Versatile Vasculature Chips for Ultrasound Localization Microscopy

Versatile Vasculature Chips for Ultrasound Localization Microscopy | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Versatile Vasculature Chips for Ultrasound Localization Microscopy Renxian Wang , Qi Liu , Xin Zhao , Wei-Ning Lee doi: https://doi.org/10.1101/2025.06.15.659747 Renxian Wang 1 Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong Special Administrative Region of China , People’s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qi Liu 2 Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China , People’s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xin Zhao 2 Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China , People’s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wei-Ning Lee 1 Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong Special Administrative Region of China , People’s Republic of China 3 Biomedical Engineering Programme, The University of Hong Kong, Hong Kong Special Administrative Region of China , People’s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: wnlee{at}hku.hk Abstract Full Text Info/History Metrics Preview PDF Abstract Ultrasound localization microscopy (ULM) has revolutionized microvasculature imaging by surpassing the diffraction limit via microbubbles. ULM demonstrates exceptional potential to resolve micrometer-scale vascular structures in both preclinical and clinical studies. However, its performance evaluation remains challenging primarily due to the lack of reference microvascular phantoms featuring micrometer-scale, hierarchical branches, and realistic vascular structures. Inspired by microfluidic chip techniques, we present an organ-on-a-chip protocol for fabricating agarose-based micro-vessel network phantoms with ground truth. The vasculature pattern offers design versatility, enabling on-demand customization. We experimentally demonstrated the feasibility of the vasculature phantom using two adapted patterns. The first was a leaf pattern, which exhibited intrinsic quasi-two-dimensional venation network with hierarchical and branching channels similar to animal vasculature. The second was a kidney pattern, which was based on a two-dimensional projection of real human vasculature obtained from micro computed tomography. The microbubble solution was perfused into the phantoms by capillary force and gravity. The ULM-reconstructed vasculature maps agreed well with the ground truth. ULM achieved a high sensitivity of 0.97 and 0.95, but a low precision of 0.37 and 0.60, for the leaf and kidney phantom, respectively. The results indicated the capability of ULM to reconstruct vessel structures while making many false positive predictions. The proposed protocol holds significant promise for the development and optimization of ultrasound microvascular imaging techniques. 1. Introduction Ultrasound localization microscopy (ULM) is an acoustic super-resolution imaging technique for mapping microcirculation in a living body with the help of gas-filled microbubbles (MBs) that are injected and flow in the circulatory system ( Errico et al . 2015 ). Through precise localization and tracking of individual micron-level MBs, ULM overcomes the acoustic diffraction limit and achieves unprecedented micrometric resolution for deep tissue imaging in vivo , closing the gap across various imaging modalities ( Christensen-Jeffries et al . 2020 ). ULM has shown great potential for broad clinical applicability across various biological tissues, including the brain ( Heiles et al . 2022 , Lowerison et al . 2022 , Demene et al . 2021 ), kidney ( Heiles et al . 2022 , Chabouh et al . 2024 ), heart ( Yan et al . 2024 ), lymph node ( Yan et al . 2022 ), and so on. ULM is a multi-step framework, typically consisting of ultrasound data acquisitions of serial frames, MB signal extraction from raw data, MB localization for subpixel accuracy and tracking, and then accumulation of sub-wavelength tracks for final vasculature. Researchers have been working for over ten years on improving the accuracy and efficiency of the ULM workflow, involving various localization and tracking algorithms ( Heiles et al . 2022 , Lerendegui et al . 2024 , Yan et al . 2022 , Chen et al . 2023 , Song et al . 2018 ). However, validation of ULM remains a critical bottleneck due to the lack of realistic vasculature phantoms that serve as ground truth. The μm diameter, branching, and hierarchical nature of blood vessels makes it challenging to fabricate microvasculature phantoms for assessment of ULM-reconstructed results. The existing validation methods for ULM mainly rely on simulations, manually-assembled tube phantoms with simple geometries, and micro-CT (micro computed tomography). A research group used the Verasonics Research Ultrasound Simulator ( Heiles et al . 2022 ) to model each MB as a point scatterer with addictive white Gaussian noise. PROTEUS ( Blanken et al . 2024 ) is a recently developed simulator that incorporates flow dynamics for contrast-enhanced ultrasound imaging of realistic vascular structures. It is a customizable framework for ULM validation. A phantom with two crossed tubes of around 200 μm in diameter was commonly used for ULM validation ( Zhang et al . 