Volumetric lean percentage measurement using dual energy mammography

Ducote, Justin L.; Klopfer, Michael; Molloi, Sabee

doi:10.1118/1.3605632

Cited by 20 publications

(39 citation statements)

References 21 publications

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“…Moreover, the current two-compartment model suffers large uncertainty from system calibration 11 due to the wide variations in the chemical composition of the fibroglandular and adipose tissue. 12,13 For example, it has been reported that the water concentration in adipose tissue may vary between 11.4% and 30.5%. 12 It has been reported that malignant tissues have a significantly higher water fraction compared to normal tissues.…”

Section: Introductionmentioning

confidence: 99%

Measurement of breast tissue composition with dual energy cone‐beam computed tomography: A postmortem study

2013

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Purpose:To investigate the feasibility of a three-material compositional measurement of water, lipid, and protein content of breast tissue with dual kVp cone-beam computed tomography (CT) for diagnostic purposes. Methods: Simulations were performed on a flat panel-based computed tomography system with a dual kVp technique in order to guide the selection of experimental acquisition parameters. The expected errors induced by using the proposed calibration materials were also estimated by simulation. Twenty pairs of postmortem breast samples were imaged with a flat-panel based dual kVp cone-beam CT system, followed by image-based material decomposition using calibration data obtained from a three-material phantom consisting of water, vegetable oil, and polyoxymethylene plastic. The tissue samples were then chemically decomposed into their respective water, lipid, and protein contents after imaging to allow direct comparison with data from dual energy decomposition. Results: Guided by results from simulation, the beam energies for the dual kVp cone-beam CT system were selected to be 50 and 120 kVp with the mean glandular dose divided equally between each exposure. The simulation also suggested that the use of polyoxymethylene as the calibration material for the measurement of pure protein may introduce an error of −11.0%. However, the tissue decomposition experiments, which employed a calibration phantom made out of water, oil, and polyoxymethylene, exhibited strong correlation with data from the chemical analysis. The average root-mean-square percentage error for water, lipid, and protein contents was 3.58% as compared with chemical analysis. Conclusions:The results of this study suggest that the water, lipid, and protein contents can be accurately measured using dual kVp cone-beam CT. The tissue compositional information may improve the sensitivity and specificity for breast cancer diagnosis.

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Section: Introductionmentioning

confidence: 99%

Measurement of breast tissue composition with dual energy cone‐beam computed tomography: A postmortem study

2013

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“…2.D, a linear correlation is expected between the two variables. Since errors in the chemical analysis are expected to be negligible, 25 we used %FGV as the gold standard for objective evaluations of the investigated methods and the effects of the bias field correction by comparing the Pearson's r coefficients. In Fig.…”

Section: Resultsmentioning

confidence: 99%

“…(8). 25,34 Equation (8), which was derived under the assumption that the chemical compositions of pure glandular and pure adipose tissue are the same among all breast samples, results in a slope generally greater than 1 and a negative intercept. However, practically, water, lipid, and protein concentrations for the glandular and adipose tissues may vary in a small range as a Gaussian broadening.…”

Section: Discussionmentioning

confidence: 99%

“…This provides a gold standard for an objective comparison, since the potential errors in chemical analysis can be controlled to be well below the required sensitivity. 25,32 Thus, it is the most straightforward way to evaluate the performance of an image-based segmentation algorithm. The results of this study suggest that although computer-assisted image segmentation based on a standard FCM algorithm can be used to estimate the volumetric breast density on breast MRI images, the presence of the bias field will reduce the accuracy of the tissue classification, and, in some cases, lead to significant errors in breast density quantification.…”

Section: Discussionmentioning

confidence: 99%

“…This definitive measurement of the chemical composition of the postmortem breasts was used as the gold standard. 25 The linear correlation between the breast density obtained from MRI and the result from chemical analysis was studied for both methods.…”

Section: Introductionmentioning

confidence: 99%

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Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: A postmortem study

et al. 2013

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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.

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Quantitative Breast Lesion Characterization with Spectral Mammography: A Feasibility Study

Ding

Molloi

2023

Photon Counting Computed Tomography

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Volumetric lean percentage measurement using dual energy mammography

Cited by 20 publications

References 21 publications

Measurement of breast tissue composition with dual energy cone‐beam computed tomography: A postmortem study

Measurement of breast tissue composition with dual energy cone‐beam computed tomography: A postmortem study

Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: A postmortem study

Quantitative Breast Lesion Characterization with Spectral Mammography: A Feasibility Study

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