Average volumetric breast density has been found to be associated with interval cancers. The association is believed to be partly due to mammographic masking. We asked if regional density may be a more sensitive descriptor of masking than average density. In this work, we propose a new method to identify high density regions based on calibrating pixel-level volumetric breast density to Breast Imaging Reporting and Data System (BI-RADS) Version 4 categories. Local breast density was measured using the single-energy X-ray absorptiometry (SXA) technique. In 583 women undergoing screening mammography, we found percent fibroglandular volume ranges that corresponded to each BI-RADS category: 0–4.9
Contrast-enhanced digital breast tomosynthesis (CEDBT) may improve contrast-enhanced lesion conspicuity and relative contrast quantification by improving three-dimensional visualization of lesion morphology, and reducing the integration of attenuation information along the axial direction. Improved visualization of patterns of contrast-enhancement and improved iodine quantification may help differentiate between malignant and benign enhancing lesions. The dependence of dual-energy contrast-enhanced lesion detectability on imaging chain design is investigated. Lesion detectability and relative iodine quantification is comparable for subtraction in either reconstruction or projection domains for both phantom and patient images. SART generally produces greater SDNR than FBP, and scatter correcting projections further improves SDNR.
Breast MRI to X-ray mammography registration usingpatient-specific biomechanical models is one challenging task in medical imaging. To solve this problem, the accurate knowledge about internal and external factors of the breast, such as internal tissues distribution, is needed for modelling a suitable physical behavior. In this work, we compare four different tissue segmentation algorithms, two intensity-based segmentation algorithms (Fuzzy C-means and Gaussian mixture model) and two improvements that incorporate spatial information (Kernelized Fuzzy C-means and Markov Random Fields, respectively), and analyze their effect to the multi-modal registration. The overall framework consists on using a density estimation software (Volpara $$^{TM}$$ ) to extract the glandular tissue from full-field digital mammograms, meanwhile, a biomechanical model is used to mimic the mammographic acquisition from the MRI, computing the glandular tissue traversed by the X-ray beam. Results with 40 patients show a high agreement between the amount of glandular tissue computed for each method.
Nonuniform phantoms are needed in order to fully characterize the impact of anatomical structures on system performance in mammography and digital breast tomosynthesis (DBT). In this work, a new type of textured physical phantom is presented, compatible for use in both 2D and 3D applications. The breast phantom was first modeled analytically, and then fabricated using inkjet printing onto parchment paper and slide transparencies. A radiographic ink solution was synthesized with 350 mg/mL iohexol and pigmented ink. The effective linear attenuation coefficient (µeff) of the parchment paper alone (0.078 ± 0.003 mm−1) was found to be very close to that of a 70
Performance assessment of breast x-ray imaging systems through clinical imaging studies is expensive and may result in unreasonable high radiation doses to the patient. As an alternative, several research groups are investigating the potential of virtual clinical trials using realistic 3D breast texture models and simulated images from those models. This paper describes a mathematically defined solid 3D breast texture model based on the analysis of segmented clinical breast computed tomography images. The model employs stochastic geometry to mimic small and medium scale fibro-glandular and adipose tissue morphologies. Medium-scale morphology of each adipose compartment is simulated by a union of overlapping ellipsoids. The boundary of each ellipsoid consists of small Voronoi cells with average volume of 0.5 mm3, introducing a small-scale texture aspect. Model parameters were first empirically determined for almost entirely adipose breasts, scattered fibro-glandular dense breasts and heterogeneously dense breasts. Preliminary evaluation has shown that simulated mammograms and digital breast tomosynthesis images have a reasonable realistic visual appearance, depending though on simulated breast density. Statistical inference of model parameters from clinical breast computed tomography images for the variety of fibro-glandular and adipose tissue distributions observed in clinical images is ongoing.
Mammographic density is a strong risk factor for breast cancer. Volumetric breast density can be estimated from a digital mammogram by modelling the imaging process; this provides a more accurate assessment than subjective and 2D area-based methods. However, reliable density estimation in the uncompressed peripheral breast region and determination of compression paddle tilt are still open and challenging problems that affect the accuracy of measurement. Here we present a complete system that is able to perform thickness correction for both the compressed and uncompressed breast regions. The system was evaluated on a dataset of 208 mammograms, and compared with results from commercial software VolparaTM (version 1.5). The proposed method yielded Pearson correlation coefficients (PCC) of volumetric breast density (VBD) between left and right breasts of 0.88 (CC view) and 0.91 (MLO view). The PCC between VolparaTM VBD and our method is 0.93.
