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Ebrahimi-Khankook A, Vejdani-Noghreiyan A, Khodajou-Chokami H, Abbasi-Khiabani Z. Designing an improved model of the adult female ICRP reference phantom dedicated to mammography procedure. JOURNAL OF RADIOLOGICAL PROTECTION : OFFICIAL JOURNAL OF THE SOCIETY FOR RADIOLOGICAL PROTECTION 2024; 44:021525. [PMID: 38688247 DOI: 10.1088/1361-6498/ad455e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 04/30/2024] [Indexed: 05/02/2024]
Abstract
Mammography is an x-ray-based imaging method to examine breast abnormalities. Since low-energy photons are used in mammography, doses to different organs would depend strongly on the phantom posture and anatomy. Until now, a few studies have been performed on doses delivered to different organs during mammography. However, in none of them, the correct posture of the patient has been considered. In the present study, the effect of accurate patient positioning, on doses to organs in the chest region were investigated through Monte Carlo simulations. The results show the rotation of the phantom head, may affect organ doses up to 60%. Also, ignoring the head in dosimetry calculations changes scattering effects and causes dose uncertainty of about 8% for these organs. Moreover, according to the obtained results, not compressing the breast causes serious dose misestimation. Finally, using developed phantoms dedicated for mammography, total doses received by different organs have been calculated for the tube voltages of 25, 28, 30 and 35 kVp and for craniocaudal and mediolateral oblique views.
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Affiliation(s)
| | | | - Hamidreza Khodajou-Chokami
- Department of Radiological Sciences, University of California, Irvine, CA 92697, United States of America
- Biomedical Engineering Group, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
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Physical and digital phantoms for 2D and 3D x-ray breast imaging: Review on the state-of-the-art and future prospects. Radiat Phys Chem Oxf Engl 1993 2022. [DOI: 10.1016/j.radphyschem.2022.110715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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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: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [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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Axelsson R, Tomic H, Zackrisson S, Tingberg A, Isaksson H, Bakic PR, Dustler M. Finite element model of mechanical imaging of the breast. J Med Imaging (Bellingham) 2022; 9:033502. [PMID: 35647217 PMCID: PMC9125329 DOI: 10.1117/1.jmi.9.3.033502] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 05/02/2022] [Indexed: 03/20/2024] Open
Abstract
Purpose: Malignant breast lesions can be distinguished from benign lesions by their mechanical properties. This has been utilized for mechanical imaging in which the stress distribution over the breast is measured. Mechanical imaging has shown the ability to identify benign or normal cases and to reduce the number of false positives from mammography screening. Our aim was to develop a model of mechanical imaging acquisition for simulation purposes. To that end, we simulated mammographic compression of a computer model of breast anatomy and lesions. Approach: The breast compression was modeled using the finite element method. Two finite element breast models of different sizes were used and solved using linear elastic material properties in open-source virtual clinical trial (VCT) software. A spherical lesion (15 mm in diameter) was inserted into the breasts, and both the location and stiffness of the lesion were varied extensively. The average stress over the breast and the average stress at the lesion location, as well as the relative mean pressure over lesion area (RMPA), were calculated. Results: The average stress varied 6.2-6.5 kPa over the breast surface and 7.8-11.4 kPa over the lesion, for different lesion locations and stiffnesses. These stresses correspond to an RMPA of 0.80 to 1.46. The average stress was 20% to 50% higher at the lesion location compared with the average stress over the entire breast surface. Conclusions: The average stress over the breast and the lesion location corresponded well to clinical measurements. The proposed model can be used in VCTs for evaluation and optimization of mechanical imaging screening strategies.
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Affiliation(s)
- Rebecca Axelsson
- Lund University, Skåne University Hospital, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Lund University, Skåne University Hospital, Diagnostic Radiology, Department of Translational Medicine, Department in Imaging and Functional Medicine, Malmö, Sweden
| | - Hanna Tomic
- Lund University, Skåne University Hospital, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
| | - Sophia Zackrisson
- Lund University, Skåne University Hospital, Diagnostic Radiology, Department of Translational Medicine, Department in Imaging and Functional Medicine, Malmö, Sweden
| | - Anders Tingberg
- Lund University, Skåne University Hospital, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
| | - Hanna Isaksson
- Lund University, Department of Biomedical Engineering, Lund, Sweden
| | - Predrag R. Bakic
- Lund University, Skåne University Hospital, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Lund University, Skåne University Hospital, Diagnostic Radiology, Department of Translational Medicine, Department in Imaging and Functional Medicine, Malmö, Sweden
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Magnus Dustler
- Lund University, Skåne University Hospital, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Lund University, Skåne University Hospital, Diagnostic Radiology, Department of Translational Medicine, Department in Imaging and Functional Medicine, Malmö, Sweden
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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] [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] [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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