Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Next Article in Journal
Aberrant Number of Vessels in the Umbilical Cord: What Do We Know?
Previous Article in Journal
Definite Change Since the Formation of the Earth. Reply to Kleber, A. Comment on “Koutsoyiannis, D. Net Isotopic Signature of Atmospheric CO2 Sources and Sinks: No Change Since the Little Ice Age. Sci 2024, 6, 17”
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sci
Volume 6
Issue 4
10.3390/sci6040064
sci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Dynamic Perviousness of Thrombi in Acute Ischemic Stroke Predicts Clinical Outcome after Reperfusion Therapy

by
Gergely Bertalan
*,
Miklos Krepuska
,
Daniel Toth
,
Jawid Madjidyar
,
Patrick Thurner
,
Tilman Schubert
and
Zsolt Kulcsar
*
Department of Neuroradiology, University Hospital Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland
*
Authors to whom correspondence should be addressed.
Sci 2024, 6(4), 64; https://doi.org/10.3390/sci6040064
Submission received: 12 August 2024 / Revised: 3 October 2024 / Accepted: 8 October 2024 / Published: 10 October 2024
(This article belongs to the Section Sports Science and Medicine)
Browse Figures
Versions Notes

Abstract

:
Background: Acute ischemic stroke (AIS) is one of the leading causes of death in the industrialized world and causes a heavy personal and economic burden. Thrombus perviousness, measured with pre-interventional computed tomography (CT), is a relatively new imaging biomarker with the potential to estimate clinical outcome in AIS and optimize therapy. However, reported findings on the relationship between thrombus perviousness and clinical parameters in AIS are conflicting. In this study, we investigated the characteristics of the time-resolved contrast agent uptake in thrombi and the predictive potential for clinical outcomes. Methods: We analyzed 55 AIS patients who underwent pre-interventional CT perfusion and recanalization with mechanical thrombectomy. A thrombus with a visible hyperdense artery sign was segmented in 2D. Thrombus standard perviousness was measured as the mean thrombus attenuation increase (TAI) between CT angiography (CTA) and NCCT. For dynamic perviousness, the time-resolved contrast agent uptake curve (CAU) was derived from a 30-phase CT perfusion (CTP) measurement. The rise time (trise) and the TAI increase rate per second (∆d), as well as the time window for the 10th (tW10), 20th (tW20), and 30th (tW30) percentiles of the CAU peak, were calculated. The standard and dynamic perviousness (trise, ∆d, tW10, tW20, and tW30) were analyzed for their associations with clinical outcomes (3-month mRS) with the Wilcoxon signed rank test. Results: Dynamic perviousness was associated with the clinical outcome. The group mean trise and ∆d for thrombi with good clinical outcomes (mRS ≤ 2) were approximately 20% lower (p = 0.04) and 36% higher (p = 0.02) than those for thrombi with poor outcomes (mRS > 2). The time windows for the 10, 20, and 30% maximum contrast agent concentrations in the thrombus were approximately 40% (p = 0.004), 18% (p = 0.02) and 33% (p = 0.004) lower in thrombi with good outcomes than in thrombi with poor outcomes, respectively. Standard perviousness showed no association with clinical outcome. Conclusion: Dynamic perviousness from perfusion imaging retrieves the CAU characteristics of thrombi with greater resolution detail than standard perviousness. Thrombi with relatively fast contrast agent uptake dynamics are more prone to good clinical outcomes than thrombi with slow uptake dynamics.

