The Experts below are selected from a list of 25491 Experts worldwide ranked by ideXlab platform
Martin J. Yaffe - One of the best experts on this subject based on the ideXlab platform.
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Quantifying masking in clinical mammograms via local detectability of simulated lesions
Medical physics, 2016Co-Authors: James G Mainprize, Olivier Alonzo-proulx, Roberta A. Jong, Martin J. YaffeAbstract:Purpose: High mammographic density is known to be associated with decreased sensitivity of mammography. Recent changes in the BI-RADS® density assessment address the effect of masking by densities, but the BI-RADS® assessment remains qualitative and achieves only moderate agreement between radiologists. An automated, quantitative algorithm that estimates the likelihood of masking of simulated masses in a mammogram by Dense Tissue has been developed. The algorithm considers both the effects of loss of contrast due to density and the distracting texture or appearance of Dense Tissue. Methods: A local detectability (dL) map is created by tessellating the mammograms into overlapping regions of interest (ROIs), for which the detectability by a non-prewhitening observer is computed using local estimates of the noise power spectrum and volumetric breast density (VBD). The dL calculation was validated in a 4-alternative forced-choice observer study on the ROIs of 150 craniocaudal digital mammograms. The dL metric was compared against the inverse threshold contrast, (ΔμT)−1 from the observer study, the anatomic noise parameter β, the radiologist's BI-RADS® density category, and a validated measure of VBD (Cumulus). Results: The mean dL had a high correlation of r = 0.915 and r = 0.699 with (ΔμT)−1 in the computerized and human observer study, respectively. In comparison, the local VBD estimate had a low correlation of 0.538 with (ΔμT)−1. The mean dL had a correlation of 0.663, 0.835, and 0.696 with BI-RADS density, β, and Cumulus VBD, respectively. Conclusions: The proposed dL metric may be useful in characterizing the potential for lesion masking by Dense Tissue. Because it uses information about the anatomic noise or Tissue appearance, it is more closely linked to lesion detectability than VBD metrics.
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towards a quantitative measure of radiographic masking by Dense Tissue in mammography
International Workshop on Digital Mammography, 2014Co-Authors: James G Mainprize, Martin J. Yaffe, Xinying WangAbstract:The detection sensitivity of screening mammography is reduced for Dense breasts where the appearance of fibroglandular Tissue can mask suspicious lesions. A measure of the degree of masking expected for a mammogram could be useful for informing the decision to direct some women to supplemental imaging procedures not affected by density. Here, we present an adaptation of a model observer to estimate the detection task SNR, d local, of a lesion embedded in various portions of the breast to indicate the level of detection difficulty. Rank correlation of mean mammogram d local with density category is ρ=–0.58. Correlation of fractional area of mammograms with low d local < 2 versus density category is ρ=0.61. This suggests that a metric based on d local may be useful in quantifying masking effects of breast density.
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Digital Mammography / IWDM - Towards a Quantitative Measure of Radiographic Masking by Dense Tissue in Mammography
Breast Imaging, 2014Co-Authors: James G Mainprize, Xinying Wang, Martin J. YaffeAbstract:The detection sensitivity of screening mammography is reduced for Dense breasts where the appearance of fibroglandular Tissue can mask suspicious lesions. A measure of the degree of masking expected for a mammogram could be useful for informing the decision to direct some women to supplemental imaging procedures not affected by density. Here, we present an adaptation of a model observer to estimate the detection task SNR, d local, of a lesion embedded in various portions of the breast to indicate the level of detection difficulty. Rank correlation of mean mammogram d local with density category is ρ=–0.58. Correlation of fractional area of mammograms with low d local < 2 versus density category is ρ=0.61. This suggests that a metric based on d local may be useful in quantifying masking effects of breast density.
