Conventional Mammograms Research Articles

Breast Cancer Screening and Diagnostic Advancements

The best tool to detect breast cancer (BC) is `screening`. Earlier screening improves prognosis and better treatment outcomes. Screening is basically looking for different signs before the appearance of actual symptoms. The process of screening specifically used for BC is known as mammography.
 Mammography is an explicit kind of imaging system that uses X-Rays (low dose) to ascertain cancer in breasts. This diagnostic procedure is in practice since the past 30 years but in the past 15 years, there is an improved set of techniques being introduced resulting in better results. Recently, three major diagnostic procedures for breast cancer which are in vogue include digital mammography, computer-aided testing and tomosynthesis. Out of these 3 diagnostic tools, tomosynthesis, furthermore branded as three-dimensional (3-D) mammography and digital tomosynthesis, is the supreme and innovative system of breast imaging where numerous pictures of the breast from diverse angles are taken and remodeled ("synthesized") resulting in formation of a three-dimensional group of images. That is why, 3-D imaging technique is alike computed tomography imaging system in which a sequences of thin "slices" are gathered to formulate a 3-D reformation of the body.
 Even though the strength of radiation for few tomosynthesis systems is higher to some extent as compared to power of the dosage used in typical mammography, but as long as it is in the predefined radiation limit approved by FDA it`s not harmful. Some of the systems have doses comparable to conventional mammography. Some previous studies on large populations have proved that breast screening with tomosynthesis have resulted in enhanced the rate of detection of breast cancer detection rates and it has also decreased the chances of recalls, especially at occasions where females are called back for screening for added testing because of a possibly uncharacteristic finding.
 Tomosynthesis has an edge on other diagnostic procedures as it may help in prior recognition of cancerous lesions that may be not be revealed on a conventional mammogram, it also eradicates the need for small number of preventable biopsies or supplementary testing and increases the probability of detection of several breast tumors. It also provides stronger imagining of irregularities within condensed breast tissue and greater precision in pinpointing the location, size and shape of breast anomalies.
 Women should at least get a standard mammogram once a year beginning at age 40. The major risk factors for BC development are old age, inactivity, reproductive history, genetic mutations, family history of ovarian or BC, being overweight or obese after menopause, history of administration of hormones. Also, if someone, have had radiation treatment to the chest in the past, it’s highly recommended that such women should have annual mammograms at a younger age (often beginning around age 30).

The role of Sonoelastography in characterization of solid breast lesions

Abstract Background Mammography and Sonography are the important sensitive imaging modalities available in detection of breast cancer. Sonoelastography is a relatively new imaging technique which acts as a complementary tool to improve the diagnostic ability of ultrasound for evaluating patients with breast masses. Objective To evaluate the role of sonoelastography characterization information on solid breast lesions over conventional sonography and mammography either benign or malignant lesions to reduce the number of benign biopsies performed. Methods A prospective study was carried at private centers, starting from august 2019 till december 2019. Results The study conducted on 40 patients with palpable breast lesions classified to BIRADS 3 and 4a according to conventional ultrasound and mammographic criteria. Their ages ranged from 18 to '75 years with a mean age 47.72 14.91, 22 cases (55%) had no family history of pervious breast disease, and 18 cases (45%) had family history of pervious breast disease. All patients were subjected to full history taking, clinical examination, conventional gray scale ultrasound, and mammography and ultrasound elastography. All patients underwent histopathological assessment to reach the final diagnosis. Among Studied cases , 28 lesions were sampled by fine needle biopsy (70%) and 12 lesions were sampled by true cut needle (30%) .the results revealed 27 breast lesions were benign (67.5%), 9 malignant lesions (22.5%) and 4 lesions shows atypia (10%) according to histopathological assessment. Among final diagnosis of breast lesions fibro adenoma was the most frequent benign breast lesion detected as 12 lesions from 27 benign breast lesions were fibroadenoma., Also infiltrative ductal carcinoma was the moste frequent among malignant breast lesions as 5 lesions from 9 malignant lesions were infiltrative ductal carcinoma. Conclusion Ultrasoundelastography has significant complementray role beside conventional mammogram and ultrasound in characterization of solid breast lesions.

