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Monnin P, Damet J, Bosmans H, Marshall NW. Task-based detectability in anatomical background in digital mammography, digital breast tomosynthesis and synthetic mammography. Phys Med Biol 2024; 69:025017. [PMID: 38214048 DOI: 10.1088/1361-6560/ad1766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 12/20/2023] [Indexed: 01/13/2024]
Abstract
Objective.Determining the detectability of targets for the different imaging modalities in mammography in the presence of anatomical background noise is challenging. This work proposes a method to compare the image quality and detectability of targets in digital mammography (DM), digital breast tomosynthesis (DBT) and synthetic mammography.Approach. The low-frequency structured noise produced by a water phantom with acrylic spheres was used to simulate anatomical background noise for the different types of images. A method was developed to apply the non-prewhitening observer model with eye filter (NPWE) in these conditions. A homogeneous poly(methyl) methacrylate phantom with a 0.2 mm thick aluminium disc was used to calculate 2D in-plane modulation transfer function (MTF), noise power spectrum (NPS), noise equivalent quanta, and system detective quantum efficiency for 30, 50 and 70 mm thicknesses. The in-depth MTFs of DBT volumes were determined using a thin tungsten wire. The MTF, system NPS and anatomical NPS were used in the NPWE model to calculate the threshold gold thickness of the gold discs contained in the CDMAM phantom, which was taken as reference. Main results.The correspondence between the NPWE model and the CDMAM phantom (linear Pearson correlation 0.980) yielded a threshold detectability index that was used to determine the threshold diameter of spherical microcalcifications and masses. DBT imaging improved the detection of masses, which depended mostly on the reduction of anatomical background noise. Conversely, DM images yielded the best detection of microcalcifications.Significance.The method presented in this study was able to quantify image quality and object detectability for the different imaging modalities and levels of anatomical background noise.
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Affiliation(s)
- P Monnin
- Institute of radiation physics (IRA), Lausanne University Hospital (CHUV) and University of Lausanne, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland
| | - J Damet
- Institute of radiation physics (IRA), Lausanne University Hospital (CHUV) and University of Lausanne, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland
| | - H Bosmans
- UZ Gasthuisberg, Department of Radiology, Herestraat 49, 3000 Leuven, Belgium
- Medical Imaging Research Center, Medical Physics and Quality Assessment, Katholieke Universiteit Leuven, 3000 Leuven, Belgium
| | - N W Marshall
- UZ Gasthuisberg, Department of Radiology, Herestraat 49, 3000 Leuven, Belgium
- Medical Imaging Research Center, Medical Physics and Quality Assessment, Katholieke Universiteit Leuven, 3000 Leuven, Belgium
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Shankari N, Kudva V, Hegde RB. Breast Mass Detection and Classification Using Machine Learning Approaches on Two-Dimensional Mammogram: A Review. Crit Rev Biomed Eng 2024; 52:41-60. [PMID: 38780105 DOI: 10.1615/critrevbiomedeng.2024051166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Breast cancer is a leading cause of mortality among women, both in India and globally. The prevalence of breast masses is notably common in women aged 20 to 60. These breast masses are classified, according to the breast imaging-reporting and data systems (BI-RADS) standard, into categories such as fibroadenoma, breast cysts, benign, and malignant masses. To aid in the diagnosis of breast disorders, imaging plays a vital role, with mammography being the most widely used modality for detecting breast abnormalities over the years. However, the process of identifying breast diseases through mammograms can be time-consuming, requiring experienced radiologists to review a significant volume of images. Early detection of breast masses is crucial for effective disease management, ultimately reducing mortality rates. To address this challenge, advancements in image processing techniques, specifically utilizing artificial intelligence (AI) and machine learning (ML), have tiled the way for the development of decision support systems. These systems assist radiologists in the accurate identification and classification of breast disorders. This paper presents a review of various studies where diverse machine learning approaches have been applied to digital mammograms. These approaches aim to identify breast masses and classify them into distinct subclasses such as normal, benign and malignant. Additionally, the paper highlights both the advantages and limitations of existing techniques, offering valuable insights for the benefit of future research endeavors in this critical area of medical imaging and breast health.
