Table of Contents

• Simulation of sequential pathology images for the virtual clinical trials with rad-path correlationPredrag R. Bakic, David D. Pokrajac, Michael D. Feldman, Andrew D. A. Maidment. [doi]
• Performance evaluation of a 3D structured phantom with simulated lesions on breast imaging systemsLiesbeth Vancoillie, Lesley Cockmartin, Kristina Tri Wigati, Dimitar Petrov, Guozhi Zhang, Nicholas Marshall, Hilde Bosmans. [doi]
• Identification of breast tissue using the x-ray image measured with an energy-resolved cadmium telluride series detector based on photon-counting techniqueMariko Sasaki, Shuji Koyama, Yoshie Kodera, Reina Suzuki, Ai Nakajima, Hiroko Nishide, Mitsuhiro Mizutani, Megumi Watanabe, Naoko Yoshida, Hiroaki Hayashi, Tsutomu Yamakawa, Shuichiro Yamamoto, Daisuke Hashimoto, Masahiro Okada. [doi]
• Volumetric breast density measurement for personalized screening: accuracy, reproducibility, and agreement with visual assessmentAndreas Fieselmann, Daniel Förnvik, Hannie Förnvik, Kristina Lång, Hanna Sartor, Sophia Zackrisson, Ludwig Ritschl, Thomas Mertelmeier. [doi]
• Clinical performance of the tomosynthesis guided breast biopsyXiaoqin Wang, Arzu Canan, Shrujal Patel, Li Chen, Richard Gibbs, Margaret Szabunio. [doi]
• Multisource x-ray system for artifact reduction in dedicated breast CTAmy E. Becker, Andrew M. Hernandez, Paul Schwoebel, John M. Boone. [doi]
• Classification of mammographic microcalcification clusters with machine learning confidence levelsAndrik Rampun, Hui Wang, Bryan W. Scotney, Philip J. Morrow, Reyer Zwiggelaar. [doi]
• Ultrasound transducer tracking system for correlation of masses in combined x-ray and manual breast ultrasound imagingQi You, Rungroj Jintamethasawat, Yuxin Wang, Jie Yuan, Marilyn A. Roubidoux, Ying Zhang, Paul L. Carson. [doi]
• Mammogram breast density classification using mean-elliptical local binary patternsMinu George, Erika R. E. Denton, Reyer Zwiggelaar. [doi]
• Development of energy-resolved photon-counting mammography with a cadmium telluride series detector to reduce radiation exposure and increase contrast-to-noise ratio using the high-energy X-raysReina Suzuki, Shuji Koyama, Yoshie Kodera, Ai Nakajima, Mariko Sasaki, Hiroto Kimura, Hiroaki Hayashi, Tsutomu Yamakawa, Shuichiro Yamamoto, Daisuke Hashimoto, Masahiro Okada. [doi]
• Diagnostic radiation dose after the implementation of digital breast tomosynthesis screeningBruno Barufaldi, Elizabeth S. McDonald, Emily F. Conant, Andrew D. A. Maidment. [doi]
• Mass detection in mammograms using pre-trained deep learning modelsRicha Agarwal, Oliver Díaz, Xavier Lladó, Robert Martí. [doi]
• Impact of angular range of digital breast tomosynthesis on mass detection in dense breastsDavid A. Scaduto, Hailiang Huang, Chunling Liu, Kim Rinaldi, Axel Hebecker, Thomas Mertelmeier, Sebastian Vogt, Paul Fisher, Wei Zhao. [doi]
• Using a convolutional neural network to predict readers' estimates of mammographic density for breast cancer risk assessmentGeorgia V. Ionescu, Martin Fergie, Michael Berks, Elaine F. Harkness, Johan Hulleman, Adam R. Brentnall, Jack Cuzick, D. Gareth Evans, Susan M. Astley. [doi]
• Automatic estimation of glandular tissue loss due to limited reconstruction voxel size in tomographic images of the breastMarco Caballo, Christian Fedon, Luca Brombal, Koen Michielsen, Renata Longo, Ioannis Sechopoulos. [doi]
• The PET/X dedicated breast-PET scanner for optimizing cancer therapyLawrence R. MacDonald, William C. J. Hunter, Chengeng Zeng, Larry A. Pierce, Sergei Dolinski, Donald DeWitt, Robert S. Miyaoka, Paul E. Kinahan. [doi]
• Development of a physical anthropomorphic breast phantom for objective task-based assessment of dedicated breast CT systemsJesse Salad, Lynda C. Ikejimba, Andrey Makeev, Christian G. Graff, Bahaa Ghammraoui, Stephen J. Glick. [doi]
