We are reporting the optimized acquisition scheme of multi-projection breast Correlation Imaging (CI)
technique, which was pioneered in our lab at Duke University. CI is similar to tomosynthesis in its image
acquisition scheme. However, instead of analyzing the reconstructed images, the projection images are directly
analyzed for pathology. Earlier, we presented an optimized data acquisition scheme for CI using mathematical
observer model. In this article, we are presenting a Computer Aided Detection (CADe)-based optimization
methodology. Towards that end, images from 106 subjects recruited for an ongoing clinical trial for
tomosynthesis were employed. For each patient, 25 angular projections of each breast were acquired. Projection
images were supplemented with a simulated 3 mm 3D lesion. Each projection was first processed by a
traditional CADe algorithm at high sensitivity, followed by a reduction of false positives by combining
geometrical correlation information available from the multiple images. Performance of the CI system was
determined in terms of free-response receiver operating characteristics (FROC) curves and the area under ROC
curves. For optimization, the components of acquisition such as the number of projections, and their angular
span were systematically changed to investigate which one of the many possible combinations maximized the
sensitivity and specificity. Results indicated that the performance of the CI system may be maximized with 7-11
projections spanning an angular arc of 44.8°, confirming our earlier findings using observer models. These
results indicate that an optimized CI system may potentially be an important diagnostic tool for improved breast
cancer detection.
Featureless, knowledge-based CAD systems are an attractive alternative to feature-based CAD because they require no
to minimal image preprocessing. Such systems compare images directly using the raw image pixel values rather than
relying on low-level image features. Specifically, information-theoretic (IT) measures such as mutual information (MI)
have been shown to be an effective, featureless, similarity measure for image comparisons. MI captures the statistical
relationship between the gray level values of corresponding image pixels. In a CAD system developed at our laboratory,
the above concept has been applied for location-specific detection of mammographic masses. The system is designed to
operate on a fixed size region of interest (ROI) extracted around a suspicious mammographic location. Since mass sizes
vary substantially, there is a potential drawback. When two ROIs are compared, it is unclear how much the parenchymal
background contributes in the calculated MI. This uncertainty could deteriorate CAD performance in the extreme cases,
namely when a small mass is present in the ROI or when a large mass extends beyond the fixed size ROI. The present
study evaluates the effect of ROI size on the overall CAD performance and proposes multisize analysis for possible
improvement. Based on two datasets of ROIs extracted from DDSM mammograms, there was a statistically significant
decline of the CAD performance as the ROI size increased. The best size ranged between 512x512 and 256x256 pixels.
Multisize fusion analysis using a linear model achieved further improvement in CAD performance for both datasets.
The purpose of this project is to study two Computer Aided Detection (CADe) systems for breast masses for
digital tomosynthesis using reconstructed slices. This study used eighty human subject cases collected as part
of on-going clinical trials at Duke University. Raw projections images were used to identify suspicious regions
in the algorithm's high sensitivity, low specificity stage using a Difference of Gaussian filter. The filtered
images were thresholded to yield initial CADe hits that were then shifted and added to yield a 3D distribution
of suspicious regions. The initial system performance was 95% sensitivity at 10 false positives per breast
volume. Two CADe systems were developed. In system A, the central slice located at the centroid depth was
used to extract a 256 X 256 Regions of Interest (ROI) database centered at the lesion coordinates. For system B,
5 slices centered at the lesion coordinates were summed before the extraction of 256 × 256 ROIs. To avoid
issues associated with feature extraction, selection, and merging, information theory principles were used to
reduce false positives for both the systems resulting in a classifier performance of 0.81 and 0.865 Area Under
Curve (AUC) with leave-one-case-out sampling. This resulted in an overall system performance of 87%
sensitivity with 6.1 FPs/ volume and 85% sensitivity with 3.8 FPs/ volume for systems A and B respectively.
This system therefore has the potential to detect breast masses in tomosynthesis data sets.