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Affiliation(s)
- N Shankari
- NITTE (Deemed to be University), Department of Electronics and Communication Engineering, NMAM Institute of Technology, Nitte 574110, Karnataka, India
| | - Vidya Kudva
- School of Information Sciences, Manipal Academy of Higher Education, Manipal, India -576104; Nitte Mahalinga Adyanthaya Memorial Institute of Technology, Nitte, India - 574110
| | - Roopa B Hegde
- NITTE (Deemed to be University), Department of Electronics and Communication Engineering, NMAM Institute of Technology, Nitte - 574110, Karnataka, India
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Kim K, Lee JH, Je Oh S, Chung MJ. AI-based computer-aided diagnostic system of chest digital tomography synthesis: Demonstrating comparative advantage with X-ray-based AI systems. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107643. [PMID: 37348439 DOI: 10.1016/j.cmpb.2023.107643] [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: 05/11/2022] [Revised: 05/26/2023] [Accepted: 06/03/2023] [Indexed: 06/24/2023]
Abstract
BACKGROUND Compared with chest X-ray (CXR) imaging, which is a single image projected from the front of the patient, chest digital tomosynthesis (CDTS) imaging can be more advantageous for lung lesion detection because it acquires multiple images projected from multiple angles of the patient. Various clinical comparative analysis and verification studies have been reported to demonstrate this, but there is no artificial intelligence (AI)-based comparative analysis studies. Existing AI-based computer-aided detection (CAD) systems for lung lesion diagnosis have been developed mainly based on CXR images; however, CAD-based on CDTS, which uses multi-angle images of patients in various directions, has not been proposed and verified for its usefulness compared to CXR-based counterparts. BACKGROUND AND OBJECTIVE This study develops and tests a CDTS-based AI CAD system to detect lung lesions to demonstrate performance improvements compared to CXR-based AI CAD. METHODS We used multiple (e.g., five) projection images as input for the CDTS-based AI model and a single-projection image as input for the CXR-based AI model to compare and evaluate the performance between models. Multiple/single projection input images were obtained by virtual projection on the three-dimensional (3D) stack of computed tomography (CT) slices of each patient's lungs from which the bed area was removed. These multiple images result from shooting from the front and left and right 30/60∘. The projected image captured from the front was used as the input for the CXR-based AI model. The CDTS-based AI model used all five projected images. The proposed CDTS-based AI model consisted of five AI models that received images in each of the five directions, and obtained the final prediction result through an ensemble of five models. Each model used WideResNet-50. To train and evaluate CXR- and CDTS-based AI models, 500 healthy data, 206 tuberculosis data, and 242 pneumonia data were used, and three three-fold cross-validation was applied. RESULTS The proposed CDTS-based AI CAD system yielded sensitivities of 0.782 and 0.785 and accuracies of 0.895 and 0.837 for the (binary classification) performance of detecting tuberculosis and pneumonia, respectively, against normal subjects. These results show higher performance than the sensitivity of 0.728 and 0.698 and accuracies of 0.874 and 0.826 for detecting tuberculosis and pneumonia through the CXR-based AI CAD, which only uses a single projection image in the frontal direction. We found that CDTS-based AI CAD improved the sensitivity of tuberculosis and pneumonia by 5.4% and 8.7% respectively, compared to CXR-based AI CAD without loss of accuracy. CONCLUSIONS This study comparatively proves that CDTS-based AI CAD technology can improve performance more than CXR. These results suggest that we can enhance the clinical application of CDTS. Our code is available at https://github.com/kskim-phd/CDTS-CAD-P.
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Affiliation(s)
- Kyungsu Kim
- Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea; Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea.
| | - Ju Hwan Lee
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea
| | - Seong Je Oh
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea
| | - Myung Jin Chung
- Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea; Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea; Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea.
