This is a literature survey for ultrasound imaging. It is done on arxiv.
First paper that I would like to consider is not directly related to ultrasound imaging but it is a tutorial related to general biomedical image reconstruction. The name of the tutorial is that Algorithms for Biomedical Image reconstruction by Michael T. Mccann and Michael Unser. (arXiv:1901.03565) They provide an introduction to a toolbox of ides and algorithms that are useful for working on modern image reconstruction problems.
Photo-acoustic computed tomography is an emerging imaging technique. There is a survey of computational frameworks for acoustic inverse problems for PACT.
arXiv:1905.03881
There is some work for segmentation (e.g. breast cancer, Intra-operative ultrasound(open brain surgery))
arXiv:1905.01902 , arXiv:1905.01344, arXiv:1904.11322,
arXiv:1904.11031, arXiv:1904.10030 ,arXiv:1904.08655,
arXiv:1904.05191, arXiv:1904.01076, arXiv:1903.08814
Articulatory to Acoustic Mapping:
arXiv:1904.05259
Ultrasound Localization Microscopy and Super-Resolution: A State of the Art( https://ieeexplore.ieee.org/document/8396283 )
Abstract— Because it drives the compromise between resolution and penetration, the diffraction limit has long represented an unreachable summit to conquer in ultrasound imaging. Within a few years after the introduction of optical localization microscopy, we proposed its acoustic alter ego that exploits the micrometric localization of microbubble contrast agents to reconstruct the finest vessels in the body in-depth. Various groups now working on the subject are optimizing the localization precision, microbubble separation, acquisition time, tracking, and velocimetry to improve the capacity of ultrasound localization microscopy (ULM) to detect and distinguish vessels much smaller than the wavelength. It has since been used in vivo in the brain, the kidney, and tumors. In the clinic, ULM is bound to improve drastically our vision of the microvasculature, which could revolutionize the diagnosis of cancer, arteriosclerosis, stroke, and diabetes.
Ultrasound Image Formation:
- 3-D Super-Resolution Ultrasound (SR-US) Imaging with a 2-D Sparse Array(arXiv:1902.01608 )
- 3-D Super-Resolution Ultrasound (SR-US) Imaging using a 2-D Sparse Array with High Volumetric Imaging Rate (arXiv:1902.02631)
- SCATGAN FOR RECONSTRUCTION OF ULTRASOUND SCATTERERS USING GENERATIVE ADVERSARIAL NETWORKS ( arXiv:1902.00469 )
- Universal Deep Beamformer for Variable Rate Ultrasound Imaging (arXiv:1901.01706 )
- Deep Learning-based Universal Beamformer for Ultrasound Imaging( arXiv:1904.02843 )
- Learning beam forming in ultrasound imaging (arXiv:1812.08043 )
- A Low-Rank and Joint-Sparse Model for Ultrasound Signal Reconstruction (arXiv:1812.04843 )
- Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound ( arXiv:1811.08252 )
- A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound (arXiv:1810.00322)
- High frame-rate cardiac ultrasound imaging with deep learning(arXiv:1808.07823)
- TOWARDS CT-QUALITY ULTRASOUND IMAGING USING DEEP LEARNING(arXiv:1710.06304 )
- DEEP CONVOLUTIONAL ROBUST PCA WITH APPLICATION TO ULTRASOUND IMAGING (arXiv:1811.08252)
- Exploiting flow dynamics for super-resolution in contrast-enhanced ultrasound(arXiv:1804.03134 )
- Sparsity-driven super-resolution in clinical contrast-enhanced ultrasound
