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Journal articles on the topic 'Medical Imaging System'

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Published: 25 May 2024

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1

Strommer, Gera M., and Uzi Eicher. "Medical imaging and navigation system." Journal of the Acoustical Society of America 124, no. 6 (2008): 3374. http://dx.doi.org/10.1121/1.3047474.

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2

Strommer, Gera, and Uzi Eichler. "Medical imaging and navigation system." Journal of the Acoustical Society of America 128, no. 4 (2010): 2260. http://dx.doi.org/10.1121/1.3500792.

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3

Jabbar, Shaima Ibraheem, Hasan Shakir Majdi, and Abathar Qahtan Aladi. "Techniques of Musculoskeletal System Imaging." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 04 (March 22, 2022): 127–42. http://dx.doi.org/10.3991/ijoe.v18i04.28229.

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Musculoskeletal models endow an opportunity to study the movement of the upper limb in vivo. The solid foundation of musculoskeletal model design is inherited from musculoskeletal parameters. Some of these parameters are tendon and muscle fiber length, pennation angle, and muscle volume. It is possible to extract these parameters based on cadaver. However, it is time-consuming and gives a generic statement about the function of the musculoskeletal system, but this is not enough to get accurate data and timely for each patient. Medical imaging has revolutionized visualization of the internal structure of the body in real time and in vivo. It is worth using medical imaging because it is impossible to imagine in real time what is inside the body unless surgery is performed; it is possible to see internal structure through cadaver dissection, but not in vivo. There are several kinds of medical imaging tools, which have been used in musculoskeletal system analysis such as Ultrasonography (US), Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DTI) and Computer Tomography (CT) scans. The work proposed aims to present principle, development and challenges of different medical imaging tools of musculoskeletal system methods. The results of this study show that the choice of imaging device for the musculoskeletal system depends mainly on the motivation, target and the strong points that present in the medical imaging devices.
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4

Takimoto, Masao. "MEDICAL DIAGNOSTIC IMAGING SYSTEM, INFORMATION PROCESSING METHOD FOR MEDICAL DIAGNOSTIC IMAGING SYSTEM, ULTRASONIC IMAGING DIAGNOSTIC APPARATUS, AND OPERATION DEVICE." Journal of the Acoustical Society of America 132, no. 3 (2012): 1876. http://dx.doi.org/10.1121/1.4752181.

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5

Andersson, P., S. Montan, and S. Svanberg. "Multispectral system for medical fluorescence imaging." IEEE Journal of Quantum Electronics 23, no. 10 (October 1987): 1798–805. http://dx.doi.org/10.1109/jqe.1987.1073216.

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6

&NA;. "GE Medical Introduces Cardiovascular Imaging System." INVESTIGATIVE RADIOLOGY 32, no. 8 (August 1997): 501. http://dx.doi.org/10.1097/00004424-199708000-00011.

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7

Takano, Hiroaki. "Medical Imaging System Division Activities and Medical DX Promotion." Japanese Journal of Radiological Technology 78, no. 7 (July 20, 2022): 787–90. http://dx.doi.org/10.6009/jjrt.2022-2056.

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8

Fatima, Shaheen. "Explicit Study on Design and Development of Content-based Image Retrieval in Medical Imaging." Journal of Advanced Research in Electronics Engineering and Technology 08, no. 1&2 (August 23, 2021): 1–5. http://dx.doi.org/10.24321/2456.1428.202101.

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Digital Image Databases and documentation provide lot of research areas. Significant among them is, the Content Based Image Retrieval (CBIR) research area for manipulating large amount of image databases and archives. The development in the field of medical imaging system has lead industries to conceptualize a complete automated system for the medical procedures, diagnosis, treatment and prediction. There is a continuous research in the area of CBIR systems typically for medical images, which provides a successive algorithm development for achieving generalized methodologies, which could be widely used. The achievement of such system mainly depends upon the strength, accuracy and speed of the retrieval systems. Content Based Image Retrieval (CBIR) system is valuable in medical systems as it provides retrieval of the images from the large dataset based on similarities. The aim of this paper is to discuss the various techniques, the assumptions and its scope suggested by various researchers and setup a further roadmap of the research in the field of CBIR system for medical image.
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9

Fatima, Shaheen. "Explicit Study on Design and Development of Content-based Image Retrieval in Medical Imaging." Journal of Advanced Research in Electronics Engineering and Technology 08, no. 1&2 (August 23, 2021): 1–5. http://dx.doi.org/10.24321/2456.1428.202101.

