# Sensing Ultrasound Localization Microscopy for the visualization of glomeruli in living rats and humans

Louise Denis, Sylvain Bodard, Vincent Hingot, Arthur Chavignon, Jacques Battaglia, Gilles Renault, Franck Lager, Abderrahmane Aissani, Olivier Hélénon, Jean-Michel Correas, et al.

► To cite this version:

Louise Denis, Sylvain Bodard, Vincent Hingot, Arthur Chavignon, Jacques Battaglia, et al. Sensing Ultrasound Localization Microscopy for the visualization of glomeruli in living rats and humans. EBioMedicine, 2023, 91, pp.104578. 10.1016/j.ebiom.2023.104578. hal-04287588

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Sensing Ultrasound Localization Microscopy for the visualization of glomeruli in living
rats and humans

Louise Denis<fnref n="1" />#*, Sylvain Bodard<fnref n="1" />,<fnref n="2" />,<fnref n="3" />#, Vincent Hingot<fnref n="1" />, Arthur Chavignon<fnref n="1" />, Jacques Battaglia<fnref n="1" />, Gilles Renault<fnref n="4" />,
Franck Lager<fnref n="4" />, Abderrahmane Aissani<fnref n="1" />, Olivier Hélénon<fnref n="2" />,<fnref n="3" />, Jean-Michel Correas<fnref n="1" />,<fnref n="2" />,<fnref n="3" />, Olivier Couture<fnref n="1" />

# These authors contributed equally to this work

* Corresponding author: louise.denis@sorbonne-universite.fr

<footnote n="1">¹ Sorbonne Université, CNRS, INSERM Laboratoire d'Imagerie Biomédicale, F-75006, Paris, France</footnote>

<footnote n="2">² AP-HP, Hôpital Necker Enfants Malades, Service d'Imagerie Adulte, F-75015, Paris, France</footnote>

<footnote n="3">³ Université de Paris Cité, F-75006, Paris, France</footnote>

<footnote n="4">⁴ Université de Paris, Institut Cochin, INSERM, CNRS, F-75014 PARIS, France</footnote>
# 1 Summary

2

# 3 Background

Estimation of glomerular function is necessary to diagnose kidney diseases. However, the study of glomeruli in the clinic is currently done indirectly through urine and blood tests. A recent imaging technique called Ultrasound Localization Microscopy (ULM) has appeared. It is based on the ability to record continuous movements of individual microbubbles in the bloodstream. Although ULM improved the resolution of vascular imaging up to tenfold, the imaging of the smallest vessels had yet to be reported.

# 9 Methods

We acquired ultrasound sequences from living humans and rats and then applied filters to divide the data set into slow-moving and fast-moving microbubbles. We performed a double tracking to highlight and characterize populations of microbubbles with singular behaviors. We decided to call this technique "sensing ULM" (sULM). We used post-mortem micro-CT for side-by-side confirmation in rats.

# 14 Findings

In this study, we report the observation of microbubbles flowing in the glomeruli in living humans and rats. We present a set of analysis tools to extract quantitative information from individual microbubbles, such as remanence time or normalized distance.

# 18 Interpretation

As glomeruli play a key role in kidney function, it would be possible that their observation yields a deeper understanding of the kidney. It could also be a tool to diagnose kidney diseases in patients. More generally, it will bring imaging capabilities closer to the functional units of organs, which is a key to understand most diseases, such as cancer, diabetes, or kidney failures.

# 23 Funding

This study was funded by the European Research Council under the European Union Horizon 2020 program (ERC Consolidator grant agreement No 772786-ResolveStroke).

# 26 Keywords

sensing Ultrasound Localization Microscopy, super resolution, glomeruli, human kidney, rat kidney, in vivo
29 **Research in Context**

30

31 **Evidence before this study**

32 Kidneys are highly vascularized organs and the observation of vessels permits the diagnosis and the prognosis of
33 kidney diseases. In clinical practice, the assessment of renal microcirculation is done indirectly by urinary or
34 blood tests. Although techniques have emerged to directly observe the renal vasculature at the microscopic level
35 (micro-CT, histology), they remain ex-vivo or invasive imaging.

36 Ultrasound Localization Microscopy (ULM) has been introduced and has proven its ability to map the renal vasculature at a microscopic scale in vivo on mice, rats, and humans. Due to the low sensitivity of ULM on very slow microbubbles, the small vessels present in the renal glomeruli have not yet been observed.

39

40 **The added value of this study**

41 In this study, we filtered the ultrasound acquisitions twice and thus made ULM sensitive to various microbubble
dynamics, including those flowing in glomeruli. By tracking fast and slow microbubbles separately, we were
42 able to image in vivo glomeruli noninvasively on rats and human grafts. With this technique, we were also able
43 to measure quantitative information - such as the relative number of glomeruli - which may be biomarkers of
44 many kidney diseases. Such an approach has been called "sensing Ultrasound Localization Microscopy"
45 (sULM).
46

47

48 Implications of all the available evidence

49 This study proves the ability of sULM to map glomeruli in 2D with both a research ultrasound scanner for rats
50 and a clinical ultrasound scanner for humans. We believe that this study could be extended worldwide thanks to
51 the availability of the code and the common scanner used in humans. Glomeruli observation and quantification
52 could become a diagnostic tool for many kidney diseases, such as hypertension, diabetes, autoimmune diseases,
53 and cancer. Due to sULM's ability to track and classify the individual behavior of each microbubble, we strongly
54 believe that this study reduces the gap between macroscopic imaging and metabolism analyses.
## 55 Introduction

Medical imaging modalities provide an invaluable window at the organ scale deep in the human body.

Meanwhile, within thin slices of biopsies or shallow depths, optical microscopy reveals cellular morphology and mechanism. Both are necessary for clinical diagnosis and fundamental studies, but the information they provide remains incomplete. Indeed, the functional units of many organs reside at an intermediate scale, tens of micrometers in size, within multicellular units too small to be observed with medical imaging and too deep for microscopy to visualize non-invasively.

