Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)最新文献
S. Lewis, Tong Li, N. Borecky, P. Brennan, Melissa L. Barron, P. Trieu
Objectives: To study the effect on radiology trainees’ observer performance through the availability of prior screening mammograms as part of seven unique education test sets. Methods: Australian radiology trainees (n=150) completed 469 readings of seven educational test sets (each set with 60 cases, 40 normal and 20 cancer cases). The percentage of cases with a prior screening mammogram was 68.7%. Mammographic density (MD) evaluated via BIRADS was spread across the test sets, with 40.5% having 25-50% glandular tissue (BIRADS “B”), 37.4% of cases having 50-75% or “C”, 12.6% have a >75% MD and 9.5% having the lowest MD rating “A”. Trainees were asked to score the cases on a scale of 1 (normal), 2 (benign), 3 (equivocal findings), 4 (suspicious finding) and 5 (highly suggestive malignancy). Mann-Whitney U was used to compare the specificity and sensitivity of radiology trainees among cases with and without prior images. Results: Radiology trainees had significantly higher sensitivity across all MD levels when prior images were not available (A-B, P=0.006; C-D, P=0.027). Specificity was also significantly higher for cases of high (C-D) MD without prior images compared with priors available by trainees who read less than 20 cases per week (P=0.008). Conclusions: In a simulated environment, radiology trainees achieved better results in cases without prior images, especially for those who read less than 20 cases per week. The utility of prior case inclusion when providing education and training in reading screening mammograms needs to be revisited, especially for women with high MD.
目的:研究作为七个独特教育测试集的一部分，预先筛查乳房x线照片的可用性对放射学受训人员观察表现的影响。方法:澳大利亚放射学学员150例，完成7组教育测试(每组60例，40例正常，20例癌症)的469次阅读。有过乳房x光筛查的病例占68.7%。通过BIRADS评估的乳腺密度(MD)分布在各个测试集，40.5%的病例有25-50%的腺体组织(BIRADS“B”)，37.4%的病例有50-75%或“C”，12.6%的病例有>75%的MD, 9.5%的病例有最低的MD评级为“a”。受训者被要求按1(正常)，2(良性)，3(模棱两可的发现)，4(可疑发现)和5(高度暗示的恶性肿瘤)的等级对病例进行评分。使用Mann-Whitney U来比较有和没有先前图像的病例中放射学受训人员的特异性和敏感性。结果:在没有先前图像的情况下，放射学培训生在所有MD水平上都具有显著更高的敏感性(A-B, P=0.006;c - d, P = 0.027)。与每周阅读少于20例的学员相比，没有先前图像的高(C-D) MD病例的特异性也显着更高(P=0.008)。结论:在模拟环境下，放射学实习生在没有事先图像的情况下取得了更好的效果，特别是对于每周阅读少于20例的病例。在提供阅读筛查乳房x光检查的教育和培训时，既往病例纳入的效用需要重新审视，特别是对于高MD的妇女。
{"title":"Prior mammogram review may affect the performance of radiology trainees in identifying breast cancers and normal cases","authors":"S. Lewis, Tong Li, N. Borecky, P. Brennan, Melissa L. Barron, P. Trieu","doi":"10.1117/12.2624189","DOIUrl":"https://doi.org/10.1117/12.2624189","url":null,"abstract":"Objectives: To study the effect on radiology trainees’ observer performance through the availability of prior screening mammograms as part of seven unique education test sets. Methods: Australian radiology trainees (n=150) completed 469 readings of seven educational test sets (each set with 60 cases, 40 normal and 20 cancer cases). The percentage of cases with a prior screening mammogram was 68.7%. Mammographic density (MD) evaluated via BIRADS was spread across the test sets, with 40.5% having 25-50% glandular tissue (BIRADS “B”), 37.4% of cases having 50-75% or “C”, 12.6% have a >75% MD and 9.5% having the lowest MD rating “A”. Trainees were asked to score the cases on a scale of 1 (normal), 2 (benign), 3 (equivocal findings), 4 (suspicious finding) and 5 (highly suggestive malignancy). Mann-Whitney U was used to compare the specificity and sensitivity of radiology trainees among cases with and without prior images. Results: Radiology trainees had significantly higher sensitivity across all MD levels when prior images were not available (A-B, P=0.006; C-D, P=0.027). Specificity was also significantly higher for cases of high (C-D) MD without prior images compared with priors available by trainees who read less than 20 cases per week (P=0.008). Conclusions: In a simulated environment, radiology trainees achieved better results in cases without prior images, especially for those who read less than 20 cases per week. The utility of prior case inclusion when providing education and training in reading screening mammograms needs to be revisited, especially for women with high MD.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"21 1","pages":"1228611 - 1228611-6"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85816149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Z. Gandomkar, S. Lewis, Somphone Siviengphanom, M. Suleiman, D. Wong, W. Reed, E. Ekpo, P. Brennan
