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Therefore, a general PCND-based extravascular monitoring technique should meet several criteria to overcome the above-mentioned problems. First, the trade-off between thermal stability and the post-activation dynamics of the chosen PCNDs is carefully tuned. Second, the target signal from concentrated PCNDs is detectable and resolvable. Third, the monitoring sensitivity is high enough for further quantitative analysis.
Accordingly, we put forward a spatiotemporally super-resolved extravascular monitoring technique by tuning the post-activation dynamics of customized mixed-core HBP PCNDs, employing a clinical ultrasound probe and tissue-mimicking phantoms. Here, recondensation events were chosen as the target signal source for monitoring non-flowing PCNDs since their stochastic nature8,11,128. M. T. Burgess, M. Aliabouzar, C. Aguilar, M. L. Fabiilli, and J. A. Ketterling, “ Slow-flow ultrasound localization microscopy using recondensation of perfluoropentane nanodroplets,” Ultrasound Med. Biol. 48, 743–759 (2022). https://doi.org/10.1016/j.ultrasmedbio.2021.12.00711. G. P. Luke, A. S. Hannah, and S. Y. Emelianov, “ Super-resolution ultrasound imaging in vivo with transient laser-activated nanodroplets,” Nano Lett. 16, 2556 (2016). https://doi.org/10.1021/acs.nanolett.6b0010812. J. Yu, X. Chen, F. S. Villanueva, and K. Kim, “ Vaporization and recondensation dynamics of indocyanine green-loaded perfluoropentane droplets irradiated by a short pulse laser,” Appl. Phys. Lett. 109, 243701 (2016). https://doi.org/10.1063/1.4972184 ensures higher signal sparsity than stable bubbles in the extravascular environment. Furthermore, a deep learning (DL)-based detection algorithm tailored to recondensation patterns was applied to present the super-resolved mapping for quantitative analysis.To obtain PCNDs with enhanced thermal stability, clinical transducer activatability, and tunable post-activation dynamics, we mixed PFCs with different bulk boiling points as the core material of PCNDs. The overall synthesis procedure was an adaptation of the conventional tip sonication method.2020. P. S. Sheeran, N. Matsuura, M. A. Borden, R. Williams, T. O. Matsunaga, P. N. Burns, and P. A. Dayton, “ Methods of generating submicrometer phase-shift perfluorocarbon droplets for applications in medical ultrasonography,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control 64, 252–263 (2017). https://doi.org/10.1109/TUFFC.2016.2619685 First, we prepared a lipid solution (1 mg/ml) containing 1,2-dipalmitoyl-sn-glycero-3-phosphate (DPPA), 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[amino (polyethylene glycol)-2000] (DSPE-PEG-2000) at a molar ratio of 7:1:2 as a shell material. The three lipid components above were purchased from CordenPharma, Switzerland. Specifically, the core material was a mixture of PFP (Strem Chemicals Inc., USA) and PFH (J&K Scientific, China) in a 2:3 volume ratio. Next, an ice-cold blend was obtained by adding 100 μl of the core material and 50 μl of Zonyl FS-63 fluorosurfactant (Sigma-Aldrich, USA) into 1 ml of the lipid solution. It was then sonicated with a 2-mm-tip probe sonicator (VX750, SONICS&MATERIALS, Inc., USA) for 60 s (8 s on and 2 s off) at 200 W. The resulting PCNDs were sized at 310 ± 30 nm (NanoSight NS300, Malvern Instruments Ltd., UK), and the mean concentration was ∼1012 droplets/ml.Tissue-mimicking polyacrylamide phantoms with different droplet concentrations (1∼9×107 droplets/ml) were then fabricated to investigate the effect of concentration on the post-activation dynamics of PCNDs. The 20 × 40 × 100 mm3 cuboid phantom consisted of de-ionized water, 40% polyacrylamide solution (Sigma-Aldrich, USA), 12% Tris solution (Sigma-Aldrich, USA), 10% ammonium persulfate solution (Sigma-Aldrich, USA), and TEMED (Aladdin, China) at a volume ratio of 1120:300:160:19:1. A pre-calculated volume of PCNDs was evenly mixed in the solution before phantom solidification.
