Relevant bibliographies by topics / Fat imaging

Contents

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

Academic literature on the topic 'Fat imaging'

Author: Grafiati
Published: 19 April 2023

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Journal articles on the topic "Fat imaging"

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Robinson, P. J. A. "Fat and the liver." Imaging 16, no. 4 (September 2004): 364–74. http://dx.doi.org/10.1259/imaging/26666175.

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Davidovich, D., A. Gastaldelli, and R. Sicari. "Imaging cardiac fat." European Heart Journal - Cardiovascular Imaging 14, no. 7 (March 28, 2013): 625–30. http://dx.doi.org/10.1093/ehjci/jet045.

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Wang, H., Y. E. Chen, and Daniel T. Eitzman. "Imaging Body Fat." Arteriosclerosis, Thrombosis, and Vascular Biology 34, no. 10 (October 2014): 2217–23. http://dx.doi.org/10.1161/atvbaha.114.303036.

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Kellman, Peter, Diego Hernando, and Andrew E. Arai. "Myocardial Fat Imaging." Current Cardiovascular Imaging Reports 3, no. 2 (March 11, 2010): 83–91. http://dx.doi.org/10.1007/s12410-010-9012-1.

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Dooms, G. C., H. Hricak, A. R. Margulis, and G. de Geer. "MR imaging of fat." Radiology 158, no. 1 (January 1986): 51–54. http://dx.doi.org/10.1148/radiology.158.1.3940397.

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Ehara, S. "MR imaging of fat necrosis." American Journal of Roentgenology 171, no. 3 (September 1998): 889. http://dx.doi.org/10.2214/ajr.171.3.9725348.

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Chan, Lai Peng, R. Gee, Ciaran Keogh, and Peter L. Munk. "Imaging Features of Fat Necrosis." American Journal of Roentgenology 181, no. 4 (October 2003): 955–59. http://dx.doi.org/10.2214/ajr.181.4.1810955.

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Axel, Leon. "Fat Suppression in MR Imaging." RadioGraphics 19, no. 5 (September 1999): 1177. http://dx.doi.org/10.1148/radiographics.19.5.g99se411177a.

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Griffith, James F. "MR imaging of marrow fat." Bone 47 (October 2010): S380. http://dx.doi.org/10.1016/j.bone.2010.09.066.

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Hernandez, R. J., D. R. Keim, T. L. Chenevert, D. B. Sullivan, and A. M. Aisen. "Fat-suppressed MR imaging of myositis." Radiology 182, no. 1 (January 1992): 217–19. http://dx.doi.org/10.1148/radiology.182.1.1727285.

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Dissertations / Theses on the topic "Fat imaging"

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An, Li. "Water-fat imaging and general chemical shift imaging with spectrum modeling." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0032/NQ38848.pdf.

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Huang, Fangping. "Water and Fat Image Reconstruction in Magnetic Resonance Imaging." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1309791802.

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Aru, Jim. "Abdominal fat distribution, measured by magnetic resonance imaging, and insulin resistance." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0018/MQ54444.pdf.

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Narayan, Sreenath Prativadi. "Magnetic Resonance Imaging of Hepatic Fat Content Measurements at 7 Tesla." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1341869672.

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Huang, Hui. "Non-destructive detection of pork intramuscular fat content using hyperspectral imaging." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=119675.

