# Assessment of Takayasu's arteritis activity by ultrasound localization microscopy

Guillaume Goudot, Anatole Jimenez, Nassim Mohamedi, Jonas Sitruk, Lina Khider, Hélène Mortelette, Clément Papadacci, Fabien Hyafil, Mickaël Tanter, Emmanuel Messas, et al.

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Guillaume Goudot, Anatole Jimenez, Nassim Mohamedi, Jonas Sitruk, Lina Khider, et al. Assessment of Takayasu's arteritis activity by ultrasound localization microscopy. EBioMedicine, 2023, 90, pp.104502. 10.1016/j.ebiom.2023.104502. inserm-04065023

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# Assessment of Takayasu's arteritis activity by ultrasound localization microscopy

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Guillaume Goudot,ᵃ,ᵇ,ғ,\* Anatole Jimenez,ᶜ,f Nassim Mohamedi,ᵃ,ᵇ Jonas Sitruk,ᵃ,ᵇ Lina Khider,ᵃ,ᵇ Hélène Mortelette,ᵃ Clément Papadacci,ᶜ
Fabien Hyafil,ᵈ Mickaël Tanter,ᶜ Emmanuel Messas,ᵃ,ᵇ Mathieu Pernot,ᶜ,g and Tristan Miraultᵃ,ᵇ,ε,g

oa

OPEN ACCESS

## Summary

**Background** Ultrasound localization microscopy (ULM) based on ultrafast ultrasound imaging of circulating microbubbles (MB) can image microvascular blood flows in vivo up to the micron scale. Takayasu arteritis (TA) has an increased vascularisation of the thickened arterial wall when active. We aimed to perform vasa vasorum ULM of the carotid wall and demonstrate that ULM can provide imaging markers to assess the TA activity.

**Methods** Patients with TA were consecutively included with assessment of activity by the National Institute of Health criteria: 5 had active TA (median age 35.8 [24.5–46.0] years) and 11 had quiescent TA (37.2 [31.7–47.3] years). ULM was performed using a 6.4 MHz probe and a dedicated imaging sequence (plane waves with 8 angles, frame rate 500 Hz), coupled with the intravenous injection of MB. Individual MB were localised at a subwavelength scale then tracked, allowing the reconstruction of the vasa vasorum flow anatomy and velocity.

**Findings** ULM allowed to show microvessels and to measure their flow velocity within the arterial wall. The number of MB detected per second in the wall was 121 [80–146] in active cases vs. 10 [6–15] in quiescent cases (p = 0.0005), with a mean velocity of 40.5 [39.0–42.9] mm.s⁻¹ in active cases.

**Interpretation** ULM allows visualisation of microvessels within the thickened carotid wall in TA, with significantly greater MB density in active cases. ULM provides a precise visualisation in vivo of the vasa vasorum and gives access to the arterial wall vascularisation quantification.

**Funding** French Society of Cardiology. ART (Technological Research Accelerator) biomedical ultrasound program of INSERM, France.

**Copyright** © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

**Keywords:** Takayasu arteritis; Contrast-enhanced ultrasound; Disease activity; Microvessels; Super-resolution imaging

## Introduction

Vasa vasorum form the microvascular network of the wall of large-sized arteries. Although this dynamic network is necessary for the supply of nutrients and oxygen to the wall, it develops particularly in the case of

artery wall pathology with an increased local metabolic activity. Vasculitis are among those situations where the inflammatory activity in the vascular wall is associated with microvessel enrichment on histology.¹⁻³ Characterisation of the arterial wall inflammation is crucial to

eBioMedicine
2023;90: 104502

Published Online xxx
https://doi.org/10.1016/j.ebiom.2023.104502

Abbreviations: ACR, American College of Rheumatology; CTA, Computed tomography angiography; FDG, Fluorodeoxyglucose; IQ, In-phase and quadrature phase signal; MB, Microbubbles; MRI, Magnetic Resonance Imaging; NIH, National Institute of Health; PET, Positron emission tomography; PSF, Point spread function; RF, Radiofrequency; SUV, Standardised uptake value; SVD, Singular value decomposition; UD, Ultrafast Doppler; ULM, Ultrasound localization microscopy; TA, Takayasu Arteritis

<footnote>ᵃVascular Medicine Department, Georges Pompidou European Hospital, APHP, Paris, France</footnote>

<footnote>ᵇUniversité Paris Cité, INSERM U970 PARCC, F-75015 Paris, France</footnote>

<footnote>ᶜPhysics for Medicine Paris, INSERM U1273, ESPCI Paris, CNRS UMR 8631, PSL Research University, Paris, France</footnote>