Next, each phantom was immersed in a 37 °C water bath and waited for temperature equilibration before ultrasound engagement. An L11–4v transducer equipped with a programmable ultrasound research platform (Verasonics Vantage 256, Kirkland, USA) was fixed where the transducer face just had contact with the upper surface of the phantom. The point spread function (PSF) and the localization precision of the ultrasound imaging system were characterized using a multi-purpose pin-target phantom (Sono403, Gammex, Inc., USA) and a previously mentioned calculation method.2121. K. Christensen-Jeffries, R. J. Browning, M. X. Tang, C. Dunsby, and R. J. Eckersley, “ In vivo acoustic super-resolution and super-resolved velocity mapping using microbubbles,” IEEE Trans. Med. Imaging 34, 433–440 (2015). https://doi.org/10.1109/TMI.2014.2359650 The axial and lateral resolution was measured as a full-width-half-maximum (FWHM) of 430 and 770 μm, while the axial and lateral localization precision was measured as 0.33 and 2.75 μm, respectively.As illustrated in Fig. 1(a), a customized pulse sequence was designed and implemented to activate and image PCNDs in the phantom using the same ultrasound probe. The parameters of the sequence were summarized in Table I. PCNDs were activated by 4 MHz, 2-cycle left-to-right sweeping focused pulses (effective focal region: ∼2 × 11 mm2) transmitted to a fixed depth of 15 mm at a peak-negative-pressure (PNP) of 3.76 MPa (Mechanical Index = 1.88). Then, 6.9 MHz, 1-cycle, 5-angle compounding plane wave imaging pulses were transmitted at a high frame rate (3 kHz) to capture millisecond-level recondensation signals11,1211. G. P. Luke, A. S. Hannah, and S. Y. Emelianov, “ Super-resolution ultrasound imaging in vivo with transient laser-activated nanodroplets,” Nano Lett. 16, 2556 (2016). https://doi.org/10.1021/acs.nanolett.6b0010812. J. Yu, X. Chen, F. S. Villanueva, and K. Kim, “ Vaporization and recondensation dynamics of indocyanine green-loaded perfluoropentane droplets irradiated by a short pulse laser,” Appl. Phys. Lett. 109, 243701 (2016). https://doi.org/10.1063/1.4972184 of PCNDs. Apparently, after a round of activation (3.5 ms), liquid-state nanodroplets and echogenic gas-state phase-change microbubbles (PCMBs) coexist in the phantom [see Fig. 1(b)]. Following another round of activation, the existing PCNDs and PCMBs will undergo several stages and reach varying final statuses, which could lead to different signal patterns in corresponding imaging frames. When the activation pulse has passed, PCNDs in the phantom can either remain unchanged (final status 1) or grow into new PCMBs—which could then either remain/coalesce (final status 2) or recondense to PCNDs (final status 3). Meanwhile, PCMBs in the phantom can either disappear (final status 4) due to dissolution—which rarely occurred around millisecond-order timescale even for uncoated bubbles,8,22–248. M. T. Burgess, M. Aliabouzar, C. Aguilar, M. L. Fabiilli, and J. A. Ketterling, “ Slow-flow ultrasound localization microscopy using recondensation of perfluoropentane nanodroplets,” Ultrasound Med. Biol. 48, 743–759 (2022). https://doi.org/10.1016/j.ultrasmedbio.2021.12.00722. K. Ferrara, R. Pollard, and M. Borden, “ Ultrasound microbubble contrast agents: Fundamentals and application to gene and drug delivery,” Annu. Rev. Biomed. Eng. 9, 415–447 (2007). https://doi.org/10.1146/annurev.bioeng.8.061505.09585223. N. Reznik, M. Seo, R. Williams, E. Bolewska-Pedyczak, M. Lee, N. Matsuura, J. Gariepy, F. S. Foster, and P. N. Burns, “ Optical studies of vaporization and stability of fluorescently labelled perfluorocarbon droplets,” Phys. Med. Biol. 57, 7205–7217 (2012). https://doi.org/10.1088/0031-9155/57/21/720524. N. Reznik, O. Shpak, E. C. Gelderblom, R. Williams, N. De Jong, M. Versluis, and P. N. Burns, “ The efficiency and stability of bubble formation by acoustic vaporization of submicron perfluorocarbon droplets,” Ultrasonics 53, 1368–1376 (2013). https://doi.org/10.1016/j.ultras.2013.04.005 or remain/coalesce (final status 5). It is important to note that only contrast agents reaching final statuses 2, 3, and 5 will generate detectable ultrasound signals during the imaging process: the PCMBs-induced remaining signal from the paths toward final statuses 2 and 5 is stable, while the recondensation-induced signal from the path toward final status 3 is dynamic.
