Anatomical complexity in breast parenchyma and its implications for optimal breast imaging strategies.
ABSTRACT: The purpose of this investigation was to assess the anatomical noise in breast images using a mathematically derived parameter β as a surrogate for detection performance, across the same patient cohort but in different imaging modalities including mammography, tomosynthesis, and breast CT.Women who were scheduled for breast biopsy were approached for participation in this IRB and HIPPA-compliant investigation. A total of 23 women had all views of each modality and represent the cohort studied in this investigation. Image data sets across all modalities were analyzed using 1000 regions of interest per image data set, and the anatomical noise power spectrum, NPS(a)(f), was computed and averaged for each breast image data set. After windowing the total noise power spectrum NPS(t)(f) to a specific frequency range corresponding to anatomical noise, the power-law slope (β) of the NPS(a)(f) was computed where NPS(a)(f) = α f(-) (β).The value of β was determined for breast CT data sets, and they were 1.75 (0.424), 1.83 (0.352), and 1.79 (0.397), for the coronal, sagittal, and axial views, respectively. For tomosynthesis, β was 3.06 (0.361) and 3.10 (0.315) for the craniocaudal (CC) and medial lateral oblique (MLO) views, respectively. For mammography, these values were 3.17 (0.226) and 3.30 (0.236), for the CC and MLO views, respectively. The values of β for breast CT were significantly different than those for tomosynthesis and mammography (p < 0.001, all 12 comparisons).Based on the parameter β which is thought to describe anatomical noise in breast images, breast CT was shown to have a statistically significant lower β than mammography or tomosynthesis. It has been suggested in the literature that a lower β may correspond to increased cancer detection performance; however, this has yet to be demonstrated unequivocally.
Project description:To analyze the frequency domain characteristics of the signal in mammography images and breast tomosynthesis projections with patient tissue texture due to detected scattered x-rays.Acquisitions of x-ray projection images of 19 different patient breasts were simulated using previously acquired volumetric patient images. Acquisition of these images was performed with a dedicated breast CT prototype system, and the images were classified into voxels representing skin, adipose, and glandular tissue with a previously validated automated algorithm. The classified three dimensional images then underwent simulated mechanical compression representing that which is performed during acquisition of mammography and breast tomosynthesis images. The acquisition of projection images of each patient breast was simulated using Monte Carlo methods with each simulation resulting in two images: one of the primary (non-scattered) signal and one of the scatter signal. To analyze the scatter signal for both mammography and breast tomosynthesis, two projections images of each patient breast were simulated, one with the x-ray source positioned at 0° (mammography and central tomosynthesis projection) and at 30° (wide tomosynthesis projection). The noise power spectra (NPS) for both the scatter signal alone and the total signal (primary + scatter) for all images were obtained and the combined results of all patients analyzed. The total NPS was fit to the expected power-law relationship NPS(f) = k/f β and the results were compared with those previously published on the power spectrum characteristics of mammographic texture. The scatter signal alone was analyzed qualitatively and a power-law fit was also performed.The mammography and tomosynthesis projections of three patient breasts were too small to analyze, so a total of 16 patient breasts were analyzed. The values of β for the total signal of the 0° projections agreed well with previously published results. As expected, the scatter power spectrum reflected a fast drop-off with increasing spatial frequency, with a reduction of four orders of magnitude by 0.1 lp/mm. The β values for the scatter signal were 6.14 and 6.39 for the 0° and 30° projections, respectively.Although the low-frequency characteristics of scatter in mammography and breast tomosynthesis were known, a quantitative analysis of the frequency domain characteristics of this signal was needed in order to optimize previously proposed software-based x-ray scatter reduction algorithms for these imaging modalities.