<footnote>ᵈNuclear Medicine Department, Georges Pompidou European Hospital, APHP, Université Paris Cité, Paris, France</footnote>

<footnote>ᵉFrench National Reference Centre for Rare Vascular Diseases, FAVA-MULTI, Member of the European Reference Network on Rare Multisystemic Vascular Diseases (VASCERN), F-75015 Paris, France</footnote>

<footnote>\*Corresponding author.</footnote>

<footnote>E-mail address: guillaume.goudot@aphp.fr (G. Goudot).</footnote>

<footnote>ᶜG.G. and A.J. contributed equally to this work.</footnote>

<footnote>ªM.P. and T.M. contributed equally to this work.</footnote>
Research in context

Evidence before this study

Characterisation of the arterial wall inflammation is crucial to determine whether a vasculitis is active or quiescent. Non-invasive imaging assessment of the vasa vasorum network in vivo would help detect neovascularisation and vasodilation associated with inflammatory activity within the arterial wall. Ultrasound localization microscopy (ULM) solved the conceptual trade-off between spatial resolution and ultrasound penetration depth while increasing sensitivity thanks to the joint use of microbubbles (MB) and ultrafast ultrasound imaging. ULM offers the unique ability to perform non-invasive deep microvascular imaging at the microscopic scale. ULM could therefore be used to assess disease activity in Takayasu arteritis by imaging the microvascular network within the arterial wall of the involved arteries.

Added value of this study

We demonstrated the capacity of ULM to track MB within the carotid wall after motion correction. This allowed the visualisation of the microvascular network within the arterial wall. MB quantification can distinguish patients with active disease from those with quiescent disease. We showed that ULM could be a reliable tool for monitoring TA disease activity at a low cost and without irradiation compared to other techniques.

Implications of all the available evidence

ULM could become a powerful tool to monitor the microvascular network in vasculitis or conditions such as atherosclerotic plaques. Its accessibility, easy repeatability, non-irradiating, and low-cost characteristics make the implementation of ULM in everyday clinical practice promising.

determine whether the vasculitis is active or quiescent. Imaging this vascular network *in vivo* would thus help detect inflammatory activity within the arterial wall and therefore allow tailoring the immunosuppressive therapy. However, precise *in vivo* microvascular imaging is particularly difficult due to the multiple physical constraints: small size of the vessels, thin arterial wall, arterial wall motion, and blood flow signal interference from the arterial lumen.

The recent development of the ultrasound localization microscopy (ULM) solved the conceptual trade-off between spatial resolution and penetration depth while increasing sensitivity thanks to the joint use of microbubbles (MB) and ultrafast ultrasound imaging. ULM offers the unique ability to perform non-invasive deep microvascular imaging at the microscopic scale in rodents and recently in humans.<sup>4-12</sup>

Based on such spatial resolution capabilities and contrast imaging using MB, we aimed in this study to evaluate the clinical value of ULM in Takayasu arteritis (TA). Patients with active TA develop intense inflammation of the arterial wall resulting in progressive thickening and stenosis of large and medium-sized arteries.<sup>13</sup> Vascular imaging is critical to diagnose and follow these patients. Assessment of TA activity remains a clinical challenge in daily practice because symptoms, physical signs and biological parameters have several limitations resulting in limited accuracy.<sup>14,15</sup> Magnetic resonance imaging (MRI) or computed tomography angiography (CTA) offers to evaluate indirectly the inflammatory activity of the arterial wall based on the detection of late contrast enhancement even though not fully reliable.<sup>16</sup> Enhancement of the arterial wall after MB injection has also been reported with conventional ultrasound imaging and may help to identify arterial wall inflammation in active TA.<sup>17-22</sup> However, with conventional contrast-enhanced ultrasound

(CEUS), the limited spatial resolution due to the ultra-
sound wavelength prevents from visualising microvessels
smaller than the wavelength. In contrast, ULM can detect
vessels of approximately 10 µm of diameter and can pro-
vide quantitative velocity measurements. The objective of
this study is to test the feasibility of *in vivo* vasa vasorum
imaging using ULM in the carotid arteries of patients with
TA and to correlate the vascular network density with TA
activity assessed by the NIH criteria (Central Illustration).

Methods

Population

Eligible patients were at least 18 years old and had TA based on ACR criteria<sup>23</sup> or Ishikawas's criteria modified by Sharma et al.<sup>24</sup> and with diagnostic validation after a multidisciplinary group meeting. Patients were excluded from the study in the absence of common carotid involvement, i.e., Numano's angiographic classification type III or type IV.<sup>25</sup>

Ethics

An independent ethics committee (CPP NORD OUEST III, 2019-A00202-55) approved the study protocol, under which patients were recruited with [ClinicalTrials.gov Identifier NCT03956394](https://ClinicalTrials.gov/Identifier NCT03956394). All the patients provided written informed consent before enrolment. The data were collected between January 2020 and June 2021 by the study investigators and monitored by the sponsor.

