| 1. |
- Bakic, Predrag R., et al.
(author)
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Evaluation of a flat fielding method for simultaneous DBT and MI acquisition
- 2020
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In: 15th International Workshop on Breast Imaging, IWBI 2020. - : SPIE. - 1996-756X .- 0277-786X. - 9781510638310 ; 11513
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Conference paper (peer-reviewed)abstract
- We are developing a prototype system for simultaneous digital breast tomosynthesis (DBT) and mechanical imaging (MI). MI maps the local pressure distribution during clinical exams, to distinguish breast abnormalities from the normal tissue. Both DBT alone, and MI when combined with digital mammography, have demonstrated the ability to reduce false positives; however, the benefit of combining DBT with MI has not been investigated. A practical limitation in simultaneous DBT and MI is the presence of the MI sensor in DBT images. Metallic elements of the sensor generate noticeable artifacts, which may interfere with clinical analysis. Previously, we shown that the sensor artifacts can be reduced by flat fielding, which combines projections of the sensor acquired with and without the breast. In this paper we evaluate the flat fielding by assessing artifact reduction and visibility of breast abnormalities. Images of a physical anthropomorphic breast phantom were acquired using a clinical wide-angle DBT system. Visual evaluation was performed by experienced medical physicists. Image quality descriptors were calculated in images with and without flat fielding. To evaluate the visibility of abnormalities we estimated the full width at half maximum (FWHM) for calcifications modeled in the phantom. Our preliminary results suggest a substantial reduction of artifacts by flat fielding (on average 83%). Few noticeable artifacts remain near the breast edge, in the reconstructed image with the sensor in focus. We observed a 17% reduction in the FWHM. Future work would include a detailed assessment, and method optimization using virtual trials as a design aid.
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| 2. |
- Bakic, Predrag R., et al.
(author)
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Pre-processing for image quality improvement in simultaneous DBT and mechanical imaging
- 2020
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In: Medical Imaging 2020 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE. - 1605-7422. - 9781510633919 ; 11312
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Conference paper (peer-reviewed)abstract
- Simultaneous digital breast tomosynthesis (DBT) and mechanical imaging (MI) offer the potential to combine anatomic information from DBT with functional information from MI. This makes it possible to associate tissue stiffness with specific anatomic structures in the breast, a combination that can reduce false-positive findings by using the MI data to discriminate between ambiguous lesions in DBT. This, in turn, will reduce the frequency of negative biopsies. Simultaneous imaging requires that the MI sensor array be present during DBT acquisition. This introduces artifacts, since the sensor is attenuating. Previously, we demonstrated that the DBT reconstruction could be modified to reduce sensor conspicuity in DBT images. In this paper, we characterize the relative attenuation of the breast and the sensor, to calculate the artifact reduction in DBT reconstruction. We concentrate on pre-processing DBT projections prior to reconstruction. Using commercially available a DBT system, we have confirmed that the sensor array does not completely attenuate the x-rays. This suggests that a pre-processing method based upon flat fielding can be used to reduce artifacts. In a proof-of-concept study, we performed flat fielding by combining DBT projections of the MI sensor with and without an anthropomorphic breast phantom. Visual evaluation confirmed substantially improved image quality. The artifacts were reduced throughout the image for all sensor elements. Few residual artifacts are noticeable where the phantom thickness decreases. The investigation of additional pre-processing, including beam hardening correction is ongoing. Future work includes quantitative validation, noise stabilization, and method optimization in virtual clinical trials and subsequent patient studies.
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| 3. |
- Bejnö, Anna, et al.
(author)
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Artificial intelligence together with mechanical imaging in mammography
- 2020
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In: 15th International Workshop on Breast Imaging, IWBI 2020. - : SPIE. - 0277-786X .- 1996-756X. - 9781510638310 ; 11513
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Conference paper (peer-reviewed)abstract
- Artificial intelligence (AI) applications are increasingly seeing use in breast imaging, particularly to assist in or automate the reading of mammograms. Another novel technique is mechanical imaging (MI) which estimates the relative stiffness of suspicious breast abnormalities by measuring the distribution of pressure on the compressed breast. This study investigates the feasibility of combining AI and MI information in breast imaging to provide further diagnostic information. Forty-six women recalled from screening were included in the analysis. Mammograms with findings scored on a suspiciousness scale by an AI tool, and corresponding pressure distributions were collected for each woman. The cases were divided into three groups by diagnosis; biopsy-proven cancer, biopsy-proven benign and non-biopsied, very likely benign. For all three groups, the relative increase of pressure at the location of the finding marked most suspicious by the AI software was recorded. A significant correlation between the relative pressure increase at the AI finding and the AI score was established in the group with cancer (p=0.043), but neither group of healthy women showed such a correlation. This study suggests that AI and MI indicate independent markers for breast cancer. The combination of these two methods has the potential to increase the accuracy of mammography screening, but further research is needed.
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| 4. |
- Dahlblom, Victor, et al.