This study aimed at conducting a review of the prior mammograms of screen-detected breast cancers, found on full-field digital mammograms based on independent double reading with arbitration. The prior mammograms of 607 women diagnosed with breast cancer during routine breast cancer screening were categorized into “Missed”, “Prior Vis”, and “Prior Invis” . The prior mammograms of “Missed” and “Prior Vis” cases showed actionable and non-actionable visible cancer signs, respectively. The “Prior Invis” cases had no overt cancer signs on the prior mammograms. The percentage of cases classified as “Missed”, “Prior Vis”, and “Prior Invis” categories were 25.5%, 21.7%, 52.7%, respectively. The proportion of high-density cases showed no significant differences among the three categories (p-values<0.05). The breakdown of cases into “Missed”, “Prior Vis”, and “Prior Invis” categories did not differ between invasive (488) and in-situ (119) cases. In the invasive category, the progesterone (p-value=0.015) and estrogen (p-value=0.007) positivity and the median ki-67 score (p-value=0.006) differed significantly among the categories with the “Prior Invis” cases exhibiting the highest percentage of hormone receptors negativity. In the invasive cases, the percentage of cancers graded as 3 (i.e., more aggressive) were significantly more in the “Prior Invis” category compared to both “Missed” and “Prior Vis” categories (both p-values<0.05). The status of receptors and breast cancer grade for the in-situ cases did not differ significantly among the three categories. Prior images categorization can predict the aggressiveness of breast cancer. Techniques to better interrogate prior images as shown elsewhere may yield important patient outcomes.
{"title":"Classification and reviewing of prior screening mammograms from screen-detected breast cancer cases","authors":"Z. Gandomkar, S. Lewis, Somphone Siviengphanom, M. Suleiman, D. Wong, W. Reed, E. Ekpo, P. Brennan","doi":"10.1117/12.2625750","DOIUrl":"https://doi.org/10.1117/12.2625750","url":null,"abstract":"This study aimed at conducting a review of the prior mammograms of screen-detected breast cancers, found on full-field digital mammograms based on independent double reading with arbitration. The prior mammograms of 607 women diagnosed with breast cancer during routine breast cancer screening were categorized into “Missed”, “Prior Vis”, and “Prior Invis” . The prior mammograms of “Missed” and “Prior Vis” cases showed actionable and non-actionable visible cancer signs, respectively. The “Prior Invis” cases had no overt cancer signs on the prior mammograms. The percentage of cases classified as “Missed”, “Prior Vis”, and “Prior Invis” categories were 25.5%, 21.7%, 52.7%, respectively. The proportion of high-density cases showed no significant differences among the three categories (p-values<0.05). The breakdown of cases into “Missed”, “Prior Vis”, and “Prior Invis” categories did not differ between invasive (488) and in-situ (119) cases. In the invasive category, the progesterone (p-value=0.015) and estrogen (p-value=0.007) positivity and the median ki-67 score (p-value=0.006) differed significantly among the categories with the “Prior Invis” cases exhibiting the highest percentage of hormone receptors negativity. In the invasive cases, the percentage of cancers graded as 3 (i.e., more aggressive) were significantly more in the “Prior Invis” category compared to both “Missed” and “Prior Vis” categories (both p-values<0.05). The status of receptors and breast cancer grade for the in-situ cases did not differ significantly among the three categories. Prior images categorization can predict the aggressiveness of breast cancer. Techniques to better interrogate prior images as shown elsewhere may yield important patient outcomes.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"45 1","pages":"122860Z - 122860Z-7"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81426874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paul Kinahan, William Hunter, K. Champley, R. Harrison, A. Lehnert, Peter Muzi, D. Dewitt, D. Hu, R. Miyaoka, L. MacDonald
