A review of deep learning ct reconstruction from incomplete projection data
Computed tomography (CT) is a widely used imaging technique in both medical and
industrial applications. However, accurate CT reconstruction requires complete projection …
industrial applications. However, accurate CT reconstruction requires complete projection …
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 …
JSR-Net: A deep network for joint spatial-radon domain CT reconstruction from incomplete data
CT image reconstruction from incomplete data, such as sparse views and limited angle
reconstruction, is an important and challenging problem in medical imaging. This work …
reconstruction, is an important and challenging problem in medical imaging. This work …
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 …
Report on the AAPM deep‐learning sparse‐view CT grand challenge
EY Sidky, X Pan - Medical physics, 2022 - Wiley Online Library
Purpose The purpose of the challenge is to find the deep‐learning (DL) technique for sparse‐
view computed tomography (CT) image reconstruction that can yield the minimum root mean …
view computed tomography (CT) image reconstruction that can yield the minimum root mean …
A deep learning reconstruction framework for X-ray computed tomography with incomplete data
As a powerful imaging tool, X-ray computed tomography (CT) allows us to investigate the
inner structures of specimens in a quantitative and nondestructive way. Limited by the …
inner structures of specimens in a quantitative and nondestructive way. Limited by the …
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 …
Deep iterative reconstruction estimation (DIRE): approximate iterative reconstruction estimation for low dose CT imaging
The image quality in low dose computed tomography (LDCT) can be severely degraded by
amplified mottle noise and streak artifacts. Although the iterative reconstruction (IR) …
amplified mottle noise and streak artifacts. Although the iterative reconstruction (IR) …
Deconvolution-based backproject-filter (bpf) computed tomography image reconstruction method using deep learning technique
For conventional computed tomography (CT) image reconstruction tasks, the most popular
method is the so-called filtered-back-projection (FBP) algorithm. In it, the acquired Radon …
method is the so-called filtered-back-projection (FBP) algorithm. In it, the acquired Radon …
Noise-generating and imaging mechanism inspired implicit regularization learning network for low dose ct reconstrution
Low-dose computed tomography (LDCT) helps to reduce radiation risks in CT scanning
while maintaining image quality, which involves a consistent pursuit of lower incident rays …
while maintaining image quality, which involves a consistent pursuit of lower incident rays …