Artificial general intelligence for radiation oncology
The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As
prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can …
prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can …
CT image denoising and deblurring with deep learning: current status and perspectives
This article reviews the deep learning methods for computed tomography image denoising
and deblurring separately and simultaneously. Then, we discuss promising directions in this …
and deblurring separately and simultaneously. Then, we discuss promising directions in this …
Synthetic CT generation from MRI using 3D transformer‐based denoising diffusion model
Background and purpose Magnetic resonance imaging (MRI)‐based synthetic computed
tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for …
tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for …
Full-dose PET synthesis from low-dose PET using high-efficiency diffusion denoising probabilistic model
To reduce the risks associated with ionizing radiation, a reduction of radiation exposure in
PET imaging is needed. However, this leads to a detrimental effect on image contrast and …
PET imaging is needed. However, this leads to a detrimental effect on image contrast and …
Cycle-guided denoising diffusion probability model for 3d cross-modality mri synthesis
This study aims to develop a novel Cycle-guided Denoising Diffusion Probability Model (CG-
DDPM) for cross-modality MRI synthesis. The CG-DDPM deploys two DDPMs that condition …
DDPM) for cross-modality MRI synthesis. The CG-DDPM deploys two DDPMs that condition …
Image‐domain material decomposition for dual‐energy CT using unsupervised learning with data‐fidelity loss
Background Dual‐energy computed tomography (DECT) and material decomposition play
vital roles in quantitative medical imaging. However, the decomposition process may suffer …
vital roles in quantitative medical imaging. However, the decomposition process may suffer …
CBCT‐based synthetic CT image generation using a diffusion model for CBCT‐Guided lung radiotherapy
Background Although cone beam computed tomography (CBCT) has lower resolution
compared to planning CTs (pCT), its lower dose, higher high‐contrast resolution, and …
compared to planning CTs (pCT), its lower dose, higher high‐contrast resolution, and …
Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
This paper presents a comprehensive systematic review of generative models (GANs, VAEs,
DMs, and LLMs) used to synthesize various medical data types, including imaging …
DMs, and LLMs) used to synthesize various medical data types, including imaging …
Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in …
Background and objective: The cone-beam computed tomography (CBCT) provides three-
dimensional volumetric imaging of a target with low radiation dose and cost compared with …
dimensional volumetric imaging of a target with low radiation dose and cost compared with …
CT-based synthetic contrast-enhanced dual-energy CT generation using conditional denoising diffusion probabilistic model
Objective. The study aimed to generate synthetic contrast-enhanced Dual-energy CT (CE-
DECT) images from non-contrast single-energy CT (SECT) scans, addressing the limitations …
DECT) images from non-contrast single-energy CT (SECT) scans, addressing the limitations …