Contrast-enhanced spectral mammography: successful initial clinical institute experience

BackgroundContrast-enhanced spectral mammography (CESM) is a relatively newly developed advanced application with modification of digital mammography by the use of a contrast agent, but still has little known efficacy among Egyptian patients. Our aim in this study is to share our initial experience in evaluating symptomatic patients with different ACR breast parenchyma especially in dense breast parenchyma as it is always challenging in diagnosis.ResultsCESM in this study gave a sensitivity of 92% and specificity of 85% in characterization of benign and malignant lesions. For postoperative cases, sensitivity was 85% and specificity was 60%. For chemotherapy cases, sensitivity was 85% and specificity was 76%. Contrast uptake was noted in 68% of masses. Cavitary benign lesions were noted in 22.1% of cases. Multifocal and multicentric carcinomas were detected in 39.7% of pathologically proved malignant masses. Statistical analysis revealed sensitivity, specificity, and accuracy of 82.9%, 76.5%, and 81.0% for conventional mammograms as compared to 92.7%, 82.4%, and 89.7% for CESM respectively.ConclusionCESM is a promising technique that can enhance the specificity of conventional mammograms. It is an easy, simple, and rapid contrast-based procedure, especially for characterization of lesions in dense breast parenchyma. It performs proper diagnosis for high-risk patients and follow-up response to different lines of management.

Using convolutional neural networks to discriminate between cysts and masses in Monte Carlo-simulated dual-energy mammography.

A substantial percentage of recalls (up to 20%) in screening mammography is attributed to extended round lesions. Benign fluid-filled breast cysts often appear similar to solid tumors in conventional mammograms. Spectral imaging (dual-energy or photon-counting mammography) has been shown to discriminate between cysts and solid masses with clinically acceptable accuracy. This work explores the feasibility of using convolutional neural networks (CNNs) for this task. A series of Monte Carlo experiments was conducted with digital breast phantoms and embedded synthetic lesions to produce realistic dual-energy images of both lesion types. We considered such factors as nonuniform anthropomorphic background, size of the mass, breast compression thickness, and variability in lesion x-ray attenuation. These data then were used to train a deep neural network (ResNet-18) to learn the differences in x-ray attenuation of cysts and masses. Our simulation results showed that the CNN-based classifier could reliably discriminate between cystic and solid mass round lesions in dual-energy images with an area under the receiver operating characteristic curve (ROC AUC) of 0.98 or greater. The proposed approach showed promising performance and ease of implementation, and could be applied to novel photon-counting detector-based spectral mammography systems.

Trade-off between breast mean glandular dose and image quality in digital and conventional mammogram systems: A multicenter study

Evaluating the patient dose or exposure parameters considering the image quality can improve the chances of accurate diagnosis and reduce the unnecessary exposures from medical devices such as mammography. This study aimed to evaluate digital and conventional mammography machines while considering the trade-off between image quality and mean glandular dose (MGD) using a phantom. In the present study, one full-field digital mammography (FFDM) and two film-screen mammography (FSM) machines were investigated. The MGD values and image quality were assessed using the American College of Radiology (ACR) phantom at various mAs and constant kVp values. The results were obtained and compared with European guidelines. Friedman and Wilcoxon statistical tests were used to show the comparison. The results from the quality control (QC) tests demonstrated that all machines are functioning well. The best image quality in the digital mammography machine was observed at the MGD of 1.8 mGy and 55 mAs. In addition, the two conventional machines had the best image quality regarding the imaging of the ACR phantom at 65 mAs with an MGD of 2.1 mGy. These values were considered as appropriate values for the studied mammography systems. Furthermore, the Friedman test demonstrated that there are significant differences between the measured image quality values obtained from the different machines (p < 0.05), however, according to the Wilcoxon test there were not any significant differences between the conventional machines at various mAs values. Owing to the results, for a medium breast size, the image quality will not be improved with increasing the exposure after a specified MGD corresponds to a certain mAs. It is notable that this value is smaller in digital mammography system at a reasonably low dose.

