[HTML][HTML] A review and experimental evaluation of deep learning methods for MRI reconstruction
Following the success of deep learning in a wide range of applications, neural network-
based machine-learning techniques have received significant interest for accelerating …
based machine-learning techniques have received significant interest for accelerating …
Sparse-view x-ray CT reconstruction via total generalized variation regularization
Sparse-view CT reconstruction algorithms via total variation (TV) optimize the data iteratively
on the basis of a noise-and artifact-reducing model, resulting in significant radiation dose …
on the basis of a noise-and artifact-reducing model, resulting in significant radiation dose …
On the data acquisition, image reconstruction, cone beam artifacts, and their suppression in axial MDCT and CBCT–a review
X Tang, EA Krupinski, H Xie, AE Stillman - Medical physics, 2018 - Wiley Online Library
Purpose In the clinic, computed tomography (CT) has evolved into an essential modality for
diagnostic imaging by multidetector row CT (MDCT) and image guided intervention by cone …
diagnostic imaging by multidetector row CT (MDCT) and image guided intervention by cone …
Compressive sensing in medical imaging
The promise of compressive sensing, exploitation of compressibility to achieve high quality
image reconstructions with less data, has attracted a great deal of attention in the medical …
image reconstructions with less data, has attracted a great deal of attention in the medical …
[HTML][HTML] Deep learning based image reconstruction algorithm for limited-angle translational computed tomography
J Wang, J Liang, J Cheng, Y Guo, L Zeng - Plos one, 2020 - journals.plos.org
As a low-end computed tomography (CT) system, translational CT (TCT) is in urgent
demand in developing countries. Under some circumstances, in order to reduce the scan …
demand in developing countries. Under some circumstances, in order to reduce the scan …
Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography
Photon-counting computed tomography (PCCT) is an emerging imaging technique that
enables multi-energy imaging with only a single scan acquisition. To enable multi-energy …
enables multi-energy imaging with only a single scan acquisition. To enable multi-energy …
Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance
Purpose: Statistical model based iterative reconstruction (MBIR) methods have been
introduced to clinical CT systems and are being used in some clinical diagnostic …
introduced to clinical CT systems and are being used in some clinical diagnostic …
Compressed sensing for longitudinal MRI: an adaptive‐weighted approach
Purpose: Repeated brain MRI scans are performed in many clinical scenarios, such as
follow up of patients with tumors and therapy response assessment. In this paper, the …
follow up of patients with tumors and therapy response assessment. In this paper, the …
Accurate and robust sparse‐view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL‐PICCS)
Background: Sparse‐view CT image reconstruction problems encountered in dynamic CT
acquisitions are technically challenging. Recently, many deep learning strategies have been …
acquisitions are technically challenging. Recently, many deep learning strategies have been …
Spectral CT reconstruction—ASSIST: Aided by self-similarity in image-spectral tensors
Spectral computed tomography (CT) reconstructs multienergy images from data in different
energy bins. However, these reconstructed images can be contaminated by noise due to the …
energy bins. However, these reconstructed images can be contaminated by noise due to the …