2019 , Riemer et al . 2023 , Wang et al . 2024 ). Recently, one study ( Parra Raad et al . 2024 ) proposed to manually fabricate a hierarchical microvascular phantom with the diameter down to around λ/6 (~ = ~350 μm) based on polydimethylsiloxane (PDMS). Kawara et al. ( Kawara et al . 2023 ) fabricated capillary-scale hydrogel microchannel networks, realizing micrometer scale flow channels but simple branches. Some studies ( Andersen et al . 2021 , Hansen et al . 2025 ) employed micro-CT to validate ultrasound microvascular imaging, but the spatial registration between the two imaging modalities is nontrivial. Overall, simulations can emulate customized and realistic microstructures but hardly replicate actual experimental data; existing phantoms generally do not incorporate micron-level sizes, hierarchical branches, and realistic vascular structures concurrently. To address the aforementioned challenges, we propose an on-chip versatile vasculature phantom protocol for ultrasound microvascular imaging, inspired by a leaf-templated microfluidic chip technique ( Mao et al . 2018 , Mao et al . 2020 , Fang et al . 2023 , Ji et al . 2023 ), with the initial application focused on ULM. We adapted microvasculature patterns as microfluidic channels with sizes of tens of microns and realistic hierarchical branching structures on an agarose layer. Customization of each microvascular structure served as a structural ground truth. In the following sections, we first introduce the protocol and then present experimental results, demonstrating its effectiveness and potential for the development and clinical adoption of ultrasound microvascular imaging techniques. 2. Methods Our protocol follows the typical fabrication workflow of a microfluidic chip, incorporating customized vasculature patterns. As figure 1(a) shows, we binarized the photo of a target pattern provided by the public datasets ( Li et al . 2022 , Walsh et al . 2021 ) and converted it into a vectograph by AutoCAD 2022. The vectograph was then used as a photomask for subsequent photolithography on a silicon wafer to replicate the pattern. A 3% agarose solution was then cast onto the silicon mould. The agarose with the negative pattern of the target vasculature was peeled off from the silicon mould after approximately 10 minutes of solidification at 25 °C. Download figure Open in new tab Figure 1. The proposed fabrication protocol of on-chip versatile vasculature phantoms for ultrasound microvascular imaging. (a) Vasculature photos were binarized and converted to vectographs for photolithography on a 4-inch silicon wafer. Micro-channels with the designed vasculature patterns were replicated from the silicon wafer, and the channel depth is approximately 240 μm. (b-d) The ultrasound imaging set-up for data acquisitions. (b) and (c) show the front view of the leaf and kidney phantoms, respectively, and (d) shows the side view. MB denotes microbubble. The red ink is to illustrate perfused vascular regions. The protocol allows for versatile customization of the vascular network. In this study, we used two vascular patterns ( figure 1(a) ) adapted from a Chinese rose leaf ( Li et al . 2022 ) and a human kidney ( Walsh et al . 2021 ). The leaf venation comprised quasi-two-dimensional (2D), hierarchical, and branching channels with high similarity to animal vasculature. As shown in figure 1(b-d) , the agarose layer with vasculature network was then tightly sealed to a flat agarose slab (3% agarose, thickness: ~ 6 mm) for MB perfusion and ultrasound imaging. The inlet and outlet were cut out to expose the stem channels of the vasculature network for better perfusion. The MB solution (2×10 7 to 3×10 7 MBs/mL, USphere, Taipei) was administered at the inlet and driven by capillary force and gravity. Ultrasound data were acquired at a compounded frame rate of 400 Hz with 5 tilted plane waves (−4; −2; 0; 2; 4°) using a linear probe L11-4v (6.25 MHz, 128 elements, pitch = 0.30 mm) connected to a Verasonics Vantage 256 system (Verasonics Inc, Kirkland, WA, USA). Each acquisition lasted for four seconds as a bloc, commencing immediately from the start of MB solution perfusion. The total number of blocs was 50 for the leaf phantom and 30 for the kidney phantom. The PALA ULM workflow was then adopted to reconstruct the vasculature network. It began with MB signal extraction by singular value decomposition. We then employed a Gaussian-fitting method for MB localization ( Heiles et al . 2022 ) and a Hungarian method ( Kuhn 1955 ) for MB tracking. The ULM-derived vasculature was lastly compared with the corresponding ground truth obtained from its original AutoCAD design. We considered the vessel ground truth from the designed pattern in the AutoCAD file as a binarized mask, with the vascular network region as the target. For quantitative comparison, ULM results were binarized using different thresholds ( th ); pixels with intensities greater than or equal to th were assigned a value of 1 (positive), while all other pixels were assigned a value of 0 (negative). The ULM results were manually registered to ground truth. Positive pixels in the binarized ULM results that fell within the vascular region of the ground truth were regarded as true positives (TPs), while those