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heritability of mammographic density a risk factor for breast cancer
The New England Journal of Medicine, 2002Co-Authors: Norman F. Boyd, David Tritchler, Gillian S Dite, Jennifer Stone, Anoma Gunasekara, Dallas R English, Margaret R E Mccredie, Graham G Giles, Anna M Chiarelli, Martin J. YaffeAbstract:Background Women with extensive Dense breast Tissue visible on a mammogram have a risk of breast cancer that is 1.8 to 6.0 times that of women of the same age with little or no density. Menopausal status, weight, and parity account for 20 to 30 percent of the age-adjusted variation in the percentage of Dense Tissue. Methods We undertook two studies of twins to determine the proportion of the residual variation in the percentage of density measured by mammography that can be explained by unmeasured additive genetic factors (heritability). A total of 353 pairs of monozygotic twins and 246 pairs of dizygotic twins were recruited from the Australian Twin Registry, and 218 pairs of monozygotic twins and 134 pairs of dizygotic twins were recruited in Canada and the United States. Information on putative determinants of breast density was obtained by questionnaire. Mammograms were digitized, randomly ordered, and read by a blinded investigator. Results After adjustment for age and measured covariates, the correlation coefficient for the percentage of Dense Tissue was 0.61 for monozygotic pairs in Australia, 0.67 for monozygotic pairs in North America, 0.25 for dizygotic pairs in Australia, and 0.27 for dizygotic pairs in North America. According to the classic twin model, heritability (the proportion of variants attributable to additive genetic factors) accounted for 60 percent of the variation in density (95 percent confidence interval, 54 to 66) in Australian twins, 67 percent (95 percent confidence interval, 59 to 75) in North American twins, and 63 percent (95 percent confidence interval, 59 to 67) in all twins studied. Conclusions These results show that the population variation in the percentage of Dense Tissue on mammography at a given age has high heritability. Because mammographic density is associated with an increased risk of breast cancer, finding the genes responsible for this phenotype could be important for understanding the causes of the disease.
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heritability of mammographic density a risk factor for breast cancer
The New England Journal of Medicine, 2002Co-Authors: Norman F. Boyd, David Tritchler, Gillian S Dite, Jennifer Stone, Anoma Gunasekara, Dallas R English, Margaret R E Mccredie, Graham G Giles, Anna M Chiarelli, Martin J. YaffeAbstract:Background Women with extensive Dense breast Tissue visible on a mammogram have a risk of breast cancer that is 1.8 to 6.0 times that of women of the same age with little or no density. Menopausal status, weight, and parity account for 20 to 30 percent of the age-adjusted variation in the percentage of Dense Tissue. Methods We undertook two studies of twins to determine the proportion of the residual variation in the percentage of density measured by mammography that can be explained by unmeasured additive genetic factors (heritability). A total of 353 pairs of monozygotic twins and 246 pairs of dizygotic twins were recruited from the Australian Twin Registry, and 218 pairs of monozygotic twins and 134 pairs of dizygotic twins were recruited in Canada and the United States. Information on putative determinants of breast density was obtained by questionnaire. Mammograms were digitized, randomly ordered, and read by a blinded investigator. Results After adjustment for age and measured covariates, the correlation coefficient for the percentage of Dense Tissue was 0.61 for monozygotic pairs in Australia, 0.67 for monozygotic pairs in North America, 0.25 for dizygotic pairs in Australia, and 0.27 for dizygotic pairs in North America. According to the classic twin model, heritability (the proportion of variants attributable to additive genetic factors) accounted for 60 percent of the variation in density (95 percent confidence interval, 54 to 66) in Australian twins, 67 percent (95 percent confidence interval, 59 to 75) in North American twins, and 63 percent (95 percent confidence interval, 59 to 67) in all twins studied. Conclusions These results show that the population variation in the percentage of Dense Tissue on mammography at a given age has high heritability. Because mammographic density is associated with an increased risk of breast cancer, finding the genes responsible for this phenotype could be important for understanding the causes of the disease. (N Engl J Med 2002;347:886-94.)
Rafael Llobet - One of the best experts on this subject based on the ideXlab platform.
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A deep learning system to obtain the optimal parameters for a threshold-based breast and Dense Tissue segmentation.
