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Prieto-Fernández A, Sánchez-Barroso G, González-Domínguez J, García-Sanz-Calcedo J. Interaction between maintenance variables of medical ultrasound scanners through multifactor dimensionality reduction. Expert Rev Med Devices 2023; 20:851-864. [PMID: 37522639 DOI: 10.1080/17434440.2023.2243208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/14/2023] [Accepted: 06/22/2023] [Indexed: 08/01/2023]
Abstract
BACKGROUND Proper maintenance of electro-medical devices is crucial for the quality of care to patients and the economic performance of healthcare organizations. This research aims to identify the interaction between Ultrasound scanners (US) maintenance variables as a function of maintenance indicators: US in service or decommissioned, excessive number of failures, and failure rate. Knowing those interactions, specific maintenance measures will be developed to improve the reliability of the US. RESEARCH DESIGN AND METHODS Multifactor Dimensionality Reduction (MDR) method was eployed to analyze data from 222 US and their four-year maintenance history. Models were developed based on the variables with the greatest influence on maintenance indicators, where US were classified according to the associated risk. RESULTS US with more than one major failure or at least one major component replacement had up to 496.4% more failures than the average. Failure rate increased by up to 188.7% over the average for those US with more than three moderate failures, three replacements, or both. CONCLUSIONS This study identifies and quantifies the causes of risk to establish a specific maintenance plan for US. It helps to better understand the degradation of US to optimize their operation and maintenance.
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Affiliation(s)
| | - Gonzalo Sánchez-Barroso
- Engineering Projects Area, School of Industrial Engineering, University of Extremadura, Badajoz, Spain
| | - Jaime González-Domínguez
- Engineering Projects Area, School of Industrial Engineering, University of Extremadura, Badajoz, Spain
| | - Justo García-Sanz-Calcedo
- Engineering Projects Area, School of Industrial Engineering, University of Extremadura, Badajoz, Spain
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2
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Abbasian Ardakani A, Bureau NJ, Ciaccio EJ, Acharya UR. Interpretation of radiomics features-A pictorial review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106609. [PMID: 34990929 DOI: 10.1016/j.cmpb.2021.106609] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 12/23/2021] [Accepted: 12/25/2021] [Indexed: 06/14/2023]
Abstract
Radiomics is a newcomer field that has opened new windows for precision medicine. It is related to extraction of a large number of quantitative features from medical images, which may be difficult to detect visually. Underlying tumor biology can change physical properties of tissues, which affect patterns of image pixels and radiomics features. The main advantage of radiomics is that it can characterize the whole tumor non-invasively, even after a single sampling from an image. Therefore, it can be linked to a "digital biopsy". Physicians need to know about radiomics features to determine how their values correlate with the appearance of lesions and diseases. Indeed, physicians need practical references to conceive of basics and concepts of each radiomics feature without knowing their sophisticated mathematical formulas. In this review, commonly used radiomics features are illustrated with practical examples to help physicians in their routine diagnostic procedures.
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Affiliation(s)
- Ali Abbasian Ardakani
- Department of Radiology Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Nathalie J Bureau
- Department of Radiology, Centre hospitalier de l'Université de Montréal (CHUM), 1000 rue Saint- Denis, Montreal, Quebec, H2×0C1, Canada; Research Center, Centre hospitalier de l'Université de Montréal (CHUM), 900 rue Saint-Denis, Montreal, Quebec, H2×0A9, Canada
| | - Edward J Ciaccio
- Department of Medicine, Columbia University, New York, NY 10032, United States of America
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia; Department of Biomedical Informatics and Medical Engineering, Asia University, Taiwan
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3
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Long Z, Zhao G, Wang J, Zhang M, Zhou S, Zhang L, Huang Z. Research on the Drivers of Entrepreneurship Education Performance of Medical Students in the Digital Age. Front Psychol 2021; 12:733301. [PMID: 34777115 PMCID: PMC8589079 DOI: 10.3389/fpsyg.2021.733301] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/04/2021] [Indexed: 11/24/2022] Open
Abstract
COVID-19 has made the entire society pay more attention to medical students training. Medicine development is inseparable from the spirit of innovation, focusing on cultivating medical students' innovative awareness and improving entrepreneurship education performance, which has an irreplaceable effect on both the students themselves and the society. This study is based on the ridge regression model to study the driving factors of the entrepreneurship education performance of medical students. Compared with traditional multiple regression, it can improve the consistency of parameter estimation and obtain more realistic results. Based on a large sample of empirical survey data of 24,677 medical students in China, this study analyzed the driving factors of the entrepreneurship education performance of medical students and found that medical students of different genders have differences in entrepreneurship education performance; the digital economy impacts entrepreneurship education performance of medical students; entrepreneurship course, entrepreneurship faculty, entrepreneurship competition, entrepreneurship practice, and entrepreneurship policy have a driving effect on the entrepreneurship education performance of medical students. Meanwhile, the impact of entrepreneurship policy is the most obvious, followed by entrepreneurship practice and entrepreneurship competition, followed by entrepreneurship course and entrepreneurship faculty.