TABLE I. Customized pulse sequence and corresponding ultrasound parameters used in the experiments.
StateTransmit frequency (MHz)Pulse length (cycle)PNP (MPa)Mechanical indexActivation423.761.88Imaging6.910.360.14With an eye on mitigating possible bioeffects2525. S. T. Kang, Y. C. Lin, and C. K. Yeh, “ Mechanical bioeffects of acoustic droplet vaporization in vessel-mimicking phantoms,” Ultrason. Sonochem. 21, 1866–1874 (2014). https://doi.org/10.1016/j.ultsonch.2014.03.007 and preventing undesired premature drug release from PCNDs caused by extravascular monitoring pulses, our tuning was about making the dynamic recondensation-induced signal dominate the total post-activation signal. Here, repetition times of left-to-right sweeping activation in each red activation section in Fig. 1(a)—which we termed activation-round-numbers (ARNs)—were investigated to obtain and analyze different post-activation signal patterns of PCNDs. Two-hundred compounded imaging frames were acquired each time as radio frequency (RF) signals and reconstructed offline. After delay-and-sum (DAS) beamforming, all subsequent steps were carried out over a 6 × 20 mm2 region of interest (ROI) selected according to the focal region of activation pulses.In order to quantify the frame-by-frame variation and the component of the post-activation signal before recondensation signal detection, the total signal amplitude (TSA) was defined as the sum of all pixel values in the chosen ROI of each frame. Furthermore, three derivative metrics “Contrast#1, Contrast#100, and RR Ratio” were calculated as follows: Contrast#1=(TSA#1−bkg)/bkg,(1) Contrast#100=(TSA#100−bkg)/bkg,(2) RR Ratio=(Contrast#1−Contrast#100)Contrast#100,(3)where TSA#1 is the TSA of frame 1, bkg is the mean amplitude of the background in the first 100 frames, and TSA#100 is the TSA of frame 100. Contrast#1, Contrast#100, and RR Ratio indicate the initial maximum post-activation signal in frame 1, the remaining post-activation signal in frame 100, and the ratio of the recondensation signal to the remaining signal in the first 100 frames, respectively. Here, we defined that RR Ratio > 1 denotes the dominance of the dynamic recondensation signal in the total post-activation signal.Figure 2 shows representative results of TSA and derivative metrics under different experimental variables. As illustrated in Fig. 2(a), at a concentration of 5 ×107 droplets/ml, the post-activation TSA curves showed a general trend of decreasing from the maximum value at frame 1 and finally leveling off within the time scope of imaging, which was observed in all groups of experiments and further confirmed the feasibility of recondensation-based monitoring, especially in groups with higher TSA#1 values. Importantly, considering the TSA curves almost plateaued after frame 100—which indicates very few recondensation signals—only the first 100 frames were selected for further processing. Corresponding derivative metrics of groups at different concentrations and ARNs are plotted in Figs. 2(b) and 2(c). Within the pre-set concentration range, higher ARNs tended to result in higher Contrast#1 and Contrast#100 values, which coincides with the existing theory that longer pulse durations facilitate the growth of nucleated bubbles2626. M. Aliabouzar, O. D. Kripfgans, W. Y. Wang, B. M. Baker, J. Brian Fowlkes, and M. L. Fabiilli, “ Stable and transient bubble formation in acoustically-responsive scaffolds by acoustic droplet vaporization: Theory and application in sequential release,” Ultrason. Sonochem. 