Computer methods and programs in biomedicine, 2020Co-Authors: Francisco Javier Pérez-benito, François Signol, Juan-carlos Perez-cortes, Marina Pollán, Beatriz Pérez-gómez, Dolores Salas-trejo, María Casals, Inmaculada Martínez, Alejandro Fuster-baggetto, Rafael LlobetAbstract:Abstract Background and Objective Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer.It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard.This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular Tissue segmentation. Methods A multi-center study, composed of 1785 women whose “for presentation” mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. Results The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. Conclusions An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.
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Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts.
Computer methods and programs in biomedicine, 2019Co-Authors: Francisco Javier Pérez-benito, François Signol, Juan-carlos Perez-cortes, Marina Pollán, Beatriz Pérez-gómez, Dolores Salas-trejo, María Casals, Inmaculada Martínez, Rafael LlobetAbstract:Abstract Background The breast Dense Tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of Dense Tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast Dense Tissue percentage. Methods A case/control study nested within a screening program covering 1563 women with craniocaudal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valenciana (Spain) was used to extract geometric and texture features. The Dense Tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients. Results The AUC score obtained by the Dense Tissue percentage (0.55) was compared to a machine learning-based classifier results. The classifier, apart from the percentage of Dense Tissue of both views, firstly included global geometric features such as the distance of Dense Tissue to the pectoral muscle, Dense Tissue eccentricity or the Dense Tissue perimeter, obtaining an accuracy of 0.56. By the inclusion of a global feature based on local histograms of oriented gradients, the accuracy of the classifier was significantly improved (0.61). The number of well-classified patients was improved up to 236 when it was 208. Conclusion Relative geometric features of Dense Tissue over the breast and histograms of standardized local texture features based on sliding windows scanning the whole breast improve risk prediction beyond the Dense Tissue percentage adjusted by geometrical variables. Other classifiers could improve the results obtained by the conventional Random Forests used in this study.
Chao Jen Lai - One of the best experts on this subject based on the ideXlab platform.
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WE‐G‐103‐07: Simulation of Contrast Enhanced Digital Mammography with a Four Dimensional Digital Breast Phantom
Medical Physics, 2013Co-Authors: Yuncheng Zhong, Chao Jen Lai, Y. Shen, Tianpeng Wang, Chris C. ShawAbstract:Purpose: Although contrast injection helps make cancerous lesions more visible in contrast enhanced digital mammograms (CEDM), the overlapping of Dense Tissue with the lesion could affect the degree of the improvement. In this study, we developed and used a simulation method to investigate such effects. Methods: A digital phantom for a compressed breast was generated from cone beam CT images of a mastectomy specimen to represent the stationary anatomy of a breast. The 3D map of a tumor was inserted into the phantom. Published contrast enhancement data and clinical contrast enhanced CT images were used to estimate and assign temporarily varying CT enhancement values to both cancerous and normal Tissues following contrast injection. These values were added to the digital phantom to form a 4D digital phantom, which was used to compute digital mammograms to visualize the lesion and measure the time density curve over the lesion. The results were used to investigate and quantify the effects of the lesion size and overlapping with Dense Tissue structures on the visibility of the lesion, degree of contrast enhancement and time of arrival in CEDM. Results: We have successfully generated and used a 4D digital breast phantom to simulate CEDM. It was found that the overlapping of the lesion with Dense Tissue could affect the degree of contrast enhancement and time of arrival information for the lesion. The effects was found to vary with the size of the lesion relative to the breast thickness in the direction of the x‐ray path. Conclusion: The benefit of contrast enhancement in digital mammography varies with the size of the lesion relative to the density Tissue in the region of interest. The overlapping Dense Tissue structure could degrade the visibility of the contrast enhanced lesion both in the image and in the time density curves measured. This work was supported in part by grants CA13852 and CA124585 from the NIH‐NCI.