1. Introduction

Acute ischemic stroke (AIS) is one of the leading causes of death in the industrialized world and an important reason for long-term disability among people over 60 years of age [1]. Around 77.2 million people worldwide experience an AIS each year; 63.5 million of them have a short or long-term disability and 3.3 million of them do not survive [2] (in Switzerland, AIS affects 16,000 people per year [3]).
The goal of AIS management is fast and complete revascularization [4], which is associated with favorable functional outcomes [5,6]. Although increasingly effective, mechanical thrombectomy (MT) still has its limitations, often based on the physical properties of the thrombus: short and softer thrombi are usually more easily and completely removed [7,8,9], whereas harder and longer clots may lead to failure [10,11,12,13,14]. Should the physical properties of the thrombus be assessable before the intervention, the technical removal concept could be adapted to the expected revascularization difficulty, which could further improve the success rate of MT.
Due to its availability in most emergency departments and fast acquisition times, computed tomography (CT) is considered the standard imaging approach to make time-critical decisions in AIS. However, standard CT in AIS focuses on infarct core and salvageable tissue, vessel occlusion location, and thrombus length, and not much information is provided about the composition and structure prediction of the clot [15]. Based on the “time is brain” concept, the analysis of the structure of an occlusive thrombus should not increase the diagnostic time, allowing patients to receive the best treatment in the shortest time possible. Therefore, comprehensive, time-consuming imaging of thrombi is not possible, and we need to rely on the data provided by the standard scans.
It is expected that the histological (red blood cell (RBC)-rich vs. fibrin-rich) and biomechanical properties (stiffness and porosity) of the thrombus influence the contrast material (CM) uptake during imaging. In turn, it is assumed that these physical parameters also influence recanalization attempts and clinical outcomes [10,11,12,13,14]. Perviousness, measured with CT, quantifies the permeability of the blood clot using information about CM penetration into the clot [16,17,18] and is a promising imaging biomarker to predict clinical parameters in AIS. Several studies have investigated the link between thrombus perviousness and recanalization success or clinical outcome, but the reported findings are conflicting. Increased perviousness was associated with better recanalization and/or better clinical outcomes after intervention [16,17,19,20,21,22,23]. He et al. [24] reported that non-porous thrombi were less pervious and more easily retrieved with MT. Wagas et al. [25] found a significantly increased likelihood for first-pass recanalization in thrombi with high perviousness, while Biglic et al. [26] reported a better response to intravenous tissue plasminogen activators in pervious thrombi. Contrary to these studies, Ye et al. [24], Byun et al. [27], and Bertalan et al. [28] observed no association between perviousness and recanalization or clinical outcome, while Dutra et al. [19] found no association between perviousness and successful recanalization.
The standard method for thrombus perviousness characterization (referred to as standard perviousness in this study) using CT imaging has some physical shortcomings, which may have led to conflicting results. Standard perviousness is measured as the mean thrombus attenuation increase (TAI) between non-contrast CT (NCCT) and CT angiography (CTA) [16,17,18]. It uses only two imaging time points to quantify the perviousness, which can lead to mischaracterization. TAI is determined by how much CM can penetrate the thrombus between NCCT and the CTA acquisition time point. However, CM penetration into the clot is a dynamic process. It can be influenced by many physical factors, such as the porosity, stiffness, shape, or location of the clot, and can show different patterns (e.g., fast uptake with early washout, late uptake with slow washout, no uptake, etc.). Using only two imaging time points and an improperly selected time delay for the CTA acquisition can miss the peak value of the CM concentration by either not permitting sufficient time for the contrast to interact with the occlusive thrombus or by hitting a phase in which the CM is already washed out from the clot. Therefore, standard perviousness can capture either the early, peak, or late phase of the CM uptake, but not all three simultaneously, which can lead to substantial mischaracterization of thrombi.
The overall aim of this paper is twofold: (1) to introduce a methodology for dynamic measurement of thrombus perviousness and (2) to emphasize the pitfalls and shortcomings of standard perviousness. To this end, we derived and characterized the entire time-resolved contrast agent uptake curve (CAU) in thrombi and computed time-dependent parameters of the CAU, such as the rise time or the time window for peak concentration. We refer to these time-dependent markers of the CAU as dynamic perviousness in this study.

2. Methods

2.1. Patients

A retrospective analysis of 475 consecutive AIS patients in our hospital between 2019 and 2022 was performed [28]. Inclusion criteria were: (1) large vessel occlusions of the intracranial internal carotid artery, proximal middle cerebral artery (MCA) up to the proximal M2 segment, and basilar artery; (2) enrollment for reperfusion therapy with MT; (3) the availability of pre-intervention NCCT, CTA, and CTP imaging; (4) relative low motion artifacts on CT images; and (5) no previous contrast agent administration for another imaging procedure. This resulted in 137 patients, from which 65 patients had visible hyperdense artery signs on NCCT. Of these 65 patients, modified Rankin scores 3 months after reperfusion (mRS3months) were available for 55 patients, who were selected for the final analysis. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of the University Hospital Zurich (Date of approval: 2022, protocol code: 2022-00422).

