Model and human observer reproducibility for detection of microcalcification clusters in digital breast tomosynthesis images of three-dimensionally structured test object-Reference-Cited by-同舟云学术

Model and human observer reproducibility for detection of microcalcification clusters in digital breast tomosynthesis images of three-dimensionally structured test object

Published:2019-03-23 Issue:01 Volume:6 Page:1
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:
Short-container-title:J. Med. Imag.

Author:

Petrov Dimitar¹

Affiliation:

1. KU Leuven, Department of Medical Physics and Quality Assessment, Leuven

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology, Nuclear Medicine and imaging

Reference38 articles.

1. Breast tomosynthesis and digital mammography: a comparison of diagnostic accuracy

2. Prospective trial comparing full-field digital mammography (FFDM) versus combined FFDM and tomosynthesis in a population-based screening programme using independent double reading with arbitration

3. Comparison of microcalcification detectability in FFDM and DBT using a virtual clinical trial

4. Detection and Classification of Calcifications on Digital Breast Tomosynthesis and 2D Digital Mammography: A Comparison

5. Detection of microcalcification clusters in 2D mammography and digital breast tomosynthesis and the relation to the standard method of measuring image quality;Hadjipanteli,2016

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Evaluating an image restoration pipeline for digital mammography across varied radiation exposures and microcalcification sizes using model observer analysis;17th International Workshop on Breast Imaging (IWBI 2024);2024-05-29

2. Computed tomography optimization using a volumetric channelized Hotelling observer approach for energy integrating and photon-counting CT scanners;Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment;2024-03-29

3. Understanding the generalizability of a convolutional neural network-based model observer for breast tomosynthesis images with different volume glandular fractions;Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment;2024-03-29

4. Convolutional neural network‐based model observer for signal known statistically task in breast tomosynthesis images;Medical Physics;2023-04-09

5. Performance evaluation of digital breast tomosynthesis systems: physical methods and experimental data;Physics in Medicine & Biology;2022-11-16