To observe such a mesoscale, we previously introduced Ultrasound Localization Microscopy (ULM)¹⁻³, which is an acoustic super-resolution technique that can map the microcirculation with unprecedented resolution at the scale of the organ of a living animal⁴⁻⁹. It relies on the tracking¹⁰ of isolated clinical contrast agents, namely microbubbles, traveling within the blood vessels. Since then, it has been investigated in humans¹¹⁻¹³. Although it provides a rich microvascular mapping, it still remains blind to the functional units of organs. This is because very slow microbubbles within capillary beds are difficult to distinguish¹⁴.

Here, we present a "sensing ULM" method using the microbubble as a sensor of its immediate environment (sULM). Such accuracy allows to classify particular microbubble motion patterns corresponding to expected microscopic structures. Thanks to filtering methods adapted to the movement of the microbubble, we can track the entirety of its dynamics, including its slowest movements. By applying sULM to the kidneys of humans and rats, we aim to observe the organ functional units, i.e. the glomeruli. This component of the nephron is a bundle of capillaries situated between two resistance vessels that provide its filtration force¹⁵. The glomerulus is impacted by several chronic diseases such as hypertension, diabetes, autoimmune diseases, and cancer that can lead to its destruction and the loss of its filtration functions¹⁶⁻¹⁹. Imaging of glomeruli is needed for the exploration, diagnosis, and understanding of these pathologies. The problem is that the human glomeruli diameter is approximately 200 microns²⁰, which is well below the resolution limit of most medical imaging techniques. Clinical imaging techniques cannot provide the observation of individual glomeruli²¹⁻²³. Currently, in the clinic, the glomerulus function is only assessed indirectly either by blood or urine tests that provide access to the global glomerular filtration rate.

In this study, we report the direct observation of individual glomerular structures in the kidney using sULM in humans with a setup available widely in hospitals. We first studied the behavior of microbubbles in glomeruli, on rat kidneys. We then compared sULM to micro-CT considered as a gold standard for glomeruli observation²⁴⁻²⁶. Finally, we demonstrated that the observation of glomeruli by sULM is also possible on human transplanted kidneys. We provide materials, such as post-processing codes, to reproduce these experiments in hospitals equipped with conventional ultrasound scanners.
## 88 Methods

## 90 Animals ethics

All animal experiments were performed in accordance with the ARRIVE guidelines and approved by the local ethics committee (ethics committee on animal experimentation n°034, Department of Regulated Research Practices Animals Used for Scientific Purposes Unit - AfiS, reference number APAFIS #33913-

## 104 Contrast ultrasound data acquisitions on rats

30 minutes before the start of the ultrasound data acquisition, 330µl/100g of buprenorphine was injected to prevent pain. The rat was then placed in the isoflurane induction cage. Once asleep, we placed it in the supine position on a thermostatically controlled warming tray under isoflurane anesthesia (Fig 1. a). An ophthalmic gel was applied to its eyes to avoid their drying. The temperature of the rat was controlled via a rectal probe and maintained between 35 and 37°C. The animal was shaved and then depilated on the abdomen, opposite the left kidney. The absence of a pinch reflex was verified before the placement of a 25G caudal venous catheter. The left kidney was externalized thanks to a short abdominal wall incision of less than one centimeter. The kidney was then stabilized with two needles on a thin plastic plate and explored by the direct contact of the ultrasound probe, assured by an ultrasound gel.

Ultrasound acquisitions were performed with an ultrasound scanner (Vantage 256, Verasonics, Kirkland) and a linear ultrasound probe L22-14v at a frequency of 15MHz (Verasonics, bandwidth [14-22] MHz, pitch 0·10mm).

The probe was positioned longitudinally to the kidney with a real-time B-mode display (Fig 1. b). A microbubbles infusion (Sonovue®, Bracco, Milan, Italy, 8µL of sulfur hexafluoride/mL) of 13 µl/min that lasted two to three minutes was then performed (Supplementary Table 1).

Ultrasound acquisitions were acquired at a frame rate of 500 Hz, a pulse repetition frequency of 12,000Hz, a pixel size of 50µm, and a voltage of 8V. A total of [60-90] blocks were saved, each consisting of 800 images resulting from a compound of 3 angles. The data were then beamformed using a conventional delay-and-sum algorithm.

We removed blocks containing the bolus starts and movements too large to perform the post-processing (see data sharing statement).

Power Doppler performed with the same acquisition and a high threshold SVD is presented in Figure 1c (parameters in Supplementary Table 1).
128 Micro Computed Tomography (micro-CT) data acquisitions on rats

129 Once the ultrasound acquisitions were completed, the rat was kept under isoflurane anesthesia to proceed with
130 the injection of the micro-CT contrast agent. This contrast agent is a mixture of barium sulfate (Micropaque
131 100g, Guerbet, France), PBS (Phosphate Buffer Saline, Cochin INSERM U1016), and gelatin. Gelatin was
132 dissolved at 90°C, then mixed with Micropaque at 40°C to obtain a homogeneous solution, and then stored at a
temperature of 4°C²⁷. After opening the thorax, a catheter was placed in the left ventricle of the heart, and the
133 right atrium was sectioned to let the liquid flow out, after passing by all the organs. We then injected 25µL of
134 heparin to avoid blood clotting, i.e. vial of 25,000 units by 5,000 units/mL. Then, a 200mL contrast agent heated
135 in a water bath at 42°C was injected at a flow rate of 750mL/h to ensure perfusion of the vasculature. The death
136 of the animal occurred under anesthesia due to this step of injection of the micro-CT contrast medium.
137 The kidney was kept for 48h in a petri dish in 4% of PFA to fix the perfusion until it was scanned with a high-
138 resolution micro-CT Skyscan 1172 (Bruker, URP2496) with a 5µm voxel size.
139

140

141 Micro-CT and sULM registration

142 We performed manually the registration between sULM and micro-CT based on the similarity of large vessels
143 (Supplementary Fig. 1). This step was done with the Amira software (ThermoFisher, 2019.4). We averaged 32
144 micro-CT slices to obtain a 160µm thickness, i.e. the probe elevation resolution. We had a 1,024 by 1,024 matrix
145 of 15µm pixel elements as the output of the Amira registration which we then analyzed in MATLAB.
146

