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Sechopoulos I, Dance DR, Boone JM, Bosmans HT, Caballo M, Diaz O, van Engen R, Fedon C, Glick SJ, Hernandez AM, Hill ML, Hulme KW, Longo R, Rabin C, Sanderink WBG, Seibert JA. Joint AAPM Task Group 282/EFOMP Working Group Report: Breast dosimetry for standard and contrast-enhanced mammography and breast tomosynthesis. Med Phys 2024; 51:712-739. [PMID: 38018710 DOI: 10.1002/mp.16842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 09/26/2023] [Accepted: 11/10/2023] [Indexed: 11/30/2023] Open
Abstract
Currently, there are multiple breast dosimetry estimation methods for mammography and its variants in use throughout the world. This fact alone introduces uncertainty, since it is often impossible to distinguish which model is internally used by a specific imaging system. In addition, all current models are hampered by various limitations, in terms of overly simplified models of the breast and its composition, as well as simplistic models of the imaging system. Many of these simplifications were necessary, for the most part, due to the need to limit the computational cost of obtaining the required dose conversion coefficients decades ago, when these models were first implemented. With the advancements in computational power, and to address most of the known limitations of previous breast dosimetry methods, a new breast dosimetry method, based on new breast models, has been developed, implemented, and tested. This model, developed jointly by the American Association of Physicists in Medicine and the European Federation for Organizations of Medical Physics, is applicable to standard mammography, digital breast tomosynthesis, and their contrast-enhanced variants. In addition, it includes models of the breast in both the cranio-caudal and the medio-lateral oblique views. Special emphasis was placed on the breast and system models used being based on evidence, either by analysis of large sets of patient data or by performing measurements on imaging devices from a range of manufacturers. Due to the vast number of dose conversion coefficients resulting from the developed model, and the relative complexity of the calculations needed to apply it, a software program has been made available for download or online use, free of charge, to apply the developed breast dosimetry method. The program is available for download or it can be used directly online. A separate User's Guide is provided with the software.
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Affiliation(s)
- Ioannis Sechopoulos
- Radboud University Medical Center, Nijmegen, The Netherlands
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
- University of Twente, Enschede, The Netherlands
| | - David R Dance
- National Co-ordinating Centre for the Physics of Mammography (NCCPM), Royal Surrey County Hospital, Guildford, UK
| | - John M Boone
- University of California, Davis, California, USA
| | | | - Marco Caballo
- Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Ruben van Engen
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
| | - Christian Fedon
- Radboud University Medical Center (now at Nuclear Research and Consultancy Group, NRG), Nijmegen, The Netherlands
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Atehortúa A, Gkontra P, Camacho M, Diaz O, Bulgheroni M, Simonetti V, Chadeau-Hyam M, Felix JF, Sebert S, Lekadir K. Cardiometabolic risk estimation using exposome data and machine learning. Int J Med Inform 2023; 179:105209. [PMID: 37729839 DOI: 10.1016/j.ijmedinf.2023.105209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/11/2023] [Accepted: 08/30/2023] [Indexed: 09/22/2023]
Abstract
BACKGROUND The human exposome encompasses all exposures that individuals encounter throughout their lifetime. It is now widely acknowledged that health outcomes are influenced not only by genetic factors but also by the interactions between these factors and various exposures. Consequently, the exposome has emerged as a significant contributor to the overall risk of developing major diseases, such as cardiovascular disease (CVD) and diabetes. Therefore, personalized early risk assessment based on exposome attributes might be a promising tool for identifying high-risk individuals and improving disease prevention. OBJECTIVE Develop and evaluate a novel and fair machine learning (ML) model for CVD and type 2 diabetes (T2D) risk prediction based on a set of readily available exposome factors. We evaluated our model using internal and external validation groups from a multi-center cohort. To be considered fair, the model was required to demonstrate consistent performance across different sub-groups of the cohort. METHODS From the UK Biobank, we identified 5,348 and 1,534 participants who within 13 years from the baseline visit were diagnosed with CVD and T2D, respectively. An equal number of participants who did not develop these pathologies were randomly selected as the control group. 109 readily available exposure variables from six different categories (physical measures, environmental, lifestyle, mental health events, sociodemographics, and early-life factors) from the participant's baseline visit were considered. We adopted the XGBoost ensemble model to predict individuals at risk of developing the diseases. The model's performance was compared to that of an integrative ML model which is based on a set of biological, clinical, physical, and sociodemographic variables, and, additionally for CVD, to the Framingham risk score. Moreover, we assessed the proposed model for potential bias related to sex, ethnicity, and age. Lastly, we interpreted the model's results using SHAP, a state-of-the-art explainability method. RESULTS The proposed ML model presents a comparable performance to the integrative ML model despite using solely exposome information, achieving a ROC-AUC of 0.78±0.01 and 0.77±0.01 for CVD and T2D, respectively. Additionally, for CVD risk prediction, the exposome-based model presents an improved performance over the traditional Framingham risk score. No bias in terms of key sensitive variables was identified. CONCLUSIONS We identified exposome factors that play an important role in identifying patients at risk of CVD and T2D, such as naps during the day, age completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status. Overall, this work demonstrates the potential of exposome-based machine learning as a fair CVD and T2D risk assessment tool.
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Affiliation(s)
- Angélica Atehortúa
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
| | - Polyxeni Gkontra
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Marina Camacho
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Oliver Diaz
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | | | | | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sylvain Sebert
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Karim Lekadir
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
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Al-Kadi OS, Diaz O. Editorial: Reviews in cancer imaging and image-directed interventions. Front Oncol 2023; 13:1183302. [PMID: 37007158 PMCID: PMC10061145 DOI: 10.3389/fonc.2023.1183302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 03/13/2023] [Indexed: 03/18/2023] Open
Affiliation(s)
- Omar S. Al-Kadi
- Department of Artificial Intelligence, King Abdullah II School for Information Technology, University of Jordan, Amman, Jordan
- *Correspondence: Omar S. Al-Kadi, ; Oliver Diaz,
| | - Oliver Diaz
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
- *Correspondence: Omar S. Al-Kadi, ; Oliver Diaz,
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Osuala R, Kushibar K, Garrucho L, Linardos A, Szafranowska Z, Klein S, Glocker B, Diaz O, Lekadir K. Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging. Med Image Anal 2023; 84:102704. [PMID: 36473414 DOI: 10.1016/j.media.2022.102704] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 11/02/2022] [Accepted: 11/21/2022] [Indexed: 11/26/2022]
Abstract
Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include inter-observer variability, class imbalance, dataset shifts, inter- and intra-tumour heterogeneity, malignancy determination, and treatment effect uncertainty. Given the recent advancements in image synthesis, Generative Adversarial Networks (GANs), and adversarial training, we assess the potential of these technologies to address a number of key challenges of cancer imaging. We categorise these challenges into (a) data scarcity and imbalance, (b) data access and privacy, (c) data annotation and segmentation, (d) cancer detection and diagnosis, and (e) tumour profiling, treatment planning and monitoring. Based on our analysis of 164 publications that apply adversarial training techniques in the context of cancer imaging, we highlight multiple underexplored solutions with research potential. We further contribute the Synthesis Study Trustworthiness Test (SynTRUST), a meta-analysis framework for assessing the validation rigour of medical image synthesis studies. SynTRUST is based on 26 concrete measures of thoroughness, reproducibility, usefulness, scalability, and tenability. Based on SynTRUST, we analyse 16 of the most promising cancer imaging challenge solutions and observe a high validation rigour in general, but also several desirable improvements. With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on data synthesis and adversarial networks in the artificial intelligence community.
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Affiliation(s)
- Richard Osuala
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain.
| | - Kaisar Kushibar
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Lidia Garrucho
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Akis Linardos
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Zuzanna Szafranowska
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
| | - Oliver Diaz
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
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Garrucho L, Kushibar K, Osuala R, Diaz O, Catanese A, del Riego J, Bobowicz M, Strand F, Igual L, Lekadir K. High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection. Front Oncol 2023; 12:1044496. [PMID: 36755853 PMCID: PMC9899892 DOI: 10.3389/fonc.2022.1044496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/19/2022] [Indexed: 01/24/2023] Open
Abstract
Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. Therefore, the sensitivity of mammography for breast cancer detection can be reduced by more than 20% in dense breasts. Additionally, extremely dense cases reported an increased risk of cancer compared to low-density breasts. This study aims to improve the mass detection performance in high-density breasts using synthetic high-density full-field digital mammograms (FFDM) as data augmentation during breast mass detection model training. To this end, a total of five cycle-consistent GAN (CycleGAN) models using three FFDM datasets were trained for low-to-high-density image translation in high-resolution mammograms. The training images were split by breast density BI-RADS categories, being BI-RADS A almost entirely fatty and BI-RADS D extremely dense breasts. Our results showed that the proposed data augmentation technique improved the sensitivity and precision of mass detection in models trained with small datasets and improved the domain generalization of the models trained with large databases. In addition, the clinical realism of the synthetic images was evaluated in a reader study involving two expert radiologists and one surgical oncologist.
