Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives
Although computer-aided diagnosis (CAD) is widely used in mammography, conventional
CAD programs that use prompts to indicate potential cancers on the mammograms have not …
CAD programs that use prompts to indicate potential cancers on the mammograms have not …
Learning to reconstruct computed tomography images directly from sinogram data under a variety of data acquisition conditions
Computed tomography (CT) is widely used in medical diagnosis and non-destructive
detection. Image reconstruction in CT aims to accurately recover pixel values from measured …
detection. Image reconstruction in CT aims to accurately recover pixel values from measured …
Improved classification of benign and malignant breast lesions using deep feature maximum intensity projection MRI in breast cancer diagnosis using dynamic …
Purpose To develop a deep transfer learning method that incorporates four-dimensional
(4D) information in dynamic contrast-enhanced (DCE) MRI to classify benign and malignant …
(4D) information in dynamic contrast-enhanced (DCE) MRI to classify benign and malignant …
PGNet: Projection generative network for sparse‐view reconstruction of projection‐based magnetic particle imaging
Background Magnetic particle imaging (MPI) is a novel tomographic imaging modality that
scans the distribution of superparamagnetic iron oxide nanoparticles. However, it is time …
scans the distribution of superparamagnetic iron oxide nanoparticles. However, it is time …
System and method for multi-architecture computed tomography pipeline
A system and method for reconstructing an image of a subject acquired using a tomographic
imaging system includes at least one computer processor configured to form an image …
imaging system includes at least one computer processor configured to form an image …
Accurate and robust sparse‐view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL‐PICCS)
Background: Sparse‐view CT image reconstruction problems encountered in dynamic CT
acquisitions are technically challenging. Recently, many deep learning strategies have been …
acquisitions are technically challenging. Recently, many deep learning strategies have been …
New horizons: artificial intelligence for digital breast tomosynthesis
The use of digital breast tomosynthesis (DBT) in breast cancer screening has become
widely accepted, facilitating increased cancer detection and lower recall rates compared …
widely accepted, facilitating increased cancer detection and lower recall rates compared …
DIR‐DBTnet: Deep iterative reconstruction network for three‐dimensional digital breast tomosynthesis imaging
T Su, X Deng, J Yang, Z Wang, S Fang… - Medical …, 2021 - Wiley Online Library
Purpose The goal of this study is to develop a three‐dimensional (3D) iterative
reconstruction framework based on the deep learning (DL) technique to improve the digital …
reconstruction framework based on the deep learning (DL) technique to improve the digital …
A denoising model based on multi-agent reinforcement learning with data transformation for digital tomosynthesis
K Nam, D Lee, S Lee - Physics in Medicine & Biology, 2023 - iopscience.iop.org
Objective. Denoising models based on the supervised learning have been proposed for
medical imaging. However, its clinical availability in digital tomosynthesis (DT) imaging is …
medical imaging. However, its clinical availability in digital tomosynthesis (DT) imaging is …
Radiomics: a primer for breast radiologists
LJ Grimm - Journal of Breast Imaging, 2021 - academic.oup.com
Radiomics has a long-standing history in breast imaging with computer-aided detection
(CAD) for screening mammography developed in the late 20th century. Although …
(CAD) for screening mammography developed in the late 20th century. Although …