have been made to explore how the activities of multiple brain cells combine to alter physiology through imaging and cell-specific manipulation in different animal models. However, the temporal regulation of peripheral organs by the neuroendocrine factors released by the brain is poorly understood. We have established a suite of adaptable methodologies to interrogate in vivo the relationship of hypothalamic regulation with the secretory output of the pituitary gland, which has complex functional networks of multiple cell types intermingled with the vasculature. These allow imaging and optogenetic manipulation of cell activities in the pituitary gland in awake mouse models, in which both neuronal regulatory activity and hormonal output are preserved. These methodologies are now readily applicable for longitudinal studies of short-lived events (e.g., calcium signals controlling hormone exocytosis) and slowly evolving processes such as tissue remodeling in health and disease over a period of days to weeks.

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Caredda, Charly, Laurent Mahieu-Williame, Raphaël Sablong, Michaël Sdika, Fabien C. Schneider, Jacques Guyotat, and Bruno Montcel. "Intraoperative Resting-State Functional Connectivity Based on RGB Imaging." Diagnostics 11, no. 11 (November 9, 2021): 2067. http://dx.doi.org/10.3390/diagnostics11112067.

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RGB optical imaging is a marker-free, contactless, and non-invasive technique that is able to monitor hemodynamic brain response following neuronal activation using task-based and resting-state procedures. Magnetic resonance imaging (fMRI) and functional near infra-red spectroscopy (fNIRS) resting-state procedures cannot be used intraoperatively but RGB imaging provides an ideal solution to identify resting-state networks during a neurosurgical operation. We applied resting-state methodologies to intraoperative RGB imaging and evaluated their ability to identify resting-state networks. We adapted two resting-state methodologies from fMRI for the identification of resting-state networks using intraoperative RGB imaging. Measurements were performed in 3 patients who underwent resection of lesions adjacent to motor sites. The resting-state networks were compared to the identifications provided by RGB task-based imaging and electrical brain stimulation. Intraoperative RGB resting-state networks corresponded to RGB task-based imaging (DICE:0.55±0.29). Resting state procedures showed a strong correspondence between them (DICE:0.66±0.11) and with electrical brain stimulation. RGB imaging is a relevant technique for intraoperative resting-state networks identification. Intraoperative resting-state imaging has several advantages compared to functional task-based analyses: data acquisition is shorter, less complex, and less demanding for the patients, especially for those unable to perform the tasks.

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Bonney, Phillip A., Brian P. Walcott, Parampreet Singh, Peggy L. Nguyen, Nerses Sanossian, and William J. Mack. "The Continued Role and Value of Imaging for Acute Ischemic Stroke." Neurosurgery 85, suppl_1 (June 14, 2019): S23—S30. http://dx.doi.org/10.1093/neuros/nyz068.

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Abstract Advances in neuroimaging in the last 2 decades have revolutionized the management of acute ischemic stroke (AIS). Here we review the development of computed tomography (CT) and magnetic resonance imaging (MRI) modalities used to guide treatment of patients with AIS characterized by large vessel occlusion. In particular, we highlight recent randomized trials and their patient selection methodologies to detail the progression of these selection paradigms. With advanced imaging, distinction between at-risk penumbra and ischemic core in AIS may be performed using either CT or MRI. While limitations exist for methodologies to quantify core and penumbra, commercially available fully automated software packages provide useful information to guide treatment decisions. Randomized controlled trials implementing perfusion imaging to patient selection algorithms have demonstrated marked success in improving functional outcomes in patients with large vessel occlusions. As such, imaging has become a vital aspect of AIS treatment in selecting patients who may benefit from mechanical thrombectomy.

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32

Wouters, Bradly G. "Proteomics: Methodologies and Applications in Oncology." Seminars in Radiation Oncology 18, no. 2 (April 2008): 115–25. http://dx.doi.org/10.1016/j.semradonc.2007.10.008.

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33

Ng, Vivian G., and Alexandra J. Lansky. "Novel QCA methodologies and angiographic scores." International Journal of Cardiovascular Imaging 27, no. 2 (February 2011): 157–65. http://dx.doi.org/10.1007/s10554-010-9787-9.

