The SenoScan full-field digital mammography scanner uses a scanning slot detector that is 10 mm wide and 220 mm long. The X-ray beam is collimated to just outside the area of the detector. One important advantage of slot scanning is its inherent scatter rejection. As previously reported, the SenoScan slot scatter rejection is better than that obtained using a 3.5:1 mammography grid, and somewhat worse than that with a 5:1 grid. Additional scatter reduction can potentially improve the contrast in images of thick breasts. We evaluate a custom-designed grid for the slot scanning system. The grid is one-dimensional, offering scatter rejection along the longitudinal axis of the detector. We evaluate the reduction in scatter fraction, grid absorption and changes in the signal-difference-to-noise ratio (SDNR). Based on phantom studies, our results show effective scatter reduction by the grid with minimal reduction of SDNR. Grid absorption and scatter elimination do not necessarily lead to an increase in patient dose, especially if there is a improvement in the number of digital values in the image that are within the useful dynamic range of the detector. A benefit of removing the scatter contribution is an improvement in system dynamic range, because electronic detector gain adjustments can compensate for the drop in the digital pixel values.
Optimization of the display of digital mammograms is an important challenge and requires knowledge of the characteristics of actual patient images. This work aims to create a description of some of the fundamental statistical properties of a large volume of images acquired on an FDA approved device as used in clinical practice. 4569 digital mammograms (1246 patients) were acquired between October
2001 and August 2002 on a GE Senograph 2000D at Sunnybrook and Women's College Health Sciences Centre. Images were saved in "raw" format. The breast was then segmented from the background on the image using a technique based on thresholding and some connectivity rules. The histogram of pixel values in the breast only is then
calculated for both the raw and processed versions of the image. The region of constant thickness, where the breast is in contact with the compression paddle, was also segmented from the CC view raw images. The histogram and statistical properties in this central region were also calculated. Assorted statistical descriptors of the histograms were examined (dynamic range, mean, standard deviations, median and mode). The effect of image processing on the dynamic range in the periphery and central area of the breast was evaluated.
The results were compared against the automatic exposure algorithm and acquisition parameters, projection (view) and breast thickness.
For digital mammography to be efficient, methods are needed to choose an initial default image presentation that maximizes the amount of relevant information perceived by the radiologist and minimizes the amount of time spent adjusting the image display parameters. The purpose of this work is to explore the possibility of using the output of computer aided detection (CAD) software to guide image enhancement and presentation. A set of 16 digital mammograms with lesions of known pathology was used to develop and evaluate an enhancement and display protocol to improve the initial softcopy presentation of digital mammograms. Lesions were identified by CAD and the DICOM structured report produced by the CAD program was
used to determine what enhancement algorithm should be applied in the identified regions of the image. An improved version of contrast limited adaptive histogram equalization (CLAHE) is used to enhance calcifications. For masses, the image is first smoothed using a non-linear diffusion technique; subsequently, local contrast is enhanced with a method based on morphological operators. A non-linear lookup table is automatically created to optimize the contrast in the regions of interest (detected lesions) without losing the context of the periphery of the breast. The effectiveness of the enhancement
will be compared with the default presentation of the images
using a forced choice preference study.
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