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Affiliation(s)
- Lidia Garrucho
- Barcelona Artificial Intelligence in Medicine Lab, Facultat de Matemàtques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Kaisar Kushibar
- Barcelona Artificial Intelligence in Medicine Lab, Facultat de Matemàtques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Richard Osuala
- Barcelona Artificial Intelligence in Medicine Lab, Facultat de Matemàtques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Oliver Diaz
- Barcelona Artificial Intelligence in Medicine Lab, Facultat de Matemàtques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Alessandro Catanese
- Unitat de Diagnòstic per la Imatge de la Mama (UDIM), Hospital Germans Trias i Pujol, Badalona, Spain
| | - Javier del Riego
- Área de Radiología Mamaria y Ginecólogica (UDIAT CD), Parc Taulí Hospital Universitari, Sabadell, Spain
| | - Maciej Bobowicz
- 2nd Department of Radiology, Medical University of Gdansk, Gdansk, Poland
| | - Fredrik Strand
- Breast Radiology, Karolinska University Hospital and Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Laura Igual
- Barcelona Artificial Intelligence in Medicine Lab, Facultat de Matemàtques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Karim Lekadir
- Barcelona Artificial Intelligence in Medicine Lab, Facultat de Matemàtques i Informàtica, Universitat de Barcelona, Barcelona, Spain
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Garrucho L, Kushibar K, Jouide S, Diaz O, Igual L, Lekadir K. Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study. Artif Intell Med 2022; 132:102386. [PMID: 36207090 DOI: 10.1016/j.artmed.2022.102386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 08/07/2022] [Accepted: 08/19/2022] [Indexed: 11/02/2022]
Abstract
Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this study, we explored the domain generalization of deep learning methods for mass detection in digital mammography and analyzed in-depth the sources of domain shift in a large-scale multi-center setting. To this end, we compared the performance of eight state-of-the-art detection methods, including Transformer based models, trained in a single domain and tested in five unseen domains. Moreover, a single-source mass detection training pipeline was designed to improve the domain generalization without requiring images from the new domain. The results show that our workflow generalized better than state-of-the-art transfer learning based approaches in four out of five domains while reducing the domain shift caused by the different acquisition protocols and scanner manufacturers. Subsequently, an extensive analysis was performed to identify the covariate shifts with the greatest effects on detection performance, such as those due to differences in patient age, breast density, mass size, and mass malignancy. Ultimately, this comprehensive study provides key insights and best practices for future research on domain generalization in deep learning based breast cancer detection.
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Affiliation(s)
- Lidia Garrucho
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Barcelona, Spain.
| | - Kaisar Kushibar
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Barcelona, Spain
| | - Socayna Jouide
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Barcelona, Spain
| | - Oliver Diaz
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Barcelona, Spain
| | - Laura Igual
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Barcelona, Spain
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Barcelona, Spain
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Diaz O, Esener D, Sacci P, Abrams E, Rose G. 170 Evaluation of Performance of Transesophageal Echocardiography by Emergency Medicine Residents After a Single Simulation-Based Training Session. Ann Emerg Med 2022. [DOI: 10.1016/j.annemergmed.2022.08.194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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Pawaskar M, Fergie J, Harley C, Samant S, Veeranki P, Diaz O, Conway JH. Impact of universal varicella vaccination on the use and cost of antibiotics and antivirals for varicella management in the United States. PLoS One 2022; 17:e0269916. [PMID: 35687559 PMCID: PMC9187103 DOI: 10.1371/journal.pone.0269916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/31/2022] [Indexed: 11/19/2022] Open
Abstract
Background
Our objective was to estimate the impact of universal varicella vaccination (UVV) on the use and costs of antibiotics and antivirals for the management of varicella among children in the United States (US).
Methods
A decision tree model of varicella vaccination, infections and treatment decisions was developed. Results were extrapolated to the 2017 population of 73.5 million US children. Model parameters were populated from published sources. Treatment decisions were derived from a survey of health care professionals’ recommendations. The base case modelled current vaccination coverage rates in the US with additional scenarios analyses conducted for 0%, 20%, and 80% coverage and did not account for herd immunity benefits.
Results
Our model estimated that 551,434 varicella cases occurred annually among children ≤ 18 years in 2017. Antivirals or antibiotics were prescribed in 23.9% of cases, with unvaccinated children receiving the majority for base case. The annual cost for varicella antiviral and antibiotic treatment was approximately $14 million ($26 per case), with cases with no complications accounting for $12 million. Compared with the no vaccination scenario, the current vaccination rates resulted in savings of $181 million (94.7%) for antivirals and $78 million (95.0%) for antibiotics annually. Scenario analyses showed that higher vaccination coverage (from 0% to 80%) resulted in reduced annual expenditures for antivirals (from $191 million to $41 million), and antibiotics ($82 million to $17 million).
Conclusions
UVV was associated with significant reductions in the use of antibiotics and antivirals and their associated costs in the US. Higher vaccination coverage was associated with lower use and costs of antibiotics and antivirals for varicella management.
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Affiliation(s)
- Manjiri Pawaskar
- Merck & Co. Inc., Rahway, New Jersey, United States of America
- * E-mail:
| | - Jaime Fergie
- Driscoll Children’s Hospital, Corpus Christi, Texas, United States of America
| | - Carolyn Harley
- PRECISIONheor, Los Angeles, California, United States of America
| | - Salome Samant
- Merck & Co. Inc., Rahway, New Jersey, United States of America
| | - Phani Veeranki
- PRECISIONheor, Los Angeles, California, United States of America
| | - Oliver Diaz
- PRECISIONheor, Los Angeles, California, United States of America
| | - James H. Conway
- School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, United States of America
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Caballo M, Rabin C, Fedon C, Rodríguez-Ruiz A, Diaz O, Boone JM, Dance DR, Sechopoulos I. Patient-derived heterogeneous breast phantoms for advanced dosimetry in mammography and tomosynthesis. Med Phys 2022; 49:5423-5438. [PMID: 35635844 PMCID: PMC9546119 DOI: 10.1002/mp.15785] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/26/2022] [Accepted: 05/24/2022] [Indexed: 12/03/2022] Open
Abstract
Background Understanding the magnitude and variability of the radiation dose absorbed by the breast fibroglandular tissue during mammography and digital breast tomosynthesis (DBT) is of paramount importance to assess risks versus benefits. Although homogeneous breast models have been proposed and used for decades for this purpose, they do not accurately reflect the actual heterogeneous distribution of the fibroglandular tissue in the breast, leading to biases in the estimation of dose from these modalities. Purpose To develop and validate a method to generate patient‐derived, heterogeneous digital breast phantoms for breast dosimetry in mammography and DBT. Methods The proposed phantoms were developed starting from patient‐based models of compressed breasts, generated for multiple thicknesses and representing the two standard views acquired in mammography and DBT, that is, cranio‐caudal (CC) and medio‐lateral‐oblique (MLO). Internally, the breast phantoms were defined as consisting of an adipose/fibroglandular tissue mixture, with a nonspatially uniform relative concentration. The parenchyma distributions were obtained from a previously described model based on patient breast computed tomography data that underwent simulated compression. Following these distributions, phantoms with any glandular fraction (1%–100%) and breast thickness (12–125 mm) can be generated, for both views. The phantoms were validated, in terms of their accuracy for average normalized glandular dose (DgN) estimation across samples of patient breasts, using 88 patient‐specific phantoms involving actual patient distribution of the fibroglandular tissue in the breast, and compared to that obtained using a homogeneous model similar to those currently used for breast dosimetry. Results The average DgN estimated for the proposed phantoms was concordant with that absorbed by the patient‐specific phantoms to within 5% (CC) and 4% (MLO). These DgN estimates were over 30% lower than those estimated with the homogeneous models, which overestimated the average DgN by 43% (CC), and 32% (MLO) compared to the patient‐specific phantoms. Conclusions The developed phantoms can be used for dosimetry simulations to improve the accuracy of dose estimates in mammography and DBT.
