| 1. |
- Axelsson, Rebecca, et al.
(author)
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Simultaneous digital breast tomosynthesis and mechanical imaging in women recalled from screening - A preliminary analysis
- 2022
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In: 16th International Workshop on Breast Imaging, IWBI 2022. - : SPIE. - 0277-786X .- 1996-756X. - 9781510655843 ; 12286
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Conference paper (peer-reviewed)abstract
- We have developed a method for simultaneous tomosynthesis and mechanical imaging, called DBTMI. Mechanical imaging measures the stress distribution over the compressed breast surface. Malignant tissue is usually stiffer than benign, which results in higher stress on the compressed breast and enables to distinguish malignant from benign findings. By combining tomosynthesis and mechanical imaging, we could improve cancer detection accuracy by reducing the number of false positive findings. In this study we have analysed clinical DBTMI data, collected from 52 women from an ongoing pilot study at the Skåne University Hospital, Malmö, Sweden. We measured the range of the average stress over the breast surface, the range of average stress over the location of suspected lesions, and the normalized stress over the lesion location. Preliminary results show that the range of stress over the breast surface was 1.23-5.84 kPa, the range over the lesion location 2.10-10.10 kPa, and the normalized stress 1.12-2.44 over the lesion location. Overall, the local stress over malignant lesions was higher than the average stress over the entire breast surface. This is the first step investigating criteria to distinguish between malignant and benign findings based upon clinical DBTMI data.
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| 2. |
- Bakic, Predrag R., et al.
(author)
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Alignment of clinical breast tomosynthesis and mechanical images : The effect of the variation in shift and rotation
- 2024
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In: 17th International Workshop on Breast Imaging, IWBI 2024. - 0277-786X .- 1996-756X. - 9781510680203 ; 13174
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Conference paper (peer-reviewed)abstract
- Simultaneous digital breast tomosynthesis and mechanical imaging (DBTMI), a novel screening approach, combines anatomic DBT with functional analysis of the stress distribution on the compressed breast by mechanical imaging (MI). Preliminary studies suggest potential to reduce false positive findings. DBTMI requires to align DBT and MI images. In this study, we have analyzed robustness to alignment variations in clinical DBTMI data. Our preliminary retrospective analysis included DBTMI of 31 women recalled from screening. We analyzed two aspects of image alignment: rotation and shift. To analyze the shift, we varied the position of suspected abnormality for ±1 cm in horizontal or vertical direction. To analyze the rotation, we varied the angle by ±1 degree between radiographic and MI images of 18 women. We compared the relative mean pressure at the lesion area (RMPA) before and after variation. Varying the shift, we observed 14.3%±12.2% difference in RMPA. Averaged separately over biopsy confirmed benign and malignant lesions, 16.2%±14.3% and 12.4%±10.2% difference was observed, respectively. In nine of 31 analyzed datasets, the shift could potentially change the clinical findings. Varying the rotation, we observed 6.4%±4.9% difference in RMPA. Averaged over biopsy confirmed benign and malignant lesions, yielded 5.8%±4.5% and 6.4%±4.8% difference, respectively. In two of 18 DBTMI datasets, the rotational variation could change the clinical findings. The larger effect of the shift may be caused by a relatively large shift variation (±1 cm) compared to the size of detected abnormalities. Analysis of more clinical DBTMI datasets and simulation studies are ongoing.
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| 3. |
- 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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| 4. |
- 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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| 5. |
- Dahlblom, Victor, et al.
(author)
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Correspondence between areas causing recall in breast cancer screening and artificial intelligence findings
- 2022
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In: 16th International Workshop on Breast Imaging, IWBI 2022. - : SPIE. - 0277-786X .- 1996-756X. - 9781510655843 ; 12286
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Conference paper (peer-reviewed)abstract
- False positive recall is a major issue in breast cancer screening and the introduction of artificial intelligence (AI) might affect which women who are unnecessarily recalled. We have investigated how an AI system works on false positive recalls at screening and compared with radiologist findings. Two-view digital mammography (DM) examinations from 656 recalled women (136 with screening detected cancer), were analysed with a commercial AI system. The AI findings were matched with the areas on the images causing the recalls. The agreement was studied both at the examination level and for individual findings. Scores were compared between true positive and false positive recalls. ROC analysis was used to study the AI-system's ability to distinguish between true and false positive recalls. It was also studied how the AI system performed on cases where there were discordant readings. AI identified the same areas as radiologists in 80% of the cases recalled on DM. For true positives both the proportion of matching areas and AI scores were higher than for false positive recalls. The AI system also had a relatively large AUC (0.83) for differentiating between false positive recalls and cancers. Further, the AI system identified most of the findings leading to recall in cases where only one of the readers had marked the case for discussion. There is a relatively large agreement between the AI system and radiologists. The AI system scores the false positives lower than true positives. AI complements a single reader in a way similar to a second reader.