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Mackenzie A, Boita J, Dance DR, Young KC. Development of an algorithm to convert mammographic images to appear as if acquired with different technique factors. J Med Imaging (Bellingham) 2022; 9:033504. [DOI: 10.1117/1.jmi.9.3.033504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/12/2022] [Indexed: 11/14/2022] Open
Affiliation(s)
- Alistair Mackenzie
- Royal Surrey NHS Foundation Trust, National Coordinating Centre for the Physics of Mammography, Guil
| | - Joana Boita
- Radboud University Medical Centre, Department of Medical Imaging, Nijmegen
| | - David R. Dance
- Royal Surrey NHS Foundation Trust, National Coordinating Centre for the Physics of Mammography, Guil
| | - Kenneth C. Young
- Royal Surrey NHS Foundation Trust, National Coordinating Centre for the Physics of Mammography, Guil
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Virtual Clinical Trials in 2D and 3D X-ray Breast Imaging and Dosimetry: Comparison of CPU-Based and GPU-Based Monte Carlo Codes. Cancers (Basel) 2022; 14:cancers14041027. [PMID: 35205775 PMCID: PMC8870739 DOI: 10.3390/cancers14041027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/12/2022] [Accepted: 02/13/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Virtual clinical trials in X-ray breast imaging may permit substantial reduction of the costs, times, and exposure risk to patient of clinical trials. Monte Carlo simulation techniques are increasingly adopted for VCT in breast imaging and dosimetry studies. This work aims to compare three different platforms for breast VCT studies, to develop real-time virtual DM, DBT and BCT examinations, where the in-silico image acquisition process takes a computational time comparable to that typical of a corresponding real clinical examination. Abstract Computational reproductions of medical imaging tests, a form of virtual clinical trials (VCTs), are increasingly being used, particularly in breast imaging research. The accuracy of the computational platform that is used for the imaging and dosimetry simulation processes is a fundamental requirement. Moreover, for practical usage, the imaging simulation computation time should be compatible with the clinical workflow. We compared three different platforms for in-silico X-ray 3D breast imaging: the Agata (University & INFN Napoli) that was based on the Geant4 toolkit and running on a CPU-based server architecture; the XRMC Monte Carlo (University of Cagliari) that was based on the use of variance reduction techniques, running on a CPU hardware; and the Monte Carlo code gCTD (University of Texas Southwestern Medical Center) running on a single GPU platform with CUDA environment. The tests simulated the irradiation of cylindrical objects as well as anthropomorphic breast phantoms and produced 2D and 3D images and 3D maps of absorbed dose. All the codes showed compatible results in terms of simulated dose maps and imaging values within a maximum discrepancy of 3%. The GPU-based code produced a reduction of the computation time up to factor 104, and so permits real-time VCT studies for X-ray breast imaging.
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Mackenzie A, Kaur S, Thomson EL, Mitchell M, Elangovan P, Warren LM, Dance DR, Young KC. Effect of glandularity on the detection of simulated cancers in planar, tomosynthesis, and synthetic 2D imaging of the breast using a hybrid virtual clinical trial. Med Phys 2021; 48:6859-6868. [PMID: 34496038 DOI: 10.1002/mp.15216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 07/19/2021] [Accepted: 08/26/2021] [Indexed: 12/15/2022] Open
Abstract
PURPOSE The purpose of this study was to measure the threshold diameter of calcifications and masses for 2D imaging, digital breast tomosynthesis (DBT), and synthetic 2D images, for a range of breast glandularities. This study shows the limits of detection for each of the technologies and the strengths and weaknesses of each in terms of visualizing the radiological features of small cancers. METHODS Mathematical voxel breast phantoms with glandularities by volume of 9%, 18%, and 30% with a thickness of 53 mm were created. Simulated ill-defined masses and calcification clusters with a range of diameters were inserted into some of these breast models. The imaging characteristics of a Siemens Inspiration X-ray system were measured for a 29 kV, tungsten/rhodium anode/filter combination. Ray tracing through the breast models was undertaken to create simulated 2D and DBT projection images. These were then modified to adjust the image sharpness, and to add scatter and noise. The mean glandular doses for the images were 1.43, 1.47, and 1.47 mGy for 2D and 1.92, 1.97, and 1.98 mGy for DBT for the three glandularities. The resultant images were processed to create 2D, DBT planes and synthetic 2D images. Patches of the images with or without a simulated lesion were extracted, and used in a four-alternative forced choice study to measure the threshold diameters for each imaging mode, lesion type, and glandularity. The study was undertaken by six physicists. RESULTS The threshold diameters of the lesions were 6.2, 4.9, and 6.7 mm (masses) and 225, 370, and 399 μm, (calcifications) for 2D, DBT, and synthetic 2D, respectively, for a breast glandularity of 18%. The threshold diameter of ill-defined masses is significantly smaller for DBT than for both 2D (p≤0.006) and synthetic 2D (p≤0.012) for all glandularities. Glandularity has a significant effect on the threshold diameter of masses, even for DBT where there is reduced background structure in the images. The calcification threshold diameters for 2D images were significantly smaller than for DBT and synthetic 2D for all glandularities. There were few significant differences for the threshold diameter of calcifications between glandularities, indicating that the background structure has little effect on the detection of calcifications. We measured larger but nonsignificant differences in the threshold diameters for synthetic 2D imaging than for 2D imaging for masses in the 9% (p = 0.059) and 18% (p = 0.19) glandularities. The threshold diameters for synthetic 2D imaging were larger than for 2D imaging for calcifications (p < 0.001) for all glandularities. CONCLUSIONS We have shown that glandularity has only a small effect on the detection of calcifications, but the threshold diameter of masses was significantly larger for higher glandularity for all of the modalities tested. We measured nonsignificantly larger threshold diameters for synthetic 2D imaging than for 2D imaging for masses at the 9% (p = 0.059) and 18% (p = 0.19) glandularities and significantly larger diameters for calcifications (p < 0.001) for all glandularities. The lesions simulated were very subtle and further work is required to examine the clinical effect of not seeing the smallest calcifications in clusters.
