Super-resolution ultrasound localization microscopy based on a high frame-rate clinical ultrasound scanner: an in-human feasibility study

Abstract

Non-invasive detection of microvascular alterations in deep tissuesin vivoprovides critical information for clinical diagnosis and evaluation of a broad-spectrum of pathologies. Recently, the emergence of super-resolution ultrasound localization microscopy (ULM) offers new possibilities for clinical imaging of microvasculature at capillary level. Currently, the clinical utility of ULM on clinical ultrasound scanners is hindered by the technical limitations, such as long data acquisition time, high microbubble (MB) concentration, and compromised tracking performance associated with low imaging frame-rate. Here we present a robust in-human ULM on a high frame-rate (HFR) clinical ultrasound scanner to achieve super-resolution microvessel imaging using a short acquisition time (<10 s). Ultrasound MB data were acquired from different human tissues, including a healthy liver and a diseased liver with acute-on-chronic liver failure, a kidney, a pancreatic tumor, and a breast mass using an HFR clinical scanner. By leveraging the HFR and advanced processing techniques including sub-pixel motion registration, MB signal separation, and Kalman filter-based tracking, MBs can be robustly localized and tracked for ULM under the circumstances of relatively high MB concentration associated with standard clinical MB administration and limited data acquisition time in humans. Subtle morphological and hemodynamic information in microvasculature were shown based on data acquired with single breath-hold and free-hand scanning. Compared with contrast-enhanced power Doppler generated based on the same MB dataset, ULM showed a 5.7-fold resolution improvement in a vessel based on a linear transducer, and provided a wide-range blood flow speed measurement that is Doppler angle-independent. Microvasculatures with complex hemodynamics can be well-differentiated at super-resolution in both normal and pathological tissues. This preliminary study implemented the ultrafast in-human ULM in various human tissues based on a clinical scanner that supports HFR imaging, indicating the potentials of the technique for various clinical applications. However, rigorous validation of the technique in imaging human microvasculature (especially for those tiny vessel structure), preferably with a gold standard, is still required.

Keywords: contrast-enhanced ultrasound; high frame-rate; microbubble; microvascular imaging; super-resolution ultrasound; ultrasound localization microscopy.

Conflict of interest statement

Competing Interests: The authors declare no competing interests.

© 2021 Institute of Physics and Engineering in Medicine.

Figures

Figure 1.

(a) Stack of B-mode images indicating original spatiotemporal ultrasound data from a healthy human liver (t represents slow time). (b) B-mode images of the MB signal extract from (a) with tissue clutter filtering. Centroids of MBs were localized and indicated by the red asterisks obtained from the cross-correlation with a predefined point-spread-function (PSF) of the system. (c) Accumulation of the MB centroid positions over time to generate an MB density map. (d) The estimated lateral tissue motion curve with respect to the first ultrasound frame before and after motion registration. (e) the microvessel density map obtained from accumulating MB positions with MB pairing, tracking, Kalman filtering, but without motion correction. (f) The microvessel density map with motion correction. Here, the example images in (c)(e)(f) were generated with 829 frames of MB data, corresponding to a data acquisition time of 2.0 s. The microvessel density images are displayed as the square root of the original accumulation intensity to compress the dynamic range for better visualization.

Figure 2.

(a) Super-resolution ULM density image of the flow channel. A subregion of the flow channel indicated by the dashed white rectangle was magnified and shown in the bottom right of the image for better visualization. (b) Super-resolution ULM velocity image of the flow channel, with colormap indicating the magnitude of the velocity. The same subregion indicated by the dashed white rectangle was magnified and shown. (c) The estimated lateral tissue motion of the phantom tissue manually induced by the hand-held probe. (d) The averaged cross-sectional profile of the flow channel measured from the ULM density image and the corresponding contrast-enhanced power Doppler (PD) image. (e) The averaged cross-sectional profile of the flow speed measured from the ULM velocity image.

Figure 3.