Dedicated breast-imaging scanners using radiotracers, e.g., PET scanners (positron emission tomography) have been proposed and evaluated since the late 1980s [1]. These systems trade a reduction in the size of the imaging field of view for improved resolution, and potentially also a lower cost, higher sensitivity, and a smaller form factor. The higher resolution can improve both detection and quantitation of concentrations of radiotracer, although for the latter to be true, tomographic imaging is mandatory. Several commercial dedicated breast PEM (positron emission mammography), PET scanners, and gamma camera systems have been developed and marketed [2].
{"title":"A sparse-readout quantitative PET scanner for breast cancer therapy optimization","authors":"Paul Kinahan, William Hunter, K. Champley, R. Harrison, A. Lehnert, Peter Muzi, D. Dewitt, D. Hu, R. Miyaoka, L. MacDonald","doi":"10.1117/12.2626724","DOIUrl":"https://doi.org/10.1117/12.2626724","url":null,"abstract":"Dedicated breast-imaging scanners using radiotracers, e.g., PET scanners (positron emission tomography) have been proposed and evaluated since the late 1980s [1]. These systems trade a reduction in the size of the imaging field of view for improved resolution, and potentially also a lower cost, higher sensitivity, and a smaller form factor. The higher resolution can improve both detection and quantitation of concentrations of radiotracer, although for the latter to be true, tomographic imaging is mandatory. Several commercial dedicated breast PEM (positron emission mammography), PET scanners, and gamma camera systems have been developed and marketed [2].","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"12286 1","pages":"1228608 - 1228608-5"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81780604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Sarno, G. Mettivier, K. Michielsen, J. J. Pautasso, I. Sechopoulos, P. Russo
This work aims at evaluating the spatial resolution and noise in 3D images acquired with a clinical Computed Tomography scanner dedicated to the breast (BCT). The presampled modulation transfer function (MTF) and the noise power spectrum (NPS) are measured. In addition, the capability of the system in showing simulated lesions and microcalcification clusters was assessed via a phantom test. The impact of the selected reconstruction algorithm on MTF, NPS, and simulated lesion visibility was evaluated. The available algorithms are the Standard (Std) and Calcification (Calc) reconstructions, which use an isotropic reconstructed voxel edge of 0.273 mm and the high-resolution (HR) reconstruction algorithm that uses an isotropic reconstructed voxel edge of 0.190 mm. The spatial frequency (expressed in mm-1 ) at which the MTF curve goes down to 10% (MTF10%) was found to be 1.0 mm-1 for the case of Std reconstruction in radial direction at the chest-wall; this value increases to 1.3 mm-1 and 1.5 mm-1 for the HR and Calc reconstructions, respectively. The distance from the isocenter did not impact the system spatial resolution. As expected, the improvement in the spatial resolution in the Calc and HR reconstruction algorithms is accompanied by an increase in the noise, especially at the higher frequencies, as shown in the 1D NPS. A phantom study showed that both simulated soft lesion with diameter of 1.8 mm and microcalcification cluster with grain diameter of 0.29 mm are visible, no matter what reconstruction algorithm is selected. Microcalcifications with diameter of 0.20 mm and 0.13 mm do not appear to be visible.