146

147 Perfusion index in micro-CT and noise reduction

148 To compare micro-CT perfusion in rats, we defined a perfusion metric as the sum of pixels with a value greater
149 than an arbitrary threshold of 0.5 divided by the total number of pixels within the cortex, with the micro-CT
150 matrix normalized by its maximum. In the Supplementary Table 2, we could see that on 3 rats over 6, perfusion
151 was above 5%, which means that the vessels were less contrasted then in the 3 other rats (Supplementary Fig. 2).
152 To reduce these differences in contrast between the vessels, we subtracted noise from micro-CT slices before
153 summing them, i.e. subtraction of the lowest 10% of values within the kidney. This 10% threshold was
154 arbitrarily chosen.
155

155

156 Human ethics

157 The Ethics Committee of the French Society of Radiology approved this study (CERF, reference number CRM-
158 2203-240). All human acquisitions were performed by following the SRQR guidelines. Informed consent has
159 been obtained from all patients.
160

160 Male and female kidney transplanted patients were included from February to August 2020, at Necker Hospital
161 (APHP, France). These subjects were present due to their clinical routine ultrasound examination and no
162 supplementary experimentation was made. Contrast-Enhanced Ultrasound Sequence (CEUS) was performed on
163 patients to search for hypo-vascular areas and necrosis. To obtain the best quality images, the patients were lying
164 in a supine position and breathing slowly during the acquisition. 14 patients were included in this study, and the
165 results of five of them are presented here (three females/two males). The exclusion of the others was mainly due
166 to respiratory motion.
167

167
## Contrast ultrasound data acquisitions on humans

Data acquisitions on humans were acquired with CEUS embedded in the clinical ultrasound scanner Applio i800 (Canon MS, Nasu, Japan) and a convex abdominal probe i8cX1 (3MHz, Canon, bandwidth [1·8-6·2] MHz), with an injection of a bolus of 1·2mL of contrast agent (Sonovue®, Bracco, Milan, Italy, 8µL of sulfur hexafluoride/mL). CEUS had the advantage to enhance the signal of the microbubbles and allow a better visualization of their behavior. The mechanical index was reduced to 0·07 during the acquisition process to reduce the destruction of the microbubbles and exploit their nonlinear properties. The frame rate was different for each patient due to imaging depth differences, i.e. between 14 and 64Hz. Data acquisition duration, which corresponds to a video clip screenshot of the clinical ultrasound scanner, varied from one to three minutes (Supplementary Table 3). Clips were then divided into blocks of 200 frames each to proceed to sULM. All data were anonymized and stored on an external USB disk to be post-processed in our laboratory.

## sULM post-processing

The codes that allowed the reconstruction of vascular mapping by sULM are available in the following GitHub repository: https://github.com/EngineerJB/akebia. A standalone application named Akebia - usable without a MATLAB license - is available in the same repository.

Several classical sULM steps have been applied to each block to obtain a density map. These are very similar in humans and rats so both will be described in this same section.

The first step consisted of a bandpass temporal (for humans) or spatiotemporal singular value decomposition filter (for rats). Frequency cutoff varied from 0·5 to 8·5Hz in the first case and was fixed at 40/800 eigenvalues for the second one. Data were filtered a second time in rats with a low-threshold SVD cumulated to a bandpass filter respectively to highlight slower microbubbles and to remove surrounding tissue (Supplementary Table 1). This way, our datasets were split into two parts: high-speed microbubbles flow with the first filter and slower microbubbles with CEUS for humans and with low-threshold SVD for rats. Previous studies showed that ULM performed better with a split dataset¹².

The second step consisted in the localization of the center of the microbubbles. In humans, this was done with a 2D Gaussian filter and targeting of the regional maximums. In rats, we applied the radial symmetry method to localize the microbubbles' center²⁸. Thus, we obtained the positions of the microbubbles in lateral and axial dimensions in pixels unit initially fixed by the acquisition itself, i.e. a pixel size between 70 and 150µm in humans, and 50µm in rats. At this step, we also had the temporal information which is the number of the frame where the microbubble appeared.

The third step consisted in applying the Hungarian algorithm method²⁹ with the simple tracker Toolbox¹⁰. We paired frame-to-frame microbubbles with specific constraints to track them. In our case, low-speed and fast-speed tracking parameters were summarized in Supplementary Tables 1 and 3.

We repeated all the precedent steps in each block of our two datasets. Thus, we accumulated all microbubble tracks of all acquisitions twice. In this way, we reconstructed the sULM composite density map. Slow flow speed track accumulation on one side and fast flow speed track accumulation on the other side.

With an interpolation step performed just before track accumulation, the final rats' sULM density maps had a pixel size of 20µm. For humans, clips were already interpolated in the clinical ultrasound scanner, so we kept the same pixel size as the original acquisitions.
All data were processed and analyzed with MATLAB.

## Manual segmentation and track classification

Kidney regions were manually delimited on sULM maps in three parts, i.e. cortex, medulla, and major vessels - including renal, interlobar, arcuate vessels, and finer structures such as afferent and efferent arterioles.

To study the behavior of microbubbles in each region, the tracks were then labeled. If one of their points belonged to the masks, then they were marked as belonging to that area.

The segmentation of these three regions was performed similarly in rats and humans.

## Glomerular mask creation

A glomeruli-specific mask was then created by considering the normalized distance value of the cortex tracks (see metrics calculation section). These normalized distance values were projected onto the same reconstruction grid used to create the sULM density maps. We then arbitrarily selected pixels whose normalized distance was greater than the 90th percentile of all pixel values by smoothing the image with a Gaussian filter. By drawing a circle of the order of the glomerulus radius from these pixels - i.e. 100µm in humans²⁰, and 50µm in rats³⁰ - we thus determined a glomerular mask. We applied the same procedure of track conservation as before. If at least one of its points belonged to the glomerular mask, we labeled the track as belonging to glomeruli.

## Metrics calculation

The normalized distance metric was defined as the cumulative distance of a track, divided by the distance separating its first point $p_I$ to its last one $p_n^{31}$.

$$ \text{Normalized distance} = \frac{\sum_{x=1}^{n-1} \|p_x - p_{x+1}\|}{\|p_1 - p_n\|} $$

The remanence time metric has been established as the period (in seconds) during which a microbubble was tracked inside a circle of glomerular size, - i.e. radius of 100µm in humans²⁰, and 50µm in rats³⁰. The center is defined as the median point of the list of points constituting the track.

