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Results for "detectability index": 5 found

Contrastive Learning and Hybrid CNN-Transformer Model for Unpaired MR Image Synthesis in Acute Cerebral Infarction

Authors: Kota Hirose, Daisuke Kawahara, Jokichi Kawazoe, Yuji Murakami

Affiliation: Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Graduate School of Biomedical and Health Sciences, Hiroshima University

Abstract Preview: Purpose: Synthesizing medical images can address the lack of or unscanned medical images, reducing scanner time and costs. However, paired image scarcity remains a challenge for image synthesis. We pr...

Dedicated Cone-Beam Breast CT: Investigation on the Effect of Lesion Type on Detectability Index Using Cascaded Systems Analysis.

Authors: Jing-Tzyh Alan Chiang, Andrew Karellas, Thomas C Larsen, Hsin Wu Tseng, Srinivasan Vedantham

Affiliation: Department of Biomedical Engineering, The University of Arizona, Department of Medical Imaging, The University of Arizona

Abstract Preview: Purpose: To investigate the performance of dedicated breast computed tomography (BCT) for the the tasks of detection of soft tissue lesions and microcalcifications using cascaded systems analysis. The...

Development of a High-Speed Digital Breast Tomosynthesis System with a Two-Dimensional Multiple X-Ray-Source Array

Authors: John M. Boone, Andrew M. Hernandez, Paul Schwoebel, Jeffrey H. Siewerdsen, Alejandro Sisniega, Wojciech B. Zbijewski

Affiliation: Johns Hopkins University, University of California, UT MD Anderson Cancer Center, University of New Mexico Albuquerque, UC Davis Health

Abstract Preview: Purpose: To significantly improve image quality relative to clinically deployed digital breast tomosynthesis (DBT) systems, which use a 1D acquisition geometry (an arc), with a 2D image acquisition ge...

Impact on image quality from reduced dose conebeam CT for pediatric fluoroscopy-guided interventional (FGI) imaging

Authors: Samuel L. Brady, Kevin Chen, Joseph G. Meier

Affiliation: University of Cincinnati, Cincinnati Childrens Hospital Med Ctr

Abstract Preview: Purpose: Conebeam-CT (CBCT) acquisition protocols typically do not distinguish between adults and pediatrics. In collaboration with a fluoroscopically-guided interventional (FGI) manufacturer, new, do...

In silico Evaluation Vs Standard Phantom Evaluation of a Deep Learning Reconstruction Algorithm

Authors: Naruomi Akino, Kirsten Lee Boedeker, Ilmar Hein, Dylan Mather, Akira Nishikori, Daniel W Shin

Affiliation: Canon Medical Systems Corporation, Canon Medical Research USA

Abstract Preview: Purpose: To validate the performance a deep learning reconstruction (DLR) algorithm in an anatomical background compared to a uniform phantom background.
Methods: An analytic forward projection mod...