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Affiliation(s)
- Marco Caballo
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Carolina Rabin
- Instituto de Física, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo, 11600, Uruguay
| | - Christian Fedon
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Alejandro Rodríguez-Ruiz
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.,epartment of Image Guided Therapy Systems, Philips Healthcare, Veenpluis 6, 5684 PC Best, the Netherlands
| | - Oliver Diaz
- Department of Mathematics and Computer Science, University of Barcelona, Spain
| | - John M Boone
- Department of Radiology and Biomedical Engineering, University of California Davis Health, 4860 "Y" Street, suite 3100 Ellison building, Sacramento, CA, 95817, USA
| | - David R Dance
- National Co-ordinating Centre for the Physics of Mammography (NCCPM), Royal Surrey County Hospital, Department of Physics, University of Surrey, Guildford, GU2 7XH, United Kingdom
| | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.,Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, The Netherlands.,Technical Medicine Centre, University of Twente, Hallenweg 5, 7522 NH, Enschede, The Netherlands
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Evin C, Quéro L, Le Malicot K, Blanchet-Deverly S, François E, Buchalet C, Lemanski C, Baba Hamed N, Rivin del Campo E, Bauwens L, Pommier P, Lièvre A, Tougeron D, Macé V, Sergent G, Diaz O, Zucman D, Mornex F, Locher C, De la Rochefordière A, Vendrely V, Huguet F. MO-0226 Clinical outcomes of HIV-positive patients with anal cancer in the ANABASE multicentric cohort. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)02328-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Van Woy L, Esener D, Sacci P, Diaz O, Murray M. 398 Factors Associated With Inconclusive Ultrasound in Pediatric Appendicitis. Ann Emerg Med 2021. [DOI: 10.1016/j.annemergmed.2021.09.413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Van Woy L, Esener D, Sacci P, Diaz O, Murray M. 311 Pediatric Emergency Departments More Accurately Diagnose Appendicitis Using Ultrasound. Ann Emerg Med 2021. [DOI: 10.1016/j.annemergmed.2021.09.325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Evin C, Quéro L, Le Malicot K, Blanchet-Deverly S, François E, Buchalet C, Lemanski C, Baba Hamed N, Rivin Del Campo E, Bauwens L, Pommier P, Lièvre A, Tougeron D, Mace V, Sergent G, Diaz O, Zucman D, Mornex F, Locher C, de La Rochefordiere A, Vendrely V, Huguet F. Efficacité et toxicité de la (chimio)radiothérapie chez les patients séropositifs pour le VIH atteints d’un carcinome épidermoïde du canal anal, analyse en sous-groupe de la cohorte multicentrique Anabase. Cancer Radiother 2021. [DOI: 10.1016/j.canrad.2021.07.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Zanca F, Avanzo M, Colgan N, Crijns W, Guidi G, Hernandez-Giron I, Kagadis GC, Diaz O, Zaidi H, Russo P, Toma-Dasu I, Kortesniemi M. Focus issue: Artificial intelligence in medical physics. Phys Med 2021; 83:287-291. [PMID: 34004585 DOI: 10.1016/j.ejmp.2021.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Affiliation(s)
- F Zanca
- Palindromo Consulting, Leuven, Belgium
| | - M Avanzo
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Department of Medical Physics, 33081 Aviano, PN, Italy
| | - N Colgan
- School of Physics, National University of Ireland Galway, Galway, Ireland
| | - W Crijns
- Department Oncology, Laboratory of Experimental Radiotherapy, KU Leuven and Department of Radiation Oncology, UZ Leuven, Belgium
| | - G Guidi
- Medical Physics, Az. Ospedaliero-Universitaria di Modena, Modena, Italy
| | - I Hernandez-Giron
- Leiden University Medical Center (LUMC), Radiology Department, Division of Image Processing, Albinusdreef 2, 2333ZA Leiden, The Netherlands
| | - G C Kagadis
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, GR 265 04, Greece
| | - O Diaz
- Faculty of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | - H Zaidi
- Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, CH-1211 Geneva, Switzerland
| | - P Russo
- Università di Napoli Federico II, Dipartimento di Fisica "Ettore Pancini", I-80126 Naples, Italy
| | - I Toma-Dasu
- Department of Physics, Medical Radiation Physics, Stockholm University, Stockholm, Sweden; Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| | - M Kortesniemi
- HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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Teuwen J, Moriakov N, Fedon C, Caballo M, Reiser I, Bakic P, García E, Diaz O, Michielsen K, Sechopoulos I. Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation. Med Image Anal 2021; 71:102061. [PMID: 33910108 DOI: 10.1016/j.media.2021.102061] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 03/22/2021] [Accepted: 03/29/2021] [Indexed: 12/12/2022]
Abstract
The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly used in breast cancer screening and diagnostics. Still, the severely limited 3rd dimension information in DBT has not been used, until now, to estimate the true breast density or the patient-specific dose. This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks. The algorithm, which we name DBToR, is based on unrolling a proximal-dual optimization method. The proximal operators are replaced with convolutional neural networks and prior knowledge is included in the model. This extends previous work on a deep learning-based reconstruction model by providing both the primal and the dual blocks with breast thickness information, which is available in DBT. Training and testing of the model were performed using virtual patient phantoms from two different sources. Reconstruction performance, and accuracy in estimation of breast density and radiation dose, were estimated, showing high accuracy (density <±3%; dose <±20%) without bias, significantly improving on the current state-of-the-art. This work also lays the groundwork for developing a deep learning-based reconstruction algorithm for the task of image interpretation by radiologists.
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Affiliation(s)
- Jonas Teuwen
- Department of Medical Imaging, Radboud University Medical Center, the Netherlands; Department of Radiation Oncology, Netherlands Cancer Institute, the Netherlands
| | - Nikita Moriakov
- Department of Medical Imaging, Radboud University Medical Center, the Netherlands; Department of Radiation Oncology, Netherlands Cancer Institute, the Netherlands
| | - Christian Fedon
- Department of Medical Imaging, Radboud University Medical Center, the Netherlands
| | - Marco Caballo
- Department of Medical Imaging, Radboud University Medical Center, the Netherlands
| | - Ingrid Reiser
- Department of Radiology, The University of Chicago, USA
| | - Pedrag Bakic
- Department of Radiology, University of Pennsylvania, USA; Department of Translational Medicine, Lund University, Sweden
| | - Eloy García
- Vall d'Hebron Institute of Oncology, VHIO, Spain
| | - Oliver Diaz
- Department of Mathematics and Computer Science, University of Barcelona, Spain
| | - Koen Michielsen
- Department of Medical Imaging, Radboud University Medical Center, the Netherlands
| | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, the Netherlands; Dutch Expert Centre for Screening (LRCB), the Netherlands.
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Fedon C, Caballo M, García E, Diaz O, Boone JM, Dance DR, Sechopoulos I. Fibroglandular tissue distribution in the breast during mammography and tomosynthesis based on breast CT data: A patient-based characterization of the breast parenchyma. Med Phys 2021; 48:1436-1447. [PMID: 33452822 PMCID: PMC7986202 DOI: 10.1002/mp.14716] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 10/30/2020] [Accepted: 01/07/2021] [Indexed: 01/19/2023] Open
Abstract
PURPOSE To develop a patient-based breast density model by characterizing the fibroglandular tissue distribution in patient breasts during compression for mammography and digital breast tomosynthesis (DBT) imaging. METHODS In this prospective study, 88 breast images were acquired using a dedicated breast computed tomography (CT) system. The breasts in the images were classified into their three main tissue components and mechanically compressed to mimic the positioning for mammographic acquisition of the craniocaudal (CC) and mediolateral oblique (MLO) views. The resulting fibroglandular tissue distribution during these compressions was characterized by dividing the compressed breast volume into small regions, for which the median and the 25th and 75th percentile values of local fibroglandular density were obtained in the axial, coronal, and sagittal directions. The best fitting function, based on the likelihood method, for the median distribution was obtained in each direction. RESULTS The fibroglandular tissue tends to concentrate toward the caudal (about 15% below the midline of the breast) and anterior regions of the breast, in both the CC- and MLO-view compressions. A symmetrical distribution was found in the MLO direction in the case of the CC-view compression, while a shift of about 12% toward the lateral direction was found in the MLO-view case. CONCLUSIONS The location of the fibroglandular tissue in the breast under compression during mammography and DBT image acquisition is a major factor for determining the actual glandular dose imparted during these examinations. A more realistic model of the parenchyma in the compressed breast, based on patient image data, was developed. This improved model more accurately reflects the fibroglandular tissue spatial distribution that can be found in patient breasts, and therefore might aid in future studies involving radiation dose and/or cancer development risk estimation.