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Intramuscular fat levels of pork affect the flavor of pork meat. In the pork industry, two quality attributes namely intramuscular fat (IMF) content and marbling score (MS) are used to represent intramuscular fat levels of pork meat. Conventional determination methods are not suitable for the current requirements of the pork industry as they are either destructive or subjective. This study investigated the use of hyperspectral imaging in evaluating intramuscular fat content and marbling score of pork. Intramuscular fat distribution along the longissmus muscle and the influences of freezing, thawing, and image pattern analysis on prediction capacity were also considered. Near infrared (NIR) hyperspectral imaging technique from 900 to 1700 nm was used for prediction of IMF content and MS. Fresh pork at the 3rd/4th last rib was imaged. Pattern analysis techniques of Gabor filter, wide line detector (WLD), and an improved grey-level co-occurrence matrix (GLCM) were studied and different image features, i.e. spectral, texture, and line features, were extracted. Key wavelengths were identified. Multiple linear regression (MLR) was used to develop prediction models. For determination of marbling score, the MLR model, using the first derivative of Gabor filtered mean spectra, performed best with a prediction accuracy of 0.90 at wavelengths of 961, 1186 and 1220 nm. For intramuscular fat content, prediction accuracy of 0.85 was obtained using the raw mean spectra at 1207 and 1279 nm. The distribution map of IMF content in pork was developed. The results showed the possibility of rapid and non-destructive evaluation of intramuscular fat level of pork using NIR images. Regarding marbling as a visual index, a method for objective evaluation of pork marbling score using red-green-blue (RGB) images was developed by applying WLD-based linear models. The possibility of non-destructive prediction of IMF content and MS using frozen and frozen-thawed pork was studied. Prediction accuracy of 0.90 for MS was achieved for frozen pork. Prediction accuracy of 0.82 for IMF content and accuracy of 0.91 for MS were realized by frozen-thawed pork. The potential of frozen and frozen-thawed pork for assessment of marbling score and frozen-thawed pork for the assessment of intramuscular fat content were demonstrated. Besides the effects of freezing and thawing, the variation of IMF content and MS across the last seven thoracic longissmus muscle was studied. Relationships between IMF content and MS at the last rib and the corresponding attribute at other ribs and the whole section of the loin were determined. The relationship between NIR images of rib end and the IMF level of pork at the six last thoracic ribs was investigated. Close relationships were indicated, especially between the images of rib end and IMF levels at the 2nd/3rd last ribs and the 2nd last/last ribs.
La teneur en matières grasses du porc affecte la saveur de la viande de porc. Dans l'industrie porcine, la graisse intramusculaire (GIM) et la cote de persillage (CP) sont deux propriétés qui déterminent la teneur en gras du porc. Les méthodes conventionnelles de détermination ne sont pas adaptées aux besoins actuels de l'industrie car elles sont destructrices ou subjectives. Cette étude porte sur l'utilisation de l'imagerie hyperspectrale dans l'évaluation de la teneur en graisse intramusculaire et du persillage du porc. Les effets de la répartition de la graisse intramusculaire le long du muscle Longissmus, de la congélation, du dégel et de l'analyse de la forme pour le traitement de l'image ont été pris en compte. Une technique d'imagerie hyperspectrale proche infrarouge (IR) allant de 900 à 1700 nm a été utilisée pour prédire le GIM ou la CP. La viande fraîche au niveau de la 3ème/4ème côte du porc a été utilisée pour recueillir les images hyperspectrales. Des analyses de la forme fondée sur les techniques du filtre de Gabor, du détecteur linéaire à large spectre (WLD) et de la matrice de cooccurrence de niveau gris améliorée (GLCM) ont été étudiées et les propriétés de l'image, i.e spectre, texture et propriétés des lignes, ont été extraites. La régression linéaire multiple (RLM) a été utilisée pour développer des modèles de prédiction. Pour la cote persillage, le modèle de RLM utilisant la moyenne de spectre filtrée pour la première dérivée de Gabor a le mieux performé avec une précision de calibration de 0,90 aux longueurs d'onde de 961, 1186 et 1220 nm. Pour le GIM, une précision de calibration de 0.85 a été obtenue avec un spectre moyen de base à 1207 et 1279 nm. La distribution du contenu de GIM a été illustrée. Les résultats démontrent la possibilité d'utiliser les images hyperspectralces proche IR pour évaluer rapidement et de façon non-destructive le taux de gras intramusculaire du porc. En ce qui concerne le persillage en tant qu'indice visuel, une méthode objective d'évaluation de la cote persillage utilisant des images rouge-vert-bleu (RGB) a été développée en appliquant un WLD basé sur un model linéaire au canal vert. La possibilité d'un contrôle non-destructif du GIM et de la CP utilisant du porc congelé et décongelé a été étudiée. Une précision de la prédiction de 0.90 pour la CP a été réalisée avec du porc congelé. Une précision de la prédiction de 0.82 pour le GIM découle du porc décongelé. Le potentiel du porc congelé et décongelé pour l'évaluation de la cote de persillage et du porc décongelé pour l'évaluation de la teneur en gras intramusculaire a été démontré. Outre l'effet du gel et du dégel, la variation du GIM et de la CP à travers les sept derniers muscles thoraciques Longissmus a été étudiée. Les relations entre le GIM et la CP à la dernière côte et les propriétés correspondantes aux autres côtes et au filet ont été déterminées avec précision. La relation entre les images de proche IR à l'extrémité et le niveau de GIM du porc six dernières côtes thoraciques a été étudiée. Des relations étroite ont été déterminées, en particulier entre les images de l'extrémité de la côte et les taux de GIM aux 2eme/3eme dernières côtes et la 2eme dernière côte.
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Mehemed, Taha Mohamed M. "Fat-Water Interface on Susceptibility-Weighted Imaging and Gradient-Echo Imaging: Comparison of Phantoms to Intracranial Lipomas." Kyoto University, 2014. http://hdl.handle.net/2433/193572.