Application of artificial intelligence-based computer-assisted diagnosis on synthetic mammograms from breast tomosynthesis: comparison with digital mammograms.

To compare the diagnostic agreement and performances of synthetic and conventional mammograms when artificial intelligence-based computer-assisted diagnosis (AI-CAD) is applied. From January 2017 to April 2017, 192 patients (mean age 53.7 ± 11.7 years) diagnosed with 203 breast cancers were enrolled in this retrospective study. All patients underwent digital breast tomosynthesis (DBT) with digital mammograms (DM) simultaneously. Commercial AI-CAD was applied to the reconstructed synthetic mammograms (SM) from DBT and DM respectively and abnormality scores were calculated. We compared the median abnormality scores between DM and SM with the Wilcoxon signed-rank test and used the Bland-Altman analysis to evaluate agreements between the two mammograms and to investigate clinicopathological factors which might affect agreement. Diagnostic performances were compared using an area under the receiver operating characteristic curve (AUC). The abnormality scores showed a mean difference (bias) of - 3.26 (95% limits of agreement: - 32.69, 26.18) between the two mammograms by the Bland-Altman analysis. The concordance correlation coefficient was 0.934 (95% CI: 0.92, 0.946), suggesting high reproducibility. SM showed higher abnormality scores in cancer with distortion and occult findings, T1 and N0 cancer, and luminal type cancer than DM (all p ≤ 0.001). Diagnostic performance did not differ between the mammograms (AUC 0.945 for conventional mammograms, 0.938 for synthetic mammograms, p = 0.499). AI-CAD can also work well on synthetic mammograms, showing good agreement and comparable diagnostic performance compared to its application to DM. • AI-CAD which was developed based on imaging findings of digital mammograms can also be applied to synthetic mammograms. • AI-CAD showed good agreement and similar diagnostic performance when applied to both synthetic and digital mammograms. • With AI-CAD, synthetic mammograms showed relatively higher abnormality scores in cancer with distortion and occult findings, T1 and N0 cancer, and luminal type cancer than digital mammograms.

Machine Learning for the Analysis of Conductivity From Mono Frequency Electrical Impedance Mammography as a Breast Cancer Risk Factor

Computational approaches have been used for analyzing risk factors together with conventional mammograms for breast cancer detection. Currently, other screening methods like electro-impedance mammography are available. Notwithstanding, as far as we know there is not related work evaluating the role of electrical-conductivity index of the mammary gland as a quantitative factor for early detection of breast cancer. This paper aims to demonstrate the importance of including breast conductivity index as a quantitative local risk-factor by analyzing a dataset of Mexican patients from a machine learning perspective. There are 12 attributes distributed into two groups: electrical-conductivity (3) and medical records (9). According to the obtained results with unsupervised methods, the performance in terms of accuracy of using only electrical-conductivity (43%) is better than using all available features (38%) and the medical records (33%). On the other hand, we identified that SVM achieves higher results in comparison with other algorithms when only the electrical-features are used. The obtained results demonstrate the important role of conductivity index as a quantitative local risk-factor for being considered in screening processes. Besides, it emerges as an important aspect to be included in the development of automatic tools for experts to perform breast cancer diagnosis.

The Effect of Breast Parenchymal Density on Breast Cancer Subtypes and Prognostic Factors

Objective: Mammographic breast parenchymal density is the radiological composition of radiodense fibroglandular tissue and radiolucent adipose tissue. Dense breasts restrict the imaging of lesions detected by mammography and reduce the sensitivity of mammography. In this study, we investigated correlation between breast parenchymal density and prognosis, tumor biology, reoperation rates and prognostic factors.
 Methods: Patients who were operated for breast cancer between January 2017-December 2019 were included. The craniocaudal and mediolateral images obtained during conventional mammograms were collected in a database. The appearance of radiologically dense and lucent areas on mammography was taken as the mammographic percentage consistent with the current literatüre
 Results: According to breast parenchymal density, there were 21 patients (14.7%) with type 1 breast density, 29 (20.3%) with type 2, 47 (32.9%) with type 3 and 46 (32.1%) with type 4. Mean survival time due to breast cancer was 27.45±17.85 months (1-63), and 139 patients (97.2%) lived, while 4 (2.8%) were exitus.
 Conclusion: Increased breast parenchymal density is a crucial risk factor for breast cancer. The effects of density on tumor biology and prognosis are still controversial. Although increased breast density seems to have an effect on indicators of poor prognosis. Additional examinations should be taken into consideration in order not to cause delay or skipping in diagnosis due to the decreased sensitivity of mammography in dense breasts