72, 105430 (2021). https://doi.org/10.1016/j.ultsonch.2020.105430—more PCMBs will, thus, appear, remain, and coalesce. However, a positive correlation was not found between RR Ratio and ARN. At higher concentrations, higher ARNs resulted in lower RR Ratios. The ratio even dropped below one at 9 ×107 droplets/ml and ARN of 9, which was probably due to the increased likelihood of PCMB coalescence when the inter-bubble distance becomes smaller at higher concentrations,7,24,27,287. A. Ishijima, J. Tanaka, T. Azuma, K. Minamihata, S. Yamaguchi, E. Kobayashi, T. Nagamune, and I. Sakuma, “ The lifetime evaluation of vapourised phase-change nano-droplets,” Ultrasonics 69, 97–105 (2016). https://doi.org/10.1016/j.ultras.2016.04.00224. N. Reznik, O. Shpak, E. C. Gelderblom, R. Williams, N. De Jong, M. Versluis, and P. N. Burns, “ The efficiency and stability of bubble formation by acoustic vaporization of submicron perfluorocarbon droplets,” Ultrasonics 53, 1368–1376 (2013). https://doi.org/10.1016/j.ultras.2013.04.00527. K. J. Haworth and O. D. Kripfgans, “ Initial growth and coalescence of acoustically vaporized perfluorocarbon microdroplets,” in IEEE Ultrasonics Symposium ( IEEE, 2008), pp. 623–626.28. Y. Yang, D. Yang, Q. Zhang, X. Guo, J. L. Raymond, R. A. Roy, D. Zhang, and J. Tu, “ The influence of droplet concentration on phase change and inertial cavitation thresholds associated with acoustic droplet vaporization,” J. Acoust. Soc. Am. 148, EL375 (2020). https://doi.org/10.1121/10.0002274 suggesting that the ARN and the concentration should be tuned together to avoid remaining post-activation contrast signals hindering the detection of recondensation signals.To extract the recondensation signal from the total post-activation signal, we examined the signal pattern of massive recondensation events. Figure 3 shows selected frames of post-activation B-mode images demonstrating the total signal dynamics over time. From frame 1 to 100, disappeared contrast signals physically represented the stochastic recondensation events, which were large in number and densely distributed within focal regions. Consequently, we captured sparsified and resolvable recondensation signals by subtracting consecutive imaging frames. Thresholding was then applied to resulting subtraction frames (hereafter “SubFrames”) for further noise rejection, as subtraction could amplify high-frequency noise.1515. H. Yoon, K. A. Hallam, C. Yoon, and S. Y. Emelianov, “ Super-resolution imaging with ultrafast ultrasound imaging of optically triggered perfluorohexane nanodroplets,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control 65, 2277–2285 (2018). https://doi.org/10.1109/TUFFC.2018.2829740 At this point, although we have obtained low-resolution images illustrating the spatial distribution of recondensation signals, they were still not suitable for direct quantitative analysis. PCMBs were immobile in the phantom, which could inevitably result in irregularly shaped signal clusters consisting of signals from multiple recondensation events in SubFrames. If conventional localization methods, such as weighted-average, were directly used here, potential signal clusters were likely to be localized as one high-intensity point, which could cause significant errors in further quantitative analysis. Moreover, temporal patterns of recondensed PCNDs have not been reflected yet. Therefore, we proposed a generalized detection algorithm based on a Modified Residual Dense Network (MRDN) from our previous study2929. S. An, M. Qu, A. Huang, H. Yu, Y. Wang, M. Wan, and Y. Zong, “ Modified residual dense network based super-resolution localization method for high-concentration microbubbles,” in IEEE Ultrasonics Symposium ( IEEE, 2022). to analyze post-activation PCNDs spatiotemporally on a super-resolved level.As illustrated in Fig. 4(a), the architecture of MRDN consists of four components: shallow feature extraction, residual dense block, global feature fusion with residual learning, and pixel shuffle, containing a total of 17 convolutional layers.2929. S. An, M. Qu, A. Huang, H. Yu, Y. Wang, M. Wan, and Y. Zong, “ Modified residual dense network based super-resolution localization method for high-concentration microbubbles,” in IEEE Ultrasonics Symposium ( IEEE, 2022). Considering the PSF of our ultrasound system and the pattern of recondensation signals in the ultrasound image, we designed 20 000 frames of paired data for the training network, with and