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Affiliation(s)
- Christian Fedon
- Department of Medical ImagingRadboud University Medical Center6500 HB Geert Grooteplein‐ZuidNijmegenThe Netherlands
| | - Marco Caballo
- Department of Medical ImagingRadboud University Medical Center6500 HB Geert Grooteplein‐ZuidNijmegenThe Netherlands
| | - Eloy García
- Vall d’ Hebron Institute of Oncology (VHIO)BarcelonaSpain
| | - Oliver Diaz
- Department of Mathematics and Computer ScienceUniversity of BarcelonaBarcelonaSpain
- CIMDParc Taulí Hospital UniversitariInstitut d’Investigació i Innovació Parc TaulíSabadellSpain
| | - John M. Boone
- Department of Radiology and Biomedical EngineeringUniversity of California Davis Health4860 “Y” Street, suite 3100 Ellison buildingSacramentoCA95817USA
| | - David R. Dance
- National Co‐ordinating Centre for the Physics of MammographyNCCPMRoyal Surrey County HospitalGuildfordGU2 7XHUK
- Department of PhysicsUniversity of SurreyGuildfordGU2 7XHUK
| | - Ioannis Sechopoulos
- Department of Medical ImagingRadboud University Medical Center6500 HB Geert Grooteplein‐ZuidNijmegenThe Netherlands
- Dutch Expert Centre for Screening (LRCB)PO Box 6873Nijmegen6503 GJThe Netherlands
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Diaz O, Guidi G, Ivashchenko O, Colgan N, Zanca F. Artificial intelligence in the medical physics community: An international survey. Phys Med 2021; 81:141-146. [DOI: 10.1016/j.ejmp.2020.11.037] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/24/2020] [Accepted: 11/30/2020] [Indexed: 12/13/2022] Open
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Diaz O, Elangovan P, Young KC, Wells K, Dance DR. Simple method for computing scattered radiation in breast tomosynthesis. Med Phys 2019; 46:4826-4836. [PMID: 31410861 DOI: 10.1002/mp.13760] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 08/05/2019] [Accepted: 08/06/2019] [Indexed: 01/15/2023] Open
Abstract
PURPOSE Virtual clinical trials (VCT) are a powerful imaging tool that can be used to investigate digital breast tomosynthesis (DBT) technology. In this work, a fast and simple method is proposed to estimate the two-dimensional distribution of scattered radiation which is needed when simulating DBT geometries in VCTs. METHODS Monte Carlo simulations are used to precalculate scatter-to-primary ratio (SPR) for a range of low-resolution homogeneous phantoms. The resulting values can be used to estimate the two-dimensional (2D) distribution of scattered radiation arising from inhomogeneous anthropomorphic phantoms used in VCTs. The method has been validated by comparing the values of the scatter thus obtained against the results of direct Monte Carlo simulation for three different types of inhomogeneous anthropomorphic phantoms. RESULTS Differences between the proposed scatter field estimation method and the ground truth data for the OPTIMAM phantom had an average modulus and standard deviation of over the projected breast area of 2.4 ± 0.9% (minimum -17.0%, maximum 27.7%). The corresponding values for the University of Pennsylvania and Duke University breast phantoms were 1.8 ± 0.1% (minimum -8.7%, maximum 8.0%) and 5.1 ± 0.1% (minimum -16.2%, maximum 7.4%), respectively. CONCLUSIONS The proposed method, which has been validated using three of the most common breast models, is a useful tool for accurately estimating scattered radiation in VCT schemes used to study current designs of DBT system.
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Affiliation(s)
- Oliver Diaz
- CVSSP, University of Surrey, Guildford, GU2 7XH, UK
- VICOROB, University of Girona, Girona, 17071, Spain
| | | | - Kenneth C Young
- NCCPM, Royal Surrey County Hospital, Guildford, GU2 7XX, UK
- Department of Physics, University of Surrey, Guildford, GU2 7XH, UK
| | - Kevin Wells
- CVSSP, University of Surrey, Guildford, GU2 7XH, UK
| | - David R Dance
- NCCPM, Royal Surrey County Hospital, Guildford, GU2 7XX, UK
- Department of Physics, University of Surrey, Guildford, GU2 7XH, UK
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Agarwal R, Diaz O, Lladó X, Yap MH, Martí R. Automatic mass detection in mammograms using deep convolutional neural networks. J Med Imaging (Bellingham) 2019; 6:031409. [PMID: 35834317 PMCID: PMC6381602 DOI: 10.1117/1.jmi.6.3.031409] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 01/18/2019] [Indexed: 08/29/2023] Open
Abstract
With recent advances in the field of deep learning, the use of convolutional neural networks (CNNs) in medical imaging has become very encouraging. The aim of our paper is to propose a patch-based CNN method for automated mass detection in full-field digital mammograms (FFDM). In addition to evaluating CNNs pretrained with the ImageNet dataset, we investigate the use of transfer learning for a particular domain adaptation. First, the CNN is trained using a large public database of digitized mammograms (CBIS-DDSM dataset), and then the model is transferred and tested onto the smaller database of digital mammograms (INbreast dataset). We evaluate three widely used CNNs (VGG16, ResNet50, InceptionV3) and show that the InceptionV3 obtains the best performance for classifying the mass and nonmass breast region for CBIS-DDSM. We further show the benefit of domain adaptation between the CBIS-DDSM (digitized) and INbreast (digital) datasets using the InceptionV3 CNN. Mass detection evaluation follows a fivefold cross-validation strategy using free-response operating characteristic curves. Results show that the transfer learning from CBIS-DDSM obtains a substantially higher performance with the best true positive rate (TPR) of 0.98 ± 0.02 at 1.67 false positives per image (FPI), compared with transfer learning from ImageNet with TPR of 0.91 ± 0.07 at 2.1 FPI. In addition, the proposed framework improves upon mass detection results described in the literature on the INbreast database, in terms of both TPR and FPI.
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Affiliation(s)
- Richa Agarwal
- University of Girona, VICOROB, Computer Vision and Robotics Institute, Girona, Spain
| | - Oliver Diaz
- University of Girona, VICOROB, Computer Vision and Robotics Institute, Girona, Spain
| | - Xavier Lladó
- University of Girona, VICOROB, Computer Vision and Robotics Institute, Girona, Spain
| | - Moi Hoon Yap
- Manchester Metropolitan University, School of Computing, Mathematics and Digital Technology, Manchester, United Kingdom
| | - Robert Martí
- University of Girona, VICOROB, Computer Vision and Robotics Institute, Girona, Spain
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20
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Fedon C, Rabin C, Caballo M, Diaz O, García E, Rodríguez-Ruiz A, González-Sprinberg GA, Sechopoulos I. Monte Carlo study on optimal breast voxel resolution for dosimetry estimates in digital breast tomosynthesis. Phys Med Biol 2018; 64:015003. [PMID: 30524034 DOI: 10.1088/1361-6560/aaf453] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Digital breast tomosynthesis (DBT) is currently used as an adjunct technique to digital mammography (DM) for breast cancer imaging. Being a quasi-3D image, DBT is capable of providing depth information on the internal breast glandular tissue distribution, which may be enough to obtain an accurate patient-specific radiation dose estimate. However, for this, information regarding the location of the glandular tissue, especially in the vertical direction (i.e. x-ray source to detector), is needed. Therefore, a dedicated reconstruction algorithm designed to localize the amount of glandular tissue, rather than for optimal diagnostic value, could be desirable. Such a reconstruction algorithm, or, alternatively, a reconstructed DBT image classification algorithm, could benefit from the use of larger voxels, rather than the small sizes typically used for the diagnostic task. In addition, the Monte Carlo (MC) based dose estimates would be accelerated by the representation of the breast tissue with fewer and larger voxels. Therefore, in this study we investigate the optimal DBT reconstructed voxel size that allows accurate dose evaluations (i.e. within 5%) using a validated Geant4-based MC code. For this, sixty patient-based breast models, previously acquired using dedicated breast computed tomography (BCT) images, were deformed to reproduce the breast during compression under a given DBT scenario. Two re-binning approaches were applied to the compressed phantoms, leading to isotropic and anisotropic voxels of different volumes. MC DBT simulations were performed reproducing the acquisition geometry of a SIEMENS Mammomat Inspiration system. Results show that isotropic cubic voxels of 2.73 mm size provide a dose estimate accurate to within 5% for 51/60 patients, while a comparable accuracy is obtained with anisotropic voxels of dimension 5.46 × 5.46 × 2.73 mm3. In addition, the MC simulation time is reduced by more than half in respect to the original voxel dimension of 0.273 × 0.273 × 0.273 mm3 when either of the proposed re-binning approaches is used. No significant differences in the effect of binning on the dose estimates are observed (Wilcoxon-Mann-Whitney test, p-value > 0.4) between the 0° the 23° (i.e. the widest angular range) exposure.
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Affiliation(s)
- Christian Fedon
- Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmgen, The Netherlands. These authors contributed equally to this work
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Vendrely V, Lemanski C, Baba-Hamed N, Barbier E, Bénézery K, de La Rochefordière A, Guibert P, Bonichon-Lamichhane N, Pommier P, Créhange G, Colliaux J, Gnep K, Ronchin P, Saliou M, Diaz O, Lepage C, Quéro L. Traitement du cancer du canal anal : premiers résultats de la cohorte nationale Anabase. Cancer Radiother 2018. [DOI: 10.1016/j.canrad.2018.07.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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22
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Vendrely V, Lemanski C, François E, Barbier E, Baba Hamed N, Bonichon-Lamichhane N, De La Rochefordière A, Bouché O, Tougeron D, Diaz O, Pommier P, Ronchin P, Saliou M, Cretin J, Lepage C, Quéro L. OC-0284: First results of the French cohort ANABASE : treatment and outcome in non-metastatic anal cancer. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)30594-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Agarwal R, Diaz O, Lladó X, Gubern-Mérida A, Vilanova JC, Martí R. Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis. Ultrason Imaging 2018; 40:97-112. [PMID: 29182056 DOI: 10.1177/0161734617737733] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Mammography is the gold standard screening technique in breast cancer, but it has some limitations for women with dense breasts. In such cases, sonography is usually recommended as an additional imaging technique. A traditional sonogram produces a two-dimensional (2D) visualization of the breast and is highly operator dependent. Automated breast ultrasound (ABUS) has also been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency, facilitating double reading and comparison with past exams. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the three-dimensional (3D) nature of the images makes the analysis difficult and tedious for radiologists. The goal of this work is to develop a semi-automatic framework for breast lesion segmentation in ABUS volumes which is based on the Watershed algorithm. The effect of different de-noising methods on segmentation is studied showing a significant impact ([Formula: see text]) on the performance using a dataset of 28 temporal pairs resulting in a total of 56 ABUS volumes. The volumetric analysis is also used to evaluate the performance of the developed framework. A mean Dice Similarity Coefficient of [Formula: see text] with a mean False Positive ratio [Formula: see text] has been obtained. The Pearson correlation coefficient between the segmented volumes and the corresponding ground truth volumes is [Formula: see text] ([Formula: see text]). Similar analysis, performed on 28 temporal (prior and current) pairs, resulted in a good correlation coefficient [Formula: see text] ([Formula: see text]) for prior and [Formula: see text] ([Formula: see text]) for current cases. The developed framework showed prospects to help radiologists to perform an assessment of ABUS lesion volumes, as well as to quantify volumetric changes during lesions diagnosis and follow-up.