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Affiliation(s)
- Alistair Mackenzie
- National Coordinating Centre for the Physics of Mammography (NCCPM), Royal Surrey NHS Foundation Trust, Guildford, UK
| | - Sukhmanjit Kaur
- National Coordinating Centre for the Physics of Mammography (NCCPM), Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
| | - Emma L Thomson
- National Coordinating Centre for the Physics of Mammography (NCCPM), Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
| | - Melissa Mitchell
- National Coordinating Centre for the Physics of Mammography (NCCPM), Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
| | - Premkumar Elangovan
- National Coordinating Centre for the Physics of Mammography (NCCPM), Royal Surrey NHS Foundation Trust, Guildford, UK
| | - Lucy M Warren
- National Coordinating Centre for the Physics of Mammography (NCCPM), Royal Surrey NHS Foundation Trust, Guildford, UK
| | - David R Dance
- National Coordinating Centre for the Physics of Mammography (NCCPM), Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
| | - Kenneth C Young
- National Coordinating Centre for the Physics of Mammography (NCCPM), Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
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Hadjipanteli A, Polyviou P, Kyriakopoulos I, Genagritis M, Kotziamani N, Moniatis D, Papoutsou A, Constantinidou A. Comparison of two-view versus single-view digital breast tomosynthesis and 2D-mammography in breast cancer surveillance imaging. PLoS One 2021; 16:e0256514. [PMID: 34587170 PMCID: PMC8480606 DOI: 10.1371/journal.pone.0256514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 08/09/2021] [Indexed: 11/18/2022] Open
Abstract
PURPOSE Limited work has been performed for the implementation of digital breast tomosynthesis (DBT) in breast cancer surveillance imaging. The aim of this study was to investigate the differences between two different DBT implementations in breast cancer surveillance imaging, for patients with a personal history of breast cancer. METHOD The DBT implementations investigated were: (1) 2-view 2D digital mammography and 2-view DBT (2vDM&2vDBT) (2) 1-view (cranial-caudal) DM and 1-view (mediolateral-oblique) DBT (1vDM&1vDBT). Clinical performance of these two implementations was assessed retrospectively using observer studies with 118 sets of real patient images, from a single imaging centre, and six observers. Sensitivity, specificity and area under the curve (AUC) using the Jack-knife alternative free-response receiver operating characteristics (JAFROC) analysis were evaluated. RESULTS Results suggest that the two DBT implementations are not significantly different in terms of sensitivity, specificity and AUC. When looking at the two main different lesion types, non-calcifications and calcifications, and two different density levels, no difference in the performance of the two DBT implementations was found. CONCLUSIONS Since 1vDM&1vDBT exposes the patient to half the dose of 2vDM&2vDBT, it might be worth considering 1vDM&1vDBT in breast cancer surveillance imaging. However, larger studies are required to conclude on this matter.