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Affiliation(s)
- Zehai Long
- Institute of China Innovation and Entrepreneurship Education, Wenzhou Medical University, Wenzhou, China
| | - Guojing Zhao
- Institute of China Innovation and Entrepreneurship Education, Wenzhou Medical University, Wenzhou, China
| | - Jing Wang
- Institute of China Innovation and Entrepreneurship Education, Wenzhou Medical University, Wenzhou, China
| | - Mengting Zhang
- Institute of China Innovation and Entrepreneurship Education, Wenzhou Medical University, Wenzhou, China
| | - Shaoyu Zhou
- Institute of China Innovation and Entrepreneurship Education, Wenzhou Medical University, Wenzhou, China
| | - Ling Zhang
- School of Education, Hangzhou Normal University, Hangzhou, China
| | - Zhaoxin Huang
- Institute of China Innovation and Entrepreneurship Education, Wenzhou Medical University, Wenzhou, China
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4
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Kesner A. The cultivation of supply side data science in medical imaging: an opportunity to define the future of global health. Eur J Nucl Med Mol Imaging 2021; 49:436-442. [PMID: 34687333 DOI: 10.1007/s00259-021-05555-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Affiliation(s)
- Adam Kesner
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1250 First Avenue, Room S-1119E (Box 84), New York, NY, 10065, USA.
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5
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Artificial Intelligence and the Medical Physicist: Welcome to the Machine. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041691] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.
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6
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Corradini S, Niyazi M, Verellen D, Valentini V, Walsh S, Grosu AL, Lauber K, Giaccia A, Unger K, Debus J, Pieters BR, Guckenberger M, Senan S, Budach W, Rad R, Mayerle J, Belka C. X-change symposium: status and future of modern radiation oncology-from technology to biology. Radiat Oncol 2021; 16:27. [PMID: 33541387 PMCID: PMC7863262 DOI: 10.1186/s13014-021-01758-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 01/28/2021] [Indexed: 02/06/2023] Open
Abstract
Future radiation oncology encompasses a broad spectrum of topics ranging from modern clinical trial design to treatment and imaging technology and biology. In more detail, the application of hybrid MRI devices in modern image-guided radiotherapy; the emerging field of radiomics; the role of molecular imaging using positron emission tomography and its integration into clinical routine; radiation biology with its future perspectives, the role of molecular signatures in prognostic modelling; as well as special treatment modalities such as brachytherapy or proton beam therapy are areas of rapid development. More clinically, radiation oncology will certainly find an important role in the management of oligometastasis. The treatment spectrum will also be widened by the rational integration of modern systemic targeted or immune therapies into multimodal treatment strategies. All these developments will require a concise rethinking of clinical trial design. This article reviews the current status and the potential developments in the field of radiation oncology as discussed by a panel of European and international experts sharing their vision during the "X-Change" symposium, held in July 2019 in Munich (Germany).