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34

Vecchiato, Giovanni, Laura Astolfi, Fabrizio De Vico Fallani, Jlenia Toppi, Fabio Aloise, Francesco Bez, Daming Wei, et al. "On the Use of EEG or MEG Brain Imaging Tools in Neuromarketing Research." Computational Intelligence and Neuroscience 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/643489.

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Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG) and magnetoencephalogram (MEG) methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI) methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

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35

Balki, Indranil, Afsaneh Amirabadi, Jacob Levman, Anne L. Martel, Ziga Emersic, Blaz Meden, Angel Garcia-Pedrero, et al. "Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review." Canadian Association of Radiologists Journal 70, no. 4 (November 2019): 344–53. http://dx.doi.org/10.1016/j.carj.2019.06.002.

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Purpose The required training sample size for a particular machine learning (ML) model applied to medical imaging data is often unknown. The purpose of this study was to provide a descriptive review of current sample-size determination methodologies in ML applied to medical imaging and to propose recommendations for future work in the field. Methods We conducted a systematic literature search of articles using Medline and Embase with keywords including “machine learning,” “image,” and “sample size.” The search included articles published between 1946 and 2018. Data regarding the ML task, sample size, and train-test pipeline were collected. Results A total of 167 articles were identified, of which 22 were included for qualitative analysis. There were only 4 studies that discussed sample-size determination methodologies, and 18 that tested the effect of sample size on model performance as part of an exploratory analysis. The observed methods could be categorized as pre hoc model-based approaches, which relied on features of the algorithm, or post hoc curve-fitting approaches requiring empirical testing to model and extrapolate algorithm performance as a function of sample size. Between studies, we observed great variability in performance testing procedures used for curve-fitting, model assessment methods, and reporting of confidence in sample sizes. Conclusions Our study highlights the scarcity of research in training set size determination methodologies applied to ML in medical imaging, emphasizes the need to standardize current reporting practices, and guides future work in development and streamlining of pre hoc and post hoc sample size approaches.

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Cereda, Maurizio, Yi Xin, Alberto Goffi, Jacob Herrmann, David W. Kaczka, Brian P. Kavanagh, Gaetano Perchiazzi, Takeshi Yoshida, and Rahim R. Rizi. "Imaging the Injured Lung." Anesthesiology 131, no. 3 (September 1, 2019): 716–49. http://dx.doi.org/10.1097/aln.0000000000002583.

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Abstract Acute respiratory distress syndrome (ARDS) consists of acute hypoxemic respiratory failure characterized by massive and heterogeneously distributed loss of lung aeration caused by diffuse inflammation and edema present in interstitial and alveolar spaces. It is defined by consensus criteria, which include diffuse infiltrates on chest imaging—either plain radiography or computed tomography. This review will summarize how imaging sciences can inform modern respiratory management of ARDS and continue to increase the understanding of the acutely injured lung. This review also describes newer imaging methodologies that are likely to inform future clinical decision-making and potentially improve outcome. For each imaging modality, this review systematically describes the underlying principles, technology involved, measurements obtained, insights gained by the technique, emerging approaches, limitations, and future developments. Finally, integrated approaches are considered whereby multimodal imaging may impact management of ARDS.

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37

Chen, Pei-Ju, Yan Li, and Chi-Hon Lee. "Calcium Imaging of Neural Activity in Fly Photoreceptors." Cold Spring Harbor Protocols 2022, no. 7 (May 31, 2022): pdb.top107800. http://dx.doi.org/10.1101/pdb.top107800.

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Functional imaging methodologies allow researchers to simultaneously monitor the neural activities of all single neurons in a population, and this ability has led to great advances in neuroscience research. Taking advantage of a genetically tractable model organism, functional imaging in Drosophila provides opportunities to probe scientific questions that were previously unanswerable by electrophysiological recordings. Here, we introduce comprehensive protocols for two-photon calcium imaging in fly visual neurons. We also discuss some challenges in applying optical imaging techniques to study visual systems and consider the best practices for making comparisons between different neuron groups.