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Groll, Emily D. "Comparison of anthropometric and DXA measurements of regional body fat." Virtual Press, 2008. http://liblink.bsu.edu/uhtbin/catkey/1398712.

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Purpose: The primary purpose of this research study was to assess the degree of agreement between simple anthropometric measurements (i.e. body mass index, waist circumference, hip circumference, and waist-to-hip ratio) and the measures of regional adiposity, with a primary focus on the androidlgynoid ratio, assessed using dual energy x-ray absorptiometry (DXA). This secondary purpose of the study was to identify any significant correlations between the measures of regional adiposity, physical activity, and cardiovascular risk factors. Methods: Forty-eight subjects, 19 males (48.7 ± 16.9 years) and 29 females (43.6 ± 16.2 years), volunteered to participate in this study. Subjects underwent laboratory testing compromised of resting blood pressure, blood lipid analysis, waist & hip circumference, total body DXA scan, and a one week physical activity assessment. Results: Significant correlations were observed between body mass index and region body fat % (r = 0.84, 0.79), waist circumference and android fat % (r = 0.79, 0.75), and waist-to-hip ratio and androidlgynoid ratio (r = 0.72, 0.61) for men and women, respectively. Fasting insulin was correlated with region body fat %, android body fat %, trunk body fat %, and the android/gynoid ratio. The android/gynoid ratio was correlated with high density lipoproteins, very low density lipoproteins, triglycerides, and fasting glucose. There was a statistically significant negative relationship observed between average steps per day and body mass index, waist circumference, hip circumference, region body fat %, android body fat %, and trunk body fat %. Conclusions: This study found that there are strong relationships between simple anthropometric measures and regional body fat measures from the DXA. According to the data in the present study, body mass index, waist circumference, and waist-to-hip ratio provide simple yet sensitive methods for the estimation of regional body fat in Caucasian males and females. In addition, this study found significant correlations between measures of the blood lipid profile, physical activity, and both simple anthropometric and DXA measures of regional body fat. Key words: android fat, body mass index, dual-energy x-ray absorptiometry, gynoid fat, obesity, waist circumference.
School of Physical Education, Sport, and Exercise Science
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Hussein, Mahamoud Omar. "Magnetic resonance imaging and spectroscopy of fat emulsions in the gastrointestinal tract." Thesis, University of Nottingham, 2013. http://eprints.nottingham.ac.uk/13582/.

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The relationship between meal structure and composition can modulate gastrointestinal processing and the resulting sense of satiety. This applies also to the fat component of meals and particularly to the surface area available for digestion. The main hypothesis underpinning this thesis work was that fat emulsion droplet size has a profound effect on fat digestion and, in turn, on the gastrointestinal and satiety responses. To test this hypothesis two fat emulsion meal systems were used. They had exactly the same composition but a small (termed the Fine emulsion, with a droplet size of 400 nm) or a large (termed the Coarse emulsion, with a droplet size of 8 μm) emulsified fat droplet size. The two fat emulsion systems were manufactured and characterised using a range of bench techniques, in vitro digestion models and MRI techniques in vitro. The difference in microstructure caused different temporal creaming characteristics for the emulsions and different percentage hydrolysis profiles in a gastric digestion model in vitro. The Fine emulsion showed initial rapid hydrolysis whilst the Coarse emulsion showed an initial slow hydrolysis phase with the hydrolysis rate increasing at later stages. This indicated that there was indeed a droplet size effect on fat hydrolysis whereby the smaller droplet size with a larger surface area hydrolysed faster than a larger droplet size. The emulsions’ performance was finally tested in vivo in healthy volunteers using MRI in a series of pilot studies leading to a main physiological study. Creaming differences in the gastric lumen were addressed by redesigning the meals using a locust bean gum (LBG) thickener that made them stable throughout the gastric emptying process. A main three-way physiological and satiety study in healthy volunteers showed that a highly emulsified, intragastrically stable emulsion delayed gastric emptying, increased small bowel water content and reduced consumption of food at the end of the study day. Finally, magnetic resonance imaging, relaxometry and spectroscopy were further evaluated to assess fat emulsion parameters in vitro and in vivo in the gastric lumen. Main static magnetic field and droplet size effects on T2 relaxation times of the Fine and the Coarse emulsions were observed. There was reasonable correlation between m-DIXON and spectroscopy methods to quantify fat fraction both in vitro and in vivo. Differences in T2 relaxation times for different droplet sizes of 20% fat emulsions were detected in vitro. These changes were however difficult to separate from creaming effects in vivo with a view of drawing meaningful inferences on droplet sizes. The main conclusion from this work was that manipulating food microstructure especially intragastric stability and fat emulsion droplet size can influence human gastrointestinal physiology and satiety responses and that MRI and MRS provide unique non invasive insights into these processes. This improved knowledge could help designing foods with desired health-promoting characteristics which could help to fight the rising tide of obesity.
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Costa, Yuri Ajala da. "A proposal for full-range fat fraction estimation using magnitude MR imaging." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-01102018-083519/.