Stereoscopic digital mammogram: Usefulness in daily practice

ObjectivesTo assess the benefit of stereoscopic digital mammography in daily practice. MethodsStereoscopic digital (SD) mammography allows a fused 3D view of mammogram. A 4-degree cranio-caudal (CC) angle view matched with a regular CC view allows generating a stereoscopic view. Three breast radiologists reviewed retrospectively 1110 stereoscopic digital mammograms (1075 women) performed between November 2011 and February 2013 with the following sequence: each evaluated firstly conventional mammogram alone, and then integrated SD mammograms. The benefit was quoted in 3-grade scale: 0 for no benefit, 1 moderate and 2 excellent. The concordance between radiologists was evaluated by the W Randall coefficient. Subgroup analysis according to the BI-RADS classification, breast density and type of abnormalities were performed by calculating odds-ratio. ResultsThe readers had the same opinion regarding the value of stereoscopic digital mammograms in 87% of cases (962/1110). Benefit was null, moderate and excellent in 8, 52 and 26% respectively. The concordance of radiologists was excellent with a W coefficient above 0.89. ConclusionsSD mammogram improved interpretation of abnormal mammograms. This potentially interesting and promising complementary tool might be beneficial in daily breast imaging practice.

Galactogram for Investigation of Pathological Nipple Discharge: A Forgotten Arrow in the Radiologists’ Quiver?

AbstractConventional X-ray galactogram (CG) is an underutilized procedure in modern breast imaging despite offering the highest spatial resolution among all modalities available for imaging of the breast ducts. The superior diagnostic performance of CG as compared to that of both conventional mammogram and high-resolution ultrasonography makes it a valuable imaging modality for the evaluation of pathological nipple discharge (PND). In addition, CG should always be considered in women with bloody nipple discharge but normal ultrasound and mammogram. CG also has an important role in the preoperative localization of intraductal lesions. CG may be especially useful in resource-restricted settings where breast magnetic resonance imaging is not readily available as it can be easily performed at any mammography facility without the need for additional equipment. In this article, we describe two cases of PND, one of benign and the other of malignant etiology, to demonstrate the value of CG in these cases. We also review the current literature and compare CG with other modalities used for imaging of ductal system of the breast.

Initial Experience with Contrast Enhanced Digital Mammography (SenoBright)—In a Comprehensive Clinical Breast Center

Objective: The purpose of initiating contrast enhanced digital mammography in our center was to evaluate the complimentary benefit of this technology with screening digital mammography and real time ultrasound in equivocal cases and high risk patients with dense breast. The intended goal was to reduce the incidence of further diagnostic and invasive procedures. Methods: Patients thought to be candidates who had good renal function confirmed by serum Blood Urea Nitrogen (BUN) and Creatinine were offered the procedure, and 225 patients had the procedure during the period of March 2013 through November 2014. The contrast enhanced digital mammograms (SenoBright) are performed on the Senograph Essential Unit. A total of 8 images are obtained: 4 conventional digital mammograms and 4 contrast enhanced digital mammograms. The patients with a positive SenoBright study had a tissue diagnosis of the lesion obtained by either a stereotactic needle biopsy, ultrasound guided core biopsy, or ultrasound directed open excisional biopsy. Results: The 225 patients who had the procedure included high risk patients with dense breast (41), patients with abnormal mammograms (92), and patients with equivocal clinical, mammographic and real time ultrasound findings (92). 31 studies were interpreted as positive and 194 as negative. 33 biopsies were performed, with 31 patients having a positive study and 2 patients with a negative study. 22 cancers were detected. Conclusion: We found that the addition of dynamic contrast enhanced digital subtraction mammography (SenoBright) was helpful in distinguishing malignant from non-malignant lesions. It was also effective in demonstrating multifocal lesions and identifying non-palpable occult carcinomas in the dense breast. It proved to be a valuable complimentary adjunctive diagnostic modality for a comprehensive clinical breast center.