The dispersity corresponded to the number of times that a track went in the same direction with a tolerance of more or less 20° - taking rounded localization of each track - divided by the number of points constituting the track (Supplementary Fig. 3).

## Glomeruli count and perfused distribution width (PDW) calculation

The glomeruli count on the micro-CT was performed with the imregionalmax function of MATLAB in the cortex region on which we applied a Gaussian filter. We arbitrarily chose to keep the pixels with an intensity higher than the 90th percentile of the whole area. The glomeruli count on the sULM has been detailed in the section "Glomerular Mask Creation". We proceeded with manual segmentation of the renal capsule to perform a count of glomeruli based on their distance from it (Supplementary Fig. 1).

We chose to represent the spatial distribution of glomeruli by the histogram of 20 bins for sULM. We used "auto" as a bin method in Matlab. This method worked in several steps. First, the Scotts rule was used to determine the width of the bins. Then bin picker function adjusted the bin width depending on its order of
magnitude and the total number of bins and positions of the left and right most histogram edges were then computed. Finally, the remaining bin edges were distributed uniformly between leftEdge and rightEdge at bandwidth intervals (Mathworks Website).

To compare sULM and micro-CT, we chose to represent the spatial distribution of glomeruli by the histogram of 20 bins for micro-CT and we fixed the constraint of the same bin width for both imaging methods. The statistical Kolmogorov-Smirnov test was done on these bins to compare the spatial distribution of sULM and micro-CT (Table 1).

On the micro-CT, the glomeruli perfused distribution width (PDW) was determined by calculating the full width at half maximum (FWHM) of the “global” glomerulus intensity profile (Supplementary Fig. 4). This intensity profile was calculated by summing all rows and columns from all glomeruli in a region of 20 pixels around its center, i.e., a 600-micrometers edge. Noisy glomeruli were removed from the calculation by selecting only those glomeruli that existed in four successive rotation of 90° of the same glomeruli. The intensity profile was then fitted by a Gaussian and interpolated, i.e. 600 points between -300 and 300 micrometers, before calculating the FWHM.

To estimate the glomeruli PDW on sULM, we took all slow tracks, i.e. tracked with slow sULM parameters, centered in zero belonging to the glomerular mask. We then took the FWHM of the "global" glomerulus density profile (Supplementary Fig. 4). This global density profile was calculated by summing all rows and columns from all glomeruli projected four times in the four directions - i.e. 90° rotation - in an interpolated grid of one µm for rats, and of 50µm for humans. The density profile was interpolated before calculating the FWHM, i.e. 600 points between -300 and 300µm for rats and 3000 points between -1500 and 1500µm for humans.

## Statistical analysis

Statistical analyses were performed to evaluate the metrics between the different regions of the kidney using an unpaired two-sided parametric student's t-test with a 95% confidence level assuming a Gaussian distribution on GraphPad Prism 9 software. The significance of the scoring was explained by the following P values: ns = P > 0.05, * = P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001, **** = P ≤ 0.0001.

Two-sided Kolmogorov-Smirnov nonparametric statistical tests³² were also performed on the spatial distributions of glomeruli as a function of their distance from the renal capsule on sULM and micro-CT images with GraphPad Prism 9 software.

## Role of funders

The funders had no role in the study design, data collection, data analysis, interpretation, or report writing. All of these steps were carried out by contributors described later. Olivier Couture is responsible for the decision to submit the manuscript.
282 **Results**

283

284 **sULM highlighted spinning microbubbles in the rat kidney**

285 Thanks to the double-filtering and the double-tracking explained above, we could obtain a composite sULM
density map with slow tracks highlighted in pink and fast ones in green (Fig. 1d). The microvasculature
reconstructed by sULM was much more resolved than with the Power Doppler of the same acquisition (Fig 1. c).

288 In the sULM map, we could observe particular microbubble behavior in the cortex: microbubble tracks that
289 entering and then exiting an area where they were spinning at velocities corresponding to the capillary flow. A
290 zoom on the cortex in Fig. 1e confirmed the presence of these slow bundles of capillaries, i.e. the glomeruli. As
291 an example, an independent microbubble was followed along this pathway leading to a glomerular shape in Fig.
292 1e and Fig. 1f. Other microbubbles within the cortex appeared to rise, then swirled before leaving again
293 (Supplementary Videos 1-4, Supplementary Fig. 5). Such microbubble behavior has only been observed along
294 the intermediate vessels of the cortex.

295 Segmentation of the different regions of the kidney, namely the medulla, main vessels, and cortex, are presented
296 in Figure 1g. Glomerular mask constructed by including high normalized distance tracks is shown in
297 Supplementary Fig. 5e. Metrics to highlight glomeruli, including normalized speeds - i.e. distance traveled by
298 the track divided by the maximum speed in each area; remanence time - i.e. time duration of the track within a
299 radius of 50µm from the track center; and dispersity - i.e. number of times that a track goes in the same direction
300 divided by the number of points constituting the track; are displayed respectively in Fig. 1h, Fig. 1i and Fig. 1j.

301 Tracks in the glomerular mask seemed specific to this area. They are slower, more stagnant, and less dispersed
302 compared to tracks in the medullary region and in the main vessels. Statistical analyses showed that these
303 differences were always significant from the main vessels to the glomeruli and from the main vessels to the
304 medullary region, but these metrics were not significantly different between the medulla and the glomeruli
305 (Table 2).