This work investigates the impact of advanced clinical displays on cancer detection in 2D digital mammograms using four-alternative-forced-choice (4AFC) and a dataset of images with inserted simulated lesions. Images were displayed on a standard monitor (Barco Coronis 5MP mammo) and an advanced monitor (Barco Coronis Uniti 12MP MDMC-12132). Ill-defined margin and spiculated mass models were inserted into mammographic regions of interest using a validated physics-based insertion framework. Experiments were conducted for mass size of 8–11 mm to 2–3 mm and density of 100 = 0.0076).
Mammography, the most effective early breast cancer detection technique, is associated with the risk of missed lesions in dense breasts, and excessive X-ray exposure. Accurate estimations of glandularity and radiation dose are important during screening. We propose a novel, inexpensive method for accurate glandularity quantification using pixel values in clinical digital mammograms and X-ray exposure spectra. Glandularities were calculated for 314 mammograms in Japanese women, and the Dance formula c-factor was applied to estimate breast doses. To investigate the relationship between breast thickness and missed lesions, images were classified into four categories based on the rate of missed lesions, and correlated with breast thickness. Glandularity decreased with increasing compressed breast thickness, indicating that commonly used breast doses (assumed 50% glandularity) significantly overestimate thin breasts and underestimate thick breasts. The missed lesion rate was higher for thinner compressed breast thicknesses. Accurate glandularity estimation could thus promote individualized screening mammography.
It has been shown that breast density and parenchymal patterns are important indicators in mammographic risk assessment. In addition, the accuracy of detecting abnormalities depends strongly on the structure and density of breast tissue. As such, mammographic parenchymal modelling and the related density estimation or classification are playing an important role in computer aided diagnosis. In this paper, we present a novel approach to the modelling of parenchymal tissue, which is directly linked to Tabar's normal breast tissue representation and based on the multi-scale distribution of dark ellipses, and the complementary distribution of bright ellipses which represent dense tissue. Our initial evaluation is based on the full MIAS database. We provide analysis of the separation between the Birads density classes, which indicates significant differences and a way towards automatic Birads based density classification.
Purpose: To perform a virtual clinical trial study to assess the justification of the grid-less mammography acquisition mode with scatter correction software, as developed by Siemens Healthcare (PRIME mode). Materials and methods: The study was performed on a Siemens mammography unit using the conventional acquisition mode (system 1) and a second system used PRIME. Mean glandular doses (MGD) were compared from data of 5981 images. A paired t-test for all thickness groups (<29 mm, 30–49 mm, 50–70 mm, >69 mm) separately and combined had shown a significantly higher average MGD for system 1 (NON-PRIME) when compared to system 2 (PRIME), with an overall decrease of 11.7 %. The next phase in justification focused on detectability performance, in particular for screening applications. A dataset mimicking an enriched screened population was created by simulating previously developed anthropomorphic mass models and microcalcification clusters in 60 out of 100 normal mammograms of system 1 (NON-PRIME). The same physical lesions were then simulated into 60 out of 100 PRIME, normal mammograms. Care was taken to simulate each lesion model in matched mammograms PRIME-NON PRIME in terms of BI-RADS score, in a region with the same background glandularity (obtained after analysis with Volpara) and in a breast of the same thickness group. All images were visualized with ViewDEX software and four radiologists performed the free search detectability study. A JAFROC analysis was executed and detectability was quantified by means of the AUC. Results: Present approach allowed the realization of paired virtual clinical data sets starting from 200 normal mammograms. The results of all readers separately as well as combined showed approximately the same AUC for PRIME and NON-PRIME (0.57 vs 0.60), and the ANOVA analysis showed no statistical significant difference in detectability of the lesions between PRIME and NON-PRIME (p-value 0.36). The same result was found if the dataset was subdivided for both types of lesions: masses (p-value 0.88) and microcalcification clusters (p-value 0.33). Conclusion: Results state that the MGD is significantly lower in PRIME mode than with the conventional acquisition while lesion detectability remained constant for all four radiologists.