without Gaussian noise. According to the reasonable number range of recondensation events in each SubFrame under our experimental conditions, the concentration of PCMBs used for training was set to 0.1–3 mm−2.Figure 4(b) shows a typical low-resolution (LR) simulation input generated by the convolution of the PSF of the ultrasound system with the PCMB-mimicking point scatterers and its super-resolved output after the processing of MRDN. When it comes to experimental data, the thresholded SubFrames were imported into the network as input, and the resulting super-resolved output was further thresholded to remove low-intensity, noise-like signals. It should be noted that in the physical sense, the non-zero points in the MRDN output of experimental data were super-resolved demonstrations of signals based on one or more recondensed PCMBs rather than definitive individual PCMBs. Unlike flowing microbubbles,3030. C. Errico, J. Pierre, S. Pezet, Y. Desailly, Z. Lenkei, O. Couture, and M. Tanter, “ Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging,” Nature 527, 499–502 (2015). https://doi.org/10.1038/nature16066 the immobile PCMBs in extreme proximity recondense in a stochastic fashion, which would inevitably generate overlapping or fused signals in the two-dimensional plane of imaging, hence the localization points in subsequent spatiotemporal mapping were super-resolved locations of probable recondensation events. Three metrics—localization error, Jaccard index,2929. S. An, M. Qu, A. Huang, H. Yu, Y. Wang, M. Wan, and Y. Zong, “ Modified residual dense network based super-resolution localization method for high-concentration microbubbles,” in IEEE Ultrasonics Symposium ( IEEE, 2022). and the number of localization events—were calculated to evaluate the localization accuracy, reliability, and capability. As shown in Fig. 4(c), MRDN outperformed the traditional weighted-average localization method for each metric, and the advantage became more evident as the concentration increased, highlighting the spatial-resolving capability of MRDN.Figure 5 shows the results at concentrations of 5 ×107 and 9 ×107 droplets/ml. In super-resolved spatiotemporal mappings at ARNs of 5 and 9 [Figs. 5(a)–5(d)], MRDN-detected locations of probable recondensation events (colored dots) were overlaid on the Maximum Intensity Projection (MIP) of 99 SubFrames. Note that different colors represent the number of frames in which the probable recondensation events appear. To corroborate that the detected probable recondensation events reflect patterns resulting from different concentrations and ARNs rather than varying degrees of initial activation, we compared the preset stepsize [Fig. 5(e)] of consecutive activation pulses in Fig. 1(a) with its counterparts calculated from experimental data. Considering the distribution of a focus-wave pressure field, if we axially sum up the intensity/number of signal points at each lateral coordinate in the focal region of one activation pulse, the maximum should fall on the lateral coordinate corresponding to the theoretical axis of this activation pulse. Accordingly, after the above-mentioned summation, we located the maximum by finding the peak of a local 2D-Gaussian fit. The distance between neighboring maxima was, thus, termed peak–peak distance (PPD) as the counterpart of preset stepsize, see Fig. 5(f). The PPDs calculated from the super-resolved mapping results were found to be more stable in distribution at different concentrations and ARNs than those calculated from low-resolution SubFrames, and their average PPDs were closer to the preset stepsize (0.9 mm), indicating the accuracy of our technique and its viability for further quantitative analysis.Next, we quantified the probable recondensation events spatially and temporally. As shown in Fig. 5(g), the total number of probable recondensation events was sensitive to the concentration and ARN. In addition, the positive correlations embodied here are consistent with the patterns of