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SU‐FF‐I‐114: Effects of Exposure Level and Anatomical Background On Detection of Simulated Microcalcifications in Digital Mammography
Medical Physics, 2009Co-Authors: Chao Jen Lai, Tao Han, Lingyun Chen, Xinming Liu, Y. Shen, Z. You, Yuncheng Zhong, William R. GeiserAbstract:Purpose: To assess the influence of quantum and anatomic noises on the detection of simulated microcalcifications (MCs) on digital mammograms.Materials and Methods: Images of an anthropomorphic breast phantom (RMI 169) overlapping with simulated MCs ranging from 160 to 224 micron in size placed at various locations were acquired with an aSi/aSe flat panel based digital mammography system (Selenia) operated with Mo‐Mo target/filter combination at 28 kVp. These mammograms were exposed with 10, 20, 40, 80, 160, and 325 mAs. All images were displayed randomly on a review workstation and reviewed by five readers independently. The readers were asked to count the number of visible MCs. The ratio of visible MCs was computed for each different combination of MC size, reader and mAs setting. The performance differences in the ratios averaged from all readers between mAs levels was assessed by performing Student t‐test to compute the p values. Results: It was found that in the Dense Tissue region the visibility reached 90% at 10, 33 and 37 mAs for 200–212, 160–180 and 180–200 micron MCs, respectively; and at 10 mAs for 180–200 and 200–212 micron MCs and at 15 mAs for 160–180 micron MCs in the fatty region. The lower visibility in the Dense Tissue region may be due to the fact that the photon flux was lower in the Dense Tissue regions therefore requiring higher mAs setting to achieve sufficient image SNRs for detection of MCs. Our results also show that in the Dense Tissue region, the visibility of 180–200 micron MCs stayed at 90% at 80 mAs. This may indicate that the anatomical noise was limiting the detectability of 180–200 micron MCs. This work was supported in part by grants CA104759 and CA124585 from NIH‐NCI, a grant EB00117 from NIH‐NIBIB, and a subcontract from NIST‐ATP.
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SU-FF-I-21: Dose Variation with Breast Density in Cone Beam Breast CT- a Monte Carlo Simulation Study
Medical Physics, 2009Co-Authors: Tao Han, Lingyun Chen, Chao Jen Lai, Xinming Liu, Y. Shen, Yuncheng Zhong, Chris C. ShawAbstract:Purpose: By using Monte Carlo(MC) dose calculation to investigate the variation of radiation dose with the breast density (Tissue composition) in cone beam breast CT, both globally and on a voxel‐by‐voxel basis. Method and Materials: To simulate cone beam breast CTimaging, we first used Rep78 to build an x‐ray spectrum for an x‐ray tube with tungsten target and a HVL of 4.08mm Al. A collimator was defined in BEAMnrc and used to form a half‐cone beam for pendant geometry used in cone beam breast CT. For validation, we compared MC dose calculations with ionization chamber measurements at 4 different locations inside the 11 cm diameter, 12 cm high cylindrical phantom under the same imaging conditions. After normalizing for the iso‐center dose, the deviations were within 3.7%. 3 cone beam CTimages were first corrected and filtered to minimize the cupping artifacts and then filtered to reduce the noise level. The processed images were then segmented using threshold method to separate the Dense Tissue from the adipose Tissue. The segmented image sets were binned into a 100×100×80 image sets with a pixel size of 1mm for MC calculation. DOSXYZnrc was used to track 1×109 incident photons distributed over 300 projection images over 360°. The resulting radiation dose was tallied for each voxel and averaged over all voxels in the breast. Results: Results from MC calculations indicated that radiation dose was significantly higher in Dense Tissue structures and lower in the adipose Tissue. The regions of higher dose were found to match the 3D Dense Tissue structures. The average breast dose was found to linearly vary with the breast density computed from the breast CT data. Conclusion: Our MC study has shown a close relationship between the radiation dose and the breast density both globally and on a voxel‐by‐voxel basis.