2.2. Imaging

CT was performed on a range of Siemens scanners, including Somatom X.cite, Somatom Definition Flash, Somatom Definition AS+, and Somatom Definition Edge Plus (Siemens, Erlangen, Germany). The clinical protocol consisted of NCCT (120 kV tube voltage, 0.8 mm slice thickness, 512 × 512 image size with 0.4 × 0.4 mm in-plane resolution), a CTA (90 kV tube voltage, 1.0 mm slice thickness, 512 × 512 image size with 0.36 × 0.36 mm in-plane resolution), and a CTP (70 kV tube voltage, 5.0 mm slice thickness, 512 × 512 image size with 0.4 × 0.4 mm in-plane resolution) after intravenous contrast agent injection. The CTP consisted of 30 acquisitions with 1.5 s time increments between the acquisitions. Figure 1 illustrates a representative thrombus with a hyperdense artery sign on NCCT and the corresponding four time points of CTP.

2.3. Image Processing and Statistical Analysis

Postprocessing and statistical analysis were performed with Python 3.16 (Python Software Foundation, Beaverton, OR, USA) and Matlab 2022 (The MathWorks, Inc., Natick, MA, USA). The postprocessing pipeline consisted of five steps: (1) image resampling and registration, (2) thrombus segmentation, (3) computation of standard perviousness (TAIstandard) between CTA and NCCT, (4) determination of the time-resolved contrast agent uptake (CAU) curve from CTP with the corresponding quantitative parameters, and (5) statistical analysis of created segments.
NCCT and CTA were resampled to a uniform 512 × 512 × 240 matrix size with 0.5 × 0.5 × 0.8 mm resolution, and CTA was co-registered to the NCCT volume using dipy (www.dipy.org, accessed on 1 August 2024) [29]. To minimize the influence of patient motion during the acquisition of the CTP on the analysis, the time-resolved CTP images were co-registered to the first image of the CTP series.
The computation of standard perviousness (TAIstandard) is illustrated in Figure 2a. Regions of interest (ROIs) covering the entire thrombus on a 2D transversal slice on NCCT were manually drawn in 3D Slicer (www.slicer.org, accessed on 1 August 2024) [30] by considering the hyperdense artery sign. Segmentation was created by consensus between G.B., D.T., and Z.K. Mean tissue intensity in Hounsfield Units (HU) was computed for the defined segment on NCCT. Mean tissue intensity on CTA was computed by overlaying the same segment on the CTA image that was previously co-registered with NCCT. TAIstandard was computed as the difference in HU between the mean densities on CTA and NCCT.
The computation of the time-dependent markers of dynamic perviousness is illustrated in Figure 2b. ROIs were drawn on a 2D transversal slice of the first CTP acquisition time point by considering the hyperdense artery sign on the image, and the same segmentation was overlaid on the other acquisition time points that were co-registered to the first image. The time-resolved CAU was determined by computing the mean thrombus density within the ROI for each imaging time point of the CTP time series. This resulted in thirty mean values for the range of 0 to 45 s with 1.5 s increments, which were fitted with a polynomial with the order of ten. A self-programmed computer algorithm identified the peak density (HUpeak) and the minimum before HUpeak (HUmin) on the CAU curve. Between HUmin and HUpeak, the TAI for dynamic perviousness (TAIdynamic = HUpeak − HUmin) and the corresponding rise time (trise = tpeak − tmin in s), as well as the CT density increase rate per second (∆d = TAIdynamic/trise in HU/s), were determined. In addition, the time windows for the 10th (tW10), 20th (tW20), and 30th (tW30) percentiles of the concentration peak were calculated.
A good clinical outcome after reperfusion therapy was defined as an mRS below three at 90 days after therapy (good outcome: mRS3months ≤ 2; poor outcome: mRS3months ≥ 3). Patients were divided into two groups: mRS3months ≤ 2 and mRS3months ≥ 3. TAIstandard, TAIdynamic, trise, ∆d, tW10, tW20, and tW30 were analyzed for their association with mRS3months with the two-sided Wilcoxon–Mann–Whitney test in Matlab. For p-values less than 0.05, differences were considered as significant.