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Current methods for estimation of proton density fat fraction (PDFF) of the liver using magnitude magnetic resonance (MR) imaging face the challenge of correctly estimating it when fat is the dominant molecule, i.e. PDFF is more than 50%. Therefore, the accuracy of the methods is limited to half-range operation. We introduce a method based on neural networks for regression capable of estimating over the full range of fat fractions. We built a neural network based on the angles and distances between the data in the discrete MR signal (ADALIFE), using these as features associated to different PDFFs and as input for the network. Tests were performed assessing ADALIFE against dual echo, triple echo, and especially Multi-interference, a state-of-the-art method to estimate PDFFs, with simulated signals at various signal-to-noise (SNR) values. Results were compared in order to verify repeatability and agreement using regression analysis, Bland-Altman and REC curves. Results for Multi-interference were similar to its in-vivo literature, showing the relevance of a simulation. ADALIFE was able to correctly estimate fat fractions up to 100%, breaking the current paradigm for full-range estimation using only off-line post processing. Within half-range, our method outperformed Multi-interference in repeatability and agreement, with narrower limits of agreement and lower expected error at any SNR.
Os métodos atuais para estimação de gordura hepática por densidade de prótons (PDFF) utilizando imagem de magnitude de ressonância magnética (RM) enfrentam o desafio de estimar corretamente quando a gordura é a molécula dominante, ou seja, PDFF é maior que 50%. Assim, a acurácia desses métodos é limitada a meio intervalo de operação. Apresentamos aqui um método baseado em redes neurais para regressão capaz de estimar pelo intervalo completo de frações de gordura. Construímos uma rede neural baseada nos ângulos e distâncias entre os dados do sinal discreto da imagem de RM (ADALIFE), usando esses atributos associados a diferentes valores de PDFF, com sinais simulados considerando diferentes relações sinal-ruído (SNR). Resultados foram comparados para verificar a repetibilidade e concordância através de análise de regressão, Bland- Altman e curvas de característica de erro de regressão (REC). Resultados para o método Multi-interferência (estado-da-arte) foram similares aos relatados in vivo pela literatura, ressaltando a relevância das simulações. ADALIFE foi capaz de estimar corretamente frações de gordura até 100%, quebrando o paradigma para intervalo completo de operação utilizando apenas processamento posterior à aquisição de imagens ou sinais. Considerando meio intervalo, nosso método superou o estado-da-arte em termos de repetibilidade e concordância, com limites mais estreitos e menor erro esperado em qualquer SNR.
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Sigar, Joseph Aduol. "Visible hyperspectral imaging for predicting intra-muscular fat content from sheep carcasses." Thesis, Sigar, Joseph Aduol (2020) Visible hyperspectral imaging for predicting intra-muscular fat content from sheep carcasses. Honours thesis, Murdoch University, 2020. https://researchrepository.murdoch.edu.au/id/eprint/54744/.