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| 6. |
- 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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| 7. |
- Dustler, Magnus, et al.
(author)
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Application of the fractal Perlin noise algorithm for the generation of simulated breast tissue
- 2015
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In: Medical Imaging 2015: Physics of Medical Imaging. - : SPIE. - 1996-756X .- 0277-786X. ; 9412, s. 94123-94123
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Conference paper (peer-reviewed)abstract
- Software breast phantoms are increasingly seeing use in preclinical validation of breast image acquisition systems and image analysis methods. Phantom realism has been proven sufficient for numerous specific validation tasks. A challenge is the generation of suitably realistic small-scale breast structures that could further improve the quality of phantom images. Power law noise follows the noise power characteristics of breast tissue, but may not sufficiently represent certain (e.g., non-Gaussian) properties seen in clinical breast images. The purpose of this work was to investigate the utility of fractal Perlin noise in generating more realistic breast tissue through investigation of its power spectrum and visual characteristics. Perlin noise is an algorithm that creates smoothly varying random structures of an arbitrary frequency. Through the use of a technique known as fractal noise or fractional Brownian motion (fBm), octaves of noise with different frequency are combined to generate coherent noise with a broad frequency range. fBm is controlled by two parameters - lacunarity and persistence - related to the frequency and amplitude of successive octaves, respectively. Average noise power spectra were calculated and beta parameters estimated in sample volumes of fractal Perlin noise with different combinations of lacunarity and persistence. Certain combinations of parameters resulted in noise volumes with beta values between 2 and 3, corresponding to reported measurements in real breast tissue. Different combinations of parameters resulted in different visual appearances. In conclusion, Perlin noise offers a flexible tool for generating breast tissue with realistic properties.
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| 8. |
- 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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| 9. |
- Förnvik, Daniel, et al.
(author)
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A human observer study for evaluation and optimization of reconstruction methods in breast tomosynthesis using clinical cases
- 2011
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In: Medical Imaging 2011: Physics of Medical Imaging. - : SPIE. - 0277-786X .- 1996-756X. ; 7961, s. 79615-79615
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Conference paper (peer-reviewed)abstract
- In breast tomosynthesis1 (BT) a number of 2D projection images are acquired from different angles along a limited arc. The imaged breast volume is reconstructed from the projection images, providing 3D information. The purpose of the study was to investigate and optimize different reconstruction methods for BT in terms of image quality using human observers viewing clinical cases. Sixty-six cases with suspected masses and calcifications were collected from 55 patients. Four different reconstructions of each image set were evaluated by four observers (two experienced radiologists, two experienced medical physicists): filtered back projection (FBP), iterative adapted FBP (iFBP) and two ML-convex iterative algorithm (MLCI) reconstructions (8 and 10 iterations) that differed in noise level and contrast of clinical details. Representation of masses and microcalcifications was evaluated. The structures were rated according to the overall appearance in a rank-order study. The differently reconstructed images of the same structure were displayed side by side in random order. The observers were forced to rank the order of the different reconstructed images and their proportions at each rank were scored. The results suggest that even though the FBP contains most noise its reconstructions are considered best overall, followed by iFBP, which contains least noise. In both FBP and iFBP methods the sharp borders and mass speculations were better represented than in iterative reconstructions while out-of-plane artifacts were better suppressed in the latter. However, in clinical practice the differences between the reconstructions may be considered negligible.
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| 10. |
- Förnvik, Daniel, et al.
(author)
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Pressure distribution in mammography: compression of breasts with malignant tumor masses
- 2013
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In: Medical Imaging 2013: Physics of Medical Imaging. - : SPIE. - 1996-756X .- 0277-786X. ; 8668, s. 86684-86684
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Conference paper (peer-reviewed)abstract
- The pressure distribution over a compressed breast is in general heterogeneous. In this study we investigated the pressure distribution over compressed breasts with tumor masses. Twenty-two women either recalled for work-up of findings suspicious for breast cancer in the screening program or with clinically suspected findings were included in the study. Twenty-one lesions turned out to be malignant and one benign. The distribution of compression pressure was measured using thin FSR (Force Sensing Resistor) pressure sensors attached to the compression plate. The pressure over the breast was ascertained by acquiring an x-ray image of the compressed breast with the pressure sensors present. The pressure data and the mammogram were used to create a composite image with pressure data displayed as a color overlay. The malignant tumor area generally matched an elevated pressure area and this pressure was generally higher than the pressure over surrounding parenchyma. In 11 out of 22 (50%) subjects the maximum pressure over the breast was located over the tumor. Only 4 out of 22 (18%) masses had a lower tumor mean pressure compared to the mean pressure over the breast (including one small < 10 mm tumor and one benign structure). The results suggest that tumors are stiffer, thus, absorbing more pressure compared to the surrounding parenchyma and that this property can be quantified. Refined pressure techniques could possibly be used to demonstrate the relative elasticity distribution in breast tissue, which might provide valuable differential diagnostic information.
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