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Breast density measurement: 3D cone beam computed tomography (CBCT) images versus 2D digital mammograms
Medical Imaging 2009: Physics of Medical Imaging, 2009Co-Authors: Tao Han, Lingyun Chen, Chao Jen Lai, Xinming Liu, Y. Shen, Tianpeng Wang, Yuncheng Zhong, Wei Tse YangAbstract:Breast density has been recognized as one of the major risk factors for breast cancer. However, breast density is currently estimated using mammograms which are intrinsically 2D in nature and cannot accurately represent the real breast anatomy. In this study, a novel technique for measuring breast density based on the segmentation of 3D cone beam CT (CBCT) images was developed and the results were compared to those obtained from 2D digital mammograms. 16 mastectomy breast specimens were imaged with a bench top flat-panel based CBCT system. The reconstructed 3D CT images were corrected for the cupping artifacts and then filtered to reduce the noise level, followed by using threshold-based segmentation to separate the Dense Tissue from the adipose Tissue. For each breast specimen, volumes of the Dense Tissue structures and the entire breast were computed and used to calculate the volumetric breast density. BI-RADS categories were derived from the measured breast densities and compared with those estimated from conventional digital mammograms. The results show that in 10 of 16 cases the BI-RADS categories derived from the CBCT images were lower than those derived from the mammograms by one category. Thus, breasts considered as Dense in mammographic examinations may not be considered as Dense with the CBCT images. This result indicates that the relation between breast cancer risk and true (volumetric) breast density needs to be further investigated.
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SU‐GG‐I‐16: A Segmentation Technique to Estimate Breast Density From Cone Beam Breast CT Images
Medical Physics, 2008Co-Authors: Tao Han, Lingyun Chen, Chao Jen Lai, Xinming Liu, Y. Shen, Tianpeng Wang, Chris C. ShawAbstract:Purpose: To describe and demonstrate the use of an improved segmentation technique to estimate breast density from cone beam breast CT (BCT) images.Method and Materials: To compute the breast density, the Dense Tissue must be separated from the adipose Tissue in the BCT images. To accomplish this task, the BCT images were first processed with a previously reported post‐reconstruction method to correct for the beam hardening and scatter induced cupping artifacts. With this method, the adipose signals were extracted from the coronal view CTimage and used to form a 3‐D background map which was then subtracted from the original images for correction. The corrected data were edge enhanced and smoothed to reduce the noise levels in the images. The results were processed by threshold segmentation to separate the Dense Tissue from the adipose Tissue and form a 3‐D Dense Tissue map which was then used to compute the Dense Tissue volume and the breast density. Results: For demonstration, the proposed technique was applied to cone beam CTimages of mastectomy breast specimens to estimate the breast density. The cupping artifacts were successively corrected for and the Dense Tissue was successfully separated from the adipose Tissue with the segmented Dense Tissue structures matching well with those visualized in the images.Conclusion: We have successfully implemented a method to use cone beam breast CTimages to estimate the breast density. The technique was successfully demonstrated with cone beam CTimages of mastectomy breast specimens. This technique lends itself to more accurate and consistent measurement of breast density which may be used an indicator for breast cancer risk. This work was supported in part by grants CA104759 and CA124585 from NIH‐NCI, a grant EB00117 from NIH‐NIBIB, and a subcontract from NIST‐ATP.
Chris C. Shaw - One of the best experts on this subject based on the ideXlab platform.