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Affiliation(s)
- Andria Hadjipanteli
- Medical School, Shacolas Educational Centre for Clinical Medicine, Palaios dromos Lefkosias Lemesou, University of Cyprus, Aglantzia, Nicosia, Cyprus
- Bank of Cyprus Oncology Centre, Strovolos, Nicosia, Cyprus
- German Oncology Center, Agios Athanasios, Limassol, Cyprus
| | - Petros Polyviou
- Medical School, Shacolas Educational Centre for Clinical Medicine, Palaios dromos Lefkosias Lemesou, University of Cyprus, Aglantzia, Nicosia, Cyprus
| | | | - Marios Genagritis
- The Breast Center of Cyprus, Karyatides Business Centre, Strovolos, Nicosia, Cyprus
| | | | | | | | - Anastasia Constantinidou
- Medical School, Shacolas Educational Centre for Clinical Medicine, Palaios dromos Lefkosias Lemesou, University of Cyprus, Aglantzia, Nicosia, Cyprus
- Bank of Cyprus Oncology Centre, Strovolos, Nicosia, Cyprus
- Cyprus Cancer Research Institute (C.C.R.I.), Aglantzia, Nicosia, Cyprus
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Vancoillie L, Cockmartin L, Marshall N, Bosmans H. The impact on lesion detection via a multi-vendor study: A phantom-based comparison of digital mammography, digital breast tomosynthesis, and synthetic mammography. Med Phys 2021; 48:6270-6292. [PMID: 34407213 DOI: 10.1002/mp.15171] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/27/2021] [Accepted: 07/27/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE The aim of this study is to perform a test object-based comparison of the imaging performance of digital mammography (DM), digital breast tomosynthesis (DBT), and synthetic mammography (SM). METHODS Two test objects were used, the CDMAM and the L1-structured phantom. Small-detail detectability was assessed using CDMAM and the microcalcification simulating specks in the L1-structured background. Detection of spiculated and non-spiculated mass-like objects was assessed using the L1 phantom. Six different systems were included: Amulet Innovality (Fujifilm), Senographe Pristina (GEHC), 3Dimensions (Hologic), Giotto Class (IMS), Clarity 2D/3D (Planmed), and Mammomat Revelation (Siemens). Images were acquired under automatic exposure control (AEC) and at adjusted levels of AEC/2 and 2 × AEC level. Threshold gold thickness (Ttr ) was established for the 0.13-mm-diameter CDMAM discs. Threshold diameters for the calcifications (dtr_c ), the spiculated masses (dtr_sm ), and for the non-spiculated masses (dtr_nsm ) were established. The threshold condition was defined as the thickness or diameter for a 62.5% correct score. RESULTS Ttr for DM was generally superior to DBT, which in turn was superior to SM, but for most systems, these differences between modes were not significant. For L1, no significant differences in dtr_c were found between DM and DBT. The increase in dtr_c from DM to SM at AEC dose was 1%, 19%, 11%, 14%, 46%, and 27% for the Fujifilm, GEHC, Hologic, IMS, Planmed, and Siemens, respectively, indicating significantly poorer performance for all vendors except for Fujifilm, Hologic, and IMS. For both mass types, DBT performed better than SM, while SM showed no significant difference with DM (except for Fujifilm spiculated masses). The dose had an impact on small-detail detectability for both phantoms but did not influence the detection of either mass type. CONCLUSIONS Both phantoms indicated potentially reduced small-detail detectability for SM versus DM and DBT and should therefore not be used in stand-alone mode. The L1 phantom demonstrated no significant difference in microcalcification detection between DM and DBT and also demonstrated the superiority of DBT, compared to DM for mass detection, for all six systems.
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Affiliation(s)
- Liesbeth Vancoillie
- Department of Imaging and Pathology, KU Leuven, Division of Medical Physics & Quality Assessment, Leuven, Belgium
| | | | - Nicholas Marshall
- Department of Imaging and Pathology, KU Leuven, Division of Medical Physics & Quality Assessment, Leuven, Belgium.,Department of Radiology, UZ Leuven, Leuven, Belgium
| | - Hilde Bosmans
- Department of Imaging and Pathology, KU Leuven, Division of Medical Physics & Quality Assessment, Leuven, Belgium.,Department of Radiology, UZ Leuven, Leuven, Belgium
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Boita J, Mackenzie A, van Engen RE, Broeders M, Sechopoulos I. Validation of a mammographic image quality modification algorithm using 3D-printed breast phantoms. J Med Imaging (Bellingham) 2021; 8:033502. [PMID: 34026921 PMCID: PMC8134780 DOI: 10.1117/1.jmi.8.3.033502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 04/28/2021] [Indexed: 11/14/2022] Open
Abstract
Purpose: To validate a previously proposed algorithm that modifies a mammogram to appear as if it was acquired with different technique factors using realistic phantom-based mammograms. Approach: Two digital mammography systems (an indirect- and a direct-detector-based system) were used to acquire realistic mammographic images of five 3D-printed breast phantoms with the technique factors selected by the automatic exposure control and at various other conditions (denoted by the original images). Additional images under other simulated conditions were also acquired: higher or lower tube voltages, different anode/filter combinations, or lower tube current-time products (target images). The signal and noise in the original images were modified to simulate the target images (simulated images). The accuracy of the image modification algorithm was validated by comparing the target and simulated images using the local mean, local standard deviation (SD), local variance, and power spectra (PS) of the image signals. The absolute relative percent error between the target and simulated images for each parameter was calculated at each sub-region of interest (local parameters) and frequency (PS), and then averaged. Results: The local mean signal, local SD, local variance, and PS of the target and simulated images were very similar, with a relative percent error of 5.5%, 3.8%, 7.8%, and 4.4% (indirect system), respectively, and of 3.7%, 3.8%, 7.7%, and 7.5% (direct system), respectively. Conclusions: The algorithm is appropriate for simulating different technique factors. Therefore, it can be used in various studies, for instance to evaluate the impact of technique factors in cancer detection using clinical images.
