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Journal articles on the topic 'Tissue imaging'

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Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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1

Ostlere, S., and R. Graham. "Imaging of soft tissue masses." Imaging 17, no. 3 (December 2005): 268–84. http://dx.doi.org/10.1259/imaging/74338804.

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2

GRAHAM, R., and S. OSTLERE. "Imaging of soft-tissue masses." Imaging 22, no. 1 (May 2013): 79953227. http://dx.doi.org/10.1259/imaging/79953227.

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3

Chase, J. Geoffrey, Elijah Van Houten, Lawrence Ray, David Bates, Jean-Paul Henderson, Cameron Ewing, and Crispin Berg. "Digital Image-Based Elasto-Tomography for Soft Tissue Imaging(Imaging & Measurement)." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2004.1 (2004): 117–18. http://dx.doi.org/10.1299/jsmeapbio.2004.1.117.

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4

Souquet, Jacques, Jeff Powers, and Patrick Pesque. "Tissue harmonic imaging." European Journal of Ultrasound 7 (February 1998): S9. http://dx.doi.org/10.1016/s0929-8266(97)80134-6.

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5

Ortega, Dulia, Peter N. Burns, David Hope Simpson, and Stephanie R. Wilson. "Tissue Harmonic Imaging." American Journal of Roentgenology 176, no. 3 (March 2001): 653–59. http://dx.doi.org/10.2214/ajr.176.3.1760653.

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6

Whittingham, T. A. "Tissue harmonic imaging." European Radiology 9, S3 (November 23, 1999): S323—S326. http://dx.doi.org/10.1007/pl00014065.

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7

Prado, Carla M. M., and Steven B. Heymsfield. "Lean Tissue Imaging." Journal of Parenteral and Enteral Nutrition 38, no. 8 (September 19, 2014): 940–53. http://dx.doi.org/10.1177/0148607114550189.

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8

Uppal, Talat. "Tissue harmonic imaging." Australasian Journal of Ultrasound in Medicine 13, no. 2 (May 2010): 29–31. http://dx.doi.org/10.1002/j.2205-0140.2010.tb00155.x.

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9

Kim, Yong Jin. "Doppler Tissue Imaging." Journal of the Korean Society of Echocardiography 11, no. 2 (2003): 63. http://dx.doi.org/10.4250/jkse.2003.11.2.63.

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10

Hedrick, W. R., and Linda Metzger. "Tissue Harmonic Imaging." Journal of Diagnostic Medical Sonography 21, no. 3 (May 2005): 183–89. http://dx.doi.org/10.1177/8756479305276477.

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11

Tanabe, Kazuaki, Marek Belohlavek, James F. Greenleaf, and James B. Seward. "Tissue Harmonic Imaging." Japanese Circulation Journal 64, no. 3 (2000): 202–6. http://dx.doi.org/10.1253/jcj.64.202.

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12

Szopinski, Kazimierz T., Anna M. Pajk, Maciej Wysocki, Dominique Amy, Malgorzata Szopinska, and Wieslaw Jakubowski. "Tissue Harmonic Imaging." Journal of Ultrasound in Medicine 22, no. 5 (May 2003): 479–87. http://dx.doi.org/10.7863/jum.2003.22.5.479.

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13

Gropler, Robert J., and Linda R. Peterson. "Adipose Tissue Imaging." JACC: Cardiovascular Imaging 3, no. 8 (August 2010): 852–53. http://dx.doi.org/10.1016/j.jcmg.2010.07.002.

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14

Yu, Cheuk-Man, John E. Sanderson, Thomas H. Marwick, and Jae K. Oh. "Tissue Doppler Imaging." Journal of the American College of Cardiology 49, no. 19 (May 2007): 1903–14. http://dx.doi.org/10.1016/j.jacc.2007.01.078.

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15

Bach, David S., and William F. Armstrong. "Doppler tissue imaging." ACC Current Journal Review 5, no. 1 (January 1996): 22–25. http://dx.doi.org/10.1016/1062-1458(95)00177-8.

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16

Akturk, Guray, Edwin Roger Parra Cuentas, Ana Lako, Evisa Gjini, Beatriz Sanchez Espiridion, Ignacio Ivan Wistuba, Magdalena Thurin, et al. "CIMAC-CIDC tissue imaging harmonization." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): 3125. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.3125.