Ultrasound examination with MB injection

Ultrasound imaging was performed using an
Aixplorer® ultrasound scanner (SuperSonic Imagine®,
Aix-en-Provence, France) equipped with a SL10-2 linear
probe. For ULM imaging, a dedicated ultrafast imaging
<figure><img src="image_3.png" /></figure>

**Central Illustration:** Experimental design of the study and analysis of the ultrasound localization microscopy data in comparison with the NIH Takayasu arteritis activity score. NIH: National Institute of Health. A boxplot comparing the number of microbubbles tracked in the carotid wall is shown in the lower left. It shows a clear separation between patients with active disease from those with quiescent disease.

sequence was used. The centre frequency for trans-
mission was 6.4 MHz, with a voltage of 8 V. The ul-
trasonic sequence was calibrated and a rarefaction peak
pressure of 0.2 MPa was measured, hence a mechanical
index of 0.2 (below 0.3 as recommended when using
MB). 8 plane waves were fired with angles ranging
from -5° to 5° at a pulse repetition frequency of 13 kHz
for a frame rate of 500 Hz. These parameters set a
diffraction-limited in-plane resolution of approximately
110 µm axially and 200 µm laterally and an out-of-plane
resolution (elevation) of 700 µm. Acquisitions were
performed on the common carotid artery with the most
visible and the thickest arterial wall. A 2.4-mL bolus of
stabilised sulfur hexafluoride MB (Sonovue®, Bracco®,
Milano, Italy) was administered into an antecubital vein
via an 18-gauge cannula, followed by a flush of 10-mL
isotonic saline solution, following posology and method
of administration of the summary of product charac-
teristics. The dedicated contrast imaging sequence was
applied for 8 s recording 4000 frames once the MB were
visually detected in the common carotid artery using B-
mode. G.G., J.S. and N.M. performed the ultrasonic
examination by handling the probe and running the
dedicated sequences. The radiofrequency ultrasound
data were stored and processed off-line. In-phase and

quadrature phase (IQ) images were reconstructed after
beamforming and coherent compounding. All the pro-
cessing steps were performed using Matlab® (The
MathWorks®, Natick, MA, USA).

Motion correction

Because of the carotid pulsatility and the manual hold-
ing of the probe, motion occurred throughout the
acquisition. To start, the 3 acquisitions with least motion
were chosen. In this context, a fast non-rigid motion due
to pulsatility and a slow rigid motion due to handheld
probe can approximate the motion.

First, we handled fast non-rigid motion selecting
blocks of 100 frames with minimal pulsatility. The pul-
satile motion was approximated based on the relative
axial velocity of the distal and proximal wall. This velocity
was computed from the IQ data in the distal and prox-
imal segments of the wall using a conventional Kasai
estimator. Diastolic intervals were detected and refined
for a standard deviation of the relative axial velocity below
5 mm/s to retrieve steady blocks of 100 frames. After this
process, 50 [45–65] % of the acquisition was kept.

Then, blocks of frames were registered to correct
potential breathing or probe movements. These dis-
placements of up to 5 mm were corrected with an
intensity-based translation registration. The registration was computed on a lumen mask based on power Doppler after Singular Value Decomposition (SVD) filtering. We extracted a fast carotid flow signal from the surrounding tissue removing the first 40 out 100 singular value contributions, this number was set visually from the resulting power Doppler. All blocks were registered on a reference block set as the temporally middle block. Translations were saved and applied during ULM processing. We assessed the quality of the motion correction with track superimposition in the carotid wall (i.e., mean track density in the wall) over all acquisitions. Computations revealed a mean increase in the mean track density within the wall of 14.4 ± 9.2% after motion correction (1.17 ± 0.15 vs. 1.34 ± 0.20).

## Ultrasound localization microscopy

ULM processing was performed on the selected blocks of 100 frames from the motion correction. Each block was processed independently and later accumulated. As Vasa Vasorum flow velocities are low, and to improve the signal-to-noise ratio, SVD filtering was set to extract slowly moving MB signal from surrounding tissue. We removed less singular value contributions than for the lumen mask's filtering with the first 20 out of 100, this number was set visually from the resulting power Doppler. Filtered data were also used to obtain a power Doppler movie from a moving averaging of 20 frames (40 ms) (Supplementary Data). Visual identification of flowing MB and tuning of ULM processing parameters were possible with this power Doppler movie.