306
<figure><img src="image_2.png" /><figcaption>Figure 1. Glomerular structures were observed in the rat kidney cortex by sULM.</figcaption></figure>

(a) Experimental setup. (b) The imaging plane is the yellow one. (c) Power Doppler of the rat 14. (d) sULM density map in rat 14 with superimposed slow-tracking (pink) and fast-tracking (green). (e) Zoom in the cortex area. (f) Zoom of yellow glomerular behavior highlighted in e. (g) Masks of the three regions in rat kidney: cortex (yellow), main vessels (blue), and medulla (red). 500 first slow and 500 first fast tracks encoded with speed are presented upon background masks. (h) Mean normalized speed of all tracks in 11 rats compared by region. The significance of the scoring is explained by the following P values: ns = P > 0.05, * = P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001, **** = P ≤ 0.0001 (unpaired student's t-tests with a 95% confidence level). The center line corresponds to the median, box limits to upper and lower quartiles, whiskers to 1.5x interquartile range and no outliers have been considered. (i) Mean remanence time of all tracks in 11 rats compared by region. Same statistical tests. (j) Mean dispersity of all tracks in 11 rats compared by region. Same statistical tests.
319 **Micro-CT confirms that these spinning microbubbles detected in sULM correspond to glomeruli**

320 Figure 2a and Figure 2b show the sULM and the micro-CT of rat seven in the same longitudinal plane.

321 Additional rats are shown in Supplementary Figure 2.

322 Figure 2c shows the glomeruli count using a high normalized distance value in the sULM and Figure 2d presents
323 the glomeruli count using the selection of local high-intensity regional maxima on the micro-CT. Both imaging
324 techniques appeared to show glomeruli in the same location.

325 In Figure 2e, we can see that the glomeruli spatial distributions from the kidney capsule were similar between the
326 glomeruli detected by micro-CT, and the glomeruli observed by sULM in rat number seven (Figure 2f).

327 Statistical analyses showed that these distributions came from the same continuous distribution in all rats
328 (Supplementary Figure 6). Thus, the glomeruli were located at the same site.

329 Glomeruli's PDW for sULM and micro-CT appeared to be in the same order of magnitude (Fig 2. f) meaning
330 that we observed the same phenomena with both modalities. Indeed, in all rats, PDW is 77±6 µm for sULM and
331 49±18µm for micro-CT (Table 3).
<figure><img src="image_3.png" /><figcaption>Figure 2. Capillary bundles observed in the sULM density map correspond to the glomeruli on micro-CT.</figcaption></figure>

(a) sULM density map in rat number seven. (b) Micro-CT in the same rat and same plane. (c) Zoom of sULM normalized distance (ND) map in rat seven (Methods). Red circles indicate glomerular count. 10,000 first slow and 5,000 first fast tracks encoded with ND are presented here. (d) Zoom of micro-CT in the same rat. Red circles indicate glomerular count. (e) Glomeruli count as a function of kidney capsule distance in micro-CT and sULM in rat seven (Methods). See Table 1 for more details on statistical analysis (two-sample Kolmogorov-Smirnov test).

(f) Glomeruli PDW calculation with a density as a function of distance to the center of glomeruli (for sULM in blue, in 11 rats), and intensity as a function of the distance from the center pixel (micro-CT in red, in six rats).

Dashed lines indicate standard deviation.
342 Glomeruli can also be observed in humans by sULM

343 Patient one's sULM composite density map presented the same glomerular shapes as rats in the cortex area (Fig.

344 3a). Another patient with the same cortical glomerular behavior is shown in Supplementary Figure 7.

345 A zoom in the cortex area confirmed the presence of arterioles with spinning microbubbles at the middle of their
346 path, i.e. the glomeruli (Fig. 3b). Examples of glomeruli tracks are shown in yellow and red respectively in
347 Figure 3c and 3d (Supplementary Videos 5-7).

348 The glomeruli count in sULM with the study of their PDW and of their spatial distribution from kidney capsules
349 are presented in all patients in Supplementary Figure 8. We could observe that glomeruli PDW in humans were
350 around 381±107µm (Table 4), which is bigger but in the same order of magnitude as the literature²⁰.

351 Manual segmentation of kidney areas is shown in Figure 3e, and the glomerular mask is presented in
352 Supplementary Figure 7c. In the same way as in rats, glomeruli tracks remained slower (Fig. 3f), more stagnant
353 (Fig. 3g), and less dispersed than the medullary region and the main vessels (Fig. 3h). The tracks inside the
354 glomeruli therefore had a similar specific behavior in humans and in rats. Statistical analyses are detailed in
355 Table 5.

356
<figure><img src="image_4.png" /><figcaption>Figure 3. Glomeruli can be observed in the cortex of human kidneys by sULM.</figcaption></figure>

(a) sULM density map in patient one with superimposed slow-tracking (pink) and fast-tracking (green). (b) Zoom in the cortex area. (c) Zoom of yellow track highlighted in b. (d) Zoom of red track highlighted in b. (e) Masks of the three regions: cortex (yellow), main vessels (blue), and medullary region (red). 500 first slow and 500 first fast tracks encoded with speed are presented upon background masks. (f) Mean normalized speed of all tracks in five patients compared by region. The significance of the scoring is explained by the following P values: ns = P > 0.05, * = P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001, **** = P ≤ 0.0001 (unpaired student's t-tests with a 95% confidence level). The center line corresponds to the median, box limits to upper and lower quartiles, whiskers to 1.5x interquartile range and no outliers have been considered. (g) Mean remanence time of all tracks in five patients compared by region. Same statistical tests. (h) Mean dispersity of all tracks in five patients compared by region. Same statistical tests.
369 **Discussion**

370 In this study, we implemented a form of ULM which exploits the kinetic of individual microbubbles - including
371 those flowing in the glomeruli - in order to map the renal microarchitecture of vessels. By classifying their
372 behavior based on known anatomical features, we sought in this case the precise complexion of glomeruli
373 microcirculation, including arterioles and capillaries. Spinning microbubbles were followed in the cortex and
374 tracks analysis confirmed that these microbubbles are slower, more stagnant, and less dispersed than in the other
375 kidney regions. The spatial distribution of glomeruli measured by sULM and by micro-CT belonged statistically
376 to the same continuous distribution in six rats. Moreover, the glomeruli PDW measured by micro-CT and sULM
377 was in the same order of magnitude with both techniques in all rats. Thanks to two these results and the fact that
378 the micro-CT is considered to be the ex-vivo gold standard to observe microcirculation including inside
379 glomeruli, we could conclude that glomeruli were observable by sULM.