Mammogram Enhancement Using Intuitionistic Fuzzy Sets.

Conventional mammogram enhancement methods use transform-domain filtering, which possibly produce some artifacts or not well highlight all local details in images. This paper presents a new enhancement method based on intuitionistic fuzzy sets. The presented algorithm initially separates a mammogram via a global threshold and then fuzzifies the image utilizing the intuitionistic fuzzy membership function that adopts restricted equivalence functions. After that, the presented scheme hyperbolizes membership degrees of foreground and background areas, defuzzifies the fuzzy plane, and achieves a filtered image via normalization. Finally, an enhanced mammogram is obtained by fusing the original image with filtered one. These implementations can be processed in parallel. This algorithm can improve the contrast and visual quality of regions of interest. Real data experiments demonstrate that our method has better performance regarding the improvement of contrast and visual quality of abnormalities in mammograms (such as masses and/or microcalcifications), compared with classical baseline methods. This algorithm has potential for understanding and determining abnormalities.

Digital breast tomosynthesis: Mean glandular dose estimation using monte carlo code

Introduction 3D breast imaging using the technique of digital tomosynthesis may have the ability to improve the visualization of tissues which would be superimposed on a conventional mammogram. Early results with digital tomosynthesis are promising. Purpose To estimate mean glandular dose (MGD) from tomosynthesis imaging of the breast using Monte Carlo code and voxel phantom in standing posture. Materials and methods EGSnrc Monte Carlo code was used to simulate the interaction of photons with matter included a female voxel phantom (breast thickness of 2-8 cm and 50% glandular fraction). For the selection of X-ray spectrum, Automatic Exposure Control of Digital Breast Tomosynthesis was used. For each imaging condition were computed the glandular dose for the zero degree projection angle and the glandular dose for non-zero projection angles in 2° steps (scan range was −25° to +25°). The calculations were made for the X-ray spectra from a W target (tube voltage range 25–40 kV) filtered by Rh. Results Acquisition of a cranio-caudal view resulted in an MGD ranging from 0,318 to 2,347 mGy. For a breast with a thickness of 5 cm and 50% glandular fraction, the MGD was 2.061 mGy. There is no significant difference between the MGD for CC and MLO mode. Conclusion The results of this study are limited to providing the MGD to voxels phantoms of various thickness and only 50% glandular fraction. Future studies of the resulting dose from various glandular fractions are needed.

Limit of Detection in X-ray Diffraction Measurements of Tissue Equivalent Samples

There is a suggestion of a new approach to mammography whereby following a conventional mammogram, the radiologist could interrogate suspicious regions using X-ray diffraction whilst the patient is still present and to establish the true extent of disease. A starting point for this work is to quantify the minimum detectable amount of breast cancer within a realistic thickness phantom. Perspex has a similar diffraction pattern to healthy breast tissue whilst water is similar to breast tumour, hence these two materials are used as tissue equivalent test objects for X-ray diffraction measurements. The preliminary results show linear agreement between the ratio of Perspex to water and the ratio of the diffraction peak intensities at 0.7 nm-1 and 1.5 nm-1. The minimum detectable limit for a component of the two ‘tissue’ mix was found to be 4.1%. This suggests that X-ray diffraction can be used to quantify tissue like mixtures down to the 4.1% / 95.9% mix level and hence has a strong potential for delineating the extent of infiltration disease.

Contrast-enhanced spectral mammography (CESM) compared with breast MRI for breast cancer detection.

11069 Background: Contrast-enhanced spectral mammography (CESM) is a new study to detect contrast enhancing malignancy that may not be visible on conventional mammogram. Limited studies have shown ...