• Breast tomosynthesis reconstruction using TIGRE software toolAlbert Malet, Diego García Pinto, Josep Fernández, Robert Martí, Oliver Díaz. [doi]
• Deep learning methods aid in predicting risk of interval cancerBenjamin Hinton, Heather Greenwood, Bonnie N. Joe, Karla Kerlikowske, Lin Ma, John Shepherd. [doi]
• Developing imaging biomarkers for mammographically-occult cancer in dense breasts using a radiologist's progress rating on cancer development: a preliminary analysisJuhun Lee, Sung Eun Song, Robert M. Nishikawa. [doi]
• Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networksShuyue Guan, Murray H. Loew. [doi]
• Orientation dependent detectability of fiber-like signals in linear iterative image reconstruction for breast tomosynthesisSean D. Rose, Ingrid S. Reiser, Emil Y. Sidky, Xiaochuan Pan. [doi]
• A framework for distinguishing benign from malignant breast histopathological images using deep residual networksZiba Gandomkar, Patrick C. Brennan, Claudia Mello-Thoms. [doi]
• Deep learning and color variability in breast cancer histopathological images: a preliminary studyGobert N. Lee, Mariusz Bajger, Kevin Clark. [doi]
• Deep radiogenomics for predicting clinical phenotypes in invasive breast cancerHong-Jun Yoon, Arvind Ramanathan, Folami Alamudun, Georgia D. Tourassi. [doi]
• Superpixel pattern graphs for identifying breast mass ROIs in dense background: a preliminary studyShelda Sajeev, Mariusz Bajger, Gobert N. Lee. [doi]
• Optimized simulation of breast anatomy for virtual clinical trialsPredrag R. Bakic, Bruno Barufaldi, David D. Pokrajac, Susan P. Weinstein, Andrew D. A. Maidment. [doi]
• A hybrid approach for virtual clinical trials for mammographic imagingFrank Schebesch, Magdalena Herbst, Thomas Mertelmeier, Andreas K. Maier, Ludwig Ritschl. [doi]
• Method for task-based evaluation of clinical FFDM and DBT systems using an anthropomorphic breast phantomLynda C. Ikejimba, Jesse Salad, Katherine Kemp, Christian G. Graff, Bahaa Ghammraoui, Joseph Y. Lo, Stephen J. Glick. [doi]
• Changes in breast density over time using automatic density measures: preliminary analysisEloy García, Arnau Oliver, Oliver Díaz, Yago Diez, Albert Gubern-Mérida, Joan Martí, Robert Martí. [doi]
• A novel nipple detection algorithm on Digital Mammography (DM)Jiayu Jiang, Yao Lu, Yanhui Guo. [doi]
• Dose reduction in breast CT by spectrum switchingKoen Michielsen, Christian Fedon, James G. Nagy, Ioannis Sechopoulos. [doi]
• Independent images: a need for phantom based image quality assessment using model observers?Ramona W. Bouwman, Christiana Balta, David R. Dance, Kenneth C. Young, Alistair Mackenzie, Ioannis Sechopoulos, Ruben E. van Engen. [doi]
• Bag of visual words based approach for the classification of benign and malignant masses in mammograms using voting-based feature encodingZobia Suhail, Arif Mahmood, Erika R. E. Denton, Reyer Zwiggelaar. [doi]
• Digital breast tomosynthesis: impact of a new beam quality on dose to patientsMargarita Chevalier, C. Viloria, P. Squair, M. S. Nogueira, O. Diaz. [doi]
• Preliminary experiences of DBT (digital breast tomosynthesis) and hybrid 18F-FDG-PET/MR (PETMR) for neoadjuvant chemotherapy (NAC) cases in breast cancerNachiko Uchiyama, Hiroaki Kurihara, Takayuki Kinoshita, Masayuki Yoshida, Mari Kikuchi, Kyoichi Otsuka, Yasuaki Arai. [doi]
• Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancersHeather M. Whitney, Karen Drukker, Alexandra Edwards, John Papaioannou, Maryellen L. Giger. [doi]
• Validation of the textural realism of a 3D anthropomorphic phantom for digital breast tomosynthesisRaymond J. Acciavatti, Meng Kang Hsieh, Aimilia Gastounioti, Yifan Hu, Jinbo Chen, Andrew D. A. Maidment, Despina Kontos. [doi]
• Developing populations of software breast phantoms for virtual clinical trialsBruno Barufaldi, Predrag R. Bakic, David D. Pokrajac, Miguel A. Lago, Andrew D. A. Maidment. [doi]