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Digital Image Databases and documentation provide lot of research areas. Significant among them is, the Content Based Image Retrieval (CBIR) research area for manipulating large amount of image databases and archives. The development in the field of medical imaging system has lead industries to conceptualize a complete automated system for the medical procedures, diagnosis, treatment and prediction. There is a continuous research in the area of CBIR systems typically for medical images, which provides a successive algorithm development for achieving generalized methodologies, which could be widely used. The achievement of such system mainly depends upon the strength, accuracy and speed of the retrieval systems. Content Based Image Retrieval (CBIR) system is valuable in medical systems as it provides retrieval of the images from the large dataset based on similarities. The aim of this paper is to discuss the various techniques, the assumptions and its scope suggested by various researchers and setup a further roadmap of the research in the field of CBIR system for medical image.
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10

YAMAMOTO, Seiji. "Autopsy Imaging and Medical Accident Investigation System." JOURNAL OF JAPAN SOCIETY FOR CLINICAL ANESTHESIA 39, no. 7 (November 15, 2019): 748–52. http://dx.doi.org/10.2199/jjsca.39.748.

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11

Fife, Michael J. "Medical ultrasonic imaging system with dynamic focusing." Journal of the Acoustical Society of America 96, no. 2 (August 1994): 1225. http://dx.doi.org/10.1121/1.410299.

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12

Erin, Onder, Mustafa Boyvat, Mehmet Efe Tiryaki, Martin Phelan, and Metin Sitti. "Magnetic Resonance Imaging System–Driven Medical Robotics." Advanced Intelligent Systems 2, no. 2 (January 20, 2020): 1900110. http://dx.doi.org/10.1002/aisy.201900110.

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13

HAYAMI, Akio. "The Integrated Electronic Medical Record System and Function of Medical Imaging Information System." Japanese Journal of Radiological Technology 55, no. 1 (1999): 9–22. http://dx.doi.org/10.6009/jjrt.kj00003110418.

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14

Dobrescu, Lidia, Silviu Stanciu, Cezar Pleșca, and Armand Ropot. "Towards an integrated medical system for radiological medical imaging investigations." Romanian Journal of Military Medicine 120, no. 1 (April 1, 2017): 5–14. http://dx.doi.org/10.55453/rjmm.2017.120.1.1.

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The continuously increasing number of medical investigations using radiological methods imposes the strong necessity of informing patients about benefits and risks regarding radiation absorbed doses. Tracking the radiation doses absorbed by patients must be a future challenge of any medical system. The effective doses received by patients in many types of medical investigations must be calculated, transformed, recorded and cumulated. Doctors and patients must be very responsible in prescribing or demanding new radiological medical investigations. Radiological standards, legislation, guidelines, programmers and practice supervised by international commissions on radiology protection must include new specific measures for patients’ cumulated doses. A pilot Romanian project had tried to accomplish some of these tasks.
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15

Takano, Hiroaki. "Medical Imaging System Division Activities and Utilization of Medical Information." Japanese Journal of Radiological Technology 76, no. 7 (2020): 768–71. http://dx.doi.org/10.6009/jjrt.2020_jjrt_76.7.768.

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16

Powers, Jeff, and Frederick Kremkau. "Medical ultrasound systems." Interface Focus 1, no. 4 (May 18, 2011): 477–89. http://dx.doi.org/10.1098/rsfs.2011.0027.

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Medical ultrasound imaging has advanced dramatically since its introduction only a few decades ago. This paper provides a short historical background, and then briefly describes many of the system features and concepts required in a modern commercial ultrasound system. The topics addressed include array beam formation, steering and focusing; array and matrix transducers; echo image formation; tissue harmonic imaging; speckle reduction through frequency and spatial compounding, and image processing; tissue aberration; Doppler flow detection; and system architectures. It then describes some of the more practical aspects of ultrasound system design necessary to be taken into account for today's marketplace. It finally discusses the recent explosion of portable and handheld devices and their potential to expand the clinical footprint of ultrasound into regions of the world where medical care is practically non-existent. Throughout the article reference is made to ways in which ultrasound imaging has benefited from advances in the commercial electronics industry. It is meant to be an overview of the field as an introduction to other more detailed papers in this special issue.
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17

Wang, Zhao, Eng Gee Lim, Yujun Tang, and Mark Leach. "Medical Applications of Microwave Imaging." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/147016.