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Affiliation(s)
- Joana Boita
- Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
| | - Alistair Mackenzie
- Royal Surrey NHS Foundation Trust, National Coordinating Centre for the Physics of Mammography, Guildford, United Kingdom
| | | | - Mireille Broeders
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
- Radboud University Medical Center, Department for Health Evidence, Nijmegen, The Netherlands
| | - Ioannis Sechopoulos
- Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
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Winter AM, Moy L, Gao Y, Bennett DL. Comparison of Narrow-angle and Wide-angle Digital Breast Tomosynthesis Systems in Clinical Practice. JOURNAL OF BREAST IMAGING 2021; 3:240-255. [PMID: 38424829 DOI: 10.1093/jbi/wbaa114] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Indexed: 03/02/2024]
Abstract
Digital breast tomosynthesis (DBT) is a pseudo 3D mammography imaging technique that has become widespread since gaining Food and Drug Administration approval in 2011. With this technology, a variable number of tomosynthesis projection images are obtained over an angular range between 15° and 50° for currently available clinical DBT systems. The angular range impacts various aspects of clinical imaging, such as radiation dose, scan time, and image quality, including visualization of calcifications, masses, and architectural distortion. This review presents an overview of the differences between narrow- and wide-angle DBT systems, with an emphasis on their applications in clinical practice. Comparison examples of patients imaged on both narrow- and wide-angle DBT systems illustrate these differences. Understanding the potential variable appearance of imaging findings with narrow- and wide-angle DBT systems is important for radiologists, particularly when comparison images have been obtained on a different DBT system. Furthermore, knowledge about the comparative strengths and limitations of DBT systems is needed for appropriate equipment selection.
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Affiliation(s)
- Andrea M Winter
- Saint Louis University, Department of Radiology, St. Louis, MO, USA
| | - Linda Moy
- NYU Langone Health, NYU School of Medicine, Department of Radiology, New York, NY, USA
| | - Yiming Gao
- NYU Langone Health, NYU School of Medicine, Department of Radiology, New York, NY, USA
| | - Debbie L Bennett
- Saint Louis University, Department of Radiology, St. Louis, MO, USA
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Ricciardi R, Mettivier G, Staffa M, Sarno A, Acampora G, Minelli S, Santoro A, Antignani E, Orientale A, Pilotti I, Santangelo V, D'Andria P, Russo P. A deep learning classifier for digital breast tomosynthesis. Phys Med 2021; 83:184-193. [DOI: 10.1016/j.ejmp.2021.03.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/04/2021] [Accepted: 03/13/2021] [Indexed: 10/21/2022] Open
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Barca P, Lamastra R, Tucciariello RM, Traino A, Marini C, Aringhieri G, Caramella D, Fantacci ME. Technical evaluation of image quality in synthetic mammograms obtained from 15° and 40° digital breast tomosynthesis in a commercial system: a quantitative comparison. Phys Eng Sci Med 2020; 44:23-35. [PMID: 33226534 DOI: 10.1007/s13246-020-00948-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 11/15/2020] [Indexed: 11/25/2022]
Abstract