Abstract P1-02-06: Retrospective comparison of sensitivity and positive predictive value (PPV) of contrast enhanced spectral mammography (CESM) to contrast enhanced breast MRI (BMRI) in 50 malignant breasts

Abstract Objective: Contrast enhanced spectral mammography (CESM) is a new study using contrast for early breast cancer detection. It utilizes dual energy digital mammographic technology to detect contrast enhanced cancer that may be invisible on conventional mammogram. Limited studies have shown that adding CESM to diagnostic workup adjunct with mammogram and breast ultrasound does increase sensitivity for breast cancer detection. More studies are needed to compare the sensitivity and positive predictive value (PPV) of CESM to BMRI to further define the role of CESM in breast cancer diagnosis. Methods: This study involved 50 malignant breasts in 48 women retrospectively chosen from of 960 patients in our institution during the period of October 2012 to March 2014. Both CESM and BMRI were done for each patient within 30 days. The cancer diagnoses were confirmed by tissue diagnoses. The number of malignant lesions was quantified in each breast. The size of lesions was classified into three categories based on standard of breast cancer stages: 1 (0.2cm -<= 2cm), 2 (2cm<lesion<=5cm), 3(>5cm). The enhancement intensity of both lesions and background has been quantified based on a scale of 0-3. Statistical significance was analyzed using T test for mean size of index cancer and mean score of enhancement intensity of background and lesions on CESM and BMRI. Sensitivity and PPV were calculated for both CESM and BMRI. Morphology consistence was calculated on both studies. Results: Both CESM and BMRI have sensitivity of 100% for breast cancer detection. CESM has a PPV of 98% versus 93% for BMRI. No statistical significance was identified on mean size of index cancer (3.7cm for CESM and 3.8cm for BMRI). The enhancement intensity of background and lesions is significantly higher on CESM than on BMRI (p<0.01). The smallest cancer can be detected by both CESM and BMRI is less than 0.5 cm. Morphology consistence was 46/50 (92%). Of the 50 breasts, there was one false-positive finding at CESM mammography, and four false-positive findings at MR imaging. Conclusions: Our study indicates that CESM and BMRI have comparable high sensitivity on breast cancer detection. CESM has a higher PPV than BMRI that may indicate a better specificity (no significant difference due to the small sample size). Significantly less background enhancement intensity on CESM than on BMRI reflect an increased specificity. More studies need to be conducted for further evaluation. Citation Format: Luna Li, Lydia Liao, Pauline Germaine, Elizabeth Tinney, Kristin Brill, Karen Hendershott. Retrospective comparison of sensitivity and positive predictive value (PPV) of contrast enhanced spectral mammography (CESM) to contrast enhanced breast MRI (BMRI) in 50 malignant breasts [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P1-02-06.

A retrospective comparison of contrast enhanced spectral mammography (CESM) to breast MRI in breast cancer detection: An initial study for sensitivity analysis.

7 Background: CESM is a new study using contrast and dual energy digital mammographic technology to detect contrast enhanced cancer that may be invisible on conventional mammogram. Limited studies have shown that adding CESM to diagnostic workup adjunct with mammogram and breast ultrasound does increase sensitivity for breast cancer detection. More studies are needed to compare the sensitivity of CESM to BMRI to further define the role of CESM in breast cancer diagnosis. Methods: This study involved 50 malignant breasts in 48 women retrospectively chosen from of 960 patients in our institution during the period of October 2012 to March 2014. Both CESM and BMRI were done for each patient within 30 days. The cancer diagnoses were confirmed by tissue diagnoses. The size of lesions was classified into three categories based on standard of breast cancer stages: 1 (0.2cm - < = 2 cm), 2 (2 cm < lesion < = 5 cm), 3 (> 5 cm). The enhancement intensity of both lesions and background has been quantified based on a scale of 0-3. Statistical significance was analyzed using T test for mean size of index cancer and mean score of enhancement intensity of background and lesions on CESM and BMRI. Sensitivity and positive predictive value (PPV) were calculated for both CESM and BMRI. Morphology consistence was calculated on both studies. Results: Both CESM and BMRI have sensitivity of 100% for breast cancer detection. CESM has a PPV of 98% versus 93% for BMRI. No statistical significance was identified on mean size of index cancer. The enhancement intensity of background and lesions is significantly higher on CESM than on BMRI (p < 0.01). The smallest cancer can be detected by both CESM and BMRI is less than 0.5 cm. Morphology consistence was 46/50 (92%). Conclusions: Our study indicates that CESM and BMRI have comparable high sensitivity on breast cancer detection. CESM has a higher PPV than BMRI that may indicate a better specificity (no significant difference due to the small sample size). Significantly less background enhancement intensity on CESM than on BMRI reflect an increased specificity. More studies need to be conducted for further evaluation.