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Ultrawide band (UWB) microwave imaging is a promising method for the detection of early stage breast cancer, based on the large contrast in electrical parameters between malignant tumour tissue and the surrounding normal breast-tissue. In this paper, the detection and imaging of a malignant tumour are performed through a tomographic based microwave system and signal processing. Simulations of the proposed system are performed and postimage processing is presented. Signal processing involves the extraction of tumour information from background information and then image reconstruction through the confocal method delay-and-sum algorithms. Ultimately, the revision of time-delay and the superposition of more tumour signals are applied to improve accuracy.
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18

CHEN Xiao-dong, 陈晓冬, 李明 LI Ming, 周浩 ZHOU Hao, 温世杰 WEN Shi-jie, and 郁道银 YU Dao-yin. "A Digital Ultrasonic Endoscope System for Medical Imaging." ACTA PHOTONICA SINICA 39, no. 4 (2010): 744–47. http://dx.doi.org/10.3788/gzxb20103904.0744.

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19

Besson, Guy M., and Morgan W. Nields. "Integrated x-ray and ultrasound medical imaging system." Journal of the Acoustical Society of America 117, no. 6 (2005): 3366. http://dx.doi.org/10.1121/1.1948311.

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20

Scaringella, M., M. Bruzzi, M. Bucciolini, M. Carpinelli, G. A. P. Cirrone, C. Civinini, G. Cuttone, et al. "A proton Computed Tomography based medical imaging system." Journal of Instrumentation 9, no. 12 (December 2, 2014): C12009. http://dx.doi.org/10.1088/1748-0221/9/12/c12009.

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21

Doi, Atsuhiro. "First Forum on JIRA Medical Imaging System Industry." Japanese Journal of Radiological Technology 68, no. 2 (2012): 181–82. http://dx.doi.org/10.6009/jjrt.2012_jsrt_68.2.181.

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22

Nanami, Shoji. "Third Forum on JIRA Medical Imaging System Industry." Japanese Journal of Radiological Technology 70, no. 3 (2014): 321–22. http://dx.doi.org/10.6009/jjrt.2014_jsrt_70.3.321.

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23

Wagner, Judith L. "Cost Containment and Computerized Medical Imaging." International Journal of Technology Assessment in Health Care 3, no. 3 (July 1987): 343–53. http://dx.doi.org/10.1017/s0266462300001161.

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AbstractToday, computers are used in several important and fast-growing medical imaging modalities, such as digital subtraction angiography, positron emission tomography, magnetic resonance imaging, nuclear medicine, and diagnostic ultrasound. The ultimate test for the computer in medical imaging will be its ability to replace traditional film-based radiography as the mechanism for displaying, communicating, and storing imaging information. This transition will require radiologists and other imagers to accept information in digital form. The speed of that acceptance depends on the economic incentives of the health care system. These are changing as a result of cost containment, which is moving away from fee-for-service toward bundled payment. The increase in capitated health plans will encourage the development of digital radiography systems that realistically trade-off the perceived quality needs of radiologists with the costs of producing and operating such systems.
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24

Lim, Hoong-Ta, and Vadakke Matham Murukeshan. "Pushbroom hyperspectral imaging system with selectable region of interest for medical imaging." Journal of Biomedical Optics 20, no. 4 (April 21, 2015): 046010. http://dx.doi.org/10.1117/1.jbo.20.4.046010.

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25

Mikami, Yuji. "MEDICAL IMAGE DIAGNOSTIC SYSTEM, MEDICAL IMAGING APPARATUS, MEDICAL IMAGE STORAGE APPARATUS, AND MEDICAL IMAGE DISPLAY APPARATUS." Journal of the Acoustical Society of America 133, no. 4 (2013): 2520. http://dx.doi.org/10.1121/1.4800169.

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26

Sotomayor, Camilo G., Marcelo Mendoza, Víctor Castañeda, Humberto Farías, Gabriel Molina, Gonzalo Pereira, Steffen Härtel, Mauricio Solar, and Mauricio Araya. "Content-Based Medical Image Retrieval and Intelligent Interactive Visual Browser for Medical Education, Research and Care." Diagnostics 11, no. 8 (August 13, 2021): 1470. http://dx.doi.org/10.3390/diagnostics11081470.