First, MB were localised. Because MB are much smaller than the ultrasound system wavelength (diameter<sub>MB</sub> = 1–3-µm << λ<sub>system</sub> = 220 µm), individually spaced MB are seen as the point spread function (PSF) of the system. Hence, local maxima computed on vessel-like regions estimated by a vesselness filter<sup>26</sup> can approximate MB. Localisation is refined from MB back-scattered high amplitude signal, for an intensity above –40 dB, and for a shape above a correlation of 0.6 with an ideal PSF (2D gaussian σ<sub>x</sub> = σ<sub>z</sub> = λ<sub>system</sub>/2 = 110 µm).<sup>4</sup>

Second, localised MB were tracked on each block with a frame-by-frame pairing of their positions based on the Hungarian method.<sup>27</sup> Tracking was refined based on a power Doppler movie considering a track speed of up to 100 mm/s right above the observed maximum velocities and a track size of at least 4 MB to remove artefact tracks. These refinements and their settings are crucial as the moving tissue signal tends to have the same signature as slowly moving MB.

Finally, the ULM image and data of the acquisition were retrieved from the accumulation of all tracks, with their positions corrected beforehand from the blocks' registration.

A reconstruction of the vasa vasorum anatomy and velocity were obtained on a grid of size 24 × 38 mm at a 7–45-mm depth with a pixel size 16 times smaller than

the system's pixel size (size<sub>pixULM</sub> = size<sub>pixUS</sub>/16 = 27.5 × 50 µm) about vasa vasorum's diameter. Fig. 1 summarises the whole process including motion correction.

## ULM analysis parameters

The MB track length and MB track velocity were computed directly from the ULM within the carotid wall. A manual segmentation of the distal and proximal walls in the longitudinal view was performed from B-mode and power Doppler images on the reference block for motion correction. This segmentation was used to compute the investigated wall area.

The mean vascular flow velocity was quantified from the length-weighted average of MB track velocities in mm/s. To describe the average flow velocity in the investigated wall, we weighted tracks' velocities with their length. A very long track's velocity, i.e., a large portion of vasa vasorum, weights more than a very short track's velocity, i.e., a small portion of vasa vasorum, in the computation of the mean vascular flow velocity.

The vascular density was quantified with two parameters: the count of MB in number and the MB path in mm, which is the sum of each MB track's lengths.

The vascular flow rate could not be directly quantified as the vessel diameter cannot be precisely assessed with short-time acquisitions. Thanks to the direct proportionality between flow velocity and vessel radius from Murray's law,<sup>28</sup> this relationship is valid under the assumption of a Poiseuille flow profile in small vessels and verified in human microvascular trees,<sup>29</sup> we introduced a perfusion score: microvessels' flow rate approximated by cubic flow velocities and integrated on the whole carotid wall. It was calculated as the sum on all MB tracks in the investigated wall N<sub>tracks</sub>, of cubic velocities V<sub>t</sub> (mm.s<sup>-1</sup>) weighted by their normalised length L<sub>t</sub> (arb.unit) in mm<sup>3</sup>.s<sup>-3</sup>.

$$Perf = \sum_{t=1}^{N_{tracks}} V_t^3 L_t$$

All those parameters were expressed per second of acquisition time. The total MB density was acquisition time-dependent but also related to the investigated area and MB concentration. Therefore, the investigated wall area and lumen power Doppler average intensity were computed for each acquisition as normalisation factors, since the latter is directly linked to the MB concentration. The normalised parameters were expressed as arbitrary units.

## Takayasu arteritis activity assessment

Activity assessment was performed using the NIH score,<sup>30</sup> with clinical assessment, measurement of acute-phase reactants, morphological evaluation by conventional colour Doppler ultrasound, and CTA or MRI. 18-fluorodeoxyglucose positron emission tomography
<figure><img src="image_4.png" /><figcaption>**Fig. 1:** Summary diagram of the processing method. (A) Acquisition: ultrafast ultrasound imaging acquisition coupled with intravenous injection of microbubbles, raw radio frequency (RF) data are saved and processed offline. (B) Beamforming: ultrasound In-phase Quadrature (IQ) image reconstruction with delay and sum beamforming. (C) Data processing: 1. pulsatility detection based on carotid wall motion; 2. selection of blocks of 100 frames with less pulsatility; 3. microbubbles filtering from tissue signal based on Singular Value Decomposition (SVD); 4. Power Doppler to retrieve the lumen in each block; 5. motion correction with lumen registration between blocks. (D) Ultrasound localization mi- croscopy on each block of filtered frames: 1. localisation of the microbubbles; 2. tracking of their positions. E. Registration of the microbubbles tracks' positions from the blocks' lumen and accumulation of the tracks over the whole acquisition.</figcaption></figure>

coupled with computed tomography (FDG-PET/CT) was also performed to investigate arterial wall metabolism of the aorta and carotids. Carotid maximum standardised uptake value (SUVmax) and carotid-to-liver SUV ratio were collected. All these examinations were then reviewed at a multidisciplinary meeting to determine the NIH score. A NIH score ≥ 2 classified TA as active.