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Intramuscular fat (IMF) content plays a key role in the quality attributes of meat, such as sensory properties and health considerations. The tenderness, flavour and juiciness of meat are examples of sensory attributes influenced by IMF content. Traditionally, IMF content in meat was determined using destructive, time consuming and at times unsuitable methods in industry applications. However, with recent advancement of technology, there has been an interest in exlporing ways to ascertain meat quality without damage. Hyperspectral imaging analysis is an emerging technology that combines the use of spectroscopy and computer imaging analysis to obtain both the spectral and spatial information of objects of interest. Hyperspectral imaging was initially developed for remote sensing, but has recently emerged as powerful tool for non-destructive analysis of quality in the food industry and has had very accurate results in the prediction of meat qualities such as IMF content. In this thesis, we use a data set of 101 hyperspectral images of sheep carcasses to investigate the ability of multivariate statistical methods to accurately predict IMF content.
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Books on the topic "Fat imaging"

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Buonincontri, Guido, Joshua Kaggie, and Martin Graves. Fast Quantitative Magnetic Resonance Imaging. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01667-7.

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Imaging history: Photography after the fact. Brussel: ASA Publishers, 2011.

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Fan thưng santiphāp (2010 Bangkok, Thailand). Fan thưng santiphāp =: Imagine peace. Krung Thēp Mahā Nakhō̜n: Krasūang Watthanatham, 2010.

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Wehrli, F. W. Fast-scan magnetic resonance: Principles and applications. New York: Raven Press, 1991.

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Chae, Jongchul, ed. Initial Results from the Fast Imaging Solar Spectrograph (FISS). Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12123-9.

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Yan, Raymond T. H. Fast radio-frequency current density imaging with spiral acquisition. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1999.

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Slater, Craig S. Studies of Photoinduced Molecular Dynamics Using a Fast Imaging Sensor. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24517-1.

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Zdenka, Badovinac, Limerick City Gallery of Art., and EV+A, eds. EV+A 2004: Imagine Limerick. Kinsale, Co. Cork: Gandon Editions, 2004.

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Siegmund, Oswald H. W. The Lyman Imaging Telescope Experiment (LITE): Final report, #NAGW - 4731. [Washington, DC: National Aeronautics and Space Administration, 1997.

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United States. National Aeronautics and Space Administration., ed. The Lyman Imaging Telescope Experiment (LITE): Final report, #NAGW - 4731. [Washington, DC: National Aeronautics and Space Administration, 1997.

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Book chapters on the topic "Fat imaging"

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Bongers, Malte Niklas. "Gastrointestinal Imaging: Liver Fat and Iron Quantification." In Spectral Imaging, 235–44. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96285-2_15.

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Wortsman, Ximena. "Imaging of Hypodermal Fat Necrosis." In Skin Necrosis, 25–31. Vienna: Springer Vienna, 2014. http://dx.doi.org/10.1007/978-3-7091-1241-0_4.

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Kuchnia, Adam J., and Neil Binkley. "Diagnosis of Osteosarcopenia – Imaging." In Osteosarcopenia: Bone, Muscle and Fat Interactions, 243–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25890-0_12.

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Marwan, Mohamed. "The Many Uses of Epicardial Fat Measurements." In Contemporary Medical Imaging, 285–94. Totowa, NJ: Humana Press, 2019. http://dx.doi.org/10.1007/978-1-60327-237-7_24.

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Altun, Ersan, Mohamed El-Azzazi, and Richard C. Semelka. "Hepatic fat and iron deposition." In Liver imaging: MRI with CT correlation, 241–54. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781118484852.ch12.

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Monti, Caterina B., Davide Capra, Francesco Secchi, Marina Codari, and Francesco Sardanelli. "Artificial Intelligence-Based Quantification of Cardiac Fat." In Artificial Intelligence in Cardiothoracic Imaging, 297–303. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92087-6_30.

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Sasaki, Hidehiko, Yoshifumi Saijo, Motonao Tanaka, and Shin-ichi Nitta. "Influence of fat Components and Tissue Preparation on the High Frequency Acoustic Properties." In Acoustical Imaging, 161–66. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4419-8606-1_21.

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França, Manuela, Ángel Alberich-Bayarri, and Luis Martí-Bonmatí. "Use Case VI: Imaging Biomarkers in Diffuse Liver Disease. Quantification of Fat and Iron." In Imaging Biomarkers, 279–94. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43504-6_20.

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Koshy, Marymol, Mazuin Mohd Razalli, Asita Elengoe, and Methil Kannan Kutty. "Imaging as a Tool for Measuring Body Fat." In Obesity and its Impact on Health, 117–24. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6408-0_9.

