Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review
Purpose In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its
superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the …
superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the …
Generating Synthesized Computed Tomography (CT) from Magnetic Resonance Imaging Using Cycle-Consistent Generative Adversarial Network for Brain Tumor …
WY Juan - International Journal of Radiation Oncology, Biology …, 2021 - redjournal.org
Purpose/Objective (s) Brain tumor is the most common malignant tumor of the head and
neck in China, postoperative radiotherapy is one of the main methods to improve the …
neck in China, postoperative radiotherapy is one of the main methods to improve the …
An improved deep learning framework for MR-to-CT image synthesis with a new hybrid objective function
There is an emerging interest in radiotherapy treatment planning that uses only magnetic
resonance (MR) imaging. Cur-rent clinical workflows rely on computed tomography (CT) …
resonance (MR) imaging. Cur-rent clinical workflows rely on computed tomography (CT) …
Attention-guided generative adversarial network to address atypical anatomy in modality transfer
H Emami, M Dong, CK Glide-Hurst - arXiv preprint arXiv:2006.15264, 2020 - arxiv.org
Recently, interest in MR-only treatment planning using synthetic CTs (synCTs) has grown
rapidly in radiation therapy. However, developing class solutions for medical images that …
rapidly in radiation therapy. However, developing class solutions for medical images that …
CT Synthesis from MRI Using Generative Adversarial Network with Frequency-Aware Discriminator
Y Li, S Xu, Z Qi - Journal of Electrical Engineering & Technology, 2024 - Springer
The pursuit of generating computed tomography (CT) from magnetic resonance imaging
(MRI) remains a key area of research with the goal of advancing modern radiation therapy …
(MRI) remains a key area of research with the goal of advancing modern radiation therapy …
Synthesizing high‐resolution magnetic resonance imaging using parallel cycle‐consistent generative adversarial networks for fast magnetic resonance imaging
Purpose The common practice in acquiring the magnetic resonance (MR) images is to
obtain two‐dimensional (2D) slices at coarse locations while keeping the high in‐plane …
obtain two‐dimensional (2D) slices at coarse locations while keeping the high in‐plane …
Contrast generalisation in deep learning-based brain MRI-to-CT synthesis
L Nijskens - 2022 - essay.utwente.nl
Background and purpose: Computed tomography (CT) is the basis for radiotherapy (RT)
planning, providing information on electron density needed for dose calculations. Magnetic …
planning, providing information on electron density needed for dose calculations. Magnetic …
[HTML][HTML] Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR) …
Background Precise patient setup is critical in radiation therapy. Medical imaging plays an
essential role in patient setup. As compared to computed tomography (CT) images …
essential role in patient setup. As compared to computed tomography (CT) images …
CBCT-to-CT translation using Registration-Based generative adversarial networks in patients with Head and Neck Cancer
C Suwanraksa, J Bridhikitti, T Liamsuwan… - Cancers, 2023 - mdpi.com
Simple Summary Cone-beam computed tomography (CBCT) not only plays an important
role in image-guided radiation therapy (IGRT) but also has the potential for dose calculation …
role in image-guided radiation therapy (IGRT) but also has the potential for dose calculation …
Attention-guided generative adversarial network to address atypical anatomy in synthetic CT generation
Recently, interest in MR-only treatment planning using synthetic CTs (synCTs) has grown
rapidly in radiation therapy. However, developing class solutions for medical images that …
rapidly in radiation therapy. However, developing class solutions for medical images that …