Artificial intelligence in image reconstruction: the change is here
Innovations in CT have been impressive among imaging and medical technologies in both
the hardware and software domain. The range and speed of CT scanning improved from the …
the hardware and software domain. The range and speed of CT scanning improved from the …
The future of CT: deep learning reconstruction
CM McLeavy, MH Chunara, RJ Gravell, A Rauf… - Clinical radiology, 2021 - Elsevier
There have been substantial advances in computed tomography (CT) technology since its
introduction in the 1970s. More recently, these advances have focused on image …
introduction in the 1970s. More recently, these advances have focused on image …
Low‐dose CT reconstruction with Noise2Noise network and testing‐time fine‐tuning
Purpose Deep learning‐based image denoising and reconstruction methods demonstrated
promising performance on low‐dose CT imaging in recent years. However, most existing …
promising performance on low‐dose CT imaging in recent years. However, most existing …
Deep learning image reconstruction for CT: technical principles and clinical prospects
Filtered back projection (FBP) has been the standard CT image reconstruction method for 4
decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in …
decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in …
Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications
The reconstruction of computed tomography (CT) images is an active area of research.
Following the rise of deep learning methods, many data-driven models have been proposed …
Following the rise of deep learning methods, many data-driven models have been proposed …
A review of deep learning CT reconstruction: concepts, limitations, and promise in clinical practice
TP Szczykutowicz, GV Toia, A Dhanantwari… - Current Radiology …, 2022 - Springer
Abstract Purpose of Review Deep Learning reconstruction (DLR) is the current state-of-the-
art method for CT image formation. Comparisons to existing filter back-projection, iterative …
art method for CT image formation. Comparisons to existing filter back-projection, iterative …
Computationally efficient deep neural network for computed tomography image reconstruction
Purpose Deep neural network‐based image reconstruction has demonstrated promising
performance in medical imaging for undersampled and low‐dose scenarios. However, it …
performance in medical imaging for undersampled and low‐dose scenarios. However, it …
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
Commercial iterative reconstruction techniques help to reduce the radiation dose of
computed tomography (CT), but altered image appearance and artefacts can limit their …
computed tomography (CT), but altered image appearance and artefacts can limit their …
Deep tomographic image reconstruction: yesterday, today, and tomorrow—editorial for the 2nd special issue “Machine Learning for Image Reconstruction”
As a follow-up to the first IEEE Transactions on Medical Imaging (TMI) special issue on the
theme of deep tomographic reconstruction, the second special issue is assembled to reflect …
theme of deep tomographic reconstruction, the second special issue is assembled to reflect …
Noise characteristics modeled unsupervised network for robust CT image reconstruction
Deep learning (DL)-based methods show great potential in computed tomography (CT)
imaging field. The DL-based reconstruction methods are usually evaluated on the training …
imaging field. The DL-based reconstruction methods are usually evaluated on the training …