Binary implementation of fractal Perlin noise to simulate fibroglandular breast tissue

Software breast phantoms are important in many applications within the field of breast imaging and mammography. This paper describes an improved method of using a previously employed in-house fractal Perlin noise algorithm to create binary software breast phantoms. The Perlin Noise algorithm creates smoothly varying structures of a frequency with a set band limit. By combining a range of frequencies (octaves) of noise, more complex structures are generated. Previously, visually realistic appearances were achieved with continuous noise values, but these do not adequately represent the breast as radiologically consisting of two types of tissue - fibroglandular and adipose. A binary implementation with a similarly realistic appearance would therefore be preferable. A library of noise volumes with continuous values between 0 and 1 were generated. A range of threshold values, also between 0 and 1, were applied to these noise volumes, creating binary volumes of different appearance, with high values resulting in a fine network of strands, and low values in nebulous clusters of tissue. These building blocks were then combined into composite volumes and a new threshold applied to make them binary. This created visually complex binary volumes with a visually more realistic appearance than earlier implementations of the algorithm. By using different combinations of threshold values, a library of pre-generated building blocks can be used to create an arbitrary number of software breast tissue volumes with desired appearance and density

Personalised breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence

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

Monte Carlo simulation of breast tomosynthesis: visibility of microcalcifications at different acquisition schemes

Microcalcifications are one feature of interest in mammography and breast tomosynthesis (BT). To achieve optimal conditions for detection of microcalcifications in BT imaging, different acquisition geometries should be evaluated. The purpose of this work was to investigate the influence of acquisition schemes with different angular ranges, projection distributions and dose distributions on the visibility of microcalcifications in reconstructed BT volumes. Microcalcifications were inserted randomly in a high resolution software phantom and a simulation procedure was used to model a MAMMOMAT Inspiration BT system. The simulation procedure was based on analytical ray tracing to produce primary images, Monte Carlo to simulate scatter contributions and flatfield image acquisitions to model system characteristics. Image volumes were reconstructed using the novel method super-resolution reconstruction with statistical artifact reduction (SRSAR). For comparison purposes, the volume of the standard acquisition scheme (50 degrees angular range and uniform projection and dose distribution) was also reconstructed using standard filtered backprojection (FBP). To compare the visibility and depth resolution of the microcalcifications, signal difference to noise ratio (SDNR) and artifact spread function width (ASFW) were calculated. The acquisition schemes with very high central dose yielded significantly lower SDNR than the schemes with more uniform dose distributions. The ASFW was found to decrease (meaning an increase in depth resolution) with wider angular range. In conclusion, none of the evaluated acquisition schemes were found to yield higher SDNR or depth resolution for the simulated microcalcifications than the standard acquisition scheme

The effect of breast density on the performance of deep learning-based breast cancer detection methods for mammography

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

Breast cancer screening with digital breast tomosynthesis : comparison of different reading strategies implementing artificial intelligence

OBJECTIVES: Digital breast tomosynthesis (DBT) can detect more cancers than the current standard breast screening method, digital mammography (DM); however, it can substantially increase the reading workload and thus hinder implementation in screening. Artificial intelligence (AI) might be a solution. The aim of this study was to retrospectively test different ways of using AI in a screening workflow.METHODS: An AI system was used to analyse 14,772 double-read single-view DBT examinations from a screening trial with paired DM double reading. Three scenarios were studied: if AI can identify normal cases that can be excluded from human reading; if AI can replace the second reader; if AI can replace both readers. The number of detected cancers and false positives was compared with DM or DBT double reading.RESULTS: By excluding normal cases and only reading 50.5% (7460/14,772) of all examinations, 95% (121/127) of the DBT double reading detected cancers could be detected. Compared to DM screening, 27% (26/95) more cancers could be detected (p < 0.001) while keeping recall rates at the same level. With AI replacing the second reader, 95% (120/127) of the DBT double reading detected cancers could be detected-26% (25/95) more than DM screening (p < 0.001)-while increasing recall rates by 53%. AI alone with DBT has a sensitivity similar to DM double reading (p = 0.689).CONCLUSION: AI can open up possibilities for implementing DBT screening and detecting more cancers with the total reading workload unchanged. Considering the potential legal and psychological implications, replacing the second reader with AI would probably be most the feasible approach.KEY POINTS: • Breast cancer screening with digital breast tomosynthesis and artificial intelligence can detect more cancers than mammography screening without increasing screen-reading workload. • Artificial intelligence can either exclude low-risk cases from double reading or replace the second reader. • Retrospective study based on paired mammography and digital breast tomosynthesis screening data