## Statistical analysis

Continuous variables are expressed as median [25th–75th percentile] and compared using a Mann–Whitney test. Categorical variables are expressed as numbers (percentage) and compared using a Chi² test or a Fisher-exact test when required. All the analyses were performed using Matlab® software (The MathWorks®, Natick, MA, United States).

## Role of funders

The funders did not have any role in the study design, data collection, data analyses, interpretation, or writing of the report.

## Results

### Clinical characteristics of patients

Among the 16 patients included, 5 patients had active TA (median age 35.8 [24.5–46.0] years, 3 women) and 11

patients had quiescent TA (median age 37.2 [31.7–47.3] years, 9 women). All characteristics of the patients are presented in Table 1. The NIH score was 3 [2–3] for patients with active TA and ≤ 1 for all patients with quiescent TA. Acute-phase reactants were elevated in active TA with C-reactive protein at 42.3 mg/L [13.9–69.8] vs. 6.7 mg/L [3.4–8.1] in quiescent TA, fibrinogen at 5.5 g/L [4.1–6.1] vs. 3.6 g/L [3.4–4.4], haptoglobin at 2.4 g/L [1.9–2.9] vs. 1.5 g/L [1.1–1.7], and alpha-1-acid glycoprotein at 1.3 g/L [1.0–1.6] vs. 0.9 g/L [0.8–1.1]. Both carotid SUVmax and carotid-to-liver SUV ratios were higher in active TA 3.6 [3.2–4.6] and 1.4 [1.2–2.1] respectively vs. 1.9 [1.7–2.3] and 0.8 [0.7–0.9] in quiescent TA.

### Ultrasound localization microscopy

ULM allowed us to localise and track individual MB within the arterial wall (Supplementary Video). The number of MB per second of the acquisition was significantly higher in active TA: 121 [80–146] vs. 10 [6–15] in quiescent TA (p < 10⁻³) and the sum of the MB track's lengths, i.e., the MB path per second of the acquisition was also significantly higher in active TA: 43 [33–58] vs 4 [2–4] mm in quiescent TA (p < 10⁻³). An increased vascular flow rate within the investigated arterial wall was detected in active TA, assessed by a
<table><thead><tr><th>Characteristics</th><th>Patients with active TA (n = 5)</th><th>Patients with quiescent TA (n = 11)</th></tr></thead><tbody><tr><td>Age (year)</td><td>35.8 [24.5-46.0]</td><td>37.2 [31.7-47.3]</td></tr><tr><td>Gender (women) - n (%)</td><td>3 (60)</td><td>9 (82)</td></tr><tr><td>Numano's angiographic classification</td><td></td><td></td></tr><tr><td>Type I</td><td>1</td><td>0</td></tr><tr><td>Type IIb</td><td>2</td><td>4</td></tr><tr><td>Type V</td><td>2</td><td>7</td></tr><tr><td>C-reactive protein (mg/L)</td><td>42.3 [13.9-69.8]</td><td>6.7 [3.4-8.1]</td></tr><tr><td>Haptoglobin (g/L)</td><td>2.4 [1.9-2.9]</td><td>1.5 [1.1-1.7]</td></tr><tr><td>Alpha-1-acid glycoprotein (g/L)</td><td>1.3 [1.0-1.6]</td><td>0.9 [0.8-1.1]</td></tr><tr><td>Fibrinogen (g/L)</td><td>5.5 [4.1-6.1]</td><td>3.6 [3.4-4.4]</td></tr><tr><td>Leucocytes (Giga/L)</td><td>6.3 [6.0-8.9]</td><td>5.8 [4.1-7.9]</td></tr><tr><td>Haemoglobin (g/L)</td><td>107 [83-122]</td><td>120 [107-128]</td></tr><tr><td>Creatinine (µmol/L)</td><td>74 [73-84]</td><td>70 [60-84]</td></tr><tr><td>NIH score</td><td>3 [2-3]</td><td>1 [0-1]</td></tr><tr><td>NIH score details</td><td></td><td></td></tr><tr><td>Systemic symptoms (n)</td><td>1</td><td>0</td></tr><tr><td>Acute-phase reactant elevation (n)</td><td>4</td><td>5</td></tr><tr><td>Ischemia or vascular inflammation (n)</td><td>4</td><td>3</td></tr><tr><td>Angiographic features (n)</td><td>5</td><td>0</td></tr><tr><td>Carotid SUV max -FDG-PET/CT (AU)</td><td>3.6 [3.2-4.6]</td><td>1.9 [1.7-2.3]</td></tr><tr><td>Carotid-to-liver SUV ratio -FDG-PET/CT (AU)</td><td>1.4 [1.2-2.1]</td><td>0.8 [0.7-0.9]</td></tr></tbody><tfoot><tr><td colspan="3">Values are presented as number (%) or median [25<sup>th</sup> - 75<sup>th</sup> percentile]. AU: arbitrary unit; FDG-PET/CT 18-fluorodeoxyglucose positron emission tomography and computed tomography; NIH: National Institute for Health; SUV: standardised uptake value; TA: Takayasu arteritis.</td></tr><tr><td colspan="3"><b>Table 1: Patients' characteristics.</b></td></tr></tfoot></table>