380 These results allowed us to observe a part of individual glomeruli in living human grafts with a clinical imaging
381 device, which was not possible with current imaging techniques²¹⁻²³. We were not only able to observe a
382 category of slow-moving microbubbles, but we showed how to reduce the gap between anatomical and
383 functional imaging. This was done because we could image the very structures responsible for the function of the
384 organ. By using sULM maps and the information given by the microbubble itself, we managed to image
385 functional microvascular units. These kinds of observations will be essential to study many organs and diseases
386 such as diabetes or cancer.

387 Still, this study has some limitations. The use of CEUS from a clinical ultrasound scanner has a frame rate
388 between 14 and 64Hz, which implies bias in the estimation of the velocities for large vessels. The maximum
389 velocity found in human sULM is 4cm/sec, whereas it is normally 100cm/sec³³,³⁴. Besides, glomeruli PDW
390 measured by sULM are larger than the diameters found in the literature for humans²⁰,³⁵, and thinner for rats³⁰.
391 This is mainly because we did not measure the same information. With micro-CT and sULM, we measured the
392 interval vascular system, i.e. the FWHM of the glomerular imaging task, whereas in the literature they measured
393 the cellular content. Additional confirmation of the glomeruli distribution and size could later be obtained on
394 immunostained histological sections.

395 For humans, the spatial resolution of CEUS, i.e. between 70 and 150µm, the localization error of 43±4µm - that
396 increases with depth due to divergent ultrasound beam - and the presence of efferent and afferent arterioles in the
397 same track as glomeruli leaded us to a rough PDW estimation with sULM (Supplementary Fig. 8,
398 Supplementary Video 5 and 6). For rats, micro-CT perfusion was not always complete because of the complex
399 injection procedure (Supplementary Table 2). Besides, fixation of the organ by PFA before scanning sometimes
400 distorted the kidney, including vascular structures. Thus, the comparison between a living organ in sULM and a
401 fixed organ in micro-CT is limited. Registrations of both techniques were done manually which also implies a
402 bias. These points may explain the difference in PDW between sULM and micro-CT.

403 The observation of glomeruli in this study is only partial compared to results found in the literature²⁵,²⁶,³⁰,³⁶⁻⁴¹
404 (Supplementary Table 4). On the one hand, this is due to a lack of acquisition time with the right concentration
405 of microbubbles⁴² (Supplementary Fig. 9). On the other hand, this is due to 2D sULM that limits the field of
406 view to a unique imaging slice.

407 The 2D sULM also has the disadvantage of creating false out-of-plane tracks. These false tracks are mostly
408 present in the medullary region because these regions were positioned transversely to the imaging plane used for
rats. Supplementary Fig. 10 shows these out-of-plane tracks inside the medulla in 4 rats. In humans, these out-of-plane tracks were less present because the clinician knew how to position the probe in the plane of the medullary vessels. These artefactual tracks could explain why the medulla tracks and the glomeruli tracks behave similarly in rats. Besides, medullary region is composed of capillaries within the vasa-recta and the juxtamedullary glomeruli, which can also explain the similarity between metrics in the medulla and in the glomeruli.

As another limitation, we can also mention the computational time which was multiplied by two due to the double tracking we performed to highlight the behavior of slow and fast microbubbles. These two regimes of microbubble behavior could not be followed in the same way, i.e. with the same tracking parameters. Indeed, if we had imposed the same tracking parameters for slow microbubbles as for fast microbubbles, i.e. high linking distances and short tracking time, then we would have risked matching microbubbles that did not belong to the same track or missing some microbubbles - because the parameters were not adapted to what we saw in the filtered acquisition that enhanced slow microbubbles. In the example of glomeruli, this could generate a deficit in their number.

In addition, movements were limited in this study because we had to externalize kidney in rats and because we had to include patients with shallower renal allografts (Supplementary Fig. 11). A future clinical trial to verify the feasibility of sULM on native human kidneys is hence needed.

In the future, 3D sULM<sup>5,8,43,44</sup> will also be needed to reduce and compensate for movements and to resolve the transverse direction, as encountered in the medullary region of rat acquisitions. Further studies will be necessary to analyze the behavior of microbubbles in more detail in the healthy and diseased glomeruli. Another study will be done to demonstrate the predictive diagnostic character based on glomerular sULM imaging. Furthermore, we believe that sULM might be a way to explore the vascular and functional status of various organs, not just kidneys. This approach could thus allow us to establish a diagnosis according to the individual behavior of each microbubble. This would eventually link macroscopic imaging and metabolism analyses.

432

### Contributors

S.B., O.H., J.M.C., and V.H. contributed to the conception of the human study. S.B. and V.H. contributed to the acquisition of the humans' sULM data. G.R., F.L., and O.C. contributed to the conception of the rats' study. A.A., L.D., S.B., F.L., G.R., and O.C. contributed to the acquisition of the rats' sULM data. A.A., L.D., S.B., F.L., G.R., and O.C. contributed to the acquisition of the micro-CT data. L.D., V.H., A.C. contributed to sULM and micro-CT post-processing. L.D., S.B., V.H., A.C., J.M.C., O.H., and O.C. contributed to the analysis and interpretation of the data. L.D., J.B., and O.C. contributed to the development of the Akebia software and the formatting of the codes. L.D., S.B., V.H., A.C., and O.C. contributed to drafting the manuscript. L.D., S.B., V.H., A.C., G.R., O.H., J.M.C., and O.C. contributed to revising the manuscript. L.D. and O.C. have direct access to the presented data. L.D. and O.C. have verified all the presented data.

All authors have read and approved the final version of the manuscript.

444

### Declaration of Interest

O.C. and V.H., hold patents in the field of ultrasound localization microscopy (EP4011299A1). O.C. and V.H. are founders and shareholders of the ResolveStroke startup. O.C. has received several grants: ERC Consolidator grant agreement no 772786-Resolve Stroke, ERC POC (2022-2023), Maturation SATT Lutech for
449 ResolveStroke Startup, ANR FightClot, CNRS Prematuration for ResolveStroke Startup, RHU Quid NASH.