Generating Synthetic Mammograms From Reconstructed Tomosynthesis Volumes

Digital breast tomosynthesis (DBT) is a promising 3-D modality that may replace mammography in the future. However, lesion search is likely to require more time in DBT volumes, while comparisons between views from different projections and prior exams might be harder to make. This may make screening with DBT cumbersome. A solution may be provided by synthesizing 2-D mammograms from DBT, which may then be used to guide the search for abnormalities. In this work we focus on synthesizing mammograms in which masses and architectural distortions are optimally visualized. Our approach first determines relevant points in a DBT volume with a computer-aided detection system and then renders a mammogram from the intersection of a surface fitted through these points and the DBT volume. The method was evaluated in a pilot observer study where three readers reported mass findings in 87 patients (25 malignant, 62 normal) for which both DBT and digital mammograms were available. We found that on average, diagnostic accuracy in the synthetic mammograms was higher (Az=0.85) than in conventional mammograms (Az=0.81), although the difference was not statistically significant. Preliminary results suggest that the synthesized mammograms are an acceptable alternative for real mammograms regarding the detection of mass lesions.

Image fusion scheme for differential phase contrast mammography

Latest progresses in breast imaging using differential phase contrast technique pose the question how to fuse multiple information (yielded by the absorption, differential phase, and scattering signals) into a single, but more informative image for clinical diagnosis and evaluation. In this work, we propose an image fusion scheme based on the multiple-resolution (MR) framework. The three signals are first transformed into multiple bands presenting information at different frequency and then a two-step processing follows: section 3.2 an intra-band processing enhances the local signal-to-noise ratio using a novel noise estimation method and context modeling; section 3.3 an inter-band processing weights each band by considering their characteristics and contributions, as well as suppressing the global noise level. The fused image, which looks similar to conventional mammogram but with significantly enhanced detail features, is then reconstructed by inverse transform. This fused image is compatible with clinical settings and enables the radiologists to use their years of diagnosis experiences in mammography.

Phantom-based comparison of conventional versus phase-contrast mammography for LCD soft-copy diagnosis

Liquid crystal display (LCD) of mammograms provides soft-copy results that differ in conventional and phase contrast mammography (PCM). PCM potentially offers the highest quality of sharpness and graininess, an edge emphasis effect on the object, and the highest image resolution. However, when the image is displayed on an LCD, the resolution depends on the pixel pitch and the PCM image data must be diminished. We investigated the observed effect on spatial resolution and contrast when conventional or phase contrast mammograms are viewed on an LCD. Using the tissue-equivalent phantom (Model 1011A), a conventional mammogram and a magnification radiography image were obtained with a PCM system. This phantom contains simulated fibers, microcalcifications, and masses. The PCM image was reduced 1/1.75 to render it consistent with life size mammography using the nearest neighbor, bilinear, and bicubic interpolation methods. The images were displayed on a five million (5M)-pixel LCD with 100% magnification. Ten mammography technicians observed the reduction images displayed on LCDs and reported their results. In the detectability of the microcalcifications, there was no significant difference between conventional mammograms and reduced PCM images. Regarding fibers and masses, detectability using reduced images was higher than those of conventional images. The detectability using images reduced by the nearest-neighbor method was lower than those of images reduced by two other interpolation methods. Bilinear interpolation was affected by the smoothing effect, while CNR was increased. In addition, since the noise of PCM image was reduced by an air gap effect, high detectability of key image features was found. Soft-copy display of phase-contrast mammograms is feasible with LCDs, while detectability of fibers and masses was best with bilinear interpolation and use of an air gap.