perfusion score of 3.9 [2.8-5.6] vs. 0.5 [0.4-0.7] × 10⁶ mm³.s⁻³ in quiescent TA (p < 10⁻³) (Table 2, Fig. 2, and Supplementary Figs. S1 and S2).

The normalised results were also significantly different (Table 2). The vascular flow velocity ranged from 20 to 70 mm/s with a median flow velocity in the investigated wall of 40.5 mm/s [39.0-42.9] in active TA (Fig. 3). The number of MB was significantly correlated with the carotid SUV max in FDG-PET/

CT (Spearman's Rho at 0.68, p = 0.004) as well as the carotid-to-liver SUV ratio (Spearman's Rho at 0.73, p = 0.002).

## Reactivation of TA and increased passage of MB

Fig. 4 illustrates the case of one patient with active TA and the ULM imaging, CTA findings with thickening of the right common carotid artery, and the overlay of the FDG-PET/CT image showing increased metabolic

<table><thead><tr><th>Parameters</th><th>Active TA disease (n = 5)</th><th>Quiescent TA disease (n = 11)</th><th>p-value</th></tr></thead><tbody><tr><td>Acquisition characteristics</td><td></td><td></td><td></td></tr><tr><td>Investigated wall area (mm²)</td><td>80 [42-86]</td><td>30 [28-43]</td><td>0.013</td></tr><tr><td>Lumen power Doppler intensity (AU)</td><td>12 [7-16]</td><td>12 [11-19]</td><td>0.913</td></tr><tr><td>ULM parameters per second of acquisition</td><td></td><td></td><td></td></tr><tr><td>Number of Microbubbles (number)</td><td>121 [80-146]</td><td>10 [6-15]</td><td>&lt;10<sup>-3</sup></td></tr><tr><td>Microbubbles path (mm)</td><td>43 [33-58]</td><td>4 [2-4]</td><td>&lt;10<sup>-3</sup></td></tr><tr><td>Perfusion score .10<sup>6</sup> (mm<sup>3</sup>.s<sup>-3</sup>)</td><td>3.9 [2.8-5.6]</td><td>0.5 [0.4-0.7]</td><td>&lt;10<sup>-3</sup></td></tr><tr><td>Normalised ULM parameters per second of acquisition</td><td></td><td></td><td></td></tr><tr><td>Number of Microbubbles (AU)</td><td>2.6 [0.1-3.6]</td><td>0.3 [0.2-0.3]</td><td>&lt;10<sup>-3</sup></td></tr><tr><td>Microbubbles path (AU)</td><td>10.9 [3.8-13.6]</td><td>0.8 [0.6-1.3]</td><td>&lt;10<sup>-3</sup></td></tr><tr><td>Perfusion score (AU)</td><td>9.0 [3.1-14.8]</td><td>1.1 [0.8-1.9]</td><td>&lt;10<sup>-3</sup></td></tr></tbody><tfoot><tr><td colspan="4">Values are presented as median [25<sup>th</sup> - 75<sup>th</sup> percentile] and p-values are obtained from Mann-Whitney test comparisons. Acquisition characteristics are presented in the first part, then ULM parameters expressed per second of acquisition time and finally ULM parameters, which are normalised according to the acquisition characteristics and expressed as arbitrary unit. P-values are obtained using the Mann-Whitney test. AU: arbitrary unit; TA: Takayasu arteritis; ULM: ultrasound localization microscopy.</td></tr><tr><td colspan="4"><b>Table 2: Parameters measured by ultrasound.</b></td></tr></tfoot></table>
<figure><img src="image_5.png" /><figcaption>**Fig. 2:** Quantification of the microbubbles (MB) within the carotid wall using ultrasound localization microscopy (ULM) in patients with active or quiescent Takayasu arteritis: number of MB tracked (A), sum of the length of the tracks (L score) (B) and perfusion score (C). P-values are obtained using the Mann-Whitney test.</figcaption></figure>

