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Volumetric lean percentage measurement using dual energy mammography.

ABSTRACT: Currently, there is no accepted standard for measuring breast density. Dual energy mammography, which has demonstrated accurate measurement in phantoms, has been proposed as one possible method. To examine the use of chemical analysis as a possible means to validate breast density measurements from dual energy mammography, a bovine tissue model was investigated. Known quantities of lean and adipose tissue were compared with composition values measured from dual energy images and chemical analysis.Theoretical simulations were performed to assess the impact variations in breast composition would have on measurement of breast density from a single calibration. Fourteen ex-vivo tissue samples composed of varying amounts of pure lean tissue and pure adipose tissue (lean percentage) from 0 to 100%, in increments of 10%, were imaged using dual energy mammography. This was followed by chemical analysis based on desiccation, trituration, and fat extraction with petroleum ether to determine water, lipid, and protein content. The volumetric lean percentage (VLP) as measured from images (VLP(I)) and as derived from chemical analysis data (VLP(CA)) were compared with the VLP calculated from measurements of sample mass with a scale (VLP(M)). Finally, data from the bovine tissue model in this study were compared to compositional data from a previous report of human tissue composition.The results from simulation suggest a substantial impact on measuring breast density is likely due to changes in anatomical breast composition. VLP(I) was related to the VLP(M) by VLP(I) = 1.53 VLP(M) + 10.0 (r2 > 0.99). VLP(CA) was related to VLP(M) by VLP(CA) = 0.76 VLP(M) + 22.8 (r2 > 0.99). VLP(I) was related to VLP(CA) by VLP(I) = 2.00 VLP(CA) - 35.6 (r2 > 0.99). Bovine adipose tissue was shown to be very similar to human adipose tissue in terms of water, lipid, and protein content with RMS differences of 1.2%. Bovine lean tissue was shown to be very similar to human skeletal muscle tissue and somewhat similar to human mammary gland tissue with RMS differences of 0.4 and 22.2%, respectively.The results of this study show strong linear relationships between volumetric lean percentage measurements using dual energy mammography, chemical analysis and the actual mass. Determining the existence of a relationship between VLP(I) and VLP(CA) was necessary before comparing density results from the dual energy technique to composition data from chemical analysis for samples of unknown composition.

SUBMITTER: Ducote JL

PROVIDER: S-EPMC3155575 | biostudies-other | 2011 Aug

REPOSITORIES: biostudies-other

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Project description:OBJECTIVES:Late contrast enhancement CT (LCE-CT) visualizes the presence of myocardial infarcts. Differentiation of the contrast-enhanced infarct from blood pool is challenging. We developed a novel method using data from first pass CT angiography (CTA) imaging to enable automatic infarct detection. MATERIALS AND METHODS:A canine model of myocardial infarction was produced in 11 animals. Two months later, first pass CTA (90 kVp) and LCE-CT (dual energy 90 kVp/150 kVp tin filtered) were performed. Late gadolinium enhancement MRI was used as reference standard. The CTA and LCE-CT were co-registered using a fully automatic non-rigid method based on curved B-splines. The method allowed for limited elastic deformation and the considerable differences in attenuation between first-pass and delayed image. The blood pool was easily identified on the CTA image by high attenuation. Because CTA and LCE-CT were registered, the blood pool segmentation can be directly transferred to the LCE-CT - thereby solving the key problem of infarct/blood pool differentiation. The remaining segmentation of infarcted vs. noninfarcted myocardium was performed using a threshold. Automatic and MRI-guided expert segmentations of LCE-CT infarcts were compared to each other on volume and area basis (intraclass correlation coefficient, ICC) and on voxel basis (dice similarity coefficient, DSC between automatic and expert CT segmentation). CT infarct volumes were compared with the reference standard MRI. RESULTS:The infarcts were mainly subendocardial (81%) and relatively small (median MRI infarct mass 7.4 g). The automatic segmentation showed excellent agreement with expert segmentation on volume and area measurements (ICC = 0.96 and 0.87, respectively). DSC showed moderately good agreement (DSC = 0.47). Compared to MRI there was modest agreement (ICC = 0.62) and excellent correlation (R = 0.9). Manual interaction was less than 1 min per exam. CONCLUSION:We propose an automatic method for infarct segmentation on LCE-CT using multiphase CT information, which showed excellent agreement with expert readers and favorable correlation with MRI.

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Volumetric lean percentage measurement using dual energy mammography.

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