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WE‐G‐103‐07: Simulation of Contrast Enhanced Digital Mammography with a Four Dimensional Digital Breast Phantom
Medical Physics, 2013Co-Authors: Yuncheng Zhong, Chao Jen Lai, Y. Shen, Tianpeng Wang, Chris C. ShawAbstract:Purpose: Although contrast injection helps make cancerous lesions more visible in contrast enhanced digital mammograms (CEDM), the overlapping of Dense Tissue with the lesion could affect the degree of the improvement. In this study, we developed and used a simulation method to investigate such effects. Methods: A digital phantom for a compressed breast was generated from cone beam CT images of a mastectomy specimen to represent the stationary anatomy of a breast. The 3D map of a tumor was inserted into the phantom. Published contrast enhancement data and clinical contrast enhanced CT images were used to estimate and assign temporarily varying CT enhancement values to both cancerous and normal Tissues following contrast injection. These values were added to the digital phantom to form a 4D digital phantom, which was used to compute digital mammograms to visualize the lesion and measure the time density curve over the lesion. The results were used to investigate and quantify the effects of the lesion size and overlapping with Dense Tissue structures on the visibility of the lesion, degree of contrast enhancement and time of arrival in CEDM. Results: We have successfully generated and used a 4D digital breast phantom to simulate CEDM. It was found that the overlapping of the lesion with Dense Tissue could affect the degree of contrast enhancement and time of arrival information for the lesion. The effects was found to vary with the size of the lesion relative to the breast thickness in the direction of the x‐ray path. Conclusion: The benefit of contrast enhancement in digital mammography varies with the size of the lesion relative to the density Tissue in the region of interest. The overlapping Dense Tissue structure could degrade the visibility of the contrast enhanced lesion both in the image and in the time density curves measured. This work was supported in part by grants CA13852 and CA124585 from the NIH‐NCI.
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SU-FF-I-21: Dose Variation with Breast Density in Cone Beam Breast CT- a Monte Carlo Simulation Study
Medical Physics, 2009Co-Authors: Tao Han, Lingyun Chen, Chao Jen Lai, Xinming Liu, Y. Shen, Yuncheng Zhong, Chris C. ShawAbstract:Purpose: By using Monte Carlo(MC) dose calculation to investigate the variation of radiation dose with the breast density (Tissue composition) in cone beam breast CT, both globally and on a voxel‐by‐voxel basis. Method and Materials: To simulate cone beam breast CTimaging, we first used Rep78 to build an x‐ray spectrum for an x‐ray tube with tungsten target and a HVL of 4.08mm Al. A collimator was defined in BEAMnrc and used to form a half‐cone beam for pendant geometry used in cone beam breast CT. For validation, we compared MC dose calculations with ionization chamber measurements at 4 different locations inside the 11 cm diameter, 12 cm high cylindrical phantom under the same imaging conditions. After normalizing for the iso‐center dose, the deviations were within 3.7%. 3 cone beam CTimages were first corrected and filtered to minimize the cupping artifacts and then filtered to reduce the noise level. The processed images were then segmented using threshold method to separate the Dense Tissue from the adipose Tissue. The segmented image sets were binned into a 100×100×80 image sets with a pixel size of 1mm for MC calculation. DOSXYZnrc was used to track 1×109 incident photons distributed over 300 projection images over 360°. The resulting radiation dose was tallied for each voxel and averaged over all voxels in the breast. Results: Results from MC calculations indicated that radiation dose was significantly higher in Dense Tissue structures and lower in the adipose Tissue. The regions of higher dose were found to match the 3D Dense Tissue structures. The average breast dose was found to linearly vary with the breast density computed from the breast CT data. Conclusion: Our MC study has shown a close relationship between the radiation dose and the breast density both globally and on a voxel‐by‐voxel basis.
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SU‐GG‐I‐16: A Segmentation Technique to Estimate Breast Density From Cone Beam Breast CT Images
Medical Physics, 2008Co-Authors: Tao Han, Lingyun Chen, Chao Jen Lai, Xinming Liu, Y. Shen, Tianpeng Wang, Chris C. ShawAbstract:Purpose: To describe and demonstrate the use of an improved segmentation technique to estimate breast density from cone beam breast CT (BCT) images.Method and Materials: To compute the breast density, the Dense Tissue must be separated from the adipose Tissue in the BCT images. To accomplish this task, the BCT images were first processed with a previously reported post‐reconstruction method to correct for the beam hardening and scatter induced cupping artifacts. With this method, the adipose signals were extracted from the coronal view CTimage and used to form a 3‐D background map which was then subtracted from the original images for correction. The corrected data were edge enhanced and smoothed to reduce the noise levels in the images. The results were processed by threshold segmentation to separate the Dense Tissue from the adipose Tissue and form a 3‐D Dense Tissue map which was then used to compute the Dense Tissue volume and the breast density. Results: For demonstration, the proposed technique was applied to cone beam CTimages of mastectomy breast specimens to estimate the breast density. The cupping artifacts were successively corrected for and the Dense Tissue was successfully separated from the adipose Tissue with the segmented Dense Tissue structures matching well with those visualized in the images.Conclusion: We have successfully implemented a method to use cone beam breast CTimages to estimate the breast density. The technique was successfully demonstrated with cone beam CTimages of mastectomy breast specimens. This technique lends itself to more accurate and consistent measurement of breast density which may be used an indicator for breast cancer risk. This work was supported in part by grants CA104759 and CA124585 from NIH‐NCI, a grant EB00117 from NIH‐NIBIB, and a subcontract from NIST‐ATP.