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3125 Background: The Cancer Immune Monitoring and Analysis Centers Cancer Immunology Data Commons (CIMAC-CIDC) network is a NCI Cancer Moonshots initiative to provide state-of-the-art technology and expertise for immunotherapy clinical trials. Multiplex tissue immunostaining is an integral assay provided that examines density and spatial distribution of immune cells and markers in tissues, for their prognostic or predictive value. Two approaches were evaluated for sensitivity, specificity, and reproducibility and subsequently harmonized: chromogenic-based Multiplex Immunohistochemical Consecutive Staining on Single Slide (MICSSS) and Multiplex Immunofluorescence (mIF) based tyramide signal amplification system. Methods: Harmonization was performed across CIMACs (Mount Sinai, Dana Farber Cancer Institute, MD Anderson Cancer Center) in multiple steps to prove that comparable data can be generated independent of site and platform. Goals: 1) harmonize image analysis platforms alone using tissues pre-stained with single chromogenic IHC for CD3 (membrane), Ki67 (nuclear), and CD68 (cytoplasmic), 2) compare image acquisition platforms, 3) streamline Antibody (Ab) clones and assess PD-L1 detection in relation to CLIA- assays, 4) harmonize staining protocols, image acquisition, and analysis platforms on 2 test head and neck tumor samples using MICSSS and mIF, 5) validate harmonization results with a tissue microarray on 27 tissues representing multiple tumors. For last steps, each CIMAC used their platforms for PD-L1, PD-1, CD3, CD8, and pan-cytokeratin (PanCK) staining on one of three consecutive slides from serial sections and compared densities of each marker. Results: Variables as PD-1 Ab clone, positive control reference tissues, sigma value for nuclear segmentation, and use of machine-learning based cell classifier were found to be key to produce accurate, reliable, comparable data. After visual quality control assessment and comparisons of each Region Of Interest (ROI), an overall inter-site Spearman correlation coefficient of ≥0.85 was achieved per marker within each tissue and across tissue types (expect pan-Cytokeratin, ≥0.7), with average coefficient of variation ≤0.1. Conclusions: These results show for the first time that two platforms can deliver harmonized data, despite differences in protocols, platforms, reagents, and analysis tools. Data resulting from retrospective and prospective CIMAC-CIDC analyses may be used with confidence for statistical associations with clinical parameters and outcome.
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17

Evangelista, Arturo. "Doppler tissue imaging. Echocardiography." Revista Española de Cardiología 51, no. 10 (January 1998): 853. http://dx.doi.org/10.1016/s0300-8932(98)74832-8.

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18

Averkiou, Michalakis A. "Tissue harmonic ultrasonic imaging." Comptes Rendus de l'Académie des Sciences - Series IV - Physics 2, no. 8 (October 2001): 1139–51. http://dx.doi.org/10.1016/s1296-2147(01)01259-8.

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19

Christopher, T. "Tissue harmonic depletion imaging." IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 56, no. 1 (January 2009): 225–30. http://dx.doi.org/10.1109/tuffc.2009.1023.

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20

Zhang, Yu Shrike, and Junjie Yao. "Imaging Biomaterial–Tissue Interactions." Trends in Biotechnology 36, no. 4 (April 2018): 403–14. http://dx.doi.org/10.1016/j.tibtech.2017.09.004.

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21

Desser, Terry S., Tom Jedrzejewicz, and Charles Bradley. "Native Tissue Harmonic Imaging." Ultrasound Quarterly 16, no. 1 (March 2000): 40–48. http://dx.doi.org/10.1097/00013644-200016010-00005.

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22

Routh, Helen. "Harmonic imaging in tissue." Journal of the Acoustical Society of America 107, no. 5 (May 2000): 2777. http://dx.doi.org/10.1121/1.428921.

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23

Benaron, D. A. "IMAGING: Enhanced: Tissue Optics." Science 276, no. 5321 (June 27, 1997): 2002–3. http://dx.doi.org/10.1126/science.276.5321.2002.

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24

Mesurolle, Benoît, Tarek Helou, Mona El-Khoury, Michael Edwardes, Elizabeth J. Sutton, and Ellen Kao. "Tissue Harmonic Imaging, Frequency Compound Imaging, and Conventional Imaging." Journal of Ultrasound in Medicine 26, no. 8 (August 2007): 1041–51. http://dx.doi.org/10.7863/jum.2007.26.8.1041.

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25

Hemke, Robert, Colleen Buckless, and Martin Torriani. "Quantitative Imaging of Body Composition." Seminars in Musculoskeletal Radiology 24, no. 04 (August 2020): 375–85. http://dx.doi.org/10.1055/s-0040-1708824.

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AbstractBody composition refers to the amount and distribution of lean tissue, adipose tissue, and bone in the human body. Lean tissue primarily consists of skeletal muscle; adipose tissue comprises mostly abdominal visceral adipose tissue and abdominal and nonabdominal subcutaneous adipose tissue. Hepatocellular and myocellular lipids are also fat pools with important metabolic implications. Importantly, body composition reflects generalized processes such as increased adiposity in obesity and age-related loss of muscle mass known as sarcopenia.In recent years, body composition has been extensively studied quantitatively to predict overall health. Multiple imaging methods have allowed precise estimates of tissue types and provided insights showing the relationship of body composition to varied pathologic conditions. In this review article, we discuss different imaging methods used to quantify body composition and describe important anatomical locations where target tissues can be measured.
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26

Balaji, P., and Nikhat Mukhtar Gazge. "Elastography: A New Dimension in Oral and Maxillofacial Imaging." Journal of Health Sciences & Research 5, no. 2 (2014): 6–9. http://dx.doi.org/10.5005/jp-journals-10042-1002.

