A survey on deep learning in medical image reconstruction
E Ahishakiye, M Bastiaan Van Gijzen… - Intelligent …, 2021 - mednexus.org
Medical image reconstruction aims to acquire high-quality medical images for clinical usage
at minimal cost and risk to the patients. Deep learning and its applications in medical …
at minimal cost and risk to the patients. Deep learning and its applications in medical …
Deep learning‐based image reconstruction for different medical imaging modalities
Image reconstruction in magnetic resonance imaging (MRI) and computed tomography (CT)
is a mathematical process that generates images at many different angles around the …
is a mathematical process that generates images at many different angles around the …
Learning with known operators reduces maximum error bounds
We describe an approach for incorporating prior knowledge into machine learning
algorithms. We aim at applications in physics and signal processing in which we know that …
algorithms. We aim at applications in physics and signal processing in which we know that …
Dynamic ct reconstruction from limited views with implicit neural representations and parametric motion fields
Reconstructing dynamic, time-varying scenes with computed tomography (4D-CT) is a
challenging and ill-posed problem common to industrial and medical settings. Existing 4D …
challenging and ill-posed problem common to industrial and medical settings. Existing 4D …
[HTML][HTML] The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review
Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
MCR toolkit: A GPU‐based toolkit for multi‐channel reconstruction of preclinical and clinical x‐ray CT data
Background The advancement of x‐ray CT into the domains of photon counting spectral
imaging and dynamic cardiac and perfusion imaging has created many new challenges and …
imaging and dynamic cardiac and perfusion imaging has created many new challenges and …
[HTML][HTML] Known operator learning and hybrid machine learning in medical imaging—a review of the past, the present, and the future
In this article, we perform a review of the state-of-the-art of hybrid machine learning in
medical imaging. We start with a short summary of the general developments of the past in …
medical imaging. We start with a short summary of the general developments of the past in …
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer
T Cheslerean-Boghiu, FC Hofmann… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Deep learning based solutions are being succesfully implemented for a wide variety of
applications. Most notably, clinical use-cases have gained an increased interest and have …
applications. Most notably, clinical use-cases have gained an increased interest and have …
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 …
Federated simulation for medical imaging
Labelling data is expensive and time consuming especially for domains such as medical
imaging that contain volumetric imaging data and require expert knowledge. Exploiting a …
imaging that contain volumetric imaging data and require expert knowledge. Exploiting a …