Blind ct image quality assessment using ddpm-derived content and transformer-based evaluator
Lowering radiation dose per view and utilizing sparse views per scan are two common CT
scan modes, albeit often leading to distorted images characterized by noise and streak …
scan modes, albeit often leading to distorted images characterized by noise and streak …
StarAN: A star attention network utilizing inter-view and intra-view correlations for sparse-view cone-beam computed tomography reconstruction
X Jin, Y Zhu, K Wu, D Hu, X Gao - Expert Systems with Applications, 2024 - Elsevier
Sparse sampling can reduce the radiation dose and accelerate the scanning speed of cone-
beam computed tomography (CBCT) by increasing the angular interval between projections …
beam computed tomography (CBCT) by increasing the angular interval between projections …
Self-supervised tomographic image noise suppression via residual image prior network
Computed tomography (CT) denoising is a challenging task in medical imaging that has
garnered considerable attention. Supervised networks require a lot of noisy-clean image …
garnered considerable attention. Supervised networks require a lot of noisy-clean image …
Synthetic lumbar MRI can aid in diagnosis and treatment strategies based on self-pix networks
K Song, W Zhu, Z Zhang, B Liu, M Zhang, T Tang… - Scientific Reports, 2024 - nature.com
CT and MR tools are commonly used to diagnose lumbar fractures (LF). However, numerous
limitations have been found in practice. The aims of this study were to innovate and develop …
limitations have been found in practice. The aims of this study were to innovate and develop …
Prior image-based generative adversarial learning for multi-material decomposition in photon counting computed tomography
J Ren, Z Zheng, Y Wang, N Liang, S Wang… - Computers in Biology …, 2024 - Elsevier
Background Photon counting detector computed tomography (PCD-CT) is a novel promising
technique providing higher spatial resolution, lower radiation dose and greater energy …
technique providing higher spatial resolution, lower radiation dose and greater energy …
[HTML][HTML] An efficient dual-domain deep learning network for sparse-view CT reconstruction
C Sun, Y Salimi, N Angeliki, S Boudabbous… - Computer methods and …, 2024 - Elsevier
Abstract Background and Objective: We develop an efficient deep-learning based dual-
domain reconstruction method for sparse-view CT reconstruction with small training …
domain reconstruction method for sparse-view CT reconstruction with small training …
LOQUAT: Low-Rank Quaternion Reconstruction for Photon-Counting CT
Z Lin, G Quan, H Qu, Y Du… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Photon-counting computed tomography (PCCT) may dramatically benefit clinical practice
due to its versatility such as dose reduction and material characterization. However, the …
due to its versatility such as dose reduction and material characterization. However, the …
Deep Inertia Half-quadratic Splitting Unrolling Network for Sparse View CT Reconstruction
Sparse view computed tomography (CT) reconstruction poses a challenging ill-posed
inverse problem, necessitating effective regularization techniques. In this letter, we employ …
inverse problem, necessitating effective regularization techniques. In this letter, we employ …
Side Information-Assisted Low-Dose CT Reconstruction
Y Zhang, R Cao, F Xu, R Zhang, F Jiang… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
CT images from individual patients or different patient populations typically share similar
radiological features such as textures and structures. In model-based iterative reconstruction …
radiological features such as textures and structures. In model-based iterative reconstruction …
One-step inverse generation network for sparse-view dual-energy CT reconstruction and material imaging
X Zhang, L Li, S Wang, N Liang, A Cai… - Physics in Medicine & …, 2024 - iopscience.iop.org
Objective. Sparse-view dual-energy spectral computed tomography (DECT) imaging is a
challenging inverse problem. Due to the incompleteness of the collected data, the presence …
challenging inverse problem. Due to the incompleteness of the collected data, the presence …