Forty post-mortem breasts were imaged with a flat-panel based cone-beam x-ray CT system at 50 kVp. The feasibility of breast density quantification has been investigated using standard histogram thresholding and an automatic segmentation method based on the fuzzy c-means algorithm (FCM). The breasts were chemically decomposed into water, lipid, and protein immediately after image acquisition was completed. The percent fibroglandular volume (%FGV) from chemical analysis was used as the gold standard for breast density comparison. Both image-based segmentation techniques showed good precision in breast density quantification with high linear coefficients between the right and left breast of each pair. When comparing with the gold standard using %FGV from chemical analysis, Pearson’s r-values were estimated to be 0.983 and 0.968 for the FCM clustering and the histogram thresholding techniques, respectively. The standard error of the estimate (SEE) was also reduced from 3.92% to 2.45% by applying the automatic clustering technique. The results of the postmortem study suggested that breast tissue can be characterized in terms of water, lipid and protein contents with high accuracy by using chemical analysis, which offers a gold standard for breast density studies comparing different techniques. In the investigated image segmentation techniques, the FCM algorithm had high precision and accuracy in breast density quantification. In comparison to conventional histogram thresholding, it was more efficient and reduced inter-observer variation.

• Adrenal adenomas are a common lesion of the adrenal glands. • Differentiation of benign adrenal adenomas from malignant adrenal lesions is important. • Dual-energy based virtual non-contrast images help to evaluate patients with adrenal adenomas.

Purpose: Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. Methods: T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-fieldcorrected images produced by CLIC method. The left-right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson's r, was used to evaluate the two image segmentation algorithms and the effect of bias field. Results: The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left-right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left-right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson's r increased from 0.86 to 0.92 with the bias field correction. Conclusions:The investigated CLIC method significantly increased the precision and accuracy of breast density quantification using breast MRI images by effectively correcting the bias field. It is expected that a fully automated computerized algorithm for breast density quantification may have great potential in clinical MRI applications.

Coronary computed tomography (CT) angiography is a noninvasive method for visualizing coronary atherosclerosis. However, CT angiography is limited in assessment of stenosis severity by the partial volume effect and calcification. Therefore, a quantitative method for assessment of stenosis severity is needed. Polyenergetic fan beam CT simulations were performed to match the geometry of a 320-slice CT scanner. Contrast-enhanced vessel lumens were modeled as 8 mg/ml Iodine solution against a lipid background. Normal vessels were simulated by circles with diameters in the range of 0.1-3 mm. To simulate lesions, 2, 3, and 4 mm diameter vessels were simulated with area stenoses in a range of 10-90 %. The occlusion was created by a circular region of lipid placed within the lumen resulting in a crescent shaped lumen. Each vessel was simulated three times to obtain multiple noise realizations for a total of 126 vessels. Two trained readers performed manual cross-sectional area measurements in simulated normal and stenotic vessels. A new, semi-automated technique based on integrated Hounsfield units was also used to calculate vessel cross-sectional area. There was an excellent correlation between the measured and the known cross-sectional area for both normal and stenotic vessels using the manual and the semi-automated techniques. However, the overall measurement error for the manual method was more than twice as compared with the integrated HU technique. Determination of vessel cross-sectional area using the semi-automated integrated Hounsfield unit technique yields more than a factor of two improvement in accuracy as compared to the existing manual technique for vessels with and without stenosis. This technique can also be used to correct for the effect of coronary calcification.

Coronary computed tomography (CT) angiography is a noninvasive method for visualizing coronary artery disease. However, coronary CT angiography is limited in assessment of stenosis severity by the partial volume effect and calcification. Therefore, an accurate method for assessment of stenosis severity is needed. A 10 cm diameter cylindrical Lucite phantom with holes in the range of 0.4-4.5 mm diameter was fitted in a chest phantom. The holes were filled with an iodine solution of 8 mg/mL. To simulate coronary artery disease, different levels of stenosis were created by inserting Lucite rods into the holes with diameter range of 2-4.5 mm. The resulting lumen cross sectional areas ranged from 1.4 to 12.3 mm. To simulate arterial calcification, calcium hydroxyapatite rods were inserted into the holes with diameter range of 2-4.5 mm. Images of the phantoms were acquired at 100 kVp using a 320-slice CT scanner. A maual and a semi-automated technique based on integrated Hounsfield units was used to calculate vessel cross-sectional area. There was an excellent correlation between the measured and the known cross-sectional area for both normal and stenotic vessels using the manual and the semi-automated techniques. However, the overall measurement error for the manual method was more than twice as compared with the integrated HU technique. Determination of vessel lumen area using the semi-automated integrated Hounsfield unit technique yields more than a factor of two improvement in precision and accuracy as compared to the existing manual technique for vessels with and without stenosis. This technique can also be used to accurately measure arterial cross-sectional area in the presence of coronary calcification.