3. Results

Figure 3 shows the statistical analysis between perviousness and clinical outcome 3 months after MT. Although not statistically significant, thrombi with good outcomes after reperfusion therapy showed a tendency for higher contrast agent uptake than thrombi with poor outcomes (TAIdynamic: 158 ± 80 HU vs. 118 ± 94 HU with p = 0.07, Figure 3b). Regarding the dynamic parameters of the time-resolved CAU curve, the CAU in thrombi with good clinical outcomes was significantly faster (approximately 20%) than in thrombi with poor clinical outcomes (trise: 16.2 ± 5.8 s vs. 19.8 ± 6.2 s with p = 0.04), while the corresponding attenuation increase rate per second was approximately 36% higher (∆d: 10.8 ± 6.3 HU/s vs. 7.0 ± 6.6 HU/s with p = 0.02), as shown in Figure 3c,d, respectively. Standard perviousness using only two imaging time points (CTA and NCCT) showed no significant association with the clinical outcome (Figure 3a).
Figure 4 shows the analysis of the group mean time windows for the CAU peak value concentration and their associations with the clinical outcome. We measured relatively short time windows for the 10th, 20th, and 30th percentile peak value areas on the CAU. The group mean times of the CAU peak value for the analyzed thrombi were 9 ± 3 s, 12 ± 4 s, and 14 ± 3 s for tW10, tW20, and tW30, respectively (Figure 4). Of particular interest is that the length of the time windows correlated with the clinical outcome. Thrombi with good clinical outcomes had significantly narrower time windows with the CAU peak than thrombi with poor outcomes (tW10: 7 ± 2 s vs. 10 ± 4 s, p = 0.004, Figure 4b; tW20: 11 ± 4 s vs. 13 ± 4 s, p = 0.02, Figure 4c; tW30: 12 ± 2 s vs. 16 ± 4 s, p = 0.004, Figure 4d). In addition, the group mean time-resolved CAU curves between good and poor clinical outcomes were shifted to each other by approximately 5 s, as shown in Figure 4a (blue for mRS ≤ 2 vs. red for mRS ≥ 3).

