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

VOLUMETRIC LOCALISATION OF DENSE BREAST TISSUE USING BREAST TOMOSYNTHESIS DATA.

This study attempted to use combined data from reconstructed digital breast tomosynthesis (DBT) volumes and density estimation of projection images to localise dense tissue inside the breast, using the assumption that the breast can be treated as consisting of only two types of tissue: fibroglandular (dense) and adipose (fatty). To be able to verify results, software breast phantoms generated using fractal Perlin noise were employed. Projection images were created using the PENELOPE Monte Carlo package. Dense tissue volume was estimated from the central projection image. The density image was used to determine the number of dense voxels at each pixel location, which were then placed using the DBT image as a template. The method proved capable of accurately determining the composition of 75±5 % of voxels

Intra- and inter-rater reliability of compressed breast thickness, applied force, and pressure distribution in screening mammography

BackgroundEnsuring equivalent and reproducible breast compression between mammographic screening rounds is important for the diagnostic performance of mammography, yet the extent to which screening mammography positioning and compression is reproducible for the individual woman is unknown.PurposeTo investigate the intra- and inter-rater reliability of breast compression in screening mammography.Materials and MethodsEleven breast-healthy women participated in the study. Two experienced radiographers independently positioned and compressed the breasts of each participant in two projections—right craniocaudal and left mediolateral oblique—and at two time points. The spatial pressure distribution on the compressed breast was measured using a pressure sensor matrix. Applied force, compressed breast thickness, force in field of view, contact area, mean pressure, and center of mass (anterio-posterior and mediolateral axes) were measured. The reliabilities of the measures between the time points for each radiographer (intra-rater reliability) and between the radiographers (inter-rater reliability) were analyzed using the intraclass correlation coefficient (ICC).ResultsIntra- and inter-rater reliabilities from both projections demonstrated good to excellent ICCs (≥0.82) for compressed breast thickness, contact area, and anterio-posterior center of mass. The other measures produced ICCs that varied from poor (≤0.42) to excellent (≥0.93) between time points and between radiographers.ConclusionIntra- and inter-rater reliability of breast compression was consistently high for compressed breast thickness, contact area, and anterio-posterior center of mass but low for mediolateral center of mass and applied force. Further research is needed to establish objective and clinically useful parameters for the standardization of breast compression

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

Evaluation of the possibility to use thick slabs of reconstructed outer breast tomosynthesis slice images

The large image volumes in breast tomosynthesis (BT) have led to large amounts of data and a heavy workload for breast radiologists. The number of slice images can be decreased by combining adjacent image planes (slabbing) but the decrease in depth resolution can considerably affect the detection of lesions. The aim of this work was to assess if thicker slabbing of the outer slice images (where lesions seldom are present) could be a viable alternative in order to reduce the number of slice images in BT image volumes. The suggested slabbing (an image volume with thick outer slabs and thin slices between) were evaluated in two steps. Firstly, a survey of the depth of 65 cancer lesions within the breast was performed to estimate how many lesions would be affected by outer slabs of different thicknesses. Secondly, a selection of 24 lesions was reconstructed with 2, 6 and 10 mm slab thickness to evaluate how the appearance of lesions located in the thicker slabs would be affected. The results show that few malignant breast lesions are located at a depth less than 10 mm from the surface (especially for breast thicknesses of 50 mm and above). Reconstruction of BT volumes with 6 mm slab thickness yields an image quality that is sufficient for lesion detection for a majority of the investigated cases. Together, this indicates that thicker slabbing of the outer slice images is a promising option in order to reduce the number of slice images in BT image volumes