in the non-vascular region were considered false positives (FPs). Negative pixels in the binarized ULM results that actually corresponded to the vascular region of the ground truth were labelled as false negatives (FNs). Precision, sensitivity, and the Jaccard (JAC) index were computed for quantitative evaluation of the ULM results and respectively defined as 3. Results Figure 2(a) shows the ULM vasculature map of the leaf phantom, corresponding to a 90° counterclockwise rotation of the imaging view shown in figure 1(b) . The upper part of the leaf venation network was reconstructed well, but the rest was not. The light blue box in figure 2(a) was zoomed-in for better visualization ( figure 2(b) ) and binarized ( figure 2(c) ) with th = 0 for comparison with the ground truth ( figure 2(d) ). Figure 2(e) shows the cross-sectional vessel profiles along lines 1, 2, and 3 in figure 2(b) with measured diameters (69.0 μm, 51.5 μm, and 58.0 μm, respectively) around or below λ / 4 (λ = 246 μm). The ULM-estimated diameters were about 45-75% of the ground truth values. The peaks of the ULM intensity profiles were all located within the ground truth vessels. Download figure Open in new tab Figure 2. ULM of a leaf phantom: (a) The ULM density map of the leaf phantom in the front view (90° counterclockwise rotation of figure 1(b) ). (b) Magnification of the venation network enclosed by the light blue box in (a). (c) Binarized vasculature map of (b) with th = 0. (d) The corresponding vasculature ground truth for (b) and (c), where (b-d) share the same scale bar. (e) The intensity profiles along the three white lines in (b). The light blue and red lines denote the ULM results and the ground truths, respectively. The kidney pattern was much more challenging to reconstruct because it has more hierarchical and branching structures. Yet, its ULM result ( figure 3(a) ) was in good qualitative agreement with the ground truth ( figure 3(c) ) despite some deviations in their details. The main branches were delineated well in the ULM map, while some complex microchannels were not reconstructed, particularly the cul-de-sac channels indicated by turquoise triangles ( figure 3 ). ULM also did not detect vessels in the positions of purple triangles ( figure 3 ). Some MBs flowed outside the micro-channels, and their flow paths were delineated as free trajectories (orange triangles in figure 3 ). Download figure Open in new tab Figure 3. ULM of a kidney phantom: (a) The ULM density map of the kidney phantom. (b) Binarized vasculature map of (a) with the threshold ( th ) of 0. (c) The corresponding vasculature ground truth. (d) Side-by-side comparison of three zoomed-in ROIs enclosed by the magenta boxes in (c) across maps in (a-c). Note that (a-c) share the same scale bar. Figure 4(a) shows the quantitative analysis of the leaf ULM map enclosed by the light blue box ( figure 2(a) ), which exhibited complex network. Empirical thresholds (0, 0.01, 0.025, 0.05, 0.1, 0.2, 0.3) were applied to the leaf ULM result for binarization and subsequent evaluation. As the threshold increased, the precision increased from 0.37 to 0.84 while the sensitivity decreased from 0.97 to 0.27. The JAC index reached its maximum, approximately 0.51, at the threshold of 0.01. Figure 4(b) shows the quantitative analysis of the ULM result for the kidney phantom from the average of the three ROIs with branching structures in figure 3(d) . The three ROIs corresponded to the three locations labelled in figure 3(c) . The thresholds were selected empirically as 0, 0.005, 0.01, 0.02, 0.035, 0.05, and 0.1 for binarization on the three ROIs. As the threshold increased, the precision increased from 0.60 to 0.92, while the sensitivity dropped from 0.95 to 0.37. The JAC index initially increased to the maximum, approximately 0.70, when th = 0.005, and then decreased to 0.35 at th = 0.1. Note that the aforementioned cul-de-sac regions were not included in the quantitative analysis of the three ROIs (marked by the yellow arrows in figure 3(d) ). Download figure Open in new tab Figure 4. Quantitative analysis of ULM at different binarization thresholds for (a) the leaf and (b) the kidney phantoms. (a) and (b) correspond to the ROIs in figure 2(b) and figure 3(d) , respectively. 4. Discussion We introduced an on-chip versatile vasculature phantom protocol that enables flexible customization of micron-sized and hierarchical microvasculature. The phantom medium is agarose, which is both acoustically and optically transparent. Optical imaging provided more flow information for further assessment (supplementary videos 1-3), and the designed pattern in the AutoCAD file served as a structural ground truth. The minimum diameter of the fabricated channels was approximately 50 μm, far below the acoustic diffraction limit of most clinical and research-used ultrasonic settings. The fabricated phantom can validate not only ULM but also other ultrasound and non-ultrasound microvascular imaging techniques. ULM resolution was assessed using the leaf phantom, showing that ULM-estimated diameters of the microchannels ( figure 2(e) ) at half-maximum intensity were smaller than the ground truth values. This was because ULM relied on MB flow dynamics, and the MB signals extracted