Tao Han - One of the best experts on this subject based on the ideXlab platform.
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SU-FF-I-21: Dose Variation with Breast Density in Cone Beam Breast CT- a Monte Carlo Simulation Study
Medical Physics, 2009Co-Authors: Tao Han, Lingyun Chen, Chao Jen Lai, Xinming Liu, Y. Shen, Yuncheng Zhong, Chris C. ShawAbstract:Purpose: By using Monte Carlo(MC) dose calculation to investigate the variation of radiation dose with the breast density (Tissue composition) in cone beam breast CT, both globally and on a voxel‐by‐voxel basis. Method and Materials: To simulate cone beam breast CTimaging, we first used Rep78 to build an x‐ray spectrum for an x‐ray tube with tungsten target and a HVL of 4.08mm Al. A collimator was defined in BEAMnrc and used to form a half‐cone beam for pendant geometry used in cone beam breast CT. For validation, we compared MC dose calculations with ionization chamber measurements at 4 different locations inside the 11 cm diameter, 12 cm high cylindrical phantom under the same imaging conditions. After normalizing for the iso‐center dose, the deviations were within 3.7%. 3 cone beam CTimages were first corrected and filtered to minimize the cupping artifacts and then filtered to reduce the noise level. The processed images were then segmented using threshold method to separate the Dense Tissue from the adipose Tissue. The segmented image sets were binned into a 100×100×80 image sets with a pixel size of 1mm for MC calculation. DOSXYZnrc was used to track 1×109 incident photons distributed over 300 projection images over 360°. The resulting radiation dose was tallied for each voxel and averaged over all voxels in the breast. Results: Results from MC calculations indicated that radiation dose was significantly higher in Dense Tissue structures and lower in the adipose Tissue. The regions of higher dose were found to match the 3D Dense Tissue structures. The average breast dose was found to linearly vary with the breast density computed from the breast CT data. Conclusion: Our MC study has shown a close relationship between the radiation dose and the breast density both globally and on a voxel‐by‐voxel basis.
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SU‐FF‐I‐114: Effects of Exposure Level and Anatomical Background On Detection of Simulated Microcalcifications in Digital Mammography
Medical Physics, 2009Co-Authors: Chao Jen Lai, Tao Han, Lingyun Chen, Xinming Liu, Y. Shen, Z. You, Yuncheng Zhong, William R. GeiserAbstract:Purpose: To assess the influence of quantum and anatomic noises on the detection of simulated microcalcifications (MCs) on digital mammograms.Materials and Methods: Images of an anthropomorphic breast phantom (RMI 169) overlapping with simulated MCs ranging from 160 to 224 micron in size placed at various locations were acquired with an aSi/aSe flat panel based digital mammography system (Selenia) operated with Mo‐Mo target/filter combination at 28 kVp. These mammograms were exposed with 10, 20, 40, 80, 160, and 325 mAs. All images were displayed randomly on a review workstation and reviewed by five readers independently. The readers were asked to count the number of visible MCs. The ratio of visible MCs was computed for each different combination of MC size, reader and mAs setting. The performance differences in the ratios averaged from all readers between mAs levels was assessed by performing Student t‐test to compute the p values. Results: It was found that in the Dense Tissue region the visibility reached 90% at 10, 33 and 37 mAs for 200–212, 160–180 and 180–200 micron MCs, respectively; and at 10 mAs for 180–200 and 200–212 micron MCs and at 15 mAs for 160–180 micron MCs in the fatty region. The lower visibility in the Dense Tissue region may be due to the fact that the photon flux was lower in the Dense Tissue regions therefore requiring higher mAs setting to achieve sufficient image SNRs for detection of MCs. Our results also show that in the Dense Tissue region, the visibility of 180–200 micron MCs stayed at 90% at 80 mAs. This may indicate that the anatomical noise was limiting the detectability of 180–200 micron MCs. This work was supported in part by grants CA104759 and CA124585 from NIH‐NCI, a grant EB00117 from NIH‐NIBIB, and a subcontract from NIST‐ATP.