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Affiliation(s)
- Richa Agarwal
- 1 Computer Vision and Robotics Institute (VICOROB), University of Girona, Girona, Spain
| | - Oliver Diaz
- 1 Computer Vision and Robotics Institute (VICOROB), University of Girona, Girona, Spain
| | - Xavier Lladó
- 1 Computer Vision and Robotics Institute (VICOROB), University of Girona, Girona, Spain
| | - Albert Gubern-Mérida
- 2 Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Robert Martí
- 1 Computer Vision and Robotics Institute (VICOROB), University of Girona, Girona, Spain
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Garcia E, Diez Y, Diaz O, Llado X, Gubern-Merida A, Marti R, Marti J, Oliver A. Multimodal Breast Parenchymal Patterns Correlation Using a Patient-Specific Biomechanical Model. IEEE Trans Med Imaging 2018; 37:712-723. [PMID: 28885152 DOI: 10.1109/tmi.2017.2749685] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this paper, we aim to produce a realistic 2-D projection of the breast parenchymal distribution from a 3-D breast magnetic resonance image (MRI). To evaluate the accuracy of our simulation, we compare our results with the local breast density (i.e., density map) obtained from the complementary full-field digital mammogram. To achieve this goal, we have developed a fully automatic framework, which registers MRI volumes to X-ray mammograms using a subject-specific biomechanical model of the breast. The optimization step modifies the position, orientation, and elastic parameters of the breast model to perform the alignment between the images. When the model reaches an optimal solution, the MRI glandular tissue is projected and compared with the one obtained from the corresponding mammograms. To reduce the loss of information during the ray-casting, we introduce a new approach that avoids resampling the MRI volume. In the results, we focus our efforts on evaluating the agreement of the distributions of glandular tissue, the degree of structural similarity, and the correlation between the real and synthetic density maps. Our approach obtained a high-structural agreement regardless the glandularity of the breast, whilst the similarity of the glandular tissue distributions and correlation between both images increase in denser breasts. Furthermore, the synthetic images show continuity with respect to large structures in the density maps.
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García E, Diaz O, Martí R, Diez Y, Gubern-Mérida A, Sentís M, Martí J, Oliver A. Local breast density assessment using reacquired mammographic images. Eur J Radiol 2017; 93:121-127. [PMID: 28668405 DOI: 10.1016/j.ejrad.2017.05.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 05/19/2017] [Accepted: 05/23/2017] [Indexed: 11/17/2022]
Abstract
PURPOSE The aim of this paper is to evaluate the spatial glandular volumetric tissue distribution as well as the density measures provided by Volpara™ using a dataset composed of repeated pairs of mammograms, where each pair was acquired in a short time frame and in a slightly changed position of the breast. MATERIALS AND METHODS We conducted a retrospective analysis of 99 pairs of repeatedly acquired full-field digital mammograms from 99 different patients. The commercial software Volpara™ Density Maps (Volpara Solutions, Wellington, New Zealand) is used to estimate both the global and the local glandular tissue distribution in each image. The global measures provided by Volpara™, such as breast volume, volume of glandular tissue, and volumetric breast density are compared between the two acquisitions. The evaluation of the local glandular information is performed using histogram similarity metrics, such as intersection and correlation, and local measures, such as statistics from the difference image and local gradient correlation measures. RESULTS Global measures showed a high correlation (breast volume R=0.99, volume of glandular tissue R=0.94, and volumetric breast density R=0.96) regardless the anode/filter material. Similarly, histogram intersection and correlation metric showed that, for each pair, the images share a high degree of information. Regarding the local distribution of glandular tissue, small changes in the angle of view do not yield significant differences in the glandular pattern, whilst changes in the breast thickness between both acquisition affect the spatial parenchymal distribution. CONCLUSIONS This study indicates that Volpara™ Density Maps is reliable in estimating the local glandular tissue distribution and can be used for its assessment and follow-up. Volpara™ Density Maps is robust to small variations of the acquisition angle and to the beam energy, although divergences arise due to different breast compression conditions.
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Affiliation(s)
- Eloy García
- Computer Vision and Robotics Institute, University of Girona, Spain
| | - Oliver Diaz
- Computer Vision and Robotics Institute, University of Girona, Spain
| | - Robert Martí
- Computer Vision and Robotics Institute, University of Girona, Spain
| | - Yago Diez
- Tokuyama Laboratory GSIS, Tohoku University, Sendai, Japan
| | | | - Melcior Sentís
- UDIAT - Centre Diagnòstic, Corporació Parc Taulí, Sabadell, Spain
| | - Joan Martí
- Computer Vision and Robotics Institute, University of Girona, Spain
| | - Arnau Oliver
- Computer Vision and Robotics Institute, University of Girona, Spain.
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García E, Oliver A, Diez Y, Diaz O, Lladó X, Martí R, Martí J. Similarity Metrics for Intensity-Based Registration Using Breast Density Maps. Pattern Recognition and Image Analysis 2017. [DOI: 10.1007/978-3-319-58838-4_24] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Fernández YT, Diaz O, Acuña E, Casanova M, Salazar O, Masaguer A. Phytostabilization of arsenic in soils with plants of the genus Atriplex established in situ in the Atacama Desert. Environ Monit Assess 2016; 188:235. [PMID: 27000320 DOI: 10.1007/s10661-016-5247-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 03/14/2016] [Indexed: 05/21/2023]
Abstract
In the ChiuChiu village (Atacama Desert, Chile), there is a high concentration of arsenic (As) in the soil due to natural causes related to the presence of volcanoes and geothermal activity. To compare the levels of As and the growth parameters among plants of the same genus, three species of plants were established in situ: Atriplex atacamensis (native of Chile), Atriplex halimus, and Atriplex nummularia. These soils have an As concentration of 131.2 ± 10.4 mg kg(-1), a pH of 8.6 ± 0.1, and an electrical conductivity of 7.06 ± 2.37 dS m(-1). Cuttings of Atriplex were transplanted and maintained for 5 months with periodic irrigation and without the addition of fertilizers. The sequential extraction of As indicated that the metalloid in these soils has a high bioavailability (38 %), which is attributed to the alkaline pH, low organic matter and Fe oxide content, and sandy texture. At day 90 of the assay, the As concentrations in the leaves of A. halimus (4.53 ± 1.14 mg kg(-1)) and A. nummularia (3.85 ± 0.64 mg kg(-1)) were significantly higher than that in A. atacamensis (2.46 ± 1.82 mg kg(-1)). However, the three species accumulated higher levels of As in their roots, indicating a phytostabilization capacity. At the end of the assay, A. halimus and A. nummularia generated 30 % more biomass than A. atacamensis without significant differences in the As levels in the leaves. Despite the difficult conditions in these soils, the establishment of plants of the genus Atriplex is a recommended strategy to generate a vegetative cover that prevents the metalloid from spreading in this arid area through the soil or by wind.
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Affiliation(s)
- Yasna Tapia Fernández
- Departamento de Ingeniería y Suelos, Universidad de Chile, Santa Rosa 11315, 8820808, Santiago, Chile.