Digital breast tomosynthesis (DBT) has recently gained interest both for breast cancer screening and diagnosis. Its employment has increased also in conjunction with digital mammography (DM), to improve cancer detection and reduce false positive recall rate. Synthetic mammograms (SMs) reconstructed from DBT data have been introduced to replace DM in the DBT + DM approach, for preserving the benefits of the dual-acquisition modality whilst reducing radiation dose and compression time. Therefore, different DBT models have been commercialized and the effective potential of each system has been investigated. In particular, wide-angle DBT was shown to provide better depth resolution than narrow-angle DBT, while narrow-angle DBT allows better identification of microcalcifications compared to wide-angle DBT. Given the increasing employment of SMs as supplement to DBT, a comparison of image quality between SMs obtained in narrow-angle and wide-angle DBT is of practical interest. Therefore, the aim of this phantom study was to evaluate and compare the image quality of SMs reconstructed from 15° (SM15) and 40° (SM40) DBT in a commercial system. Spatial resolution, noise and contrast properties were evaluated through the modulation transfer function (MTF), noise power spectrum, maps of signal-to-noise ratio (SNR), image contrast, contrast-to-noise ratio (CNR) and contrast-detail (CD) thresholds. SM40 expressed higher MTF than SM15, but also lower SNR and CNR levels. SM15 and SM40 were characterized by slight different texture, and a different behavior in terms of contrast was found. SM15 provided better CD performances than SM40. These results suggest that the employment of wide/narrow-angle DBT + SM images should be optimized based on the specific image task.
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Affiliation(s)
- Patrizio Barca
- Department of Physics, University of Pisa, Largo Bruno Pontecorvo 3, 56127, Pisa, Italy.
| | - Rocco Lamastra
- Department of Physics, University of Pisa, Largo Bruno Pontecorvo 3, 56127, Pisa, Italy
- INFN, Pisa Section, Pisa, Italy
| | - Raffaele Maria Tucciariello
- Department of Physics, University of Pisa, Largo Bruno Pontecorvo 3, 56127, Pisa, Italy
- INFN, Pisa Section, Pisa, Italy
| | - Antonio Traino
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy
| | - Carolina Marini
- S.D. Radiologia Senologica, "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy
| | - Giacomo Aringhieri
- Department of Translational Research and of New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Davide Caramella
- Department of Translational Research and of New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Maria Evelina Fantacci
- Department of Physics, University of Pisa, Largo Bruno Pontecorvo 3, 56127, Pisa, Italy
- INFN, Pisa Section, Pisa, Italy
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Bliznakova K. The advent of anthropomorphic three-dimensional breast phantoms for X-ray imaging. Phys Med 2020; 79:145-161. [DOI: 10.1016/j.ejmp.2020.11.025] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 11/16/2020] [Accepted: 11/19/2020] [Indexed: 10/22/2022] Open
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Abadi E, Segars WP, Tsui BMW, Kinahan PE, Bottenus N, Frangi AF, Maidment A, Lo J, Samei E. Virtual clinical trials in medical imaging: a review. J Med Imaging (Bellingham) 2020; 7:042805. [PMID: 32313817 PMCID: PMC7148435 DOI: 10.1117/1.jmi.7.4.042805] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 03/23/2020] [Indexed: 12/13/2022] Open
Abstract
The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.