• Lesion assessment and radiation dose in contrast-enhanced digital breast tomosynthesisHailiang Huang, David A. Scaduto, Chunling Liu, Jie Yang, Chencan Zhu, Kim Rinaldi, Jason Eisenberg, Jingxuan Liu, Mathias Hoernig, Julia Wicklein, Sebastian Vogt, Thomas Mertelmeier, Paul R. Fisher, Wei Zhao. [doi]
• Optimization of acquisition parameters for the detection of secondary breast lesions applying temporal contrast enhanced digital mammographyJ. P. Castillo-Lopez, J. C. Cruz-Rodríguez, H. A. Galván-Espinoza, F. Berumen, Y. Villaseñor-Navarro, María-Ester Brandan. [doi]
• How does wide-angle breast tomosynthesis depict calcifications in comparison to digital mammography? A retrospective observer studyAlejandro Rodríguez-Ruiz, Ruben E. van Engen, Koen Michielsen, Ramona W. Bouwman, Suzan Vreemann, Nico Karssemeijer, Ritse M. Mann, Ioannis Sechopoulos. [doi]
• Acquisition parameters for dual-energy contrast-enhanced digital mammography using a micelle-based all-in-one nanoparticle (AION) contrast agent: a phantom studyKristen C. Lau, Jessica C. Hsu, Pratap C. Naha, Peter Chhour, Renee Hastings, Joel M. Stein, Elizabeth S. McDonald, David P. Cormode, Andrew D. A. Maidment. [doi]
• Towards an analytic model describing the effect of scan angle and slice thickness on the in-plane spatial resolution of calcifications in digital breast tomosynthesisChristoph Luckner, Frank Schebesch, Thomas Mertelmeier, Andreas Fieselmann, Andreas K. Maier, Ludwig Ritschl. [doi]
• Towards clinic-friendly solutions for patient trials in breast cancer phase contrast imagingSarah J. Lewis, Timur E. Gureyev, Patrycja Baran, Seyedamir Tavakoli Taba, Serena Pacilè, Christian Dullin, Giuliana Tromba, Daniel Hausermann, Andrew Peele, Darren Lockie, Patrick C. Brennan. [doi]
• Mammographic breast density over time among women who have participated in BreastScreen NorwayNataliia Moshina, Gunvor G. Wåde, Marta Roman, Sofie Sebuødegård, Solveig Hofvind. [doi]
• Mammogram denoising to improve the calcification detection performance of convolutional netsClaudio Marrocco, Alessandro Bria, Valerio Di Sano, Lucas R. Borges, Benedetta Savelli, Mario Molinara, Jan-Jurre Mordang, Nico Karssemeijer, Francesco Tortorella. [doi]
• Development of an automated detection algorithm for patient motion blur in digital mammogramsMelissa L. Hill, Patsy J. Whelehan, Sarah J. Vinnicombe, Christopher E. Tromans, Andrew Evans, Violet R. Warwick, J. Michael Brady, Ralph P. Highnam. [doi]
• Measuring breast motion at multiple DBT compression levels using ultrasound speckle-tracking techniquesRongping Zeng, Congxian Jia, Nima Akhlaghi, Mahsa Torkaman, Brian S. Garra, Karen Alton, Rachel Brem, Tahira Ahmed, Richard Kaczmarek, Kyle J. Myers. [doi]
• Masking risk predictors in screening mammographyJames G. Mainprize, Olivier Alonzo-Proulx, Taghreed Alshafeiy, James T. Patrie, Jennifer A. Harvey, Martin J. Yaffe. [doi]
• Breast phantom validation of a mammographic image modification methodJoana Boita, Alistair Mackenzie, Ioannis Sechopoulos. [doi]
• Front Matter: Volume 107181071801 [doi]
• Breast compression parameters among women imaged with full field digital mammography and breast tomosynthesis in BreastScreen NorwayGunvor G. Wåde, Åsne Holen, B. Hanestad, S. Sebuødegård, N. Moshina, K. Pedersen, Hofvind. 1071802 [doi]
• Can radiologists improve their breast cancer detection in mammography when using a deep learning based computer system as decision support?Alejandro Rodríguez-Ruiz, Jan-Jurre Mordang, Nico Karssemeijer, Ioannis Sechopoulos, Ritse M. Mann. 1071803 [doi]
• Detection of the abnormal gist in the prior mammograms even with no overt sign of breast cancerZiba Gandomkar, Ernest U. Ekpo, Sarah J. Lewis, Karla K. Evans, Kriscia Tapia, Phuong Dung Trieu, Jeremy M. Wolfe, Patrick C. Brennan. 1071804 [doi]