| | - O Diaz
- Departamento de Biología, Universidad de Santiago de Chile, Casilla 40, Correo 33, Santiago, Chile
| | - E Acuña
- Departamento de Ingeniería y Suelos, Universidad de Chile, Santa Rosa 11315, 8820808, Santiago, Chile
| | - M Casanova
- Departamento de Ingeniería y Suelos, Universidad de Chile, Santa Rosa 11315, 8820808, Santiago, Chile
| | - O Salazar
- Departamento de Ingeniería y Suelos, Universidad de Chile, Santa Rosa 11315, 8820808, Santiago, Chile
| | - A Masaguer
- Departamento de Producción Agraria, Universidad Politécnica de Madrid, CP 28040, Madrid, Spain
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Lavine SD, Cockroft K, Hoh B, Bambakidis N, Khalessi AA, Woo H, Riina H, Siddiqui A, Hirsch JA, Chong W, Rice H, Wenderoth J, Mitchell P, Coulthard A, Signh TJ, Phatorous C, Khangure M, Klurfan P, Ter Brugge K, Iancu D, Gunnarsson T, Jansen O, Muto M, Szikora I, Pierot L, Brouwer P, Gralla J, Renowden S, Andersson T, Fiehler J, Turjman F, White P, Januel AC, Spelle L, Kulcsar Z, Chapot R, Biondi A, Dima S, Taschner C, Szajner M, Krajina A, Sakai N, Matsumaru Y, Yoshimura S, Diaz O, Lylyk P, Jayaraman MV, Patsalides A, Gandhi CD, Lee SK, Abruzzo T, Albani B, Ansari SA, Arthur AS, Baxter BW, Bulsara KR, Chen M, Almandoz JED, Fraser JF, Heck DV, Hetts SW, Hussain MS, Klucznik RP, Leslie-Mawzi TM, Mack WJ, McTaggart RA, Meyers PM, Mocco J, Prestigiacomo CJ, Pride GL, Rasmussen PA, Starke RM, Sunenshine PJ, Tarr RW, Frei DF, Ribo M, Nogueira RG, Zaidat OO, Jovin T, Linfante I, Yavagal D, Liebeskind D, Novakovic R, Pongpech S, Rodesch G, Soderman M, Ter Brugge K, Taylor A, Krings T, Orbach D, Biondi A, Picard L, Suh DC, Tanaka M, Zhang HQ. Training Guidelines for Endovascular Stroke Intervention: An International Multi-Society Consensus Document. Interv Neurol 2016; 5:51-6. [PMID: 27610121 DOI: 10.1159/000444945] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Lavine SD, Cockroft K, Hoh B, Bambakidis N, Khalessi AA, Woo H, Riina H, Siddiqui A, Hirsch JA, Chong W, Rice H, Wenderoth J, Mitchell P, Coulthard A, Signh TJ, Phatorous C, Khangure M, Klurfan P, terBrugge K, Iancu D, Gunnarsson T, Jansen O, Muto M, Szikora I, Pierot L, Brouwer P, Gralla J, Renowden S, Andersson T, Fiehler J, Turjman F, White P, Januel AC, Spelle L, Kulcsar Z, Chapot R, Spelle L, Biondi A, Dima S, Taschner C, Szajner M, Krajina A, Sakai N, Matsumaru Y, Yoshimura S, Ezura M, Fujinaka T, Iihara K, Ishii A, Higashi T, Hirohata M, Hyodo A, Ito Y, Kawanishi M, Kiyosue H, Kobayashi E, Kobayashi S, Kuwayama N, Matsumoto Y, Miyachi S, Murayama Y, Nagata I, Nakahara I, Nemoto S, Niimi Y, Oishi H, Satomi J, Satow T, Sugiu K, Tanaka M, Terada T, Yamagami H, Diaz O, Lylyk P, Jayaraman MV, Patsalides A, Gandhi CD, Lee SK, Abruzzo T, Albani B, Ansari SA, Arthur AS, Baxter BW, Bulsara KR, Chen M, Delgado Almandoz JE, Fraser JF, Heck DV, Hetts SW, Hussain MS, Klucznik RP, Leslie-Mawzi TM, Mack WJ, McTaggart RA, Meyers PM, Mocco J, Prestigiacomo CJ, Pride GL, Rasmussen PA, Starke RM, Sunenshine PJ, Tarr RW, Frei DF, Ribo M, Nogueira RG, Zaidat OO, Jovin T, Linfante I, Yavagal D, Liebeskind D, Novakovic R, Pongpech S, Rodesch G, Soderman M, terBrugge K, Taylor A, Krings T, Orbach D, Biondi A, Picard L, Suh DC, Tanaka M, Zhang HQ. Training Guidelines for Endovascular Ischemic Stroke Intervention: An International Multi-Society Consensus Document. AJNR Am J Neuroradiol 2016; 37:E31-4. [PMID: 26892982 DOI: 10.3174/ajnr.a4766] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Karmonik C, Anderson JR, Beilner J, Ge JJ, Partovi S, Klucznik RP, Diaz O, Zhang YJ, Britz GW, Grossman RG, Lv N, Huang Q. Relationships and redundancies of selected hemodynamic and structural parameters for characterizing virtual treatment of cerebral aneurysms with flow diverter devices. J Biomech 2015; 49:2112-2117. [PMID: 26654675 DOI: 10.1016/j.jbiomech.2015.11.035] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 11/13/2015] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND PURPOSE To quantify the relationship and to demonstrate redundancies between hemodynamic and structural parameters before and after virtual treatment with a flow diverter device (FDD) in cerebral aneurysms. METHODS Steady computational fluid dynamics (CFD) simulations were performed for 10 cerebral aneurysms where FDD treatment with the SILK device was simulated by virtually reducing the porosity at the aneurysm ostium. Velocity and pressure values proximal and distal to and at the aneurysm ostium as well as inside the aneurysm were quantified. In addition, dome-to-neck ratios and size ratios were determined. Multiple correlation analysis (MCA) and hierarchical cluster analysis (HCA) were conducted to demonstrate dependencies between both structural and hemodynamic parameters. RESULTS Velocities in the aneurysm were reduced by 0.14m/s on average and correlated significantly (p<0.05) with velocity values in the parent artery (average correlation coefficient: 0.70). Pressure changes in the aneurysm correlated significantly with pressure values in the parent artery and aneurysm (average correlation coefficient: 0.87). MCA found statistically significant correlations between velocity values and between pressure values, respectively. HCA sorted velocity parameters, pressure parameters and structural parameters into different hierarchical clusters. HCA of aneurysms based on the parameter values yielded similar results by either including all (n=22) or only non-redundant parameters (n=2, 3 and 4). CONCLUSION Hemodynamic and structural parameters before and after virtual FDD treatment show strong inter-correlations. Redundancy of parameters was demonstrated with hierarchical cluster analysis.
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Affiliation(s)
- C Karmonik
- MRI Core, Houston Methodist Research Institute, Houston, TX, USA; Cerebrovascular Center, Neurosurgery, Houston Methodist, Houston, TX, USA.
| | - J R Anderson
- MRI Core, Houston Methodist Research Institute, Houston, TX, USA
| | | | - J J Ge
- Siemens AX, Shanghai, China
| | - S Partovi
- Department of Radiology, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, OH, USA
| | - R P Klucznik
- Cerebrovascular Center, Radiology, Houston Methodist, Houston, TX, USA
| | - O Diaz
- Cerebrovascular Center, Radiology, Houston Methodist, Houston, TX, USA
| | - Y J Zhang
- Cerebrovascular Center, Neurosurgery, Houston Methodist, Houston, TX, USA
| | - G W Britz
- Cerebrovascular Center, Neurosurgery, Houston Methodist, Houston, TX, USA
| | - R G Grossman
- Cerebrovascular Center, Neurosurgery, Houston Methodist, Houston, TX, USA
| | - N Lv
- Neurosurgery, The Affiliated Changhai Hospital of Second Military Medical University, Shanghai, China
| | - Q Huang
- Neurosurgery, The Affiliated Changhai Hospital of Second Military Medical University, Shanghai, China
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Hanel R, Bonafe A, Fischer S, Diaz O, Kallmes D, Barnwell S, Woo H. O-020 treatment of giant intracranial aneurysms with pipeline: aspire (aneurysm study of pipeline in an observational registry) results. J Neurointerv Surg 2015. [DOI: 10.1136/neurintsurg-2015-011917.20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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Zhang Y, Scranton R, Britz G, Diaz O, Klucznik R. E-012 flow diversion: safe and efficacious method in the treatment of cerebrovascular disease. J Neurointerv Surg 2015. [DOI: 10.1136/neurintsurg-2015-011917.87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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Ferrandon S, Magné N, Battiston-Montagne P, Hau-Desbat NH, Diaz O, Beuve M, Constanzo J, Chargari C, Poncet D, Chautard E, Ardail D, Alphonse G, Rodriguez-Lafrasse C. Cellular and molecular portrait of eleven human glioblastoma cell lines under photon and carbon ion irradiation. Cancer Lett 2015; 360:10-6. [PMID: 25657111 DOI: 10.1016/j.canlet.2015.01.025] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Revised: 01/19/2015] [Accepted: 01/20/2015] [Indexed: 10/25/2022]
Abstract
This study aimed to examine the cellular and molecular long-term responses of glioblastomas to radiotherapy and hadrontherapy in order to better understand the biological effects of carbon beams in cancer treatment. Eleven human glioblastoma cell lines, displaying gradual radiosensitivity, were irradiated with photons or carbon ions. Independently of p53 or O(6)-methylguanine-DNA methyltransferase(1) status, all cell lines responded to irradiation by a G2/M phase arrest followed by the appearance of mitotic catastrophe, which was concluded by a ceramide-dependent-apoptotic cell death. Statistical analysis demonstrated that: (i) the SF2(2) and the D10(3) values for photon are correlated with that obtained in response to carbon ions; (ii) regardless of the p53, MGMT status, and radiosensitivity, the release of ceramide is associated with the induction of late apoptosis; and (iii) the appearance of polyploid cells after photon irradiation could predict the Relative Biological Efficiency(4) to carbon ions. This large collection of data should increase our knowledge in glioblastoma radiobiology in order to better understand, and to later individualize, appropriate radiotherapy treatment for patients who are good candidates.