{"title":"Noise and spatial resolution characteristics of a clinical computed tomography scanner dedicated to the breast","authors":"A. Sarno, G. Mettivier, K. Michielsen, J. J. Pautasso, I. Sechopoulos, P. Russo","doi":"10.1117/12.2624289","DOIUrl":"https://doi.org/10.1117/12.2624289","url":null,"abstract":"This work aims at evaluating the spatial resolution and noise in 3D images acquired with a clinical Computed Tomography scanner dedicated to the breast (BCT). The presampled modulation transfer function (MTF) and the noise power spectrum (NPS) are measured. In addition, the capability of the system in showing simulated lesions and microcalcification clusters was assessed via a phantom test. The impact of the selected reconstruction algorithm on MTF, NPS, and simulated lesion visibility was evaluated. The available algorithms are the Standard (Std) and Calcification (Calc) reconstructions, which use an isotropic reconstructed voxel edge of 0.273 mm and the high-resolution (HR) reconstruction algorithm that uses an isotropic reconstructed voxel edge of 0.190 mm. The spatial frequency (expressed in mm-1 ) at which the MTF curve goes down to 10% (MTF10%) was found to be 1.0 mm-1 for the case of Std reconstruction in radial direction at the chest-wall; this value increases to 1.3 mm-1 and 1.5 mm-1 for the HR and Calc reconstructions, respectively. The distance from the isocenter did not impact the system spatial resolution. As expected, the improvement in the spatial resolution in the Calc and HR reconstruction algorithms is accompanied by an increase in the noise, especially at the higher frequencies, as shown in the 1D NPS. A phantom study showed that both simulated soft lesion with diameter of 1.8 mm and microcalcification cluster with grain diameter of 0.29 mm are visible, no matter what reconstruction algorithm is selected. Microcalcifications with diameter of 0.20 mm and 0.13 mm do not appear to be visible.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"35 1","pages":"1228619 - 1228619-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75174554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Riveira-Martín, A. Rodríguez-Ruiz, M. Chevalier
Screening programs for the early detection of breast cancer have significantly reduced mortality in women. The limitations of these programmes are primarily due to the use of 2D techniques and the high number of mammograms to be read by radiologists. Artificial Intelligence (AI) systems may lead to new tools to help radiologists read mammograms and classify the examination based on the malignancy of the detected lesions. Several factors related to breast characteristics (thickness and density), technical factors of image acquisition, X-ray system performance and image processing algorithms can influence the outcome of a mammogram and thus also the detection capability of an AI system. The aim of this work is to analyze the robustness of an AI system for breast cancer detection and its dependence on breast characteristics and technical factors. For this purpose, mammograms from a population-based screening program were scored with the AI system. The AUC (area under the ROC curve) index generated from the scoring ROC curve was 0.92 (CI(95%) = 0.89 - 0.95), demonstrating the robust performance of the AI system. Moreover, the statistical analysis performed showed that the AUC index was independent of breast characteristics, the type of mammographic system and most of the technical parameters considered, demonstrating the effectiveness of the AI system.
