Super-resolution Ultrasound Localization Microscopy through Deep Learning

Ruud J.G. van Sloun (Corresponding author), Oren Solomon, Matthew Bruce, Zin Z. Khaing, Hessel Wijkstra, Yonina C Eldar, Massimo Mischi

Research output: Contribution to journalArticleAcademicpeer-review

25 Citations (Scopus)
202 Downloads (Pure)

Abstract

Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents (microbubbles) across numerous imaging frames. However, analysis of high-density regions with significant overlaps among the microbubble point spread responses yields high localization errors, constraining the technique to low-concentration conditions. As such, long acquisition times are required to sufficiently cover the vascular bed. In this work, we present a fast and precise method for obtaining super-resolution vascular images from high-density contrast-enhanced ultrasound imaging data. This method, which we term Deep Ultrasound Localization Microscopy (Deep-ULM), exploits modern deep learning strategies and employs a convolutional neural network to perform localization microscopy in dense scenarios, learning the nonlinear image-domain implications of overlapping RF signals originating from such sets of closely spaced microbubbles. Deep-ULM is trained effectively using realistic on-line synthesized data, enabling robust inference in-vivo under a wide variety of imaging conditions. We show that deep learning attains super-resolution with challenging contrast-agent densities, both in-silico as well as in-vivo. Deep-ULM is suitable for real-time applications, resolving about 70 high-resolution patches (128×128 pixels) per second on a standard PC. Exploiting GPU computation, this number increases to 1250 patches per second.

Original languageEnglish
Article number9257449
Pages (from-to)829-839
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume40
Issue number3
Early online date12 Nov 2020
DOIs
Publication statusPublished - Mar 2021

Keywords

  • deep learning
  • neural network
  • super resolution
  • Ultrasound
  • ultrasound localization microscopy

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