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Affiliation(s)
- S Ferrandon
- Laboratoire de Radiobiologie Cellulaire et Moléculaire, EMR3738, Faculté Médecine Lyon-Sud, Université de Lyon, Université Lyon1, 69921 Oullins, France
| | - N Magné
- Laboratoire de Radiobiologie Cellulaire et Moléculaire, EMR3738, Faculté Médecine Lyon-Sud, Université de Lyon, Université Lyon1, 69921 Oullins, France; Départment de Radiothérapie, Institut de Cancérologie Lucien Neuwirth, 42271 St Priest-en-Jarez, France
| | - P Battiston-Montagne
- Laboratoire de Radiobiologie Cellulaire et Moléculaire, EMR3738, Faculté Médecine Lyon-Sud, Université de Lyon, Université Lyon1, 69921 Oullins, France
| | - N-H Hau-Desbat
- Laboratoire de Radiobiologie Cellulaire et Moléculaire, EMR3738, Faculté Médecine Lyon-Sud, Université de Lyon, Université Lyon1, 69921 Oullins, France
| | - O Diaz
- Laboratoire de Radiobiologie Cellulaire et Moléculaire, EMR3738, Faculté Médecine Lyon-Sud, Université de Lyon, Université Lyon1, 69921 Oullins, France
| | - M Beuve
- IPNL-LIRIS-CNRS-IN2P3, 69622 Villeurbanne, France
| | - J Constanzo
- IPNL-LIRIS-CNRS-IN2P3, 69622 Villeurbanne, France
| | - C Chargari
- Service de Radiothérapie, Hôpital du Val de Grâce, 75230 Paris, France
| | - D Poncet
- Laboratoire de Radiobiologie Cellulaire et Moléculaire, EMR3738, Faculté Médecine Lyon-Sud, Université de Lyon, Université Lyon1, 69921 Oullins, France; Hospices Civils de Lyon, Centre Hospitalier Lyon-Sud, 69495 Pierre-Bénite, France
| | - E Chautard
- Centre Jean Perrin, Laboratoire de Radio-Oncologie Expérimentale, Clermont Université, EA7283 CREaT, Université d'Auvergne, 63011 Clermont-Ferrand, France
| | - D Ardail
- Laboratoire de Radiobiologie Cellulaire et Moléculaire, EMR3738, Faculté Médecine Lyon-Sud, Université de Lyon, Université Lyon1, 69921 Oullins, France; Hospices Civils de Lyon, Centre Hospitalier Lyon-Sud, 69495 Pierre-Bénite, France
| | - G Alphonse
- Laboratoire de Radiobiologie Cellulaire et Moléculaire, EMR3738, Faculté Médecine Lyon-Sud, Université de Lyon, Université Lyon1, 69921 Oullins, France; Hospices Civils de Lyon, Centre Hospitalier Lyon-Sud, 69495 Pierre-Bénite, France
| | - C Rodriguez-Lafrasse
- Laboratoire de Radiobiologie Cellulaire et Moléculaire, EMR3738, Faculté Médecine Lyon-Sud, Université de Lyon, Université Lyon1, 69921 Oullins, France; Hospices Civils de Lyon, Centre Hospitalier Lyon-Sud, 69495 Pierre-Bénite, France.
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Teigen C, Moyle H, Patel R, Fischman A, Kim E, Baxter B, Quarfordt S, Heck D, Klucznik R, Diaz O, Reeves A, Abraham M, Madarang E, Zwiebel B, Brant-Zawadzki M, Peck W, Nguyen B, Whitaker L, Gailloud P, Hagino R, Liu K, Moskovitz J, Luong E, Lai J, Kuo S, Hak S, Nguyen N, Bose A, Sit S. Experience using large volume detachable coils in the peripheral vasculature: preliminary results from the ACE multicenter study. J Vasc Interv Radiol 2015. [DOI: 10.1016/j.jvir.2014.12.071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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Teigen C, Moyle H, Patel R, Fischman A, Kim E, Baxter B, Quarfordt S, Heck D, Klucznik R, Diaz O, Reeves A, Abraham M, Madarang E, Zwiebel B, Brant-Zawadzki M, Peck W, Nguyen B, Whitaker L, Gailloud P, Hagino R, Liu K, Moskovitz J, Luong E, Lai J, Kuo S, Hak S, Buell H, Bose A, Sit S. Experience Using the Penumbra Ruby Coil in the Peripheral Vasculature: ACE Multicenter Study Preliminary Results. J Vasc Interv Radiol 2015. [DOI: 10.1016/j.jvir.2014.10.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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Mackenzie A, Dance DR, Diaz O, Young KC. Image simulation and a model of noise power spectra across a range of mammographic beam qualities. Med Phys 2014; 41:121901. [PMID: 25471961 DOI: 10.1118/1.4900819] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 08/13/2014] [Accepted: 10/13/2014] [Indexed: 02/11/2024] Open
Abstract
PURPOSE The aim of this work is to create a model to predict the noise power spectra (NPS) for a range of mammographic radiographic factors. The noise model was necessary to degrade images acquired on one system to match the image quality of different systems for a range of beam qualities. METHODS Five detectors and x-ray systems [Hologic Selenia (ASEh), Carestream computed radiography CR900 (CRc), GE Essential (CSI), Carestream NIP (NIPc), and Siemens Inspiration (ASEs)] were characterized for this study. The signal transfer property was measured as the pixel value against absorbed energy per unit area (E) at a reference beam quality of 28 kV, Mo/Mo or 29 kV, W/Rh with 45 mm polymethyl methacrylate (PMMA) at the tube head. The contributions of the three noise sources (electronic, quantum, and structure) to the NPS were calculated by fitting a quadratic at each spatial frequency of the NPS against E. A quantum noise correction factor which was dependent on beam quality was quantified using a set of images acquired over a range of radiographic factors with different thicknesses of PMMA. The noise model was tested for images acquired at 26 kV, Mo/Mo with 20 mm PMMA and 34 kV, Mo/Rh with 70 mm PMMA for three detectors (ASEh, CRc, and CSI) over a range of exposures. The NPS were modeled with and without the noise correction factor and compared with the measured NPS. A previous method for adapting an image to appear as if acquired on a different system was modified to allow the reference beam quality to be different from the beam quality of the image. The method was validated by adapting the ASEh flat field images with two thicknesses of PMMA (20 and 70 mm) to appear with the imaging characteristics of the CSI and CRc systems. RESULTS The quantum noise correction factor rises with higher beam qualities, except for CR systems at high spatial frequencies, where a flat response was found against mean photon energy. This is due to the dominance of secondary quantum noise in CR. The use of the quantum noise correction factor reduced the difference from the model to the real NPS to generally within 4%. The use of the quantum noise correction improved the conversion of ASEh image to CRc image but had no difference for the conversion to CSI images. CONCLUSIONS A practical method for estimating the NPS at any dose and over a range of beam qualities for mammography has been demonstrated. The noise model was incorporated into a methodology for converting an image to appear as if acquired on a different detector. The method can now be extended to work for a wide range of beam qualities and can be applied to the conversion of mammograms.
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Affiliation(s)
- Alistair Mackenzie
- National Coordinating Centre for the Physics of Mammography, Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom and Department of Physics, University of Surrey, Guildford GU2 7XH, United Kingdom
| | - David R Dance
- National Coordinating Centre for the Physics of Mammography, Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom and Department of Physics, University of Surrey, Guildford GU2 7XH, United Kingdom
| | - Oliver Diaz
- Centre for Vision, Speech and Signal Processing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, United Kingdom and Computer Vision and Robotics Research Institute, University of Girona, Girona 17071, Spain
| | - Kenneth C Young
- National Coordinating Centre for the Physics of Mammography, Royal Surrey County Hospital, Guildford GU2 7XX, United Kingdom and Department of Physics, University of Surrey, Guildford GU2 7XH, United Kingdom
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Elangovan P, Warren LM, Mackenzie A, Rashidnasab A, Diaz O, Dance DR, Young KC, Bosmans H, Strudley CJ, Wells K. Development and validation of a modelling framework for simulating 2D-mammography and breast tomosynthesis images. Phys Med Biol 2014; 59:4275-93. [PMID: 25029333 DOI: 10.1088/0031-9155/59/15/4275] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Planar 2D x-ray mammography is generally accepted as the preferred screening technique used for breast cancer detection. Recently, digital breast tomosynthesis (DBT) has been introduced to overcome some of the inherent limitations of conventional planar imaging, and future technological enhancements are expected to result in the introduction of further innovative modalities. However, it is crucial to understand the impact of any new imaging technology or methodology on cancer detection rates and patient recall. Any such assessment conventionally requires large scale clinical trials demanding significant investment in time and resources. The concept of virtual clinical trials and virtual performance assessment may offer a viable alternative to this approach. However, virtual approaches require a collection of specialized modelling tools which can be used to emulate the image acquisition process and simulate images of a quality indistinguishable from their real clinical counterparts. In this paper, we present two image simulation chains constructed using modelling tools that can be used for the evaluation of 2D-mammography and DBT systems. We validate both approaches by comparing simulated images with real images acquired using the system being simulated. A comparison of the contrast-to-noise ratios and image blurring for real and simulated images of test objects shows good agreement ( < 9% error). This suggests that our simulation approach is a promising alternative to conventional physical performance assessment followed by large scale clinical trials.