{"title":"Evaluation of an AI system designed for breast cancer detection","authors":"M. Riveira-Martín, A. Rodríguez-Ruiz, M. Chevalier","doi":"10.1117/12.2623686","DOIUrl":"https://doi.org/10.1117/12.2623686","url":null,"abstract":"Screening programs for the early detection of breast cancer have significantly reduced mortality in women. The limitations of these programmes are primarily due to the use of 2D techniques and the high number of mammograms to be read by radiologists. Artificial Intelligence (AI) systems may lead to new tools to help radiologists read mammograms and classify the examination based on the malignancy of the detected lesions. Several factors related to breast characteristics (thickness and density), technical factors of image acquisition, X-ray system performance and image processing algorithms can influence the outcome of a mammogram and thus also the detection capability of an AI system. The aim of this work is to analyze the robustness of an AI system for breast cancer detection and its dependence on breast characteristics and technical factors. For this purpose, mammograms from a population-based screening program were scored with the AI system. The AUC (area under the ROC curve) index generated from the scoring ROC curve was 0.92 (CI(95%) = 0.89 - 0.95), demonstrating the robust performance of the AI system. Moreover, the statistical analysis performed showed that the AUC index was independent of breast characteristics, the type of mammographic system and most of the technical parameters considered, demonstrating the effectiveness of the AI system.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"160 1","pages":"1228610 - 1228610-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86455665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rebecca Axelsson, Victor Dahlblom, A. Tingberg, S. Zackrisson, M. Dustler, P. Bakic
We have developed a method for simultaneous tomosynthesis and mechanical imaging, called DBTMI. Mechanical imaging measures the stress distribution over the compressed breast surface. Malignant tissue is usually stiffer than benign, which results in higher stress on the compressed breast and enables to distinguish malignant from benign findings. By combining tomosynthesis and mechanical imaging, we could improve cancer detection accuracy by reducing the number of false positive findings. In this study we have analysed clinical DBTMI data, collected from 52 women from an ongoing pilot study at the Skåne University Hospital, Malmö, Sweden. We measured the range of the average stress over the breast surface, the range of average stress over the location of suspected lesions, and the normalized stress over the lesion location. Preliminary results show that the range of stress over the breast surface was 1.23-5.84 kPa, the range over the lesion location 2.10-10.10 kPa, and the normalized stress 1.12-2.44 over the lesion location. Overall, the local stress over malignant lesions was higher than the average stress over the entire breast surface. This is the first step investigating criteria to distinguish between malignant and benign findings based upon clinical DBTMI data.
{"title":"Simultaneous digital breast tomosynthesis and mechanical imaging in women recalled from screening: a preliminary analysis","authors":"Rebecca Axelsson, Victor Dahlblom, A. Tingberg, S. Zackrisson, M. Dustler, P. Bakic","doi":"10.1117/12.2625715","DOIUrl":"https://doi.org/10.1117/12.2625715","url":null,"abstract":"We have developed a method for simultaneous tomosynthesis and mechanical imaging, called DBTMI. Mechanical imaging measures the stress distribution over the compressed breast surface. Malignant tissue is usually stiffer than benign, which results in higher stress on the compressed breast and enables to distinguish malignant from benign findings. By combining tomosynthesis and mechanical imaging, we could improve cancer detection accuracy by reducing the number of false positive findings. In this study we have analysed clinical DBTMI data, collected from 52 women from an ongoing pilot study at the Skåne University Hospital, Malmö, Sweden. We measured the range of the average stress over the breast surface, the range of average stress over the location of suspected lesions, and the normalized stress over the lesion location. Preliminary results show that the range of stress over the breast surface was 1.23-5.84 kPa, the range over the lesion location 2.10-10.10 kPa, and the normalized stress 1.12-2.44 over the lesion location. Overall, the local stress over malignant lesions was higher than the average stress over the entire breast surface. This is the first step investigating criteria to distinguish between malignant and benign findings based upon clinical DBTMI data.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"604 1","pages":"1228607 - 1228607-7"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89852866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Vancoillie, L. Cockmartin, Ferdinand Lueck, N. Marshall, R. Nanke, S. Kappler, H. Bosmans