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38

Nerella, Sridhar Goud, Ahana Bhattacharya, Pavitra S. Thacker, and Sanam Tulja. "Synthetic methodologies and PET imaging applications of fluorine-18 radiotracers: a patent review." Expert Opinion on Therapeutic Patents 32, no. 4 (January 28, 2022): 455–73. http://dx.doi.org/10.1080/13543776.2022.2032649.

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39

Sargent, D. J., L. Rubinstein, L. Schwartz, J. E. Dancey, C. Gatsonis, L. E. Dodd, and L. K. Shankar. "Validation of novel imaging methodologies for use as cancer clinical trial end-points." European Journal of Cancer 45, no. 2 (January 2009): 290–99. http://dx.doi.org/10.1016/j.ejca.2008.10.030.

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40

Francis, Susan T., and Sally Eldeghaidy. "Imaging methodologies and applications for nutrition research: what can functional MRI offer? – ERRATUM." Proceedings of the Nutrition Society 74, no. 2 (January 12, 2015): 185. http://dx.doi.org/10.1017/s002966511400175x.

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41

Alali, Sanaz, and Alex Vitkin. "Polarized light imaging in biomedicine: emerging Mueller matrix methodologies for bulk tissue assessment." Journal of Biomedical Optics 20, no. 6 (March 20, 2015): 061104. http://dx.doi.org/10.1117/1.jbo.20.6.061104.

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42

Lara Hernandez, Karen Andrea, Theresa Rienmüller, Daniela Baumgartner, and Christian Baumgartner. "Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability." Computers in Biology and Medicine 130 (March 2021): 104200. http://dx.doi.org/10.1016/j.compbiomed.2020.104200.

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43

Lee, Hyochul, Roh-Eul Yoo, and Seung Hong Choi. "Glymphatic Magnetic Resonance Imaging: Part I—Methodologies for Evaluation of the Glymphatic System." Investigative Magnetic Resonance Imaging 27, no. 4 (2023): 196. http://dx.doi.org/10.13104/imri.2023.0031.

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44

Jalpaben Kandoriya and Dr Sheshang Degadwala Degadwala. "Advanced Machine Learning Techniques for Liver Tumor Classification in MRI Imaging." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 2 (April 3, 2024): 388–94. http://dx.doi.org/10.32628/cseit2410233.

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In this research into liver tumor categorization within MRI images, diverse machine learning methodologies were scrutinized for their efficacy. The study delved into the integration of shape and texture features, aiming to bolster classification accuracy. Among the algorithms explored, the Extra Trees model emerged as the most promising contender, exhibiting superior performance compared to its counterparts. Leveraging the distinctive capabilities of the Extra Trees model, the study underscored its effectiveness in accurately categorizing liver tumors. This highlights its potential to enhance diagnostic precision in clinical contexts. Through rigorous experimentation and analysis, the research elucidated the significance of incorporating shape and texture features into machine learning frameworks for improved tumor classification. The findings not only contribute to advancing the field of medical imaging but also underscore the importance of leveraging innovative methodologies to address healthcare challenges. Overall, the study sheds light on the promising prospects of employing advanced machine learning techniques in medical imaging for more accurate and efficient diagnosis of liver tumors.

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Udupa, Jayaram K. "Three-dimensional Visualization and Analysis Methodologies: A Current Perspective." RadioGraphics 19, no. 3 (May 1999): 783–806. http://dx.doi.org/10.1148/radiographics.19.3.g99ma13783.

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Castro, Simão, Vitor Pereira, and Rui Silva. "Improved Segmentation of Cellular Nuclei Using UNET Architectures for Enhanced Pathology Imaging." Electronics 13, no. 16 (August 22, 2024): 3335. http://dx.doi.org/10.3390/electronics13163335.

