Super-resolution Ultrasound Imaging

Abstract

The majority of exchanges of oxygen and nutrients are performed around vessels smaller than 100 μm, allowing cells to thrive everywhere in the body. Pathologies such as cancer, diabetes and arteriosclerosis can profoundly alter the microvasculature. Unfortunately, medical imaging modalities only provide indirect observation at this scale. Inspired by optical microscopy, ultrasound localization microscopy has bypassed the classic compromise between penetration and resolution in ultrasonic imaging. By localization of individual injected microbubbles and tracking of their displacement with a subwavelength resolution, vascular and velocity maps can be produced at the scale of the micrometer. Super-resolution ultrasound has also been performed through signal fluctuations with the same type of contrast agents, or through switching on and off nano-sized phase-change contrast agents. These techniques are now being applied pre-clinically and clinically for imaging of the microvasculature of the brain, kidney, skin, tumors and lymph nodes.

Keywords: Brain; Contrast agents; Localization; Microbubbles; Microscopy; Microvessels; Super-resolution; Tumor; Ultrasound.

Figure 1.

Steps in ultrasound super-resolution processing. A) Acquisition of ultrasound data over time from contrast-enhanced vascular region. B) Detection of signals from microbubble contrast agents. C) Isolation of individual microbubble signals, overlapping or interfering signals are rejected. D) Localization of the microbubble to a precision far beyond the diffraction-limited resolution. E) Tracking of the microbubbles through consecutive frames to establish velocity profiles. F) Mapping of the accumulated localizations gathered over the series of frames produces an image of the vascular structure far beyond the diffraction limit.

Figure 2:

Fast ultrasound super-resolution imaging achieved by fast-AWSALM (a) Conventional B-mode image (b) SVD-filtered image (c) super-resolution image by a 200 milisecond data acquisition (d) optical image of the 200 μm cross-tube phantom. (e) and (f) show the resolution measurements at different lateral ROIs indicated by the red lines on the images. (Figure from Zhang et al. UFFC 2019)

Figure 3:

An overview of several non-localization methods that leverage priors through signal structure or by learning from data. An input CEUS sequence (a) can be modelled to derive estimators that exploit signal structure (b), or to generate realistic data to train data-driven estimators in the form of deep neural networks (c).

Figure 4.

Application of microvascular imaging to assess the angiogenic biomarker of cancer: Maximum intensity projections of 3-D acoustic angiography imaging of microvasculature for healthy (control) tissue and tissue surrounding fibrosarcoma tumors in a rat model. Dotted circles indicate approximate location of solid tumor mass, and field of view is approximately 2.5×2cm. Reproduced with permission from R. C. Gessner, S. R. Aylward, and P. A. Dayton, “Mapping microvasculature with acoustic angiography yields quantifiable differences between healthy and tumor-bearing tissue volumes in a rodent model,” Radiology, vol. 264, no. 3, pp. 733-40, Sep 2012 ©RSNA.

Figure 5.

Maximum intensity projections of 3D super-resolution imaging of healthy microvasculature (a-b) and tumor associated microvasculature (c-d). Smallest vessels resolved were approximately 25 microns in diameter, approximately 6x improved from achievable with acoustic angiography at a similar depth. Reproduce with permission from: F. Lin, S. E. Shelton, D. Espindola, J. D. Rojas, G. Pinton, and P. A. Dayton, “3-D Ultrasound Localization Microscopy for Identifying Microvascular Morphology Features of Tumor Angiogenesis at a Resolution Beyond the Diffraction Limit of Conventional Ultrasound,” Theranostics, vol. 7, no. 1, pp. 196-204, 2017.

Figure 6:

2D projection of popliteal lymph node through 17 super-resolution image slices covering 1.7 mm thickness: (left) Depth color-coded super-resolution maximum intensity projection (MIP) images, where hue encodes the image slice with the maximum intensity and saturation represents the number of microbubbles localized at that depth. (right) Velocity color-coded MIP, where color shows velocity and direction. Top and bottom groups of images obtained from 2 different lymph nodes. (Adapted from Zhu J. et al., Radiology 2019)

Figure 7:

B-mode image (A) of a breast cancer patient showing a hypointense, irregular lesion with unsharp margins. The MIP image after microbubble injection (B) confirms the lesion to be highly vascularized but details in the vascular architecture can hardly be captured. Profound information on the vascularization is available from the microbubble tracking analysis illustrating microbubble tracks (C), the directions of blood flow (D) and individual vessels’ velocities (E).

Figure 8 –

From left to right: Micro CT image of a spiral tube phantom with nominal internal diameter of 255 μm; Super-resolution ultrasound image of the phantom through a human skull cap using a hemispherical array (transmit 306 kHz, receive 612 kHz); Merged image showing agreement between ultrasound and microCT. (reproduced from O’Reilly 2013 by permissions of Wiley company, all rights reserved)

Figure 9 –

Super-resolution image (15 MHz) of rat brain vasculature through thinned skull bone using an ultrafast scanner at 500 frames per second. 150 seconds of acquisition were required to localize and track approximatively 1 million separable sources per hemisphere (From Errico 2015, Nature PG all rights reserved).

Figure 10:

Volumetric Ultrasound Localization Microscopy implemented on an anesthetised rat brain using a 2D matrix array with center frequency 9MHz (Heiles et al. 2018). Voxel size is 10 μm, region of imaging is 13.28mm deep, 10.5mm wide, and 11.4mm in length. The “glow” colormap was used to encode microbubble density. The brighter the color of the voxel, the more microbubbles have passed through that voxel.