PURPOSE: To investigate differences in microcalcification detection performance for different acquisition setups in digital breast tomosynthesis (DBT), a convex dose distribution and sparser number of projections compared to the standard set-up was evaluated via a virtual clinical trial (VCT). METHODS AND MATERIALS: Following the Institutional Review Board (IRB) approval and patient consent, mediolateral oblique (MLO) DBT views were acquired at twice the automatic exposure controlled (AEC) dose level; omitting the craniocaudal (CC) view limited the total examination dose. Microcalcification clusters were simulated into the DBT projections and noise was added to simulate lower dose levels. Three set-ups were evaluated: (1) 25 DBT projections acquired with a fixed dose/projection at the clinically used AEC dose level, (2) 25 DBT projections with dose/projection following a convex dose distribution along the scan arc, and (3) 13 DBT projections at higher dose with the total scan dose equal to the AEC dose level and preserving the angular range of 50° (sparse). For the convex set-up, dose/projection started at 0.035 mGy at the extremes and increased to 0.163 mGy for the central projection. A Siemens prototype algorithm was used for reconstruction. An alternative free-response receiver operating characteristic (AFROC) study was conducted with 6 readers to compare the microcalcification detection between the acquisition set-ups. Sixty cropped VOIs of 50x50x(breast thickness) mm3 per set-up were included, of which 50% contained a microcalcification cluster. In addition to localization of the cluster, the readers were asked to count the individual calcifications. The area under the AFROC curve was used to compare the different acquisition set-ups and a paired t-test was used to test significance. RESULTS: The AUCs for the standard, convex and sparse set-up were 0.97±0.01, 0.95±0.02 and 0.89±0.03, respectively, indicating no significant difference between standard and convex set-up (p=0.309), but a significant decrease in detectability was found for the sparse set-up (p=0.001). The number of detected calcifications per cluster was not significantly different between standard and convex set-ups (p=0.049), with 42%±9% and 40%±8%, respectively. The sparse set-up scored lower with a relative number of detected microcalcifications of 34%±11%, but this decrease was not significant (p=0.031). CONCLUSION: A convex dose distribution that increased dose along the scan arc towards the central projections did not increase detectability of microcalcifications in the DBT planes compared to the current AEC set-up. Conversely, a sparse set of projections acquired over the total scan arc decreased microcalcification detectability compared to the variable dose and current clinical set-up.
{"title":"Optimized signal of calcifications in wide-angle digital breast tomosynthesis systems: a virtual clinical study","authors":"L. Vancoillie, L. Cockmartin, Ferdinand Lueck, N. Marshall, R. Nanke, S. Kappler, H. Bosmans","doi":"10.1117/12.2625772","DOIUrl":"https://doi.org/10.1117/12.2625772","url":null,"abstract":"PURPOSE: To investigate differences in microcalcification detection performance for different acquisition setups in digital breast tomosynthesis (DBT), a convex dose distribution and sparser number of projections compared to the standard set-up was evaluated via a virtual clinical trial (VCT). METHODS AND MATERIALS: Following the Institutional Review Board (IRB) approval and patient consent, mediolateral oblique (MLO) DBT views were acquired at twice the automatic exposure controlled (AEC) dose level; omitting the craniocaudal (CC) view limited the total examination dose. Microcalcification clusters were simulated into the DBT projections and noise was added to simulate lower dose levels. Three set-ups were evaluated: (1) 25 DBT projections acquired with a fixed dose/projection at the clinically used AEC dose level, (2) 25 DBT projections with dose/projection following a convex dose distribution along the scan arc, and (3) 13 DBT projections at higher dose with the total scan dose equal to the AEC dose level and preserving the angular range of 50° (sparse). For the convex set-up, dose/projection started at 0.035 mGy at the extremes and increased to 0.163 mGy for the central projection. A Siemens prototype algorithm was used for reconstruction. An alternative free-response receiver operating characteristic (AFROC) study was conducted with 6 readers to compare the microcalcification detection between the acquisition set-ups. Sixty cropped VOIs of 50x50x(breast thickness) mm3 per set-up were included, of which 50% contained a microcalcification cluster. In addition to localization of the cluster, the readers were asked to count the individual calcifications. The area under the AFROC curve was used to compare the different acquisition set-ups and a paired t-test was used to test significance. RESULTS: The AUCs for the standard, convex and sparse set-up were 0.97±0.01, 0.95±0.02 and 0.89±0.03, respectively, indicating no significant difference between standard and convex set-up (p=0.309), but a significant