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Affiliation(s)
- Ehsan Abadi
- Duke University, Department of Radiology, Durham, North Carolina, United States
| | - William P. Segars
- Duke University, Department of Radiology, Durham, North Carolina, United States
| | - Benjamin M. W. Tsui
- Johns Hopkins University, Department of Radiology, Baltimore, Maryland, United States
| | - Paul E. Kinahan
- University of Washington, Department of Radiology, Seattle, Washington, United States
| | - Nick Bottenus
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
- University of Colorado Boulder, Department of Mechanical Engineering, Boulder, Colorado, United States
| | - Alejandro F. Frangi
- University of Leeds, School of Computing, Leeds, United Kingdom
- University of Leeds, School of Medicine, Leeds, United Kingdom
| | - Andrew Maidment
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Joseph Lo
- Duke University, Department of Radiology, Durham, North Carolina, United States
| | - Ehsan Samei
- Duke University, Department of Radiology, Durham, North Carolina, United States
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Monnin P, Verdun FR, Bosmans H, Marshall NW. In-plane image quality and NPWE detectability index in digital breast tomosynthesis. Phys Med Biol 2020; 65:095013. [PMID: 32191923 DOI: 10.1088/1361-6560/ab8147] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
A rigorous 2D analysis of signal and noise transfer applied to reconstructed planes in digital breast tomosynthesis (DBT) is necessary for system characterization and optimization. This work proposes a method for assessing technical image quality and system detective quantum efficiency (DQEsys) for reconstructed planes in DBT. Measurements of 2D in-plane modulation transfer function (MTF) and noise power spectrum (NPS) were made on five DBT systems using different acquisition parameters, reconstruction algorithms and plane spacing. This work develops the noise equivalent quanta (NEQ), DQEsys and detectability index (d') calculated using a non-prewhitening model observer with eye filter (NPWE) for reconstructed DBT planes. The images required for this implementation were acquired using a homogeneous test object of thickness 40 mm poly(methyl) methacrylate plus 0.5 mm Al; 2D MTF was calculated from an Al disc of thickness 0.2 mm and diameter 50 mm positioned within the phantom. The radiant contrast of the MTF disc and the air kerma at the system input were used as normalization factors. The NPWE detectability index was then compared to the in-plane contrast-detail (c-d) threshold measured using the CDMAM phantom. The MTF and NPS measured on the different systems showed a strong anisotropy, consistent with the cascaded models developed in the literature for DBT. Detectability indices calculated from the measured MTF and NPS successfully predicted changes in c-d detectability for details between 0.1 mm and 2.0 mm, for DBT plane spacings between 0.5 mm and 10 mm, and for air kerma values at the system input between 157 µGy and 1170 μGy. The linear Pearson correlation between the detectability index and threshold gold thickness of the CDMAM phantom was -0.996. The method implements a parametric means of assessing the technical image quality of reconstructed DBT planes, providing valuable information for optimization of DBT systems.
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Affiliation(s)
- P Monnin
- Institute of Radiation Physics (IRA), Lausanne University Hospital (CHUV) and University of Lausanne, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland. Author to whom any correspondence should be addressed
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Hadjipanteli A, Kontos M, Constantinidou A. The role of digital breast tomosynthesis in breast cancer screening: a manufacturer- and metrics-specific analysis. Cancer Manag Res 2019; 11:9277-9296. [PMID: 31802947 PMCID: PMC6827571 DOI: 10.2147/cmar.s210979] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 08/03/2019] [Indexed: 12/21/2022] Open
Abstract
Aim Digital Breast Tomosynthesis (DBT), with or without Digital Mammography (DM) or Synthetic Mammography (SM), has been introduced or is under consideration for its introduction in breast cancer screening in several countries, as it has been shown that it has advantages over DM. Despite this there is no agreement on how to implement DBT in screening, and in many cases there is a lack of official guidance on the optimum usage of each commercially available system. The aim of this review is to carry out a manufacturer-specific summary of studies on the implementation of DBT in breast cancer screening. Methods An exhaustive literature review was undertaken to identify clinical observer studies that evaluated at least one of five common metrics: sensitivity, specificity, area under the curve (AUC) of the receiver-operating characteristics (ROC) analysis, recall rate and cancer detection rate. Four common DBT implementation methods were discussed in this review: (1) DBT, (2) DM with DBT, (3) 1-view DBT with or without 1-view DM or 2-view DM and (4) DBT with SM. Results A summary of 89 studies, selected from a database of 677 studies, on the assessment of the implementation of DBT in breast cancer screening is presented in tables and discussed in a manufacturer- and metric-specific approach. Much more studies were carried out using some DBT systems than others. For one implementation method of DBT by one manufacturer there is a shortage of studies, for another implementation there are conflicting results. In some cases, there is a strong agreement between studies, making the advantages and disadvantages of each system clear. Conclusion The optimum implementation method of DBT in breast screening, in terms of diagnostic benefit and patient radiation dose, for one manufacturer does not necessarily apply to other manufacturers.
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Affiliation(s)
- A Hadjipanteli
- Medical School, University of Cyprus, Nicosia, Cyprus.,Bank of Cyprus Oncology Centre, Nicosia, Cyprus
| | - M Kontos
- 1st Department of Surgery, National and Kapodistrian University of Athens, Athens, Greece
| | - A Constantinidou
- Medical School, University of Cyprus, Nicosia, Cyprus.,Bank of Cyprus Oncology Centre, Nicosia, Cyprus
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