450 O.C. gave a lecture at Bracco SAS and was an invited speaker at several conferences such as BMT Innsbruck.

451 Other authors have no conflict of interest to disclose.

452

453 Acknowledgments

454 We thank the “Plateforme d’Imagerie du Vivant” (Université de Paris), and specifically Lotfi Slimani for the
455 micro-CT acquisitions with the Skyscan 1172 Bruker.

456 This study was funded by the European Research Council under the European Union Horizon 2020 program
457 (ERC Consolidator grant agreement No 772786-ResolveStroke).

458

459 Data Sharing Statement

460 ZENODO repository is accessible: https://zenodo.org/record/6811910#.ZA8Mkh-ZPic (DOI
461 10.5281/zenodo.6811910). All 60 blocks of rat 14 have been made available. To get the same image as in the
462 article only blocks 10 to 20 and 27 to 32 have to be loaded (see README). This is due to the presence of
463 motions on the last 28 blocks, and too many microbubbles at the beginning of the bolus (Methods). Patients'
464 imaging data are not available due to ethical, medical, and legislative considerations toward personal
465 information.

466 The GitHub repository is accessible with scripts and functions used to perform sULM. Matlab 2021a is needed
467 to run the programs: https://github.com/EngineerJB/akebia. Moreover, a standalone application named Akebia,
468 i.e. usable without a MATLAB license, is available in the same repository.

469

470
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576
<table><caption>579 Table 1. A detailed comparison between glomeruli spatial distribution in micro-CT and in sULM³²</caption><thead><tr><th>Test statistic</th><th>Metric</th><th>Rat number</th><th>Confidence Intervals micro-CT</th><th>Confidence Intervals sULM</th><th>Size (n)</th><th>Kolmogorov-Smirnov D</th><th>P-value</th></tr></thead><tbody><tr><td rowspan="15">Two-sample Kolmogorov-Smirnov test</td><td rowspan="15">Glomeruli spatial distribution between micro-CT and sULM</td><td>7</td><td>0.02<br>To<br>0.08</td><td>0.02<br>To<br>0.08</td><td>20</td><td>0.30</td><td>0.33</td></tr><tr><td>8</td><td>0.03<br>To<br>0.07</td><td>0.03<br>To<br>0.07</td><td>20</td><td>0.20</td><td>0.82</td></tr><tr><td>11</td><td>0.03<br>To<br>0.07</td><td>0.03<br>To<br>0.07</td><td>20</td><td>0.20</td><td>0.82</td></tr><tr><td>12</td><td>0.03<br>To<br>0.07</td><td>0.02<br>To<br>0.08</td><td>20</td><td>0.40</td><td>0.08</td></tr><tr><td>13</td><td>0.03<br>To<br>0.08</td><td>0.02<br>To<br>0.08</td><td>20</td><td>0.35</td><td>0.17</td></tr><tr><td>15</td><td>0.03<br>To<br>0.07</td><td>0.03<br>To<br>0.06</td><td>20</td><td>0.25</td><td>0.56</td></tr></tbody></table>
<table><caption>Table 2. A detailed comparison between main vessels, medulla, and glomeruli in rats</caption><thead><tr><th>Test statistic</th><th>Metric</th><th>Region compared</th><th>Confidence intervals</th><th>Size (n)</th><th>Degrees of freedom</th><th>P-value</th></tr></thead><tbody><tr><td rowspan="10">Two-sided student t-test</td><td rowspan="3">Normalized Speed</td><td>Main vessels and medulla</td><td>-0.14 to -0.07</td><td>11</td><td>10</td><td>&lt;0.001</td></tr><tr><td>Main vessels and glomeruli</td><td>-0.13 to -0.06</td><td>11</td><td>10</td><td>&lt;0.0001</td></tr><tr><td>Medulla and glomeruli</td><td>-0.02 to 0.04</td><td>11</td><td>10</td><td>0.39</td></tr><tr><td rowspan="3">Remanence</td><td>Main vessels and medulla</td><td>0.02 to 0.04</td><td>11</td><td>10</td><td>&lt;0.001</td></tr><tr><td>Main vessels and glomeruli</td><td>0.02 to 0.03</td><td>11</td><td>10</td><td>&lt;0.001</td></tr><tr><td>Medulla and glomeruli</td><td>-0.02 to 0.00</td><td>11</td><td>10</td><td>0.12</td></tr><tr><td rowspan="3">Dispersity</td><td>Main vessels and medulla</td><td>-0.09 to -0.04</td><td>11</td><td>10</td><td>&lt;0.001</td></tr><tr><td>Main vessels and glomeruli</td><td>-0.09 to -0.04</td><td>11</td><td>10</td><td>&lt;0.001</td></tr><tr><td>Medulla and glomeruli</td><td>-0.01 to 0.02</td><td>11</td><td>10</td><td>0.80</td></tr></tbody></table>
<table><caption>Table 3. Glomeruli PDW estimation by sULM and by micro-CT in rats</caption><thead><tr><td>Rat n°</td><td>sULM (μm)</td><td>Micro-CT (μm)</td></tr></thead><tbody><tr><td>1</td><td>80</td><td>/</td></tr><tr><td>2</td><td>74</td><td>/</td></tr><tr><td>5</td><td>65</td><td>/</td></tr><tr><td>6</td><td>74</td><td>/</td></tr><tr><td>7</td><td>76</td><td>67</td></tr><tr><td>8</td><td>75</td><td>56</td></tr><tr><td>11</td><td>75</td><td>49</td></tr><tr><td>12</td><td>88</td><td>36</td></tr><tr><td>13</td><td>85</td><td>46</td></tr><tr><td>14</td><td>78</td><td>/</td></tr><tr><td>15</td><td>83</td><td>43</td></tr><tr><td>Mean ± std</td><td>78 ± 6</td><td>49 ± 18</td></tr></tbody></table>
585

<table><caption>Table 4. Glomeruli PDW estimation by sULM in patients</caption><thead><tr><td>Patient n°</td><td>sULM (μm)</td></tr></thead><tbody><tr><td>1</td><td>389</td></tr><tr><td>2</td><td>236</td></tr><tr><td>3</td><td>203</td></tr><tr><td>4</td><td>462</td></tr><tr><td>13</td><td>372</td></tr><tr><td>Mean ± std</td><td>332 ± 109</td></tr></tbody></table>