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Chen, Xin, Emmanouil Moschidis, Chris Taylor, and Susan Astley. "A Novel Framework for Fat, Glandular Tissue, Pectoral Muscle and Nipple Segmentation in Full Field Digital Mammograms." In Breast Imaging, 201–8. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07887-8_29.

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Conference papers on the topic "Fat imaging"

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Kriston, Andras, Paulo Mendonça, Alvin Silva, Robert G. Paden, William Pavlicek, Dushyant Sahani, Benedek Janos Kis, Laszlo Rusko, Darin Okerlund, and Rahul Bhotika. "Liver fat quantification using fast kVp-switching dual energy CT." In SPIE Medical Imaging, edited by Benoit M. Dawant and David R. Haynor. SPIE, 2011. http://dx.doi.org/10.1117/12.878206.

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Stevenson, Kevin, Mark Schweitzer, and Ghassan Hamarneh. "Multi-angle deformation analysis of Hoffa's fat pad." In Medical Imaging, edited by Armando Manduca and Amir A. Amini. SPIE, 2006. http://dx.doi.org/10.1117/12.654301.

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Tong, Yubing, Jayaram K. Udupa, and Drew A. Torigian. "Standardized anatomic space for abdominal fat quantification." In SPIE Medical Imaging, edited by Sebastien Ourselin and Martin A. Styner. SPIE, 2014. http://dx.doi.org/10.1117/12.2044254.

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Sacha, Jaroslaw P., Michael D. Cockman, Thomas E. Dufresne, and Darren Trokhan. "Quantification of regional fat volume in rat MRI." In Medical Imaging 2003, edited by Anne V. Clough and Amir A. Amini. SPIE, 2003. http://dx.doi.org/10.1117/12.480405.

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Ding, Xiaowei, Demetri Terzopoulos, Mariana Diaz-Zamudio, Daniel S. Berman, Piotr J. Slomka, and Damini Dey. "Automated epicardial fat volume quantification from non-contrast CT." In SPIE Medical Imaging, edited by Sebastien Ourselin and Martin A. Styner. SPIE, 2014. http://dx.doi.org/10.1117/12.2043326.

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Tong, Yubing, Jayaram K. Udupa, Caiyun Wu, Gargi Pednekar, Janani Rajan Subramanian, David J. Lederer, Jason Christie, and Drew A. Torigian. "Fat segmentation on chest CT images via fuzzy models." In SPIE Medical Imaging, edited by Robert J. Webster and Ziv R. Yaniv. SPIE, 2016. http://dx.doi.org/10.1117/12.2217864.

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Sussman, Daniel L., Jianhua Yao, and Ronald M. Summers. "Automated fat measurement and segmentation with intensity inhomogeneity correction." In SPIE Medical Imaging, edited by Benoit M. Dawant and David R. Haynor. SPIE, 2010. http://dx.doi.org/10.1117/12.843860.

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Tisdall, M. Dylan, and M. Stella Atkins. "Fat/water separation in a single MRI image with arbitrary phase shift." In Medical Imaging, edited by Michael J. Flynn and Jiang Hsieh. SPIE, 2006. http://dx.doi.org/10.1117/12.655128.

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Abrahim, Banazier A., Zeinab A. Mustafa, and Yasser M. Kadah. "Fast phase aberration correction in ultrasound imaging using fat layer model." In 2007 International Conference on Computer Engineering & Systems. IEEE, 2007. http://dx.doi.org/10.1109/icces.2007.4447048.

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Agarwal, Chirag, Ahmed H. Dallal, Mohammad R. Arbabshirani, Aalpen Patel, and Gregory Moore. "Unsupervised quantification of abdominal fat from CT images using Greedy Snakes." In SPIE Medical Imaging, edited by Martin A. Styner and Elsa D. Angelini. SPIE, 2017. http://dx.doi.org/10.1117/12.2254139.

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Reports on the topic "Fat imaging"

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Tao, Yang, Victor Alchanatis, and Yud-Ren Chen. X-ray and stereo imaging method for sensitive detection of bone fragments and hazardous materials in de-boned poultry fillets. United States Department of Agriculture, January 2006. http://dx.doi.org/10.32747/2006.7695872.bard.