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Breast density measurement: 3D cone beam computed tomography (CBCT) images versus 2D digital mammograms
Medical Imaging 2009: Physics of Medical Imaging, 2009Co-Authors: Tao Han, Lingyun Chen, Chao Jen Lai, Xinming Liu, Y. Shen, Tianpeng Wang, Yuncheng Zhong, Wei Tse YangAbstract:Breast density has been recognized as one of the major risk factors for breast cancer. However, breast density is currently estimated using mammograms which are intrinsically 2D in nature and cannot accurately represent the real breast anatomy. In this study, a novel technique for measuring breast density based on the segmentation of 3D cone beam CT (CBCT) images was developed and the results were compared to those obtained from 2D digital mammograms. 16 mastectomy breast specimens were imaged with a bench top flat-panel based CBCT system. The reconstructed 3D CT images were corrected for the cupping artifacts and then filtered to reduce the noise level, followed by using threshold-based segmentation to separate the Dense Tissue from the adipose Tissue. For each breast specimen, volumes of the Dense Tissue structures and the entire breast were computed and used to calculate the volumetric breast density. BI-RADS categories were derived from the measured breast densities and compared with those estimated from conventional digital mammograms. The results show that in 10 of 16 cases the BI-RADS categories derived from the CBCT images were lower than those derived from the mammograms by one category. Thus, breasts considered as Dense in mammographic examinations may not be considered as Dense with the CBCT images. This result indicates that the relation between breast cancer risk and true (volumetric) breast density needs to be further investigated.
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SU‐GG‐I‐16: A Segmentation Technique to Estimate Breast Density From Cone Beam Breast CT Images
Medical Physics, 2008Co-Authors: Tao Han, Lingyun Chen, Chao Jen Lai, Xinming Liu, Y. Shen, Tianpeng Wang, Chris C. ShawAbstract:Purpose: To describe and demonstrate the use of an improved segmentation technique to estimate breast density from cone beam breast CT (BCT) images.Method and Materials: To compute the breast density, the Dense Tissue must be separated from the adipose Tissue in the BCT images. To accomplish this task, the BCT images were first processed with a previously reported post‐reconstruction method to correct for the beam hardening and scatter induced cupping artifacts. With this method, the adipose signals were extracted from the coronal view CTimage and used to form a 3‐D background map which was then subtracted from the original images for correction. The corrected data were edge enhanced and smoothed to reduce the noise levels in the images. The results were processed by threshold segmentation to separate the Dense Tissue from the adipose Tissue and form a 3‐D Dense Tissue map which was then used to compute the Dense Tissue volume and the breast density. Results: For demonstration, the proposed technique was applied to cone beam CTimages of mastectomy breast specimens to estimate the breast density. The cupping artifacts were successively corrected for and the Dense Tissue was successfully separated from the adipose Tissue with the segmented Dense Tissue structures matching well with those visualized in the images.Conclusion: We have successfully implemented a method to use cone beam breast CTimages to estimate the breast density. The technique was successfully demonstrated with cone beam CTimages of mastectomy breast specimens. This technique lends itself to more accurate and consistent measurement of breast density which may be used an indicator for breast cancer risk. This work was supported in part by grants CA104759 and CA124585 from NIH‐NCI, a grant EB00117 from NIH‐NIBIB, and a subcontract from NIST‐ATP.