Digital mammography has limitations in sensitivity, in particular for patients with a dense breast. Phase contrast techniques (phase contrast mammography, PCM) might increase the tissue contrast for breast imaging. Propagation based PCM with a dedicated 0.1-mm-focal spot size mammography unit was investigated in past years, showing higher image quality in magnification PCM than in absorption based DM. In this work the authors investigated, using breast phantoms, the dependence of image quality on increasing mean glandular dose with a 0.007-mm-focal spot size W-anode microfocus X-ray tube. They compared PCM imaging (magnification M ≅ 2) to absorption based contact imaging (M ≅ 1) and then to phase retrieval for phase imaging, at low (40 kV) as well as high (80 kV) beam energy. Phase imaging shows higher image contrast for glandular masses and microcalcifications with MGD similar to one-view mammography. The phase contrast power spectrum assumes higher values than for absorption imaging. Possibility of dose reduction was suggested by the adoption of phase retrieval PCM.
Virtual clinical trials (VCTs) are increasingly being seen as a viable pre-clinical method for evaluation of imaging systems in breast cancer screening. The CR-UK funded OPTIMAM project is aimed at producing modelling tools for use in such VCTs. In the initial phase of the project, modelling tools were produced to simulate 2D-mammography and digital breast tomosynthesis (DBT) imaging systems. This paper elaborates on the new tools that have recently been developed for the current phase of the OPTIMAM project. These new additions to the framework include tools for simulating synthetic breast tissue, spiculated masses and variable-angle DBT systems. These tools are described in the paper along with the preliminary validation results. Four-alternative forced choice (4-AFC) type studies deploying these new tools are underway. The results of the ongoing 4AFC studies investigating minimum detectable contrast/size of masses/microcalcifications for different modalities and system designs are presented.
Aim: Understanding both normal mammographic appearance and how false positive (FP) errors occur is paramount to improving the efficiency and diagnostic accuracy of screening mammography services. While much of the focus of research is on increasing knowledge about the appearances and imaging of breast cancers, this study reports on findings where breast screen readers are asked to comment on past incorrect decisions by assigning a lexicon that best describes a known FP region. Method: Fifteen breast screen readers were given two tasks. The first was to assess nine normal screening cases which had attracted a high number of FP decisions in a test set of 60 cases in a previous study with 129 readers. In the second task, the 15 readers in this study, who were made aware that the nine cases were normal, were directed to view distinct regions of interest (ROI) that represented the FP markings from past readings in the blinded observer performance study. A list of descriptors derived from literature was used to assist readers to describe the mammographic appearance within those ROIs. Results: In the first task, readers identified breast density as the greatest difficulty in determining normality. In the second task, asymmetry of breast tissue and a suspicion of architectural distortion (AD) were the top two reasons our readers gave to explain the high number of past FP decisions. Additionally, our readers believed past FP decisions were less likely to reflect a suspicion of breast lesions or masses (second task). Conclusion: The classification of normal cases remains a challenging task, influenced by asymmetry and breast density. FP decisions may reflect a suspicion of AD and appear less related to suspicion of masses.
Solitary, well-defined lesions are a common mammographic finding contributing more than 20 % of overall screening recalls. Discrimination of cystic from solid breast lesions therefore has the potential to reduce unnecessary recalls in mammography screening. A pre-clinical study, measuring the energy-dependent X-ray attenuation of tissue specimen and cystic fluid, revealed a measurable difference of the photon detection rate in the two energy bins of an energy-resolving photon-counting mammography system for these two tissue types. Based on these differences, a spectral lesion characterization algorithm has been developed, which estimates the lesion composition from spectral measurements in a lesion and a lesion-free reference region. In this work, we present a simulation study to estimate the dependence of this lesion characterization algorithm on various types of uncertainties including the biological variation of cyst fluid and tumor tissue, variations in the mammographic background texture, and errors in the spectral measurements. The simulation study uses the receiver operating characteristics (ROC) for the task of identifying solid lesions (‘positive result’) to predict an expected area under the curve (AUC) and the specificity at the 99 % sensitivity level for a simulated screening population. The results of this simulation study are compared to those of a recently published pilot study.
This paper investigates the use of mereotopological barcodes to help non-experts classify microcalcification clusters as either benign or malignant. When compared against classification using the microcalcification cluster segmentation maps, the use of barcodes is able to see a significant improvement in classification performance with the AUC significantly increasing ( $$p < 0.01$$ ) from 0.62 for images to 0.82 for barcodes on the MIAS dataset. This shows that barcodes could prove useful to aid clinicians with interpreting and classifying mammographic microcalcifications.