by SVD was related to the velocity distribution within the flow channel, resulting in a quasi-parabolic intensity profile ( figure 2(e) ). For both leaf and kidney phantoms, the high sensitivity (0.97 and 0.95) but low precision (0.37 and 0.60) at th = 0 indicated that ULM successfully reconstructed the vessels within the selected ROIs but with a large number of FPs, particularly in the branching regions ( figure 2(c) and 3(d)). As the binarization threshold increased, precision and JAC index increased while sensitivity decreased in both phantom cases. Yet, the kidney case had a higher precision than the leaf one. We attributed this observation to the less complex microvasculature in the selected ROIs of the kidney case than that in the leaf case, which contained many branches. This finding highlighted the challenge for ULM to reconstruct branching structures more complex than straight channels. ULM could not reconstruct the lower half of the leaf vasculature in the deep image zone probably due to channel congestion by air. This could be caused by the pumpless nature of our phantom, and the MB solution was perfused by capillary force and gravity. The kidney phantom had a more severe problem with air congestion (purple triangles in figure 3 and supplementary video 2) because of its much more complex vasculature, including multiscale microvascular network and more bifurcations, than that in the leaf phantom. The kidney vasculature map reconstructed by ULM generally agreed with the ground truth image, but its quality was multifactorial. The cul-de-sac channels, marked by the turquoise triangles in figure 3 and shown in the supplementary video 1, had no circulatory flow so that ULM failed to detect them. Since the agarose microfluidic chip was sealed spontaneously by hydrogen bonds, the adhesion could not be guaranteed as reliably as that of traditional PDMS-based microfluidic chips, especially at high flow velocities. In our experiments, fluid leakage occurred in the areas that were indicated by the orange triangles in figure 3 and supplementary video 3, where free MB trajectories were delineated by ULM. It is important to note that the issue of fluid leakage could be mitigated through proper pattern design, such as avoiding cul-de-sac channels. This may explain why there was no fluid leakage observed in the leaf phantom. Manual spatial registration between ULM and ground truth images may have led to alignment errors although the high sensitivity at th = 0 indicated good registration. In particular, the channel depth of our phantoms was 240 μm, which was deemed to be relatively large for a typical microfluidic chip. The large channel depth was designed to facilitate pumpless perfusion but might cause photolithography defect and could exacerbate the mis-alignment between the ultrasound imaging plane and the vasculature channel. Moreover, generation of ULM images involved many parameters in the localization and tracking steps, including SVD cut-off threshold, the number of MBs per frame, localization methods, the minimum trajectory length, and so on. Finding the optimal set of parameters is an exhaustive task. The parameters that were used for the leaf and kidney data in this study were summarized in Table S1 of the Supplementary Material. Overall, the proposed on-chip vasculature protocol provides a valuable method to validate and assess the performance of ULM, showing significant potential for the development and optimization of ultrasound microvascular imaging techniques. The methodology also paves the way for developing versatile organ-on-a-chip devices. Future work could focus on performance evaluation of volumetric ULM and micro-Doppler imaging with flow velocity information. Data availability statements The data that support the findings of this study are openly available at the following URL/DOI: https://doi.org/10.5281/zenodo.15614916 . Data will be available from 01 December 2025. Acknowledgements Renxian Wang is supported by ACCESS – AI Chip Center for Emerging Smart Systems, sponsored by the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government. This work was in part supported by Collaborative Research with World-leading Research Groups from The Hong Kong Polytechnic University (P0039523). 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Share Versatile Vasculature Chips for Ultrasound Localization Microscopy Renxian Wang , Qi Liu , Xin Zhao , Wei-Ning Lee bioRxiv 2025.06.15.659747; doi: https://doi.org/10.1101/2025.06.15.659747 Share This Article: Copy Citation Tools Versatile Vasculature Chips for Ultrasound Localization Microscopy Renxian Wang , Qi Liu , Xin Zhao , Wei-Ning Lee bioRxiv 2025.06.15.659747; doi: https://doi.org/10.1101/2025.06.15.659747 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Bioengineering Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17690) Bioengineering (13892) Bioinformatics (41935) Biophysics (21451) Cancer Biology (18587) Cell Biology (25499) Clinical Trials (138) Developmental Biology (13377) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24318) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88601) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15152) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)