PET image denoising based on denoising diffusion probabilistic model
Purpose Due to various physical degradation factors and limited counts received, PET
image quality needs further improvements. The denoising diffusion probabilistic model …
image quality needs further improvements. The denoising diffusion probabilistic model …
Convex optimization algorithms in medical image reconstruction—in the age of AI
The past decade has seen the rapid growth of model based image reconstruction (MBIR)
algorithms, which are often applications or adaptations of convex optimization algorithms …
algorithms, which are often applications or adaptations of convex optimization algorithms …
CBCT-guided adaptive radiotherapy using self-supervised sequential domain adaptation with uncertainty estimation
Adaptive radiotherapy (ART) is an advanced technology in modern cancer treatment that
incorporates progressive changes in patient anatomy into active plan/dose adaption during …
incorporates progressive changes in patient anatomy into active plan/dose adaption during …
Ultralow‐parameter denoising: trainable bilateral filter layers in computed tomography
Background Computed tomography (CT) is widely used as an imaging tool to visualize three‐
dimensional structures with expressive bone‐soft tissue contrast. However, CT resolution …
dimensional structures with expressive bone‐soft tissue contrast. However, CT resolution …
Joint synthesis and registration network for deformable MR-CBCT image registration for neurosurgical guidance
Objective. The accuracy of navigation in minimally invasive neurosurgery is often
challenged by deep brain deformations (up to 10 mm due to egress of cerebrospinal fluid …
challenged by deep brain deformations (up to 10 mm due to egress of cerebrospinal fluid …
Real-Time 3D Video Reconstruction for Guidance of Transventricular Neurosurgery
Neuroendoscopic approach to deep-brain targets imparts deformation of the ventricles and
adjacent parenchyma, limiting the accuracy of conventional neuronavigation. We report a …
adjacent parenchyma, limiting the accuracy of conventional neuronavigation. We report a …
Combining physics‐based models with deep learning image synthesis and uncertainty in intraoperative cone‐beam CT of the brain
Background Image‐guided neurosurgery requires high localization and registration
accuracy to enable effective treatment and avoid complications. However, accurate …
accuracy to enable effective treatment and avoid complications. However, accurate …
Structure-preserved meta-learning uniting network for improving low-dose CT quality
Objective. Deep neural network (DNN) based methods have shown promising performances
for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based …
for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based …
Stationary CT Imaging of Intracranial Hemorrhage with Diffusion Posterior Sampling Reconstruction
A Lopez-Montes, T McSkimming, A Skeats… - arxiv preprint arxiv …, 2024 - arxiv.org
Diffusion Posterior Sampling (DPS) can be used in Computed Tomography (CT)
reconstruction by leveraging diffusion-based generative models for unconditional image …
reconstruction by leveraging diffusion-based generative models for unconditional image …
[HTML][HTML] Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising
Background: Most computed tomography (CT) denoising algorithms have been evaluated
using image quality analysis (IQA) methods developed for natural image, which do not …
using image quality analysis (IQA) methods developed for natural image, which do not …