A Technical Review of Convolutional Neural Network‐Based Mammographic Breast Cancer Diagnosis
This study reviews the technique of convolutional neural network (CNN) applied in a specific
field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on …
field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on …
[HTML][HTML] 3D image scanning of gravel soil using in-situ X-ray computed tomography
S Matsumura, A Kondo, K Nakamura, T Mizutani… - Scientific Reports, 2023 - nature.com
A typical ground investigation for characterizing geotechnical properties of soil requires
sampling soils to test in a laboratory. Laboratory X-ray computed tomography (CT) has been …
sampling soils to test in a laboratory. Laboratory X-ray computed tomography (CT) has been …
Adaptive iterative reconstruction based on relative total variation for low-intensity computed tomography
C Gong, L Zeng - Signal Processing, 2019 - Elsevier
Low-intensity projections (high-level noise) may degrade computed tomography (CT) image
quality in some applications. CT image reconstruction from projections with low intensity has …
quality in some applications. CT image reconstruction from projections with low intensity has …
A deep learning framework for prostate localization in cone beam CT‐guided radiotherapy
Purpose To develop a deep learning‐based model for prostate planning target volume
(PTV) localization on cone beam computed tomography (CBCT) to improve the workflow of …
(PTV) localization on cone beam computed tomography (CBCT) to improve the workflow of …
A deep unsupervised learning framework for the 4D CBCT artifact correction
Objective. Four-dimensional cone-beam computed tomography (4D CBCT) has unique
advantages in moving target localization, tracking and therapeutic dose accumulation in …
advantages in moving target localization, tracking and therapeutic dose accumulation in …
Can signal-to-noise ratio perform as a baseline indicator for medical image quality assessment
Natural image quality assessment (NIQA) wins increasing attention, while NIQA models are
rarely used in the medical community. A couple of studies employ the NIQA methodologies …
rarely used in the medical community. A couple of studies employ the NIQA methodologies …
[HTML][HTML] A deep unsupervised learning model for artifact correction of pelvis cone-beam CT
Purpose In recent years, cone-beam computed tomography (CBCT) is increasingly used in
adaptive radiation therapy (ART). However, compared with planning computed tomography …
adaptive radiation therapy (ART). However, compared with planning computed tomography …
Ring artifact suppression in X-ray computed tomography using a simple, pixel-wise response correction
We present a pixel-specific, measurement-driven correction that effectively reduces errors in
detector response that give rise to the ring artifacts commonly seen in X-ray computed …
detector response that give rise to the ring artifacts commonly seen in X-ray computed …
Removing ring artefacts for photon-counting detectors using neural networks in different domains
The development of energy-resolving photon-counting detectors provides a new approach
for obtaining spectral information in computed tomography. However, the responses of …
for obtaining spectral information in computed tomography. However, the responses of …
[HTML][HTML] A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images
Background Quality assessment of medical images is highly related to the quality
assurance, image interpretation and decision making. As to magnetic resonance (MR) …
assurance, image interpretation and decision making. As to magnetic resonance (MR) …