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ABSTRACT Elastography or elasticity imaging is a new non-invasive imaging modality that involves tissue stiffness assessment based on hardness (elasticity) of normal or pathological tissues. The principle being that tissue compression produces strain within the tissue leading to tissue displacement due to which tissue hardness can be estimated. This tissue elasticity resulting from compression is displayed as an image called elastogram. This technique can be particularly useful in preoperative assessment of pathological tissues which are generally harder than normal surrounding tissues. Hence, the purpose of this article is to highlight this technique and its various applications in oral and maxillofacial region. How to cite this article Gazge NM, Balaji P. Elastography: A New Dimension in Oral and Maxillofacial Imaging. J Health Sci Res 2014;5(2):6-9.
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27

Uchida, Yasumi. "Coronary angioscopy: From tissue imaging to molecular imaging." Current Cardiovascular Imaging Reports 2, no. 4 (August 2009): 284–92. http://dx.doi.org/10.1007/s12410-009-0033-6.

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28

Simha Mohan Rao, Prasanna, Rashmi Arunkumar Kotecha, Sunil P.K., Parimala Prasanna Simha, and Sadiq Sheriff. "Tissue Doppler Imaging and Wall Acceleration During Weaning from Cardiopulmonary Bypass (CPB)." Journal of Cardiovascular Medicine and Surgery 5, no. 1 (2019): 20–25. http://dx.doi.org/10.21088/jcms.2454.7123.5119.4.

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29

Noguchi, Y., S. Takashima, J. Ikezoe, M. Yoshii, T. Koide, and T. Kozuka. "Hyperparathyroidism — Comparison of Flash Imaging with Spin ECHO MR Imaging." Acta Radiologica 34, no. 6 (November 1993): 625–30. http://dx.doi.org/10.1177/028418519303400619.

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MR images of the neck were prospectively studied in 19 patients with hyperparathyroidism. Fast low angle shot (FLASH) sequence was performed in addition to T1- and T2-weighted spin echo (SE) sequences. FLASH images were obtained with 320/12/20° (TR/TE/flip angle) using presaturation technique. TE of 12 ms was chosen to eliminate high signal of fat tissue. In the evaluation of detectability, a combination of T1-weighted SE and FLASH images (T1WI + FLASH) was compared with a combination of T1- and T2-weighted SE images (T1WI + T2WI). MR imaging correctly depicted 20 of 30 abnormal glands on both T1WI + FLASH and T1WI + T2WI. FLASH imaging effectively eliminated high signal of fat tissue. Nineteen abnormal glands demonstrated higher signal than surrounding tissues on FLASH images, whereas 12 glands were high-intense on T2-weighted SE images. We conclude that FLASH imaging provides improved tissue contrast and anatomic delineation and, thus, may replace T2-weighted SE imaging in the neck.
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30

Bell, Donald M. "Imaging morphogenesis." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1720 (March 27, 2017): 20150511. http://dx.doi.org/10.1098/rstb.2015.0511.

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The hostile environment of the microscope stage poses numerous challenges to successful imaging of morphogenesis in live tissues. This review aims to highlight some of the main practical considerations to take into account when embarking on a project to image cell behaviour in the context of cells' normal surroundings. Scrutiny of these activities is likely to be the most informative approach to understanding mechanical morphogenesis but is often confounded by the substantial technical difficulties involved in imaging samples over extended periods of time. Repeated observation of cells in live tissue requires that strategies be adopted to prioritize the stability of the sample, ensuring that it remains viable and develops normally while being held in a manner accessible to microscopic examination. Key considerations when creating reliable protocols for time-lapse imaging may be broken down into three main criteria; labelling, mounting and image acquisition. Choices and compromises made here, however, will directly influence image quality, and even small refinements can substantially improve what information may be extracted from images. Live imaging of tissue is difficult but paying close attention to the basics along with a little innovation is likely to be well rewarded. This article is part of the themed issue ‘Systems morphodynamics: understanding the development of tissue hardware’.
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31

Yasko, Alan W. "Imaging of Soft Tissue Tumors." Journal of Bone and Joint Surgery (American Volume) 79, no. 8 (August 1997): 1277–78. http://dx.doi.org/10.2106/00004623-199708000-00032.

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32

Oppenheimer, Stacey R., and Dieter M. Drexler. "Tissue analysis by imaging MS." Bioanalysis 4, no. 1 (January 2012): 95–112. http://dx.doi.org/10.4155/bio.11.282.