Project description:To develop a set of accurate 2D models of compressed breasts undergoing mammography or breast tomosynthesis, based on objective analysis, to accurately characterize mammograms with few linearly independent parameters, and to generate novel clinically realistic paired cranio-caudal (CC) and medio-lateral oblique (MLO) views of the breast.We seek to improve on an existing model of compressed breasts by overcoming detector size bias, removing the nipple and non-mammary tissue, pairing the CC and MLO views from a single breast, and incorporating the pectoralis major muscle contour into the model. The outer breast shapes in 931 paired CC and MLO mammograms were automatically detected with an in-house developed segmentation algorithm. From these shapes three generic models (CC-only, MLO-only, and joint CC/MLO) with linearly independent components were constructed via principal component analysis (PCA). The ability of the models to represent mammograms not used for PCA was tested via leave-one-out cross-validation, by measuring the average distance error (ADE).The individual models based on six components were found to depict breast shapes with accuracy (mean ADE-CC = 0.81 mm, ADE-MLO = 1.64 mm, ADE-Pectoralis = 1.61 mm), outperforming the joint CC/MLO model (P ? 0.001). The joint model based on 12 principal components contains 99.5% of the total variance of the data, and can be used to generate new clinically realistic paired CC and MLO breast shapes. This is achieved by generating random sets of 12 principal components, following the Gaussian distributions of the histograms of each component, which were obtained from the component values determined from the images in the mammography database used.Our joint CC/MLO model can successfully generate paired CC and MLO view shapes of the same simulated breast, while the individual models can be used to represent with high accuracy clinical acquired mammograms with a small set of parameters. This is the first step toward objective 3D compressed breast models, useful for dosimetry and scatter correction research, among other applications.
Project description:The use of contrast agents in breast imaging has the capability of enhancing nodule detectability and providing physiological information. Accordingly, there has been a growing trend toward using iodine as a contrast medium in digital mammography (DM) and digital breast tomosynthesis (DBT). Widespread use raises concerns about the best way to use iodine in DM and DBT, and thus a comparison is necessary to evaluate typical iodine-enhanced imaging methods. This study used a task-based observer model to determine the optimal imaging approach by analyzing six imaging paradigms in terms of their ability to resolve iodine at a given dose: unsubtracted mammography and tomosynthesis, temporal subtraction mammography and tomosynthesis, and dual energy subtraction mammography and tomosynthesis.Imaging performance was characterized using a detectability index d', derived from the system task transfer function (TTF), an imaging task, iodine signal difference, and the noise power spectrum (NPS). The task modeled a 10 mm diameter lesion containing iodine concentrations between 2.1 mg/cc and 8.6 mg/cc. TTF was obtained using an edge phantom, and the NPS was measured over several exposure levels, energies, and target-ﬁlter combinations. Using a structured CIRS phantom, d' was generated as a function of dose and iodine concentration.For all iodine concentrations and dose, temporal subtraction techniques for mammography and tomosynthesis yielded the highest d', while dual energy techniques for both modalities demonstrated the next best performance. Unsubtracted imaging resulted in the lowest d' values for both modalities, with unsubtracted mammography performing the worst out of all six paradigms.At any dose, temporal subtraction imaging provides the greatest detectability, with temporally subtracted DBT performing the highest. The authors attribute the successful performance to excellent cancellation of inplane structures and improved signal difference in the lesion.
Project description:SUMMARY: BACKGROUND: The aim of this study was to investigate the efficacy of the rolled views taken in craniocaudal (CC) and mediolateral oblique (MLO) projections in solving equivocal mammography findings. PATIENTS AND METHODS: The rolled views were taken by changing the positioning of the breast but not the obliquity of the X-ray beams. The breast was rolled medially or laterally in the rolled CC view, and inferiorly or superiorly in the rolled MLO view to separate overlapping structures from each other. RESULTS: We evaluated equivocal findings in 87 asymptomatic women undergoing either CC (n = 48, 55%) or MLO (n = 39, 45%) rolled views between 2001 and 2008. The rolled views were helpful in solving equivocal mammographic findings and making proper decisions on management in 85 of the 87 (97.7%) women. This technique was used for breast asymmetries in 55 of the 87 (63.2%) women, and was sufficient to directly show summation artifacts in 59 of 79 (74.6%) women. The rolled views revealed 4 intramammary lymph nodes, 2 circumscribed masses out of 6 obscured masses, 7 summation artifacts, and 2 circumscribed masses out of 9 questionable masses. CONCLUSIONS: The rolled view is an effective method of differentiating summation artifacts from real lesions on mammography in both the CC and the MLO view.