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As Americans become increasingly health conscious, they have increased their consumptionof boneless white and skinless poultry meat. To the poultry industry, accurate detection of bonefragments and other hazards in de-boned poultry meat is important to ensure food quality andsafety for consumers. X-ray imaging is widely used for internal material inspection. However,traditional x-ray technology has limited success with high false-detection errors mainly becauseof its inability to consistently recognize bone fragments in meat of uneven thickness. Today’srapid grow-out practices yield chicken bones that are less calcified. Bone fragments under x-rayshave low contrast from meat. In addition, the x-ray energy reaching the image detector varieswith the uneven meat thickness. Differences in x-ray absorption due to the unevenness inevitablyproduce false patterns in x-ray images and make it hard to distinguish between hazardousinclusions and normal meat patterns even by human visual inspection from the images.Consequently, the false patterns become camouflage under x-ray absorptions of variant meatthickness in physics, which remains a major limitation to detecting hazardous materials byprocessing x-ray images alone.Under the support of BARD, USDA, and US Poultry industries, we have aimed todeveloping a new technology that uses combined x-ray and laser imaging to detect bonefragments in de-boned poultry. The technique employs the synergism of sensors of differentprinciples and has overcome the deficiency of x-rays in physics of letting x-rays work alone inbone fragment detection. X-rays in conjunction of laser-based imaging was used to eliminatefalse patterns and provide higher sensitivity and accuracy to detect hazardous objects in the meatfor poultry processing lines.Through intensive research, we have met all the objectives we proposed during the researchperiod. Comprehensive experiments have proved the concept and demonstrated that the methodhas been capable of detecting frequent hard-to-detect bone fragments including fan bones andfractured rib and pulley bone pieces (but not cartilage yet) regardless of their locations anduneven meat thickness without being affected by skin, fat, and blood clots or blood vines.
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Ismaiel, Abdulrahman, Oana Ciobanu, Mohamed Ismaiel, Daniel-Corneliu Leucuta, Stefan-Lucian Popa, Liliana David, Dilara Ensar, Nahlah Al Srouji, and Dan L. Dumitrascu. Atherogenic Index of Plasma in Non-Alcoholic Fatty Liver Disease: Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2022. http://dx.doi.org/10.37766/inplasy2022.8.0043.

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Review question / Objective: P - Non-alcoholic fatty liver disease (NAFLD) I - Atherogenic index of plasma (AIP) C - Imaging and histopathology O - Mean difference and Area Under the Curve S - Observational studies. Condition being studied: Non-alcoholic fatty liver disease (NAFLD), is a common liver disease characterized by the presence of excessive fat build up within hepatocytes, in the absence of other conditions that result in hepatic steatosis and with little to no alcohol consumption. It refers to a broad range of conditions including steatosis, non-alcoholic steatohepatitis (NASH) and cirrhosis.
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Zhong He. Fast Neutron Imaging Systems. Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/895007.

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Kim, J., T. LaGrange, B. Reed, G. Campbell, and N. Browning. Directly Imaging Fast Reaction Fronts. Office of Scientific and Technical Information (OSTI), February 2007. http://dx.doi.org/10.2172/902297.

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Brockington, Samuel, Andrew Case, and Franklin Douglas Witherspoon. Fast Fiber-Coupled Imaging Devices. Office of Scientific and Technical Information (OSTI), April 2018. http://dx.doi.org/10.2172/1433921.

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Mihalczo, John T., Michael C. Wright, Seth M. McConchie, Daniel E. Archer, and Blake A. Palles. Transportable, Low-Dose Active Fast-Neutron Imaging. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1400208.

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Birge, Noah, Verena Geppert-Kleinrath, Christopher Danly, Valerie Fatherley, Harold Jorgenson, Matthew Freeman, and Carl Wilde. Fast Neutron Imaging and Tomography at NIF. Office of Scientific and Technical Information (OSTI), April 2022. http://dx.doi.org/10.2172/1864964.

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Basu, Samit. Dynamic Imaging and Fast Reconstruction Algorithms in Tomography. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada368306.

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Brockington, Samuel, Ajoke Williams, Andrew Case, Franklin D. Witherspoon, Robert Horton, and David Hwang. Fast Fiber-Coupled Imaging of Low Light Events. Office of Scientific and Technical Information (OSTI), July 2019. http://dx.doi.org/10.2172/1545653.

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Brubaker, Erik, James S. Brennan, Peter Marleau, Aaron B. Nowack, John T. Steele, Melinda Sweany, and Daniel J. Throckmorton. Bubble masks for time-encoded imaging of fast neutrons. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1096263.

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