scholarly journals Compressed sensing-based super-resolution ultrasound imaging for faster acquisition and high quality images

2021 ◽  
pp. 1-1
Author(s):  
Jihun Kim ◽  
Qingfei Wang ◽  
Siyuan Zhang ◽  
Sangpil Yoon
Keyword(s):  
Compressed Sensing ◽  
Ultrasound Imaging ◽  
Super Resolution ◽  
High Quality

scholarly journals Compressed sensing-based super-resolution ultrasound imaging for faster acquisition and high quality images

2020 ◽  
Author(s):  
Jihun Kim ◽  
Qingfei Wang ◽  
Siyuan Zhang ◽  
Sangpil Yoon
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Ultrasound Imaging ◽  
Imaging Technique ◽  
Super Resolution ◽  
Tumor Model ◽  
Acquisition Time ◽  
Mouse Tumor ◽  
Conventional Ultrasound

AbstractSuper-resolution ultrasound (SRUS) imaging technique has overcome the diffraction limit of conventional ultrasound imaging, resulting in an improved spatial resolution while preserving imaging depth. Typical SRUS images are reconstructed by localizing ultrasound microbubbles (MBs) injected in a vessel using normalized 2-dimensional cross-correlation (2DCC) between MBs signals and the point spread function of the system. However, current techniques require isolated MBs in a confined area due to inaccurate localization of densely populated MBs. To overcome this limitation, we developed the ℓ1-homotopy based compressed sensing (L1H-CS) based SRUS imaging technique which localizes densely populated MBs to visualize microvasculature in vivo. To evaluate the performance of L1H-CS, we compared the performance of 2DCC, interior-point method based compressed sensing (CVX-CS), and L1H-CS algorithms. Localization efficiency was compared using axially and laterally aligned point targets (PTs) with known distances and randomly distributed PTs generated by simulation. We developed post-processing techniques including clutter reduction, noise equalization, motion compensation, and spatiotemporal noise filtering for in vivo imaging. We then validated the capabilities of L1H-CS based SRUS imaging technique with high-density MBs in a mouse tumor model, kidney, and zebrafish dorsal trunk, and brain. Compared to 2DCC, and CVX-CS algorithm, L1H-CS algorithm, considerable improvement in SRUS image quality and data acquisition time was achieved. These results demonstrate that the L1H-CS based SRUS imaging technique has the potential to examine the microvasculature with reduced acquisition and reconstruction time of SRUS image with enhanced image quality, which may be necessary to translate it into the clinics.

scholarly journals Current Development and Applications of Super-Resolution Ultrasound Imaging

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2417
Author(s):  
Qiyang Chen ◽  
Hyeju Song ◽  
Jaesok Yu ◽  
Kang Kim
Keyword(s):  
Kidney Disease ◽  
Ultrasound Imaging ◽  
Atherosclerotic Plaque ◽  
Super Resolution ◽  
Emerging Technology ◽  
Diffraction Limit ◽  
Disease Models ◽  
Current Development ◽  
Future Directions ◽  
Acoustic Diffraction

Abnormal changes of the microvasculature are reported to be key evidence of the development of several critical diseases, including cancer, progressive kidney disease, and atherosclerotic plaque. Super-resolution ultrasound imaging is an emerging technology that can identify the microvasculature noninvasively, with unprecedented spatial resolution beyond the acoustic diffraction limit. Therefore, it is a promising approach for diagnosing and monitoring the development of diseases. In this review, we introduce current super-resolution ultrasound imaging approaches and their preclinical applications on different animals and disease models. Future directions and challenges to overcome for clinical translations are also discussed.

scholarly journals Super-resolution Ultrasound Imaging of the Renal Microvasculature in Rats with Metabolic syndrome

2020 ◽  
Author(s):  
Stinne Byrholdt Sogaard ◽  
Sofie Bech Andersen ◽  
Iman Taghavi ◽  
Carlos Armando Villagomez Hoyos ◽  
Kristoffer Lindskov Hansen ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Ultrasound Imaging ◽  
Super Resolution ◽  
Renal Microvasculature
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