4. Discussion

In this study, we propose a quantitative method for dynamic thrombus perviousness characterization based on the evaluation of the time-resolved CAU curve. By computing the time-dependent parameters of the CAU derived from a 30-phase CTP, we show that patients with clots demonstrating relatively fast contrast agent uptake dynamics (short trise, large ∆d), as well as with corresponding relatively fast contrast agent washout (short tW10, tW20, tW30), are significantly more prone to good clinical outcomes than the patients with thrombi demonstrating slow uptake dynamics and slow washout. We measured a relatively narrow time window (tW10, tW20, tW30) in which the contrast agent concentration in the thrombus was at its maximum that was even correlated with the clinical outcome. As a consequence of that, we argue that standard perviousness with the use of only two imaging time points (NCCT and CTA) and a constant time delay between contrast injection and CTA acquisition seems to be suboptimal for in-depth thrombus characterization.
Reports on the predictive value of thrombus standard perviousness are conflicting. Regarding perviousness and thrombus composition, Benson et al. [31,32] and Hund et al. [33] found a positive correlation between the perviousness and RBC content, whereas Berndt et al. [34], Patel et al. [35], and Wei et al. [36] found negative correlations. In addition, Ye et al. observed no association between RBC content and perviousness [24]. Santos et al. [16,37] and Berndt et al. [17] reported a positive correlation between perviousness, recanalization rate, and functional outcome. Dutra et al. [19] and Patel et al. [35] observed a correlation between perviousness and functional outcome, while they found no association between perviousness and recanalization. Kappelhof et al. [38] observed better responses to thrombolytic treatment in patients with more pervious clots, but perviousness had no effect on reperfusion success. Ye et al. [24], Byun et al. [27], and Bertalan et al. [28] reported no association between perviousness and recanalization or clinical outcome. The contradictory findings may be attributed, at least partly, to the use of only two imaging time points (NCCT and CTA), which, in the context of sampling a dynamic curve such as the CAU (Figure 2b and Figure 4a), leads to mischaracterization through a signal processing phenomenon known in the literature as “under-sampling”.
In this study, standard perviousness was computed for the entire 2D thrombus area on a transversal slice. In our previous study, standard perviousness was computed for the same population using full 3D volumetric segmentation [28]. We assume that both approaches are more precise than the standard 2D method in the literature, in which three spherical ROIs are placed on the thrombus on a transversal slice [16,17,19,20,21,22,23,24,26,27]. For the population used in this study, standard perviousness using only two imaging time points did not show any association with the clinical outcome. In our previous study, we obtained the same result using a full 3D volumetric evaluation [28]. This finding is in line with Ye et al. [24] and Byun et al. [27] and suggests that standard perviousness is not as strongly associated with clinical parameters as previously reported. Ye et al. [24] analyzed the link between thrombus structural properties, clinical parameters, and perviousness in 55 AIS patients. While they reported a negative correlation between perviousness and platelet fraction, they found no association between perviousness and successful recanalization nor between perviousness and functional outcome. Byun et al. [27] studied the link between several CT imaging markers (including perviousness) and revascularization outcome after MT with stent retrievers in 52 AIS patients using multiphase CTA. They found no association between thrombus imaging characteristics on multiphase CTA (including perviousness) and revascularization.
In contrast to standard perviousness, the rise time between the min–max values of the CAU curve (trise), as well as the uptake rate per second (∆d), were significantly correlated with the clinical outcome 3 months after MT (Figure 3). In addition, thrombi in patients with good outcomes had significantly shorter periods of contrast maximum than poor-outcome samples (Figure 4), indicating that thrombi with fast contrast uptake and corresponding fast washout are predictive of better clinical outcomes than thrombi with slow uptake (Figure 4). The only other publication focusing on the time-resolved CAU curve reported similar results as in this study. Chen et al. [39] used a similar approach to the one presented herein and found a significantly better clinical outcome for pervious thrombi. Our findings are in line with Chen et al. [39]. Using standard perviousness and two imaging time points, several studies have reported better outcomes for pervious thrombi [16,17,19,20,21,22,23,24,26,27]. Our findings are consistent with these observations, although, in the current study, the use of two measurement time points (TAIstandard) was not sufficient to reach this conclusion.
It has been reported that thrombus composition affects the mechanical properties and is an important factor influencing successful reperfusion and corresponding clinical outcome [40,41]. In general, RBC-rich thrombi are softer and are associated with higher reperfusion rates and better clinical outcomes than fibrin-rich thrombi [41,42,43,44], although platelet-driven forces during thrombus maturation can compact RBCs into impermeable layers of polyhedrocytes, which leads to higher overall stiffness [45,46]. Correlating RBC to perviousness produced conflicting results. Benson et al. [31] and Wei et al. [36] found that perviousness was associated with a higher degree of RBC, which suggests that perviousness is inversely correlated with clot stiffness. On the other hand, Berndt et al. [34] and Patel et al. [35] found that permeable thrombi were fibrin-rich, which suggests that perviousness is positively correlated with overall thrombus stiffness. In a previous study, we demonstrated that dynamic perviousness is associated with higher RBC content in the clot [47]. Since, in the literature, soft thrombi have been associated with good clinical outcomes, we speculate that the here-measured fast CAU dynamics of thrombi with good clinical outcomes may be related to low stiffness and high porosity, leading to rapid penetration and washout of the contrast agent.
Our findings illustrate the pitfalls of thrombus standard perviousness using only two imaging time points. We measured a very narrow time window (ca. 9–16 s) in which the contrast agent concentration was at its maximum in the clot. In addition, the maximum contrast concentration between the analyzed parameter groups was shifted in time (Figure 4a), and thrombi with good outcomes had significantly shorter periods of contrast maximum than thrombi with poor outcomes (Figure 4). These results indicate how critical the timing of the CTA acquisition is if we use only two imaging time points. For example, to hit the CAU at a maximum contrast concentration of plus-or-minus 30 percent, the CTA acquisition has to hit a 14 ± 4 s time window (the black group mean curve in Figure 4a and corresponding boxplot in Figure 4c), which is technically very challenging. One of the main motivations for thrombus perviousness characterization is that it is related to the biophysical properties of thrombi, which, in turn, influence recanalization success and clinical outcome. From a biophysical point of view, it is expected that the shape of the time-resolved CAU curve (e.g., peak value, slope, width, etc.) is influenced by the clot composition and physical properties such as porosity. As a consequence of that, if we use only two imaging time points and characterize perviousness as density on CTA minus density on NCCT, the time delay for the CTA should be adjusted to the thrombus structure to match the peak concentration of CAU with the CTA acquisition. Otherwise, we will measure the CAU curve at different phases and introduce a structure-dependent bias into the measurement. This is, however, not possible for two reasons: (1) clot characteristics are unknown a priori and (2) in general, we would like to go exactly the other way around and estimate thrombus structure, corresponding revascularization, and clinical parameters with perviousness. Therefore, based on the findings of this study, standard perviousness using only two imaging time points and a constant time delay for CTA seems to be suboptimal. Our results suggest that the dynamic nature of the time-resolved CAU curve cannot be neglected and that two imaging time points cannot capture the complex CAU characteristics of thrombi adequately.
Although we obtained encouraging results, our study has some limitations. First, because we applied manual segmentation, only patients with visible hyperdense artery signs on NCCT or on the first CTV image phase were included in the analysis, and our findings are, therefore, valid only for this patient group. In the future, it would be of great interest to apply the image analysis method presented herein to a larger AIS data set and to thrombi without visible hyperdense artery signs. Second, since we used a standard clinical protocol for CTP, the slice thickness was relatively large (5 mm), and we could segment the clot on CTP only on a 2D transversal slice. It would be of great interest to test the results presented herein with a smaller slice thickness (e.g., 1 mm) and to use full 3D thrombus segmentation, which could further increase the robustness of the method presented herein. Third, the small sample size (55 patients) and the retrospective study design may limit the generalizability of the findings.