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Affiliation(s)
- Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
| | - Maximilian Niyazi
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Dirk Verellen
- Department of Radiotherapy, Iridium Network, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Vincenzo Valentini
- Department of Radiation Oncology and Hematology, Fondazione Policlinico Universitario A.Gemelli IRCCS, Università Cattolica S. Cuore, Rome, Italy
| | | | - Anca-L Grosu
- Department of Radiation Oncology, Medical Center, Medical Faculty, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - Kirsten Lauber
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Amato Giaccia
- Division of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University, Stanford, USA
| | - Kristian Unger
- Integrative Biology Group, Helmholtz Zentrum Munich, Munich, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Bradley R Pieters
- Department of Radiation Oncology, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Suresh Senan
- Department of Radiation Oncology, Amsterdam University Medical Centers, Location VUmc, Amsterdam, The Netherlands
| | - Wilfried Budach
- Department of Radiation Oncology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Roland Rad
- Center for Translational Cancer Research (TranslaTUM), TU Munich, Munich, Germany
| | - Julia Mayerle
- Department of Internal Medicine II, University Hospital, LMU, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
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7
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Kesner AL. Data-driven motion correction in clinical PET - a joint accomplishment of creative academia and industry. J Nucl Med 2020; 62:jnumed.120.248187. [PMID: 32513901 DOI: 10.2967/jnumed.120.248187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 05/07/2020] [Indexed: 11/16/2022] Open
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8
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Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, Keek SA, Sanduleanu S, Primakov SP, Beuque MPL, Marcus D, van der Wiel AMA, Zerka F, Oberije CJG, van Timmeren JE, Woodruff HC, Lambin P. Radiomics: from qualitative to quantitative imaging. Br J Radiol 2020; 93:20190948. [PMID: 32101448 DOI: 10.1259/bjr.20190948] [Citation(s) in RCA: 144] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.
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Affiliation(s)
- William Rogers
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Thoracic Oncology, IRCCS Foundation National Cancer Institute, Milan, Italy
| | - Sithin Thulasi Seetha
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Thoracic Oncology, IRCCS Foundation National Cancer Institute, Milan, Italy
| | - Turkey A G Refaee
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Relinde I Y Lieverse
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Renée W Y Granzier
- Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Surgery, Maastricht University Medical Centre, Grow-School for Oncology and Developmental Biology, Maastricht, The Netherlands
| | - Abdalla Ibrahim
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Nuclear Medicine and Comprehensive diagnostic center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany.,Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium
| | - Simon A Keek
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Sebastian Sanduleanu
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Sergey P Primakov
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Manon P L Beuque
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Damiënne Marcus
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Alexander M A van der Wiel
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Fadila Zerka
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Cary J G Oberije
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Janita E van Timmeren
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Radiation Oncology, University Hospital Zürich, Zürich, Switzerland.,University of Zürich, Zürich, Switzerland
| | - Henry C Woodruff
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
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9
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Radiology workflow for RECIST assessment in clinical trials: Can we reconcile time-efficiency and quality? Eur J Radiol 2019; 118:257-263. [DOI: 10.1016/j.ejrad.2019.07.030] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 06/10/2019] [Accepted: 07/23/2019] [Indexed: 01/01/2023]
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10
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Abstract
PET imaging has been, and continues to be, an evolving diagnostic technology. In recent years, the modernizing digital landscape has opened new opportunities for data-driven innovation. One such facet has been data-driven motion correction (DDMC) in PET. As both research and industry propel this technology forward, we can recognize prospects and opportunities for further development. The concept of clinical practicality is supported by DDMC approaches—it is what sets them apart from traditional hardware-driven motion correction strategies that have largely not gained acceptance in routine diagnostic PET; the ease of use of DDMC may help propel acceptance of motion correction solutions in clinical practice. As we reflect on the present field, we should consider that DDMC can be made even more practical, and likely more impactful, if further developed to fit within a real-time acquisition framework. This vision for development is not new, but has been made more feasible with contemporary electronics, and has begun to be revisited in contemporary literature. The opportunities for development lie on a new forefront of innovation where medical physics integrates with engineering, data science, and modern computing capacities. Real-time DDMC is a systems integration challenge, and achieving it will require cooperation between hardware and software developers, and likely academia and industry. While challenges for development do exist, it is likely that we will see real-time DDMC come to fruition in the coming years. This effort may establish groundwork for developing similar innovations in the emerging digital innovation age.