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Medical imaging is essential nowadays throughout medical education, research, and care. Accordingly, international efforts have been made to set large-scale image repositories for these purposes. Yet, to date, browsing of large-scale medical image repositories has been troublesome, time-consuming, and generally limited by text search engines. A paradigm shift, by means of a query-by-example search engine, would alleviate these constraints and beneficially impact several practical demands throughout the medical field. The current project aims to address this gap in medical imaging consumption by developing a content-based image retrieval (CBIR) system, which combines two image processing architectures based on deep learning. Furthermore, a first-of-its-kind intelligent visual browser was designed that interactively displays a set of imaging examinations with similar visual content on a similarity map, making it possible to search for and efficiently navigate through a large-scale medical imaging repository, even if it has been set with incomplete and curated metadata. Users may, likewise, provide text keywords, in which case the system performs a content- and metadata-based search. The system was fashioned with an anonymizer service and designed to be fully interoperable according to international standards, to stimulate its integration within electronic healthcare systems and its adoption for medical education, research and care. Professionals of the healthcare sector, by means of a self-administered questionnaire, underscored that this CBIR system and intelligent interactive visual browser would be highly useful for these purposes. Further studies are warranted to complete a comprehensive assessment of the performance of the system through case description and protocolized evaluations by medical imaging specialists.
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27

Zhou, Xiaojuan, and Wenjun Ouyang. "The Application of the Big Data Medical Imaging System in Improving the Medical and Health Examination." Journal of Healthcare Engineering 2021 (September 16, 2021): 1–4. http://dx.doi.org/10.1155/2021/8251702.

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To explore the application effect of the big data medical imaging tertiary diagnostic system in improving the medical and health examination, cases in township health centers were collected by the medical imaging tertiary diagnosis system. Clinical cases examined by the tertiary diagnostic system of big data medical imaging will be set as the observation group. Clinical cases not involved in the tertiary diagnostic system of big data medical imaging were set as the control group. The qualified rate, film positive rate, and film diagnosis accuracy between the two groups are compared, and X-ray perspective, X-ray examination, and CT multiple medical imaging examinations are used in two groups. The experimental results showed that the pass rate was 86.57%, positive rate was 72.32%, and diagnosis rate was 80.17%. Pass rate, positive rate, and diagnostic accuracy were higher than the control group ( P < 0.05 ). X-line film is the most cost effective. CT examination has a high diagnostic sensitivity and can achieve a clear diagnosis of the benign and malignant diseases. The three-level diagnosis system of medical imaging has significantly improved and improved the technical level in the medical and health examination, which has good practical value.
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28

Suligoj, Filip, Christoff M. Heunis, Jakub Sikorski, and Sarthak Misra. "RobUSt–An Autonomous Robotic Ultrasound System for Medical Imaging." IEEE Access 9 (2021): 67456–65. http://dx.doi.org/10.1109/access.2021.3077037.

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29

Elmissaoui, Taoufik, Nabila Soudani, and Ridha Bouallegue. "Optimization of the UWB Radar System in Medical Imaging." Journal of Signal and Information Processing 02, no. 03 (2011): 227–31. http://dx.doi.org/10.4236/jsip.2011.23031.

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30

Itaya, Hidehiko. "Domestic Market Trend for Medical Imaging and Radiological System." Japanese Journal of Radiological Technology 78, no. 8 (August 20, 2022): 912–14. http://dx.doi.org/10.6009/jjrt.2022-2070.

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31

Itaya, Hidehiko. "Domestic Market Trend for Medical Imaging and Radiological System." Japanese Journal of Radiological Technology 77, no. 8 (2021): 896–98. http://dx.doi.org/10.6009/jjrt.2021_jsrt_77.8.896.

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32

Andersson-Engels, Stefan, Jonas Johansson, and Sune Svanberg. "Medical diagnostic system based on simultaneous multispectral fluorescence imaging." Applied Optics 33, no. 34 (December 1, 1994): 8022. http://dx.doi.org/10.1364/ao.33.008022.

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33

Gaud, Emmanuel. "Medical Imaging System Based On A Targeted Contrast Agent." Journal of the Acoustical Society of America 129, no. 6 (2011): 4101. http://dx.doi.org/10.1121/1.3600978.

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34

Leavitt, Steven C. "Medical ultrasound imaging system with velocity‐dependent rejection filtering." Journal of the Acoustical Society of America 87, no. 6 (June 1990): 2805. http://dx.doi.org/10.1121/1.399547.

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35

Kasai, Toshifumi, K. Sugimura, K. Morimoto, K. Ishihara, K. Hirota, S. Hara, M. Haramoto, et al. "Construction of medical imaging network system with internet protocol." Japanese Journal of Radiological Technology 52, no. 9 (1996): 1201. http://dx.doi.org/10.6009/jjrt.kj00001354943.

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36

Choi, Heung-Kook, Se-Myung Park, Jae-Hyo Kang, Sang-Kyoon Kim, and Hang-Mook Choi. "Tele-medical imaging conference system based on the Web." Computer Methods and Programs in Biomedicine 68, no. 3 (June 2002): 223–31. http://dx.doi.org/10.1016/s0169-2607(01)00174-2.