586
<table><caption>587 Table 5. A detailed comparison between main vessels, medulla, and glomeruli in humans</caption><thead><tr><th>Test statistic</th><th>Metric</th><th>Region compared</th><th>Confidence intervals</th><th>Size (n)</th><th>Degrees of freedom</th><th>P-value</th></tr></thead><tbody><tr><td rowspan="10">Two-sided student t-test</td><td rowspan="3">Normalized Speed</td><td>Main vessels and medulla</td><td>-0.14 to 0.04</td><td>5</td><td>4</td><td>0.22</td></tr><tr><td>Main vessels and glomeruli</td><td>-0.20 to 0.04</td><td>5</td><td>4</td><td>0.01</td></tr><tr><td>Medulla and glomeruli</td><td>-0.16 to 0.02</td><td>5</td><td>4</td><td>0.12</td></tr><tr><td rowspan="3">Remanence</td><td>Main vessels and medulla</td><td>0.00 to 0.04</td><td>5</td><td>4</td><td>0.09</td></tr><tr><td>Main vessels and glomeruli</td><td>0.03 to 0.08</td><td>5</td><td>4</td><td>0.00</td></tr><tr><td>Medulla and glomeruli</td><td>0.01 to 0.07</td><td>5</td><td>4</td><td>0.01</td></tr><tr><td rowspan="3">Dispersity</td><td>Main vessels and medulla</td><td>-0.08 to 0.00</td><td>5</td><td>4</td><td>0.04</td></tr><tr><td>Main vessels and glomeruli</td><td>-0.17 to 0.07</td><td>5</td><td>4</td><td>&lt;0.001</td></tr><tr><td>Medulla and glomeruli</td><td>-0.13 to 0.03</td><td>5</td><td>4</td><td>0.01</td></tr></tbody></table>
589 **Figure Legends**

590

591 **Figure 1. Glomerular structures were observed in the rat kidney cortex by sULM.**

592 (a) Experimental setup. (b) The imaging plane is the yellow one. (c) Power Doppler of the rat 14. (d) sULM
593 density map in rat 14 with superimposed slow-tracking (pink) and fast-tracking (green). (e) Zoom in the cortex
594 area. (f) Zoom of yellow glomerular behavior highlighted in e. (g) Masks of the three regions in rat kidney:
595 cortex (yellow), main vessels (blue), and medulla (red). 500 first slow and 500 first fast tracks encoded with
596 speed are presented upon background masks. (h) Mean normalized speed of all tracks in 11 rats compared by
597 region. The significance of the scoring is explained by the following P values: ns = P > 0.05, * = P ≤ 0.05, ** =
598 P ≤ 0.01, *** = P ≤ 0.001, **** = P ≤ 0.0001 (unpaired student's t-tests with a 95% confidence level). The center
599 line corresponds to the median, box limits to upper and lower quartiles, whiskers to 1.5x interquartile range and
600 no outliers have been considered. (i) Mean remanence time of all tracks in 11 rats compared by region. Same
601 statistical tests. (j) Mean dispersity of all tracks in 11 rats compared by region. Same statistical tests.
602

603

603 **Figure 2. Capillary bundles observed in the sULM density map correspond to the glomeruli on micro-CT.**

604 (a) sULM density map in rat number seven. (b) Micro-CT in the same rat and same plane. (c) Zoom of sULM
605 normalized distance (ND) map in rat seven (Methods). Red circles indicate glomerular count. 10,000 first slow
606 and 5,000 first fast tracks encoded with ND are presented here. (d) Zoom of micro-CT in the same rat. Red
circles indicate glomerular count. (e) Glomeruli count as a function of kidney capsule distance in micro-CT and
607 sULM in rat seven (Methods). See Table 1 for more details on statistical analysis (two-sample Kolmogorov-
608 Smirnov test). (f) Glomeruli PDW calculation with a density as a function of distance to the center of glomeruli
609 (for sULM in blue, in 11 rats), and intensity as a function of the distance from the center pixel (micro-CT in red,
610 in six rats). Dashed lines indicate standard deviation.
611

611

612 **Figure 3. Glomeruli can be observed in the cortex of human kidneys by sULM.**

613 (a) sULM density map in patient one with superimposed slow-tracking (pink) and fast-tracking (green). (b) Zoom in the cortex area. (c) Zoom of yellow track highlighted in b. (d) Zoom of red track highlighted in b. (e) Masks of the three regions: cortex (yellow), main vessels (blue), and medullary region (red). 500 first slow and 500 first fast tracks encoded with speed are presented upon background masks. (f) Mean normalized speed of all tracks in five patients compared by region. The significance of the scoring is explained by the following P values: ns = P > 0.05, * = P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001, **** = P ≤ 0.0001 (unpaired student's t-tests with a 95% confidence level). The center line corresponds to the median, box limits to upper and lower quartiles, whiskers to 1.5x interquartile range and no outliers have been considered. (g) Mean remanence time of all tracks in five patients compared by region. Same statistical tests. (h) Mean dispersity of all tracks in five patients compared by region. Same statistical tests.
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614 Zoom in the cortex area. (c) Zoom of yellow track highlighted in b. (d) Zoom of red track highlighted in b. (e)

615 Masks of the three regions: cortex (yellow), main vessels (blue), and medullary region (red). 500 first slow and
616 500 first fast tracks encoded with speed are presented upon background masks. (f) Mean normalized speed of all
617 tracks in five patients compared by region. The significance of the scoring is explained by the following P
618 values: ns = P > 0.05, * = P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001, **** = P ≤ 0.0001 (unpaired

619 with a 95% confidence level). The center line corresponds to the median, box limits to upper and lower quartiles,
620 whiskers to 1.5x interquartile range and no outliers have been considered. (g) Mean remanence time of all tracks
621 in five patients compared by region. Same statistical tests. (h) Mean dispersity of all tracks in five patients
622 compared by region. Same statistical tests.

622
623 Supplementary Materials

624 This paper contains supplementary material available online.

625 Correspondence and requests for materials should be addressed to:

626 Louise Denis: louise.denis@sorbonne-universite.fr

627 Olivier Couture: olivier.couture@sorbonne-universite.fr

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