We present the outcomes of combined feasibility studies carried out at Elettra and Australian Synchrotron to evaluate novel protocols for three-dimensional (3D) mammographic phase contrast imaging. A custom designed plastic phantom and some tissue samples have been studied at diverse resolution scales and experimental conditions. Several computed tomography (CT) reconstruction algorithms with different pre-processing and post-processing steps have been considered. Special attention was paid to the effect of phase retrieval on the diagnostic value of the reconstructed images. The images were quantitatively evaluated using objective quality indices in comparison with subjective assessments performed by three experienced radiologists and one pathologist.We show that the propagation-based phase-contrast imaging (PBI) leads to substantial improvement to the contrast-to-noise and to the intrinsic quality of the reconstructed CT images compared with conventional techniques as well as to an important reduction of the delivered doses, thus opening the way to clinical implementations.
Our purpose was to investigate the influence of phantom and biological materials on a 3-component decomposition using dual-energy mammography protocol (3CB). Materials and Methods: A novel dual-energy 3CB mammography technique concludes in quantifying of the lipid, protein, and water thicknesses. The protocol was designed to be used on full-field digital mammography system by including an additional high-energy image with the clinical image. We study influence of calibration phantom and regression techniques on three component outputs. Two types of phantoms were used: solid water/wax/Delrin phantom and bovine phantom consisted of fat and lean muscle compartments. The linear and quadratic model equations were analyzed using linear and ridge regressions. The elaborated calibration protocol was applied to breast images with different compositions and sizes. In addition, the protocol was validated using cadaver breasts of known compositions. Results: We found that there were many negative values of protein components when we applied our solid water/wax/Delrin calibrations using 51 ROIs for clinical dual energy mammogram analysis. This behavior could be explained by potential over fitting and not exact correspondence of biological and phantom material. Creating a calibration related to bovine tissue provided higher accuracy and realizable thicknesses for clinical breast composition components, and achieved satisfactory results for cadaver breast compositions. Conclusion: Using a bovine calibration, the 3CB technique provides higher accuracy for lipid, water and protein compositional breast measurements than using plastic tissue equivalents alone.
A texture analysis aimed at finding correlations between textural descriptors and lesion diagnosis was applied to Contrast-Enhanced Digital Mammography (CEDM) subtracted images acquired under single-energy temporal subtraction modality using iodine-based contrast medium. The study, based on textural descriptors from Gray Level Co-occurrence Matrix (GLCM), included 68 CEDM images of 17 patients, 10 cancer and 7 benign, acquired 1 to 5 min after iodine injection. Seventeen GLCM descriptors were analyzed. Image processing consisted of geometric registration, logarithmic subtraction, and selection of regions-of-interest (adipose, glandular and lesion ROIs) by the radiologist. Results for lesion ROIs showed that homogeneity, normalized homogeneity, second-order inverse moment, energy and inverse variance were insensitive to the presence of iodine; a linear correlation existed between the sum mean and mean pixel value. Logistic regression showed that a linear combination of entropy and diagonal momentum discriminated between malignant and benign lesions with 79 % specificity, 93 % sensitivity and 87 % accuracy.
There are several methods to evaluate objectively the quality of a digital image. For digital mammography, objective quality assessment must be performed without references. In a previous study, the authors investigated the use of a normalized anisotropic quality index (NAQI) to assess mammography images blindly in terms of noise and spatial resolution. Since the NAQI is used as a quality metric, it must not be highly dependent on the breast anatomy. Thus, in this work, we analyze the NAQI behavior with different breast anatomies. A computerized system was used to synthesize 2,880 anthropomorphic breast phantom images with a realistic range of anatomical variations. The results show that NAQI is only marginally dependent on breast anatomy when images are acquired without degradation (< 12 %). However, for realizations that simulate the acquisition process in digital mammography, the NAQI is more sensitive ( 33 %) to variations arising from quantum noise. Thus, NAQI can be used in clinical practice to assess mammographic image quality.
Virtual clinical trials (VCT) currently represent key tools for breast imaging optimisation, especially in two-dimensional planar mammography and digital breast tomosynthesis. Voxelised breast models are a crucial part of VCT as they allow the generation of synthetic image projections of breast tissue distribution. Therefore, realistic breast models containing an accurate representation of women breasts are needed. Current voxelised breast models show, in their compressed version, a very round contour which might not be representative of the entire population. This work pretends to develop an imaging framework, based on depth cameras, to investigate breast deformation during mammographic compression. Preliminary results show the feasibility of depth sensors for such task, however post-processing steps are needed to smooth the models. The proposed framework can be used in the future to produce more accurate compressed breast models, which will eventually generate more realistic images in VCT.