The purpose of this project is to study Computer Aided Detection (CADe) of breast masses for digital
tomosynthesis. It is believed that tomosynthesis will show improvement over conventional mammography in
detection and characterization of breast masses by removing overlapping dense fibroglandular tissue. This study
used the 60 human subject cases collected as part of on-going clinical trials at Duke University. Raw projections
images were used to identify suspicious regions in the algorithm's high-sensitivity, low-specificity stage using a
Difference of Gaussian (DoG) filter. The filtered images were thresholded to yield initial CADe hits that were then
shifted and added to yield a 3D distribution of suspicious regions. These were further summed in the depth direction
to yield a flattened probability map of suspicious hits for ease of scoring. To reduce false positives, we developed an
algorithm based on information theory where similarity metrics were calculated using knowledge databases
consisting of tomosynthesis regions of interest (ROIs) obtained from projection images. We evaluated 5 similarity
metrics to test the false positive reduction performance of our algorithm, specifically joint entropy, mutual
information, Jensen difference divergence, symmetric Kullback-Liebler divergence, and conditional entropy. The
best performance was achieved using the joint entropy similarity metric, resulting in ROC Az of 0.87 ± 0.01. As a
whole, the CADe system can detect breast masses in this data set with 79% sensitivity and 6.8 false positives per
scan. In comparison, the original radiologists performed with only 65% sensitivity when using mammography alone,
and 91% sensitivity when using tomosynthesis alone.
The purpose of this study was to investigate feasibility of computer-aided detection of masses and calcification clusters in breast tomosynthesis images and obtain reliable estimates of sensitivity and false positive rate on an independent test set. Automatic mass and calcification detection algorithms developed for film and digital mammography images were applied without any adaptation or retraining to tomosynthesis projection images. Test set contained 36 patients including 16 patients with 20 known malignant lesions, 4 of which were missed by the radiologists in conventional mammography images and found only in retrospect in tomosynthesis. Median filter was applied to tomosynthesis projection images. Detection algorithm yielded 80% sensitivity and 5.3 false positives per breast for calcification and mass detection algorithms combined. Out of 4 masses missed by radiologists in conventional mammography images, 2 were found by the mass detection algorithm in tomosynthesis images.
KEYWORDS: Performance modeling, Solid modeling, Visual process modeling, Tumor growth modeling, Databases, Image filtering, Digital mammography, Mammography, Breast cancer, Human vision and color perception
Human vision models have been shown to capture the response of the visual system; their incorporation into the classification stage of a Computer Aided Detection system could improve performance. This study seeks to improve the performance of an automated breast mass detection system by using the Watson filter model versus a Laguerre Gauss Channelized Hotelling Observer (LG-CHO). The LG-CHO and the Watson filter model were trained and tested on a 512x512 ROI database acquired from the Digital Database of Screening Mammography consisting of 800 total ROIs; 200 of which were malignant, 200 were benign and 400 were normal. Half of the ROIs were used to train the weights for ten LG-CHO templates that were later used during the testing stage. For the Watson filter model, the training cases were used to optimize the frequency filter parameter empirically to yield the best ROC Az performance. This set of filter parameters was then tested on the remaining cases. The training Az for the LG-CHO and the Watson filter was 0.896 +/- 0.016 and 0.924 +/- 0.014 respectively. The testing Az for the LG-CHO and Watson filter was 0.849 +/- 0.019 and 0.888 +/- 0.017. With a p-value of 0.029, the difference in testing performance was statistically significant, thus implying that the Watson filter model holds promise for better detection of masses.
A prototype breast tomosynthesis system has been developed, allowing a total angular view of ±25°. The detector used in this system is an amorphous selenium direct-conversion digital flat-panel detector suitable for digital tomosynthesis. The system is equipped with various readout sequences to allow the investigation of different tomosynthetic data acquisition modes. In this paper, we will present basic physical properties -- such as MTF, NPS, and DQE -- measured for the full resolution mode and a binned readout mode of the detector. From the measured projections, slices are reconstructed employing a special version of filtered backprojection algorithm. In a phantom study, we compare binned and full resolution acquisition modes with respect to image quality. Under the condition of same dose, we investigate the impact of the number of views on artifacts. Finally, we show tomosynthesis images reconstructed from first clinical data.
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