Contrast#1, Contrast#100, and RR Ratio. Note that groups with ARN = 1 were excluded for the extremely low signal intensity [Fig. 2(c)] caused by insufficient activation, stressing the importance of ARN tuning. Further, we counted the number of probable recondensation events in chosen mapping groups by time in three periods [see Fig. 5(h)]. In line with previous studies7,8,247. A. Ishijima, J. Tanaka, T. Azuma, K. Minamihata, S. Yamaguchi, E. Kobayashi, T. Nagamune, and I. Sakuma, “ The lifetime evaluation of vapourised phase-change nano-droplets,” Ultrasonics 69, 97–105 (2016). https://doi.org/10.1016/j.ultras.2016.04.0028. M. T. Burgess, M. Aliabouzar, C. Aguilar, M. L. Fabiilli, and J. A. Ketterling, “ Slow-flow ultrasound localization microscopy using recondensation of perfluoropentane nanodroplets,” Ultrasound Med. Biol. 48, 743–759 (2022). https://doi.org/10.1016/j.ultrasmedbio.2021.12.00724. N. Reznik, O. Shpak, E. C. Gelderblom, R. Williams, N. De Jong, M. Versluis, and P. N. Burns, “ The efficiency and stability of bubble formation by acoustic vaporization of submicron perfluorocarbon droplets,” Ultrasonics 53, 1368–1376 (2013). https://doi.org/10.1016/j.ultras.2013.04.005 that have observed the diminishing of resondensation events on a longer timescale, the number of probable recondensation events showed an overall tendency to decrease throughout the imaging period, reflecting the temporal-resolving capability of our technique.In summary, a spatiotemporally super-resolved ultrasound monitoring technique was proposed to dig deeper into the potential of theranostic PCNDs as controllable contrast agents beyond vasculature. First, we assumed that the tunable recondensation ratio of our homemade PCNDs could be the bridge to a one-probe-only solution for extravascular monitoring, and the results of the phantom experiment indicate that co-restricted concentration and pulse duration could help create ideal detectability (higher Contrast#1), sparsity, and dominance (RR Ratio > 1) of recondensation signals for accurate extravascular monitoring. Then we tailored a DL-based detection algorithm to recondensation signal patterns as a generalized approach to map concentrated PCNDs on a super-resolved level, which outperformed a widely used conventional localization algorithm in simulation. The final quantitative analysis results calculated from the super-resolved mapping were in good agreement with the patterns of TSA-derived metrics and existing theories on post-activation dynamics of PCNDs, highlighting the feasibility and spatiotemporal sensitivity of our method. In all, this technique may benefit in-depth extravascular monitoring and dose analysis for PCNDs-involved therapies, and we will investigate the in vivo extension of this technique in the future work.
The authors greatly appreciated the financial support from the National Natural Science Foundation of China (No. 32071369), the National Major Scientific Research Instrument Development Special Project (No. 81827801), and the National Natural Science Foundation of China (No. 31771075).
Conflict of Interest
The authors have no conflicts to disclose.
Author Contributions
Anqi Huang: Conceptualization (lead); Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Project administration (lead); Software (lead); Validation (lead); Visualization (lead); Writing – original draft (lead); Writing – review & editing (lead). Shizhe An: Data curation (equal); Formal analysis (equal); Investigation (equal); Software (equal). Yuebo Wang: Investigation (equal). Kangyi Feng: Methodology (supporting). Haiyang Yu: Software (supporting). Zhuonan Chen: Investigation (supporting). Mingxi Wan: Funding acquisition (equal); Resources (equal). Yujin Zong: Funding acquisition (equal); Project administration (equal); Resources (equal); Supervision (equal); Writing – review & editing (equal).
The data that support the findings of this study are available within the article.
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