(author)
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Personalised breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence
- 2020
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In: 15th International Workshop on Breast Imaging, IWBI 2020. - : SPIE. - 0277-786X .- 1996-756X. - 9781510638310 ; 11513
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Conference paper (peer-reviewed)abstract
- Breast cancer screening is predominantly performed using digital mammography (DM), but higher sensitivity has been demonstrated with digital breast tomosynthesis (DBT). A partial DBT screening in selected groups with a clear benefit from DBT might be more feasible than a full implementation, and using artificial intelligence (AI) to select women for DBT might be a possibility. This study used data from Malmö Breast Tomosynthesis Screening Trial, where all women prospectively were examined with separately read DM and DBT. We retrospectively analysed DM examinations (n=14768) with a breast cancer detection software and used the provided risk score (1-10) for risk stratification. We tested how different score thresholds for adding DBT to an initial DM affects the number of detected cancers, additional DBT examinations needed, detection rate, and false positives. If using a threshold of 9.0, 25 (26 %) more cancers would be detected compared to using DM alone. Of the 41 cancers only detected on DBT, 61 % would be detected, with only 1797 (12 %) of the women examined with both DM and DBT. The detection rate for the added DBT would be 14/1000 women, while the false positive recalls would be increased with 58 (21 %). Using DBT only for selected high gain cases could be an alternative to a complete DBT screening. AI could be used for analysing DM to identify high gain cases, where DBT can be added during the same visit. There might be logistical challenges and further studies in a prospective setting are necessary.
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| 5. |
- Dustler, Magnus, et al.
(author)
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The effect of breast density on the performance of deep learning-based breast cancer detection methods for mammography
- 2020
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In: 15th International Workshop on Breast Imaging, IWBI 2020. - : SPIE. - 1996-756X .- 0277-786X. - 9781510638310 ; 11513
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Conference paper (peer-reviewed)abstract
- Mammographic sensitivity in breasts with higher density has been questioned. Higher breast density is also linked to an increased risk for breast cancer. Even though digital breast tomosynthesis (DBT) offers an attractive solution, for varied reasons it has not yet been widely adopted in screening. An alternative could be to boost the performance of standard mammography by using computer-aided detection based on deep learning, but it remains to be proven how such methods are affected by density. A deep-learning based computer-aided detection program was used to score the suspicion of cancer on a scale of 1 to 10. A set of 13838 mammography screening exams were used. All cases had BIRADS density values available. The set included 2304 exams (11 cancers) in BIRADS 1, 5310 (51 cancers) in BIRADS 2, 4844 (73 cancers) in BIRADS 3 and 1223 (22 cancers) in BIRADS 4. A Kruskal-Wallis analysis of variance showed no statistically significant differences between the cancer risk scores of the density categories for cases diagnosed with cancer (P=0.9225). An identical analysis for cases without cancer, showed significant differences between the density categories (P<0.0001). The results suggest that the risk categorization of the deep-learning software is not affected by density, as though some density categories receive higher risk assessments in general, this does not hold for cancer cases, which show uniformly high risk values despite density. This shows the potential for deep-learning to improve screening sensitivity even for women with high density breasts.
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| 6. |
- Hellgren, Gustav, et al.
(author)
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Evaluation of digital breast tomosynthesis systems
- 2020
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In: Medical Imaging 2020 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE. - 1605-7422. - 9781510633919 ; 11312
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Conference paper (peer-reviewed)abstract
- In this study, two digital breast tomosynthesis (DBT) systems were evaluated: Siemens Mammomat Inspiration TOMO (Siemens Healthineers, Erlangen, Germany) and GE Senographe Pristina (GE, Buc, France). Along with differences such as angular range and detectors type, the systems use different reconstruction algorithms. One was available for the GE system, based on iterative reconstruction (IR). Two algorithms were available for the Siemens system: TOMO_STANDARD, using filtered back projection (FBP) and EMPIRE, FBP with statistically based artifact reduction. Two commercially available DBT phantoms (CIRS model 020 & 021), with heterogeneous and homogenous background respectively, were used to calculate signal-difference-to-noise-ratio (SDNR) in key structures for varying phantom thickness (30, 45 & 70 mm) and average glandular dose (AGD). Key phantom structures include calcifications and lesion masses of different sizes. Results show a positive correlation between SDNR and AGD except for the EMPIRE algorithm where there was a negative SDNR/AGD trend for one of the microcalcification specks in the heterogeneous phantom. The highest overall SDNR was acquired using the EMPIRE algorithm. Both systems are well within the recommended dose limits but could increase their dose levels in order to achieve higher SDNR. This indicates that there may be room for dose optimization in DBT systems used in screening programs, confirming the importance of continuous evaluation and optimization.
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| 7. |
- Torlegård, B., et al.
(author)
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Identifying and modelling clinical subpopulations from the Malmö breast tomosynthesis screening trial
- 2020
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In: 15th International Workshop on Breast Imaging, IWBI 2020. - : SPIE. - 0277-786X .- 1996-756X. - 9781510638310 ; 11513
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Conference paper (peer-reviewed)abstract
- Virtual Clinical Trials (VCT) are an effective tool to evaluate the performance of novel imaging systems using computer simulations. VCT results depend on the selection of virtual patient populations. In the case of breast imaging, virtual patients should be matched to a desired clinical population in terms of selected anatomical or demographic descriptors. We are developing a virtual population of women who participated in the Malmö Breast Tomosynthesis Screening Trial (MBTST). We have used clinical values of the compressed breast thickness and volumetric breast density to develop a multidimensional distribution of women in MBTST. Breast density and thickness values were obtained from anonymized, previously collected tomosynthesis images of 14,746 women. In this paper, we compare several approaches to identify clinical subpopulations and select virtual patients that represent various groups of clinical subjects. We performed two methods to identify clinical subpopulations by clustering clinical data using the K-means algorithm or woman's age. The obtained clusters have been explored and compared using the silhouette mean. The K-means algorithm yielded grouping of MBTST data into two clusters; however, that grouping was, shown to be suboptimal by the silhouette analysis. The agebased clustering showed significant overlap in terms of breast thickness and density. We also compared two approaches to select sets of representative phantoms. Our analysis has emphasized benefits and limitations of different clustering methods. The preferred method depends on the specific task that should be addressed using VCTs. Simulation of representative phantoms is ongoing. Potential correlations with pathological findings and/or parenchymal properties will be investigated.
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