Project description:To compare the diagnostic performance of breast tomosynthesis versus supplemental mammography views in classification of masses, distortions, and asymmetries.Eight radiologists who specialized in breast imaging retrospectively reviewed 217 consecutively accrued lesions by using protocols that were HIPAA compliant and institutional review board approved in 182 patients aged 31-60 years (mean, 50 years) who underwent diagnostic mammography and tomosynthesis. The lesions in the cohort included 33% (72 of 217) cancers and 67% (145 of 217) benign lesions. Eighty-four percent (182 of 217) of the lesions were masses, 11% (25 of 217) were asymmetries, and 5% (10 of 217) were distortions that were initially detected at clinical examination in 8% (17 of 217), at mammography in 80% (173 of 217), at ultrasonography (US) in 11% (25 of 217), or at magnetic resonance imaging in 1% (2 of 217). Histopathologic examination established truth in 191 lesions, US revealed a cyst in 12 lesions, and 14 lesions had a normal follow-up. Each lesion was interpreted once with tomosynthesis and once with supplemental mammographic views; both modes included the mediolateral oblique and craniocaudal views in a fully crossed and balanced design by using a five-category Breast Imaging Reporting and Data System (BI-RADS) assessment and a probability-of-malignancy score. Differences between modes were analyzed with a generalized linear mixed model for BI-RADS-based sensitivity and specificity and with modified Obuchowski-Rockette approach for probability-of-malignancy-based area under the receiver operating characteristic (ROC) curve.Average probability-of-malignancy-based area under the ROC curve was 0.87 for tomosynthesis versus 0.83 for supplemental views (P < .001). With tomosynthesis, the false-positive rate decreased from 85% (989 of 1160) to 74% (864 of 1160) (P < .01) for cases that were rated BI-RADS category 3 or higher and from 57% (663 of 1160) to 48% (559 of 1160) for cases rated BI-RADS category 4 or 5 (P < .01), without a meaningful change in sensitivity. With tomosynthesis, more cancers were classified as BI-RADS category 5 (39% [226 of 576] vs 33% [188 of 576]; P = .017) without a decrease in specificity.Tomosynthesis significantly improved diagnostic accuracy for noncalcified lesions compared with supplemental mammographic views.
Project description:To develop models of compressed breasts undergoing mammography based on objective analysis, that are capable of accurately representing breast shapes in acquired clinical images and generating new, clinically realistic shapes.An automated edge detection algorithm was used to catalogue the breast shapes of clinically acquired cranio-caudal (CC) and medio-lateral oblique (MLO) view mammograms from a large database of digital mammography images. Principal component analysis (PCA) was performed on these shapes to reduce the information contained within the shapes to a small number of linearly independent variables. The breast shape models, one of each view, were developed from the identified principal components, and their ability to reproduce the shape of breasts from an independent set of mammograms not used in the PCA, was assessed both visually and quantitatively by calculating the average distance error (ADE).The PCA breast shape models of the CC and MLO mammographic views based on six principal components, in which 99.2% and 98.0%, respectively, of the total variance of the dataset is contained, were found to be able to reproduce breast shapes with strong fidelity (CC view mean ADE = 0.90 mm, MLO view mean ADE = 1.43 mm) and to generate new clinically realistic shapes. The PCA models based on fewer principal components were also successful, but to a lesser degree, as the two-component model exhibited a mean ADE = 2.99 mm for the CC view, and a mean ADE = 4.63 mm for the MLO view. The four-component models exhibited a mean ADE = 1.47 mm for the CC view and a mean ADE = 2.14 mm for the MLO view. Paired t-tests of the ADE values of each image between models showed that these differences were statistically significant (max p-value = 0.0247). Visual examination of modeled breast shapes confirmed these results. Histograms of the PCA parameters associated with the six principal components were fitted with Gaussian distributions. The six-component model was also used to generate CC and MLO view mammogram breast shapes, using the mean PCA parameter values of these distributions and randomly generated values based on the fitted Gaussian distributions, which resemble clinically encountered breasts. A spreadsheet with the data necessary to apply this model is provided as the supplementary material.Our PCA models of breast shapes in both mammographic views successfully reproduce analyzed breast shapes and generate new clinically relevant shapes. This work can aid in research applications which incorporate breast shape modeling, such as x-ray scatter correction, dosimetry, and image registration.