decrease in detectability was found for the sparse set-up (p=0.001). The number of detected calcifications per cluster was not significantly different between standard and convex set-ups (p=0.049), with 42%±9% and 40%±8%, respectively. The sparse set-up scored lower with a relative number of detected microcalcifications of 34%±11%, but this decrease was not significant (p=0.031). CONCLUSION: A convex dose distribution that increased dose along the scan arc towards the central projections did not increase detectability of microcalcifications in the DBT planes compared to the current AEC set-up. Conversely, a sparse set of projections acquired over the total scan arc decreased microcalcification detectability compared to the variable dose and current clinical set-up.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"7 1","pages":"1228604 - 1228604-7"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89958397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rubèn Sanchez De la Rosa, C. Jailin, A. Carton, Pablo Milioni, Laurence Casteignau, S. Muller
Description of purpose Contrast-enhanced spectral mammography can be used to guide needle biopsies. However, in vertical approach the compressed breast is deformed generating a so-called bump in the paddle aperture, which may interfere with the visibility of contrast-uptakes. Local thickness estimation would provide an enhanced image quality of the recombined image, increasing the visibility of the contrast-uptakes to be targeted during the biopsy procedure. In this work we propose a method to estimate the shape of the breast bump in biopsy vertical approach. Materials and Methods Our method consists on two steps: first, we compute a raw thickness which does not take into account the presence of contrast-uptakes; second, we use a physical model to separate the sparse iodine texture from the breast shape. This physical model is composed by a sum of Fourier components, describing the main shape of the bump, a series of low-order polynomials, describing the main compressed thickness, paddle tilt and deflection, and non-linear components describing the translation and rotation of the paddle aperture. A 3D object mimicking a bump was fabricated to test the pertinence of our shape model. Also, clinical images of 21 patients which followed CESM-guided biopsy were visually assessed. Results Comparison between raw and final estimated thickness of our 3D test object shows an error standard deviation of 0.37 mm similar to the noise standard deviation equals to 0.32 mm. The visual assessment of clinical cases showed that the thickness correction removes the superimposed low-frequency pattern due to non-uniform thickness of the bump, improving the identification of the lesion to be targeted. Conclusion The proposed method for thickness estimation is adapted to CESM-guided biopsies in vertical approach and it improves the identification of the contrast-uptakes that need to be targeted during the procedure.
{"title":"Breast shape estimation and correction in CESM biopsy","authors":"Rubèn Sanchez De la Rosa, C. Jailin, A. Carton, Pablo Milioni, Laurence Casteignau, S. Muller","doi":"10.1117/12.2625779","DOIUrl":"https://doi.org/10.1117/12.2625779","url":null,"abstract":"Description of purpose Contrast-enhanced spectral mammography can be used to guide needle biopsies. However, in vertical approach the compressed breast is deformed generating a so-called bump in the paddle aperture, which may interfere with the visibility of contrast-uptakes. Local thickness estimation would provide an enhanced image quality of the recombined image, increasing the visibility of the contrast-uptakes to be targeted during the biopsy procedure. In this work we propose a method to estimate the shape of the breast bump in biopsy vertical approach. Materials and Methods Our method consists on two steps: first, we compute a raw thickness which does not take into account the presence of contrast-uptakes; second, we use a physical model to separate the sparse iodine texture from the breast shape. This physical model is composed by a sum of Fourier components, describing the main shape of the bump, a series of low-order polynomials, describing the main compressed thickness, paddle tilt and deflection, and non-linear components describing the translation and rotation of the paddle aperture. A 3D object mimicking a bump was fabricated to test the pertinence of our shape model. Also, clinical images of 21 patients which followed CESM-guided biopsy were visually assessed. Results Comparison between raw and final estimated thickness of our 3D test object shows an error standard deviation of 0.37 mm similar to the noise standard deviation equals to 0.32 mm. The visual assessment of clinical cases showed that the thickness correction removes the superimposed low-frequency pattern due to non-uniform thickness of the bump, improving the identification of the lesion to be targeted. Conclusion The proposed method for thickness estimation is adapted to CESM-guided biopsies in vertical approach and it improves the identification of the contrast-uptakes that need to be targeted during the procedure.