AI Article Synopsis

  • Microvascular processes are important in diseases like diabetes, and this study aimed to improve imaging techniques to visualize these changes using super-resolution ultrasound (SR-US).
  • Different sizes and concentrations of microbubble contrast agents were tested on mice, with various imaging parameters to optimize resolution in skeletal muscle microvascularity.
  • Results showed larger microbubbles and specific doses improved image detail, with specific settings (at least 20 fps and 8 minutes of imaging) yielding the best clarity in microvascular structures.

Article Abstract

Purpose: Microvascular processes play key roles in many diseases including diabetes. Improved understanding of the microvascular changes involved in disease development could offer crucial insight into the relationship of these changes to disease pathogenesis. Super-resolution ultrasound (SR-US) imaging has showed the potential to visualize microvascular detail down to the capillary level (i.e., subwavelength resolution), but optimization is still necessary. The purpose of this study was to investigate in vivo SR-US imaging of skeletal muscle microvascularity using microbubble (MB) contrast agents of various size and concentration while evaluating different ultrasound (US) system level parameters such as imaging frame rate and image acquisition length.

Methods: An US system equipped with a linear array transducer was used in a harmonic imaging mode at low transmit power. C57BL/6J mice fed a normal diet were used in this study. An assortment of size-selected MB contrast agents (1-2 μm, 3-4 μm, and 5-8 μm in diameter) were slowly infused in the tail vein at various doses (1.25 × 10 , 2.5 × 10 , or 5 × 10  MBs). US image data were collected before MB injection and thereafter for 10 min at 30 frames per s (fps). The US transducer was fixed throughout and between each imaging period to help capture microvascular patterns along the same image plane. An adaptive SR-US image processing technique was implemented using custom Matlab software.

Results: Experimental findings illustrate the use of larger MB results in better SR-US images in terms of skeletal muscle microvascular detail. A dose of 2.5 × 10  MBs resulted in SR-US images with optimal spatial resolution. An US imaging rate of at least 20 fps and image acquisition length of at least 8 min also resulted in SR-US images with pronounced microvascular detail.

Conclusions: This study indicates that MB size and dose and US system imaging rate and data acquisition length have significant impact on the quality of in vivo SR-US images of skeletal muscle microvascularity.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5734623PMC
http://dx.doi.org/10.1002/mp.12606DOI Listing

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Article Synopsis
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  • Deep learning methods, specifically a new fully convolutional neural network called SRUSnet, enhance the detection and localization of microbubble contrast agents, drastically improving efficiency and performance.
  • The SRUSnet model shows remarkable accuracy in detecting and localizing microbubbles, with over 99.9% detection accuracy and a quick processing time of about 64.5 ms per image, facilitating faster ultrasound imaging.
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