(a) Full FOV super-resolution ULM microvessel image of the healthy human liver overlaid on the B-mode image from a 70-year-old patient with no significant liver diseases. (b) The zoomed-in local region as indicated by the white rectangle in (a). (c) The magnified region indicated by the white rectangle in (b) highlighting a small branch of vessels. (d) The contrast-enhanced power Doppler image of the same branch of vessels in (c), which is obtained by accumulating the power of the MB signal over time. (e) Plots of a vessel profile along the white dashed line indicated in (c) using ULM (red curve)and power Doppler (blue curve). (f) Bi-directional super-resolution microvessel density image, similar to Fig. 3b but with red color indicates the upward flow and blue color represents the downward flow. (g) Super-resolution ULM microvessel velocity image of the healthy human liver, with color bar indicating the magnitude of the velocity. The direction of the blood flow can be found in Sup. Fig. 1. (h) The histogram of the blood flow speed distribution.

Figure 4.

(a) Full FOV super-resolution ULM microvessel density image of human liver overlaid on the B-mode image from a 38-year-old patient with acute-on-chronic liver failure. (b) The zoomed-in region as indicated by the white rectangle in (a). (c) Corresponding bi-directional microvessel density image, similar to Fig. 4b but with red color indicates the upward flow and blue color represents the downward flow. (d) Corresponding super-resolution microvessel velocity image, with colormap indicating the magnitude of the velocity. The direction of the blood flow can be found in Sup. Fig. 2. (e) Zoom-in region (indicated by the white rectangle in Fig. 4b) of the contrast-enhanced power Doppler image (see Sup. Fig. 2 for full FOV power Doppler). (f) Corresponding zoom-in region of the super-resolution microvessel density image. (g) Corresponding zoom-in region of the super-resolution microvessel velocity image. Some of the distorted vessels with tortuosity and/or tapering in the main branches are denoted by white arrows.

Figure 5.

(a) Super-resolution microvessel density image of a human kidney overlap on the ultrasound B-mode image. (b) corresponding bi-directional microvessel density image, similar to Fig. 5a, but with red color indicates the upward flow and blue color represents the downward flow. Neighboring cortical arteries and veins with opposite flow directions can be well-differentiated at high spatial resolution. (c) Corresponding super-resolution microvessel velocity image of the human kidney, with colormap indicating the magnitude of the velocity. The direction of the blood flow can be found in Sup. Fig. 3(b). (d) Zoom-in region of the bi-directional super-resolution microvessel density image and (c) Zoom-in region of super-resolution microvessel velocity image indicated by the white rectangle in (c) showing dense microvasculature and complex hemodynamics in the renal cortex.

Figure 6.

(a) B-mode image of a pancreatic tumor from a 70-year-old patient, with white arrows roughly indicating the boundary of the lesion. (b) Super-resolution microvessel density image of the human pancreatic tumor. (c) Corresponding bi-directional microvessel density image, similar to Fig. 6b but with red color indicates the upward flow and blue color represents the downward flow. (d) Corresponding super-resolution microvessel velocity image, with colormap indicating the magnitude of the velocity. The direction of the blood flow can be found in Sup. Fig. 4b. (e) Zoom-in region (indicated by the white rectangle in Fig. 6b) of the contrast-enhanced power Doppler image. (f) Corresponding zoom-in region of the super-resolution microvessel density image. (g) Corresponding zoom-in region of the super-resolution microvessel velocity image.

Figure 7.

(a) B-mode image of a breast tumor diagnosed as ductal carcinoma in situ from a 49-year-old patient after neoadjuvant chemotherapies, with white arrows roughly indicating the boundary of the lesion. (b) Corresponding super-resolution microvessel density image of the human breast tumor. (c) Corresponding bi-directional microvessel density image, similar to Fig. 7b but with red color indicates the upward flow and blue color represents the downward flow. (d) Corresponding super-resolution microvessel velocity image, with colormap indicating the magnitude of the velocity. (e) Zoom-in region (indicated by the white rectangle in Fig. 7b) of the contrast-enhanced power Doppler image. (f) Corresponding zoom-in region of the super-resolution microvessel density image. (g) Corresponding zoom-in region of the super-resolution microvessel velocity image.