activity within the carotid wall. In another case, we had the opportunity to repeat the examination due to a flare of the disease, although the patient had been initially assessed with quiescent TA months earlier. A comparison of the two examinations is shown in Fig. 5. Initially, acute-phase reactants were within the normal

range, with C-reactive protein: 4.9 mg/L, fibrinogen: 3.1 g/L, and ULM found 28 MB per second of acquisition and a MB path per second of acquisition of 9.3 mm. Nine months later, the patient had active TA with increased acute-phase reactants; C-reactive protein: 13.9 mg/L, fibrinogen: 4.1 g/L, and contrast

<figure><img src="image_6.png" /><figcaption>**Fig. 3:** Graph of microbubble velocity distribution within the arterial wall of patients with active Takayasu arteritis (n = 5).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4:** Example of a case of active Takayasu arteritis with CTA reconstruction in frontal view of the right common carotid artery (A). In B is adapted the image at the same site obtained in ultrasound localization microscopy (obtained from 8 s long acquisition after motion correction) with visualisation of microvessels (B). FDG-PET/CT imaging shows intense fixation localised to the common carotid walls (C).</figcaption></figure>

enhancement in the aortic wall on MRI. ULM found 83 MB per second of acquisition and a MB path per second of acquisition of 34 mm.

Discussion

In this study, ULM provided a direct *in vivo* visualisation of the vasa vasorum within the arterial wall. When

<figure><img src="image_8.png" /><figcaption>**Fig. 5:** Ultrasound localization microscopy of the common carotid wall of the same patient with initially quiescent Takayasu arteritis (A), and with a reactivation of the disease 9 months later with new imaging showing a significant passage of microbubbles in the arterial wall (B), from 8 s long acquisition after motion correction and Ultrasound localization microscopy data accumulated.</figcaption></figure>

applied to the carotid arteries in TA, it allowed us to quantify the passage of MB and assess the increased vascularisation secondary to inflammation within the arterial wall. Therefore, we clearly distinguished patients with active TA (>40 MB per second of acquisition and a MB path per second of acquisition >20 mm) from those with quiescent TA (<30 MB per second of acquisition and a MB path per second of acquisition <10 mm).

Due to their size, direct imaging of the vasa vasorum in humans remains challenging. CEUS has been used to visualise the MB circulating within the arterial wall, but the ultrasound wavelength limited the spatial resolution to approximately 200 µm diameter. Hoogi et al.³¹ developed an algorithm to detect and track MB using multidimensional dynamic programming to quantify neovascularisation in carotid plaques, paving the way for vasa vasorum imaging. Although there are existing CEUS methods differentiating active and quiescent disease states in TA patients, CEUS cannot achieve such spatial resolution to direct imaging vasa vasorum.¹⁷⁻²² After its first validation using ultrafast ultrasound imaging,⁴,³² and then achieving microvascularisation analysis of parenchyma such as the brain, kidney, or liver,⁵,³³,³⁴ ULM now gives access to the vasa vasorum of arteries. Additionally, ULM allows to track the MB and to quantify the flow velocity which ranges from 20 to 70 mm/s in the vasa vasorum of the carotid of our patients with active TA. Hemodynamic flow information is indeed crucial to better characterise the flow in the vasa vasorum and could be used to ultimately assess quantitatively the perfusion flow rate at a microscopic level.

In addition to the small size of the vasa vasorum, the arterial wall motion and pulsatility represent a major
challenge for direct imaging. Several techniques have been proposed to register the acquisition in time over the cardiac cycle and spatially for the visualisation of vasa vasorum in rabbits.³⁵,³⁶

