Exploration of strategies for implementation of screen-printed mercuric iodide converters in direct detection AMFPIs for digital breast tomosynthesis

2017 ◽  
Author(s):  
Larry E. Antonuk ◽  
Youcef El-Mohri ◽  
Qihua Zhao ◽  
Hao Jiang
Keyword(s):  
Direct Detection ◽  
Digital Breast Tomosynthesis ◽  
Mercuric Iodide ◽  
Breast Tomosynthesis ◽  
Screen Printed

scholarly journals Comparison of radiation dose between 2D digital stereotactic versus digital breast tomosynthesis-guided breast biopsies

2021 ◽  
Vol 134 ◽  
pp. 109407
Author(s):  
T. Amir ◽  
S.P Zuckerman ◽  
B. Barufaldi ◽  
A.D Maidment ◽  
E.F Conant
Keyword(s):  
Radiation Dose ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis

scholarly journals A Decision Support System Based on BI-RADS and Radiomic Classifiers to Reduce False Positive Breast Calcifications at Digital Breast Tomosynthesis: A Preliminary Study

2021 ◽  
Vol 11 (6) ◽  
pp. 2503
Author(s):  
Marco Alì ◽  
Natascha Claudia D’Amico ◽  
Matteo Interlenghi ◽  
Marina Maniglio ◽  
Deborah Fazzini ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Breast Imaging ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Preliminary Study ◽  
Personalized Risk ◽  
Fold Cross Validation ◽  
Malignant Diagnosis

Digital breast tomosynthesis (DBT) studies were introduced as a successful help for the detection of calcification, which can be a primary sign of cancer. Expert radiologists are able to detect suspicious calcifications in DBT, but a high number of calcifications with non-malignant diagnosis at biopsy have been reported (false positives, FP). In this study, a radiomic approach was developed and applied on DBT images with the aim to reduce the number of benign calcifications addressed to biopsy and to give the radiologists a helpful decision support system during their diagnostic activity. This allows personalizing patient management on the basis of personalized risk. For this purpose, 49 patients showing microcalcifications on DBT images were retrospectively included, classified by BI-RADS (Breast Imaging-Reporting and Data System) and analyzed. After segmentation of microcalcifications from DBT images, radiomic features were extracted. Features were then selected with respect to their stability within different segmentations and their repeatability in test–retest studies. Stable radiomic features were used to train, validate and test (nested 10-fold cross-validation) a preliminary machine learning radiomic classifier that, combined with BI-RADS classification, allowed a reduction in FP of a factor of 2 and an improvement in positive predictive value of 50%.

scholarly journals A deep learning classifier for digital breast tomosynthesis

2021 ◽  
Vol 83 ◽  
pp. 184-193
Author(s):  
R. Ricciardi ◽  
G. Mettivier ◽  
M. Staffa ◽  
A. Sarno ◽  
G. Acampora ◽  
...  
Keyword(s):  
Deep Learning ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Learning Classifier
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