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Affiliation(s)
- Premkumar Elangovan
- Centre for Vision, Speech, and Signal Processing, Medical Imaging Group, University of Surrey, Guildford, GU2 7XH, UK
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Abstract
Digital breast tomosynthesis (DBT) is a promising technique to overcome the tissue superposition limitations found in planar 2D x-ray mammography. However, as most DBT systems do not employ an anti-scatter grid, the levels of scattered radiation recorded within the image receptor are significantly higher than that observed in planar 2D x-ray mammography. Knowledge of this field is necessary as part of any correction scheme and for computer modelling and optimisation of this examination. Monte Carlo (MC) simulations are often used for this purpose, however they are computationally expensive and a more rapid method of calculation is desirable. This issue is addressed in this work by the development of a fast kernel-based methodology for scatter field estimation using a detailed realistic DBT geometry. Thickness-dependent scatter kernels, which were validated against the literature with a maximum discrepancy of 4% for an idealised geometry, have been calculated and a new physical parameter (air gap distance) was used to estimate more accurately the distribution of scattered radiation for a series of anthropomorphic breast phantom models. The proposed methodology considers, for the first time, the effects of scattered radiation from the compression paddle and breast support plate, which can represent more than 30% of the total scattered radiation recorded within the image receptor. The results show that the scatter field estimator can calculate scattered radiation images in an average of 80 min for projection angles up to 25° with equal to or less than a 10% error across most of the breast area when compared with direct MC simulations.
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Affiliation(s)
- O Diaz
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK
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Fiorella D, Derdeyn C, Turk A, Boulos A, Diaz O, Pride G, Jabbour P, Woo H. O-004 Final Results of the US Humanitarian Device Exemption Study of the Low-profile Visualised Intraluminal Support (LVIS) Device. J Neurointerv Surg 2014. [DOI: 10.1136/neurintsurg-2014-011343.4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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Diaz O. Rich new training technologies for professional development. Occup Health Saf 2014; 83:84-85. [PMID: 25181889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
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Karmonik C, Diaz O, Klucznik R, Grossman RG, Zhang YJ, Britz G, Lv N, Huang Q. Quantitative comparison of hemodynamic parameters from steady and transient CFD simulations in cerebral aneurysms with focus on the aneurysm ostium. J Neurointerv Surg 2014; 7:367-72. [DOI: 10.1136/neurintsurg-2014-011182] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Accepted: 03/24/2014] [Indexed: 11/03/2022]
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42
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Teigen C, Moyle H, Patel R, Fischman A, Kim E, Baxter B, Quarfordt S, Heck D, Klucznik R, Diaz O, Reeves A, Abraham M, Madarang E, Zwiebel B, Brant-Zawadzki M, Peck W, Nguyen B, Whitaker L, Gailloud P, Hagino R, Lai J, Bose A, Sit S. Multicenter experience with the Ruby Coil in the peripheral vasculature: preliminary results from the penumbra ace post market registry. J Vasc Interv Radiol 2014. [DOI: 10.1016/j.jvir.2013.12.441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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Fumagalli I, Faivre JC, Thureau S, Bibault JE, Diaz O, Leroy T, Pichon B, Riou O, Fournier C, Hannoun-Lévi JM, Peiffert D. État des lieux de la formation des internes français d’oncologie radiothérapie en curiethérapie. Cancer Radiother 2014; 18:28-34. [DOI: 10.1016/j.canrad.2013.07.150] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2013] [Revised: 07/19/2013] [Accepted: 07/24/2013] [Indexed: 12/01/2022]
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Thureau S, Challand T, Bibault JE, Biau J, Cervellera M, Diaz O, Faivre JC, Fumagalli I, Leroy T, Lescut N, Martin V, Pichon B, Riou O, Dubray B, Giraud P, Hennequin C. Delegation of medical tasks in French radiation oncology departments: Current situation and impact on residents’ training. Cancer Radiother 2013; 17:370-7. [DOI: 10.1016/j.canrad.2013.07.144] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2013] [Accepted: 07/15/2013] [Indexed: 11/15/2022]
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Oulmoudne N, Hatime M, Merle P, Diaz O, Mornex F, Mehiri S. Radiothérapie hépatique pour carcinome hépatocellulaire : analyse de la tolérance digestive haute, selon la localisation tumorale. Cancer Radiother 2013. [DOI: 10.1016/j.canrad.2013.06.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Diaz O, Lorchel F, Revault C, Mornex F. [Task sharing with radiotherapy technicians in image-guided radiotherapy]. Cancer Radiother 2013; 17:383-8. [PMID: 24007955 DOI: 10.1016/j.canrad.2013.07.138] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2013] [Accepted: 07/10/2013] [Indexed: 11/17/2022]
Abstract
The development of accelerators with on-board imaging systems now allows better target volumes reset at the time of irradiation (image-guided radiotherapy [IGRT]). However, these technological advances in the control of repositioning led to a multiplication of tasks for each actor in radiotherapy and increase the time available for the treatment, whether for radiotherapy technicians or radiation oncologists. As there is currently no explicit regulatory framework governing the use of IGRT, some institutional experiments show that a transfer is possible between radiation oncologists and radiotherapy technicians for on-line verification of image positioning. Initial training for every technical and drafting procedures within institutions will improve audit quality by reducing interindividual variability.
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Affiliation(s)
- O Diaz
- Service de radiothérapie, hospices civils de Lyon, CHU Lyon-Sud, 65, chemin du Grand-Revoyet, 69310 Pierre-Bénite, France; EMR 3738, université Claude-Bernard Lyon-1, domaine Rockefeller, 8, avenue Rockefeller, 69373 Lyon cedex 08, France.
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Rashidnasab A, Elangovan P, Yip M, Diaz O, Dance DR, Young KC, Wells K. Simulation and assessment of realistic breast lesions using fractal growth models. Phys Med Biol 2013; 58:5613-27. [DOI: 10.1088/0031-9155/58/16/5613] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Bouwman RW, Diaz O, van Engen RE, Young KC, den Heeten GJ, Broeders MJM, Veldkamp WJH, Dance DR. Phantoms for quality control procedures in digital breast tomosynthesis: dose assessment. Phys Med Biol 2013; 58:4423-38. [DOI: 10.1088/0031-9155/58/13/4423] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Tapia Y, Diaz O, Pizarro C, Segura R, Vines M, Zúñiga G, Moreno-Jiménez E. Atriplex atacamensis and Atriplex halimus resist As contamination in Pre-Andean soils (northern Chile). Sci Total Environ 2013; 450-451:188-196. [PMID: 23474264 DOI: 10.1016/j.scitotenv.2013.02.021] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2012] [Revised: 02/08/2013] [Accepted: 02/08/2013] [Indexed: 06/01/2023]
Abstract
The Pre-Andean area of Chile exhibits saline soils of volcanic origin naturally contaminated with arsenic (As), and we hypothesise that revegetation with resistant species may be a valid alternative for soil management in this area. Thus, the xerophytic and halophytic shrubs Atriplex halimus and Atriplex atacamensis were cultivated in containers for 90 days in Pre-Andean soil, As-soil, (111±19 mg As kg(-1), pH8.4±0.1) or control soil (12.7±1.1 mg As kg(-1), pH7.8±0.1) to evaluate As accumulation and resistance using stress bioindicators (chlorophylls, malondialdehyde (MDA) and total thiols). Sequential extraction of As-soil indicated that 52.3% of As was found in the most available fraction. The As distribution was significantly different between the species: A. halimus translocated the As to leaves, whilst A. atacamensis retained the As in roots. At 30 and 90 days, A. halimus showed similar As concentrations in the leaves (approximately 5.5 mg As kg(-1)), and As increased in stems and roots (up to 4.73 and 16.3 mg As kg(-1), respectively). In A. atacamensis, As concentration was lower (2.6 in leaves; 3.2 in stems and 6.9 in roots in mg As kg(-1)). Both species exhibited a high concentration of B in leaves (362-389 mg kg(-1)). If the plants are used for animal feed, it should be considered that A. halimus accumulates higher concentration of As and B in the leaves than A. atacamensis. Neither plant growth nor stress bioindicators were negatively affected by the high levels of available As, with the exception of MDA in the leaves of A. halimus. The results indicate that these plants resist contamination by arsenic, accumulating mainly the metalloid in the roots and can be recommended to generate plant cover in As-contaminated soils in the Pre-Andean region, under saline conditions controlled, preventing the dispersion of this metalloid via wind and leaching.
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Affiliation(s)
- Y Tapia
- Departamento de Ingeniería y Suelos, Universidad de Chile, 8820808 Santiago, Chile.
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Rashidnasab A, Elangovan P, Diaz O, Mackenzie A, Young K, Dance D, Wells K. Simulation of 3D DLA masses in digital breast tomosynthesis. ACTA ACUST UNITED AC 2013. [DOI: 10.1117/12.2008333] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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