Project description:To compare radiologists' diagnostic accuracy and recall rates for breast tomosynthesis combined with digital mammography versus digital mammography alone.Institutional review board approval was obtained at each accruing institution. Participating women gave written informed consent. Mediolateral oblique and craniocaudal digital mammographic and tomosynthesis images of both breasts were obtained from 1192 subjects. Two enriched reader studies were performed to compare digital mammography with tomosynthesis against digital mammography alone. Study 1 comprised 312 cases (48 cancer cases) with images read by 12 radiologists; study 2, 312 cases (51 cancer cases) with 15 radiologists. Study 1 readers recorded only that an abnormality requiring recall was present; study 2 readers had additional training and recorded both lesion type and location. Diagnostic accuracy was compared with receiver operating characteristic analysis. Recall rates of noncancer cases, sensitivity, specificity, and positive and negative predictive values determined by analyzing Breast Imaging Reporting and Data System scores were compared for the two methods.Diagnostic accuracy for combined tomosynthesis and digital mammography was superior to that of digital mammography alone. Average difference in area under the curve in study 1 was 7.2% (95% confidence interval [CI]: 3.7%, 10.8%; P < .001) and in study 2 was 6.8% (95% CI: 4.1%, 9.5%; P < .001). All 27 radiologists increased diagnostic accuracy with addition of tomosynthesis. Recall rates for noncancer cases for all readers significantly decreased with addition of tomosynthesis (range, 6%-67%; P < .001 for 25 readers, P < .03 for all readers). Increased sensitivity was largest for invasive cancers: 15% and 22% in studies 1 and 2 versus 3% for in situ cancers in both studies.Addition of tomosynthesis to digital mammography offers the dual benefit of significantly increased diagnostic accuracy and significantly reduced recall rates for noncancer cases.http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12120674/-/DC1.
Project description:For digital breast tomosynthesis (DBT) systems, we investigate the effects of the reconstruction filters for different data acquisition angles on signal detection. We simulated a breast phantom with a 30% volume glandular fraction (VGF) of breast anatomy using the power law spectrum and modeled the breast mass as a spherical object with a 1 mm diameter. Projection data were acquired using two different data acquisition angles and numbers of projection view pairs, and in-plane breast images were reconstructed using the Feldkamp-Davis-Kress (FDK) algorithm with three different reconstruction filter schemes. To measure the ability to detect a signal, we conducted the human observer study with a binary detection task and compared the signal detectability of human to that of channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) channels and dense difference-of-Gaussian (D-DOG) channels. We also measured the contrast-to-noise ratio (CNR), signal power spectrum (SPS), and ? values of the anatomical noise power spectrum (NPS) to show the association between human observer performance and these traditional metrics. Our results show that using a slice thickness (ST) filter degraded the signal detection performance of human observers at the same data acquisition angle. This could be predicted by D-DOG CHO with internal noise, but the correlation between the traditional metrics and signal detectability was not observed in this work.
Project description:This paper concerns the feasibility of x-ray differential phase contrast (DPC) tomosynthesis imaging using a grating-based DPC benchtop experimental system, which is equipped with a commercial digital flat-panel detector and a medical-grade rotating-anode x-ray tube. An extensive system characterization was performed to quantify its imaging performance.The major components of the benchtop system include a diagnostic x-ray tube with a 1.0 mm nominal focal spot size, a flat-panel detector with 96 μm pixel pitch, a sample stage that rotates within a limited angular span of ± 30°, and a Talbot-Lau interferometer with three x-ray gratings. A total of 21 projection views acquired with 3° increments were used to reconstruct three sets of tomosynthetic image volumes, including the conventional absorption contrast tomosynthesis image volume (AC-tomo) reconstructed using the filtered-backprojection (FBP) algorithm with the ramp kernel, the phase contrast tomosynthesis image volume (PC-tomo) reconstructed using FBP with a Hilbert kernel, and the differential phase contrast tomosynthesis image volume (DPC-tomo) reconstructed using the shift-and-add algorithm. Three inhouse physical phantoms containing tissue-surrogate materials were used to characterize the signal linearity, the signal difference-to-noise ratio (SDNR), the three-dimensional noise power spectrum (3D NPS), and the through-plane artifact spread function (ASF).While DPC-tomo highlights edges and interfaces in the image object, PC-tomo removes the differential nature of the DPC projection data and its pixel values are linearly related to the decrement of the real part of the x-ray refractive index. The SDNR values of polyoxymethylene in water and polystyrene in oil are 1.5 and 1.0, respectively, in AC-tomo, and the values were improved to 3.0 and 2.0, respectively, in PC-tomo. PC-tomo and AC-tomo demonstrate equivalent ASF, but their noise characteristics quantified by the 3D NPS were found to be different due to the difference in the tomosynthesis image reconstruction algorithms.It is feasible to simultaneously generate x-ray differential phase contrast, phase contrast, and absorption contrast tomosynthesis images using a grating-based data acquisition setup. The method shows promise in improving the visibility of several low-density materials and therefore merits further investigation.