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37

Dikshit, Aditya, Dawei Wu, Chunyan Wu, and Weizhao Zhao. "An online interactive simulation system for medical imaging education." Computerized Medical Imaging and Graphics 29, no. 6 (September 2005): 395–404. http://dx.doi.org/10.1016/j.compmedimag.2005.02.001.

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38

Go´mez, E. J., F. del Pozo, J. A. Quiles, M. T. Arredondo, H. Rahms, M. Sanz, and P. Cano. "A telemedicine system for remote cooperative medical imaging diagnosis." Computer Methods and Programs in Biomedicine 49, no. 1 (January 1996): 37–48. http://dx.doi.org/10.1016/0169-2607(95)01706-2.

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39

Nishimura, Masatoshi. "Domestic Market Trend for Medical Imaging and Radiological System." Japanese Journal of Radiological Technology 68, no. 10 (2012): 1428–31. http://dx.doi.org/10.6009/jjrt.2012_jsrt_68.10.1428.

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40

Nishimura, Masatoshi. "Domestic Market Trend for Medical Imaging and Radiological System." Japanese Journal of Radiological Technology 70, no. 8 (2014): 849–51. http://dx.doi.org/10.6009/jjrt.2014_jsrt_70.8.849.

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41

Kajiyama, Koji. "Domestic Market Trend for Medical Imaging and Radiological System." Japanese Journal of Radiological Technology 72, no. 8 (2016): 717–19. http://dx.doi.org/10.6009/jjrt.2016_jsrt_72.8.717.

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42

Kajiyama, Koji. "Domestic Market Trend for Medical Imaging and Radiological System." Japanese Journal of Radiological Technology 73, no. 8 (2017): 714–16. http://dx.doi.org/10.6009/jjrt.2017_jsrt_73.8.714.

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43

Kajiyama, Koji. "Domestic Market Trend for Medical Imaging and Radiological System." Japanese Journal of Radiological Technology 74, no. 8 (2018): 846–48. http://dx.doi.org/10.6009/jjrt.2018_jsrt_74.8.846.

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44

Kajiyama, Koji. "Domestic Market Trend for Medical Imaging and Radiological System." Japanese Journal of Radiological Technology 75, no. 8 (2019): 866–68. http://dx.doi.org/10.6009/jjrt.2019_jsrt_75.8.866.

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45

Itaya, Hidehiko. "Domestic Market Trend for Medical Imaging and Radiological System." Japanese Journal of Radiological Technology 76, no. 8 (2020): 873–75. http://dx.doi.org/10.6009/jjrt.2020_jsrt_76.8.873.

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46

Thomas, Lewis Jones. "Medical diagnositc ultrasound system using contrast pulse sequence imaging." Journal of the Acoustical Society of America 113, no. 6 (2003): 2967. http://dx.doi.org/10.1121/1.1588856.

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47

Itaya, Hidehiko. "Domestic Market Trend for Medical Imaging and Radiological System." Japanese Journal of Radiological Technology 79, no. 8 (August 20, 2023): 886–88. http://dx.doi.org/10.6009/jjrt.2023-2231.

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48

Takano, Hiroaki. "Medical Imaging System Division Activities and Digital Healthcare Promotion." Japanese Journal of Radiological Technology 79, no. 7 (July 20, 2023): 747–50. http://dx.doi.org/10.6009/jjrt.2023-2222.

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49

Valluru, Keerthi S., Bhargava K. Chinni, and Navalgund A. Rao. "Photoacoustic Imaging: Opening New Frontiers in Medical Imaging." Journal of Clinical Imaging Science 1 (May 6, 2011): 24. http://dx.doi.org/10.4103/2156-7514.80522.

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In today's world, technology is advancing at an exponential rate and medical imaging is no exception. During the last hundred years, the field of medical imaging has seen a tremendous technological growth with the invention of imaging modalities including but not limited to X-ray, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography, and single-photon emission computed tomography. These tools have led to better diagnosis and improved patient care. However, each of these modalities has its advantages as well as disadvantages and none of them can reveal all the information a physician would like to have. In the last decade, a new diagnostic technology called photoacoustic imaging has evolved which is moving rapidly from the research phase to the clinical trial phase. This article outlines the basics of photoacoustic imaging and describes our hands-on experience in developing a comprehensive photoacoustic imaging system to detect tissue abnormalities.
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50

Marian, Vaughn R. "Medical diagnostic ultrasound imaging system transmitter control in a modulator transducer system." Journal of the Acoustical Society of America 115, no. 5 (2004): 1881. http://dx.doi.org/10.1121/1.1757215.

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