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Giansante L, Martins JC, Nersissian DY, Kiers KC, Kay FU, Sawamura MVY, Lee C, Gebrim EMMS, Costa PR. Organ doses evaluation for chest computed tomography procedures with TL dosimeters: Comparison with Monte Carlo simulations. J Appl Clin Med Phys 2018; 20:308-320. [PMID: 30508315 PMCID: PMC6333138 DOI: 10.1002/acm2.12505] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 09/22/2018] [Accepted: 10/25/2018] [Indexed: 12/05/2022] Open
Abstract
Purpose To evaluate organ doses in routine and low‐dose chest computed tomography (CT) protocols using an experimental methodology. To compare experimental results with results obtained by the National Cancer Institute dosimetry system for CT (NCICT) organ dose calculator. To address the differences on organ dose measurements using tube current modulation (TCM) and fixed tube current protocols. Methods An experimental approach to evaluate organ doses in pediatric and adult anthropomorphic phantoms using thermoluminescent dosimeters (TLDs) was employed in this study. Several analyses were performed in order to establish the best way to achieve the main results in this investigation. The protocols used in this study were selected after an analysis of patient data collected from the Institute of Radiology of the School of Medicine of the University of São Paulo (InRad). The image quality was evaluated by a radiologist from this institution. Six chest adult protocols and four chest pediatric protocols were evaluated. Lung doses were evaluated for the adult phantom and lung and thyroid doses were evaluated for the pediatric phantom. The irradiations were performed using both a GE and a Philips CT scanner. Finally, organ doses measured with dosimeters were compared with Monte Carlo simulations performed with NCICT. Results After analyzing the data collected from all CT examinations performed during a period of 3 yr, the authors identified that adult and pediatric chest CT are among the most applied protocol in patients in that clinical institution, demonstrating the relevance on evaluating organ doses due to these examinations. With regards to the scan parameters adopted, the authors identified that using 80 kV instead of 120 kV for a pediatric chest routine CT, with TCM in both situations, can lead up to a 28.7% decrease on the absorbed dose. Moreover, in comparison to the standard adult protocol, which is performed with fixed mAs, TCM, and ultra low‐dose protocols resulted in dose reductions of up to 35.0% and 90.0%, respectively. Finally, the percent differences found between experimental and Monte Carlo simulated organ doses were within a 20% interval. Conclusions The results obtained in this study measured the impact on the absorbed dose in routine chest CT by changing several scan parameters while the image quality could be potentially preserved.
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Affiliation(s)
- Louise Giansante
- Group of Radiation Dosimetry and Medical Physics, Institute of Physics, University of São Paulo (IFUSP), São Paulo, SP, Brazil
| | - Juliana C Martins
- Group of Radiation Dosimetry and Medical Physics, Institute of Physics, University of São Paulo (IFUSP), São Paulo, SP, Brazil.,Ludwig-Maximilians-Universität München (LMU), Munich, Germany
| | - Denise Y Nersissian
- Group of Radiation Dosimetry and Medical Physics, Institute of Physics, University of São Paulo (IFUSP), São Paulo, SP, Brazil
| | - Karen C Kiers
- Group of Radiation Dosimetry and Medical Physics, Institute of Physics, University of São Paulo (IFUSP), São Paulo, SP, Brazil.,Vrije Universiteit Amsterdam (VU), Amsterdam, The Netherlands
| | - Fernando U Kay
- Institute of Radiology, School of Medicine, University of São Paulo (InRad), São Paulo, SP, Brazil
| | - Marcio V Y Sawamura
- Institute of Radiology, School of Medicine, University of São Paulo (InRad), São Paulo, SP, Brazil
| | - Choonsik Lee
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Eloisa M M S Gebrim
- Institute of Radiology, School of Medicine, University of São Paulo (InRad), São Paulo, SP, Brazil
| | - Paulo R Costa
- Group of Radiation Dosimetry and Medical Physics, Institute of Physics, University of São Paulo (IFUSP), São Paulo, SP, Brazil
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12
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Kesner AL, Bodei L. Modern Radiopharmaceutical Dosimetry Should Include Robust Biodistribution Reporting. J Nucl Med 2018; 59:1507-1509. [DOI: 10.2967/jnumed.118.208603] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 03/23/2018] [Indexed: 11/16/2022] Open
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