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Medical imaging is essential for pathology diagnosis and treatment, enhancing decision making and reducing costs, but despite various computational methodologies proposed to improve imaging modalities, further optimization is needed for broader acceptance. This study explores deep learning (DL) methodologies for classifying and segmenting pathological imaging data, optimizing models to accurately predict and generalize from training to new data. Different CNN and U-Net architectures are implemented for segmentation tasks, with their performance evaluated on histological image datasets using enhanced pre-processing techniques such as resizing, normalization, and data augmentation. These are trained, parameterized, and optimized using metrics such as accuracy, the DICE coefficient, and intersection over union (IoU). The experimental results show that the proposed method improves the efficiency of cell segmentation compared to networks, such as U-NET and W-UNET. The results show that the proposed pre-processing has improved the IoU from 0.9077 to 0.9675, about 7% better results; also, the values of the DICE coefficient obtained improved from 0.9215 to 0.9916, about 7% better results, surpassing the results reported in the literature.

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Lim, Teik-Cheng. "Review of Data Mining Methodologies for Healthcare Applications." Journal of Medical Imaging and Health Informatics 3, no. 2 (June 1, 2013): 288–93. http://dx.doi.org/10.1166/jmihi.2013.1164.

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Kryl, Martin, Lukas Danys, Rene Jaros, Radek Martinek, Pavel Kodytek, and Petr Bilik. "Wood Recognition and Quality Imaging Inspection Systems." Journal of Sensors 2020 (September 17, 2020): 1–19. http://dx.doi.org/10.1155/2020/3217126.

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Forestry is an undoubtedly crucial part of today’s industry; thus, automation of certain visual tasks could lead to a significant increase in productivity and reduction of labor costs. Eye fatigue or lack of attention during manual visual inspections can lead to falsely categorized wood, thus leading to major loss of earnings. These mistakes could be eliminated using automated vision inspection systems. This article focuses on the comparison of researched methodologies related to wood type classification and wood defect detection/identification; hence, readers with an intention of building a similar vision-based system have summarized review to build upon.

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Ricci, S., F. Antonelli, and C. Sacco Perasso. "ASPECTS OF BIODETERIORATION OF LAPIDEOUS SUBMERGED ARTEFACTS: 3D METHODOLOGIES APPLICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W5 (April 9, 2015): 191–94. http://dx.doi.org/10.5194/isprsarchives-xl-5-w5-191-2015.

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Submerged stone archaeological artefacts are bioeroded by endolithic microbiota (cyanobacteria, algae and fungi) and macroborers (Porifera, Bivalvia and Sipuncula). Optical microscope and SEM observations permit to analyse the bioerosion traces and to identify bioeroders. Data obtained with these techniques cannot be used to estimate volumes of material bioeroded. This aspect require the need to collect three-dimensional, close-range data from artefact. In this work we illustrate two 3D imaging techniques used to study bioerosion phenomena of underwater Cultural Heritage. In particular Digital Video Microscope permit the elaboration of 3D images, which are widely employed for close-range acquisitions. Underwater Laser Scanner documents the in situ degradation of submerged artefacts. This research aims to sensitize specialist figures in the study 3D offering a starting point for future collaborations that could lead to interesting results.

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Cruz-Guerrero, Ines A., Raquel Leon, Daniel U. Campos-Delgado, Samuel Ortega, Himar Fabelo, and Gustavo M. Callico. "Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing." Applied Sciences 10, no. 16 (August 17, 2020): 5686. http://dx.doi.org/10.3390/app10165686.

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Hyperspectral imaging is a multidimensional optical technique with the potential of providing fast and accurate tissue classification. The main challenge is the adequate processing of the multidimensional information usually linked to long processing times and significant computational costs, which require expensive hardware. In this study, we address the problem of tissue classification for intraoperative hyperspectral images of in vivo brain tissue. For this goal, two methodologies are introduced that rely on a blind linear unmixing (BLU) scheme for practical tissue classification. Both methodologies identify the characteristic end-members related to the studied tissue classes by BLU from a training dataset and classify the pixels by a minimum distance approach. The proposed methodologies are compared with a machine learning method based on a supervised support vector machine (SVM) classifier. The methodologies based on BLU achieve speedup factors of ~459× and ~429× compared to the SVM scheme, while keeping constant and even slightly improving the classification performance.

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