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"85 1","pages":"122860H - 122860H-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74582841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital breast tomosynthesis (DBT) is an important imaging modality for breast cancer screening. The morphology of breast masses and the shape of the microcalcifications are important factors to detect and determine the malignancy of breast cancer. Recently, convolutional neural networks (CNNs) have been used for denoising in medical imaging and have shown potential to improve the performance of radiologists. However, they can impose noise spatial correlation in the restoration process. Noise correlation can negatively impact radiologists’ performance, creating image signals that can resemble breast lesions. In this work, we propose a deep CNN that restores low-dose DBT projections by partially filtering out the noise, but imposes fidelity of the noise correlation between the original and restored images, avoiding artifacts that may resemble signs of breast cancer. The combination of a loss function that calculates the difference in the power spectra (PS) of the input and output images and another one that seeks image visual perception is proposed. We compared the performance of the proposed neural network with traditional denoising methods that do not consider the noise correlation in the restoration process and found superior results in terms of PS for our approach.
{"title":"Imposing noise correlation fidelity on digital breast tomosynthesis restoration through deep learning techniques","authors":"R. B. Vimieiro, L. Borges, Ge Wang, M. Vieira","doi":"10.1117/12.2626634","DOIUrl":"https://doi.org/10.1117/12.2626634","url":null,"abstract":"Digital breast tomosynthesis (DBT) is an important imaging modality for breast cancer screening. The morphology of breast masses and the shape of the microcalcifications are important factors to detect and determine the malignancy of breast cancer. Recently, convolutional neural networks (CNNs) have been used for denoising in medical imaging and have shown potential to improve the performance of radiologists. However, they can impose noise spatial correlation in the restoration process. Noise correlation can negatively impact radiologists’ performance, creating image signals that can resemble breast lesions. In this work, we propose a deep CNN that restores low-dose DBT projections by partially filtering out the noise, but imposes fidelity of the noise correlation between the original and restored images, avoiding artifacts that may resemble signs of breast cancer. The combination of a loss function that calculates the difference in the power spectra (PS) of the input and output images and another one that seeks image visual perception is proposed. We compared the performance of the proposed neural network with traditional denoising methods that do not consider the noise correlation in the restoration process and found superior results in terms of PS for our approach.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"42 1","pages":"122861C - 122861C-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75820335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Martí, Pablo G. del Campo, Joel Vidal, X. Cufí, J. Martí, M. Chevalier, J. Freixenet
The paper presents a framework for the detection of mass-like lesions in 3D digital breast tomosynthesis. It consists of several steps, including pre and post-processing, and a main detection block based on a Faster RCNN deep learning network. In addition to the framework, the paper describes different training steps to achieve better performance, including transfer learning using both mammographic and DBT data. The presented approach obtained third place in the recent DBT Lesion detection Challenge, DBTex, being the top approach without using an ensemble based method.
{"title":"Lesion detection in digital breast tomosynthesis: method, experiences and results of participating to the DBTex challenge","authors":"R. Martí, Pablo G. del Campo, Joel Vidal, X. Cufí, J. Martí, M. Chevalier, J. Freixenet","doi":"10.1117/12.2625733","DOIUrl":"https://doi.org/10.1117/12.2625733","url":null,"abstract":"The paper presents a framework for the detection of mass-like lesions in 3D digital breast tomosynthesis. It consists of several steps, including pre and post-processing, and a main detection block based on a Faster RCNN deep learning network. In addition to the framework, the paper describes different training steps to achieve better performance, including transfer learning using both mammographic and DBT data. The presented approach obtained third place in the recent DBT Lesion detection Challenge, DBTex, being the top approach without using an ensemble based method.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"49 1","pages":"122860W - 122860W-6"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74111985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)
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