Digital Mammography

In digital mammography, the processes of image acquisition, display, and storage are separated, which allows optimization of each. Radiation transmitted through the breast is absorbed by an electronic detector, the response of which is faithful over a wide range of intensities. Once this information is recorded, it can be displayed by using computer image-processing techniques to allow arbitrary settings of image brightness and contrast, without the need for further exposure to the patient. In this article, the current state of the art in technology for digital mammography and data from clinical trials that support the use of the technology will be reviewed. In addition, several potentially useful applications that are being developed with digital mammography will be described

Issues to Consider in Converting to Digital Mammography

This paper will outline the reasons that many radiology practices are converting to digital mammography. In addition, we will provide basic information on the issues that must be considered in making the transformation. These include technical matters regarding image display, storage and retrieval, as well as clinical and ergonomic considerations

Two-Modality Mammography May Confer an Advantage Over Either Full-Field Digital Mammography or Screen-Film Mammography

To compare the cancer detection rate and ROC area under the curve of full-field digital mammography, screen-film mammography, and a combined technique that allowed diagnosis if a finding was suspicious on film, on digital, or both

Discrimination of benign from malignant breast lesions in dense breasts with model-based analysis of regions-of-interest using directional diffusion-weighted images.

BACKGROUND: There is an increasing interest in non-contrast-enhanced magnetic resonance imaging (MRI) for detecting and evaluating breast lesions. We present a methodology utilizing lesion core and periphery region of interest (ROI) features derived from directional diffusion-weighted imaging (DWI) data to evaluate performance in discriminating benign from malignant lesions in dense breasts. METHODS: We accrued 55 dense-breast cases with 69 lesions (31 benign; 38 cancer) at a single institution in a prospective study; cases with ROIs exceeding 7.50 cm RESULTS: The region-growing algorithm for 3D lesion model generation improved inter-observer variability over hand drawn ROIs (DSC: 0.66 vs 0.56 (p \u3c 0.001) with substantial agreement (DSC \u3e 0.8) in 46% vs 13% of cases, respectively (p \u3c 0.001)). The overall classifier improved discrimination over mean ADC, (ROC- area under the curve (AUC): 0.85 vs 0.75 and 0.83 vs 0.74 respectively for the two readers). CONCLUSIONS: A classifier generated from directional DWI information using lesion core and lesion periphery information separately can improve lesion discrimination in dense breasts over mean ADC and should be considered for inclusion in computer-aided diagnosis algorithms. Our model-based ROIs could facilitate standardization of breast MRI computer-aided diagnostics (CADx)

A pilot study of eye movement during mammography interpretation: Eyetracker results and workstation design implications

Digital mammography can potentially improve mammography image and interpretation quality. On-line interpretation from a workstation may improve interpretation logistics and increase availability of comparison images. Interpretation of eight 4k- x 5k-pixel mammograms on two to four 2k- x 2.5k-pixel monitors is problematic because of the time spent in choosing which images display on which monitors, and zooming and roaming on individual images that are too large to display completely at full resolution. The authors used an eyetracker to measure radiologists viewing behavior during mammography interpretation with film on a viewbox. It was observed that a significant portion of the mammographers' time is spent viewing "comparison pairs" (typically two or more comparisons per case), such as the left mediolateral and craniocaudal images or old and new images. From the eyetracker measurements, we estimated that the number of image display, roam, and zoom operations decreases from an average of 64 for one monitor to 31 for four monitors, with the largest change going from one to two monitors. We also show that fewer monitors with a faster response time is superior to more monitors with a slower response time. Finally, the authors demonstrate the applicability of time-motion analysis to mammographic workstation design

Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms

The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms. Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep. Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied. A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied. The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image. Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds. Twenty student observers were asked to detect the orientation of the spiculation in the image. There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4. The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved

Comparison of Acquisition Parameters and Breast Dose in Digital Mammography and Screen-Film Mammography in the American College of Radiology Imaging Network Digital Mammographic Imaging Screening Trial

The purpose of our study was to compare the technical performance of full-field digital mammography (FFDM) and screen-film mammography

Does intensity windowing improve the detection of simulated calcifications in dense mammograms?

This study attempts to determine whether intensity windowing (IW) improves detection of simulated calcifications in dense mammograms. Clusters of five simulated calcifications were embedded in dense mammograms digitized at 50-microns pixels, 12 bits deep. Film images with no windowing applied were compared with film images with nine different window widths and levels applied. A simulated cluster was embedded in a realistic background of dense breast tissue, with the position of the cluster varied. The key variables involved in each trial included the position of the cluster, contrast level of the cluster, and the IW settings applied to the image. Combining the ten IW conditions, four contrast levels and four quadrant positions gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 160 backgrounds. The entire experiment consisted of 800 trials. Twenty student observers were asked to detect the quadrant of the image in which the mass was located. There was a statistically significant improvement in detection performance for clusters of calcifications when the window width was set at 1024 with a level of 3328, and when the window width was set at 1024 with a level of 3456. The selected IW settings should be tested in the clinic with digital mammograms to determine whether calcification detection performance can be improved

The effect of intensity windowing on the detection of simulated masses embedded in dense portions of digitized mammograms in a laboratory setting

The purpose of this study was to determine whether intensity windowing (IW) improves detection of simulated masses in dense mammograms. Simulated masses were embedded in dense mammograms digitized at 50 microns/pixel, 12 bits deep. Images were printed with no windowing applied and with nine window width and level combinations applied. A simulated mass was embedded in a realistic background of dense breast tissue, with the position of the mass (against the background) varied. The key variables involved in each trial included the position of the mass, the contrast levels and the IW setting applied to the image. Combining the 10 image processing conditions, 4 contrast levels, and 4 quadrant positions gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 160 backgrounds. The entire experiment consisted of 800 trials. Twenty observers were asked to detect the quadrant of the image into which the mass was located. There was a statistically significant improvement in detection performance for masses when the window width was set at 1024 with a level of 3328. IW should be tested in the clinic to determine whether mass detection performance in real mammograms is improved

Consequences of False-Positive Screening Mammograms

False-positive mammograms, a common occurrence in breast cancer screening programs, represent a potential screening harm that is currently being evaluated by the United States Preventive Services Task Force