• Automated lesion detection and segmentation in digital mammography using a u-net deep learning networkTimothy de Moor, Alejandro Rodríguez-Ruiz, Albert Gubern-Mérida, Ritse Mann, Jonas Teuwen. 1071805 [doi]
• Deep learning in computer-aided diagnosis incorporating mammographic characteristics of both tumor and parenchyma stromaHui Li 0024, Deepa Sheth, Kayla R. Mendel, Li Lan, Maryellen L. Giger. 1071806 [doi]
• Comparing the performance of various deep networks for binary classification of breast tumoursAzam Hamidinekoo, Zobia Suhail, Erika R. E. Denton, Reyer Zwiggelaar. 1071807 [doi]
• Improving the automated detection of calcifications by combining deep cascades and deep convolutional netsAlessandro Bria, Claudio Marrocco, Mario Molinara, Benedetta Savelli, Jan-Jurre Mordang, Nico Karssemeijer, Francesco Tortorella. 1071808 [doi]
• Retrieval of reference images of breast masses on mammograms by similarity space modelingChisako Muramatsu, Shunichi Higuchi, Takako Morita, Mikinao Oiwa, Tomonori Kawasaki, Hiroshi Fujita 0001. 1071809 [doi]
• Phantom-based comparison of microcalcification visibility between digital and synthetic mammography using humans and a deep neural network as observersMaria Castillo-García, Alejandro Rodríguez-Ruiz, Joao Emilio Peixoto, Margarita Chevalier. 1071810 [doi]
• A deep learning framework for micro-calcification detection in 2D mammography and C-viewGiovanni Trovini, Christian Napoli, Robert Marti, Amaya Martin, Alessandro Bria, Claudio Marrocco, Mario Molinara, Francesco Tortorella, Oliver Díaz. 1071811 [doi]
• Multi-scale morphological feature extraction for the classification of micro-calcificationsZobia Suhail, Erika R. E. Denton, Reyer Zwiggelaar. 1071812 [doi]
• Transfer deep learning mammography diagnostic model from public datasets to clinical practice: a comparison of model performance and mammography datasetsQuan Chen, Jinze Liu, Kyle Luo, Xiaofei Zhang, Xiaoqin Wang. 1071813 [doi]
• Radiation dose reduction in digital breast tomosynthesis (DBT) by means of neural network convolution (NNC) deep learningJunchi Liu, Amin Zarshenas, Syed Ammar Qadir, Limin Yang, Laurie Lee Fajardo, Kenji Suzuki. 1071814 [doi]
• Metastatic breast cancer: characterization of axillary sentinel lymph node (SLN) on the preoperative spectral CTRutong Zeng, Sitao Zhang, Xiang Zhang, Chushan Zheng, Jialun Zhang, Ke Xue, Jun Wei 0002, Yao Lu, Jun Shen. 1071815 [doi]
• Automatic classification of clustered microcalcifications in digitized mammogram using ensemble learningNashid Alam, Reyer Zwiggelaar. 1071816 [doi]
• Creation of new artificial calcification shadows for breast cancer and verification of effectiveness of CAD development technique that uses no actual casesK. Abe, Hideya Takeo, Y. Nagai, Yoshifumi Kuroki, Shigeru Nawano. 1071817 [doi]
• Fully automated pectoral muscle identification on MLO-view mammograms with deep convolutional neural networkXiangyuan Ma, Jun Wei 0002, Chuan Zhou, Heang-Ping Chan, Lubomir M. Hadjiiski, Yao Lu. 1071818 [doi]
• First results with a deep learning (feed-forward CNN) approach for daily quality control in digital breast tomosynthesisDimitar Petrov, Nicholas Marshall, Lesley Cockmartin, Hilde Bosmans. 1071819 [doi]
• Comparison of screening full-field digital mammography and digital breast tomosynthesis technical recallsLonie R. Salkowski, Mai Elezaby, Amy M. Fowler, Elizabeth S. Burnside, Ryan W. Woods, Roberta M. Strigel. 1071820 [doi]
• An anthropomorphic model observer for spiculated massesAli R. N. Avanaki, Kathryn S. Espig, Albert Xthona, Tom R. L. Kimpe. 1071821 [doi]
• Increasing display luminance as a means to enhance interpretation accuracy and efficiency when reducing full-field digital mammography doseElizabeth A. Krupinski. 1071822 [doi]