## Limitations

One limitation is the absence of a control group with subjects without a carotid anomaly. However, the thin arterial wall of the normal carotid would not allow for catching any signal. This is the reason why we choose TA, a large vessel vasculitis with thickened arteries. The controls are the patients with quiescent TA in our study. Another limitation is the small number of patients included. TA is indeed a very rare disease, and active forms are sparse and rapidly treated with immunosuppressive therapies. However, the small number of patients did not prevent us from demonstrating the proof-of-concept of vasa vasorum imaging with ULM and quantification with significant difference between active and quiescent TA in this pilot study. We acknowledge that imaging vascular networks within arterial walls does not directly detect inflammation but can provide clues that inflammatory activity is increased, resulting in increased vascularity. Although we did not find any discrepancy between carotid inflammatory involvement on the one hand and global disease activity, on the other hand, it is important to emphasise the local nature of the investigation. Consequently, only patients with carotid involvement could be assessed. However, it represents most of the patients with TA.³⁷ Should the acquisition of other arterial trunks be enabled, a more extensive arterial evaluation would be possible by ultrasound during the same examination. However, if the subclavian arteries assessment is still accessible with the same technology, technological developments remain to perform ULM of the abdominal aorta with a 1–5 MHz curvilinear probe, or the thoracic aorta with a phased array probe. With ULM, only microvessels in which at least one microbubble has flowed during the acquisition can be visualised. The microvascular density cannot be fully represented, as the microvascular network is partially seen. Moreover, slowly moving MB can be identified as tissue by the spatiotemporal filter used in the post-processing step. Therefore, we shall acknowledge that ULM may miss MB in the smaller vessels. For this reason, it also appears challenging to image vasa vasorum in healthy individuals with this technique because of the thin arterial wall and the less developed microvascular network. Also, ULM does not allow us to estimate accurately the size of the vasa vasorum imaged. However, the range of MB velocities between 10 and 70 mm/s led us to estimate approximately to 100 µm the diameter of the visualised microvessels, according to the vessel diameter-red blood cell velocity relationship described by Piechnik et al.³⁸ The diameter of these microvessels falls within the one observed for vasa

vasorum in humans, i.e., 50–300 µm.³⁹,⁴⁰ Finally, the perfusion assessment through the perfusion score, based on MB velocities and according to Murray’s law, is only valid when considering the region of interest as a microvascular network. This score will require further validation in complex and inflammatory microvascular networks. In this study, the acquisition was limited to two dimensions and did not rely on concomitant ECG recording but on direct assessment of tissue motion followed by a rigid spatial registration. This method could be further improved using 3D ULM with matrix transducers⁴¹,⁴² and non-rigid registration algorithms.

## Future perspective

Future perspective will be first to enable rapid clinical translation through an acquisition and measurement interface integrated into a ready-to-use ultrasound scanner. Here, the acquisitions were post-processed, but this analysis could be easily transferred to a commercial, user-friendly interface. Second, a 3D analysis would have the advantage of a less operator-dependent evaluation of probe positioning. Volume acquisition would also give access to the MB perfusion rate within the wall, a parameter inaccessible on a 2D analysis.

Finally, the translation into clinical practice for TA and more broadly for large vessel vasculitis could open the way to a close arterial wall inflammation follow-up due to the quantification of disease activity offered by ULM, a fast, low-cost, and non-irradiating tool compared to other techniques. TA should be considered here as a model to test ULM’s capability to visualise vasa vasorum. Analysis of vasa vasorum extends well beyond vasculitis, with applications to evaluate the neovascularisation of carotid plaques such as in the ongoing dedicated study ULTRA-VASC NCT04470687, and more broadly, to the microvascularisation of moving structures.

## Conclusion

Ultrasound localization microscopy is a valuable method to evaluate the microvascularisation within the arterial wall. In addition to the fine detection of the microvascular network, it allows quantifying the passage of microbubbles, a biomarker associated with the inflammatory activity in the arterial wall. In a model of large vessel vasculitis, it allowed us to efficiently distinguish active TA from quiescent TA, which makes it a very promising tool in clinical practice.

## Contributors

T.M., G.G., M.P., and E.M. conceived and supervised the study. G.G., J.S., N.M., and L.K. collected the data. A.J. and M.P. performed post-processing of the imaging data. A.J. performed the statistical analysis. T.M. supervised the statistical analysis. G.G., A.J., T.M. and M.P. wrote the manuscript. All authors interpreted the data, drafted, and revised the manuscript, and approved the final version. M.P. and T.M. verified the underlying data. G.G, A.J., M.P., and T.M. were responsible for the decision to submit the manuscript.
**Data sharing statement**

Data that support the findings of this study will be available from the corresponding author of the study.

**Declaration of interests**

All authors have no conflicts of interest to disclose.

**Acknowledgements**

We thank the French Society of Cardiology for promoting this study and for its financial support (grant from the aorta and peripheral vessels group). We acknowledge the ART (Technological Research Accelerator) biomedical ultrasound program of INSERM.

Funding: Research grant of the aorta and peripheral vessels group of the French Society of Cardiology. We acknowledge the ART (Technological Research Accelerator) biomedical ultrasound program of INSERM.

**Appendix A. Supplementary data**

Supplementary data related to this article can be found at https://doi.org/10.1016/j.ebiom.2023.104502.

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