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33

Struk, Daniel W., Peter L. Munk, Mark J. Lee, Stephen G. F. Ho, and Dan F. Worsley. "Imaging of Soft Tissue Infections." Radiologic Clinics of North America 39, no. 2 (March 2001): 277–303. http://dx.doi.org/10.1016/s0033-8389(05)70278-5.

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34

Jagannathan, Jyothi P., Sree Harsha Tirumani, and Nikhil H. Ramaiya. "Imaging in Soft Tissue Sarcomas." Surgical Oncology Clinics of North America 25, no. 4 (October 2016): 645–75. http://dx.doi.org/10.1016/j.soc.2016.05.002.

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35

Grigioni, Mauro, and Giuseppe D'Avenio. "Non-invasive imaging: tissue characterisation." Paediatrics and Child Health 19 (December 2009): S106—S110. http://dx.doi.org/10.1016/j.paed.2009.08.015.

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36

Heslin, Martin J., and J. Kevin Smith. "Imaging of Soft Tissue Sarcomas." Surgical Oncology Clinics of North America 8, no. 1 (January 1999): 91–107. http://dx.doi.org/10.1016/s1055-3207(18)30227-8.

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37

Ridley, Lloyd. "Imaging of soft tissue tumors." Pathology 29, no. 4 (1997): 454. http://dx.doi.org/10.1016/s0031-3025(16)35012-7.

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38

Patel, Dakshesh B., and George R. Matcuk Jr. "Imaging of soft tissue sarcomas." Chinese Clinical Oncology 7, no. 4 (August 2018): 35. http://dx.doi.org/10.21037/cco.2018.07.06.

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39

Thind, SS. "Imaging of Soft Tissue Tumours." Medical Journal Armed Forces India 60, no. 4 (October 2004): 413. http://dx.doi.org/10.1016/s0377-1237(04)80031-5.

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40

Hughes, T. M. D., and A. J. Spillane. "Imaging of soft tissue tumours." British Journal of Surgery 87, no. 3 (March 2000): 259–60. http://dx.doi.org/10.1046/j.1365-2168.2000.01412.x.

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41

Altmayer, Stephan, Nupur Verma, Elizabeth A. Dicks, and Amy Oliveira. "Imaging musculoskeletal soft tissue infections." Seminars in Ultrasound, CT and MRI 41, no. 1 (February 2020): 85–98. http://dx.doi.org/10.1053/j.sult.2019.09.005.

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42

Listinsky, Jay J. "Tissue Characterization in MR Imaging." Radiology 179, no. 1 (April 1991): 290. http://dx.doi.org/10.1148/radiology.179.1.290.

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43

Ram, Roopa, Tarun Pandey, Richard Nicholas, and Kedar Jambhekar. "Imaging of Soft Tissue Sarcomas." Contemporary Diagnostic Radiology 34, no. 2 (January 2011): 1–6. http://dx.doi.org/10.1097/01.cdr.0000392957.06238.d0.

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44

&NA;. "Imaging of Soft Tissue Sarcomas." Contemporary Diagnostic Radiology 34, no. 2 (January 2011): 6. http://dx.doi.org/10.1097/01.cdr.0000392958.83366.bb.

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45

Tarns, P. M., V. M. Rao, B. A. Hinrichs, and Thomas Jefferson. "ADENOID TISSUE IMAGING BY CT." Investigative Radiology 26, no. 12 (December 1991): 1157. http://dx.doi.org/10.1097/00004424-199112000-00165.

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46

Szabo, Thomas L. "Physics of tissue harmonic imaging." Journal of the Acoustical Society of America 114, no. 4 (October 2003): 2436. http://dx.doi.org/10.1121/1.4779100.

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47

Nayak, Sundeep M. "Imaging of Soft Tissue Tumors." Radiology 205, no. 1 (October 1997): 94. http://dx.doi.org/10.1148/radiology.205.1.94.

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48

Parekattil, Sijo, Lawrence L. Yeung, and Li-Ming Su. "Intraoperative Tissue Characterization and Imaging." Urologic Clinics of North America 36, no. 2 (May 2009): 213–21. http://dx.doi.org/10.1016/j.ucl.2009.02.008.

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49

Kelly, Jeffrey J., Dawn A. Rorvik, Keith N. Richmond, and Clyde H. Barlow. "Videofluorometer for imaging tissue metabolism." Review of Scientific Instruments 60, no. 11 (November 1989): 3498–502. http://dx.doi.org/10.1063/1.1140500.

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50

Rajkumar, K., and V. Padmaja. "Optical Realization for Tissue Imaging." i-manager's Journal on Image Processing 2, no. 3 (September 15, 2015): 14–18. http://dx.doi.org/10.26634/jip.2.3.3601.

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