A tutorial on sparse signal reconstruction and its applications in signal processing
Sparse signals are characterized by a few nonzero coefficients in one of their transformation
domains. This was the main premise in designing signal compression algorithms …
domains. This was the main premise in designing signal compression algorithms …
[HTML][HTML] SYNAPSE: An international roadmap to large brain imaging
APJ Stampfl, Z Liu, J Hu, K Sawada, H Takano… - Physics Reports, 2023 - Elsevier
Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been
collaborating in a major project called “SYNAPSE”(Synchrotrons for Neuroscience: an Asia …
collaborating in a major project called “SYNAPSE”(Synchrotrons for Neuroscience: an Asia …
3D feature constrained reconstruction for low-dose CT imaging
Low-dose computed tomography (LDCT) images are often highly degraded by amplified
mottle noise and streak artifacts. Maintaining image quality under low-dose scan protocols is …
mottle noise and streak artifacts. Maintaining image quality under low-dose scan protocols is …
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 …
DuDoSS: Deep‐learning‐based dual‐domain sinogram synthesis from sparsely sampled projections of cardiac SPECT
Purpose Myocardial perfusion imaging (MPI) using single‐photon emission‐computed
tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. In …
tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. In …
Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT: A review
Statistical image reconstruction (SIR) methods have shown potential to substantially improve
the image quality of low‐dose x‐ray computed tomography (CT) as compared to the …
the image quality of low‐dose x‐ray computed tomography (CT) as compared to the …
Artifact reduction for sparse-view CT using deep learning with band patch
Sparse-view computed tomography (CT), an imaging technique that reduces the number of
projections, can reduce the total scan duration and radiation dose. However, sparse data …
projections, can reduce the total scan duration and radiation dose. However, sparse data …
On some common compressive sensing recovery algorithms and applications-Review paper
Compressive Sensing, as an emerging technique in signal processing is reviewed in this
paper together with its common applications. As an alternative to the traditional signal …
paper together with its common applications. As an alternative to the traditional signal …
Deep learning-based solvability of underdetermined inverse problems in medical imaging
Recently, with the significant developments in deep learning techniques, solving
underdetermined inverse problems has become one of the major concerns in the medical …
underdetermined inverse problems has become one of the major concerns in the medical …
Compressed medical imaging based on average sparsity model and reweighted analysis of multiple basis pursuit
In medical imaging and applications, efficient image sampling and transfer are some of the
key fields of research. The compressed sensing (CS) theory has shown that such …
key fields of research. The compressed sensing (CS) theory has shown that such …