5. Conclusions

Here, we presented a 2D evaluation of thrombus dynamic perviousness in AIS by determining the time-dependent parameters of the time-resolved contrast agent uptake curve in thrombi with relatively large time resolutions. Our results suggest that the dynamic nature of contrast agent uptake in thrombi may play an important role in estimating thrombus perviousness and corresponding clinical outcome, whereas the state-of-the-art measure of thrombus perviousness on single-phase CTA seems to be suboptimal for a precise characterization of occlusive thrombi in AIS.

Author Contributions

Conceptualization, G.B., P.T., T.S. and Z.K.; methodology, G.B. and M.K.; software, G.B.; validation, D.T., J.M., P.T. and Z.K.; formal Analysis, G.B., M.K., D.T. and Z.K.; investigation, G.B., M.K. and D.T.; resources, J.M., P.T. and T.S.; data curation, G.B. and D.T.; writing—original draft preparation, all authors; writing—review and editing, all authors; visualization, G.B.; supervision, Z.K.; project administration, Z.K.; funding acquisition, Z.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Iten-Kohaut-Foundation in Switzerland (www.iten-kohaut-stiftung.ch, accessed on 1 August 2024).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the University Hospital Zurich. Date of approval: 22 April 2022, protocol code: 2022-00422.

Informed Consent Statement

Informed consent was waived by the research ethics committee of the University Hospital Zurich.

Data Availability Statement

Data are available upon reasonable request.

Acknowledgments

The authors acknowledge the financial support of the Iten-Kohaut-Foundation in Switzerland (www.iten-kohaut-stiftung.ch, accessed on 1 August 2024).

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Abbreviations

AIS: acute ischemic stroke; CT, computed tomography; NCCT, non-contrast CT; CTA, CT angiography; CTP, CT perfusion; HU, Hounsfield units; MT, mechanical thrombectomy; TAI, thrombus attenuation increase; TAIstandard, standard perviousness; mRS, modified Rankin score; mRS3months, mRS 3 months after MT; ROI, region of interest; CM, contrast material; CAU, time-resolved contrast agent uptake curve; HUpeak, the max value of the CAU in HU; HUmin, the minimum value of the CAU before the HUpeak; trise, rise time to HUpeak; ∆d, attenuation increase rate of the CAU per second; tW10/20/30, time window for the 10th, 20th, and 30th percentiles of CAU maximum; RBC, red blood cell.

References

  1. Feske, S.K. Ischemic Stroke. Am. J. Med. 2021, 134, 1457–1464. [Google Scholar] [CrossRef] [PubMed]
  2. Feigin, V.L.; Stark, B.A.; Johnson, C.O.; Roth, G.A.; Bisignano, C.; Abady, G.G.; Abbasifard, M.; Abbasi-Kangevari, M.; Abd-Allah, F.; Abedi, V.; et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021, 20, 795–820. [Google Scholar] [CrossRef] [PubMed]
  3. Bundesamt für Statistik, Switzerland, Hirnschlag, 2015–2019, Datentabelle. 2019. Available online: https://ind.obsan.admin.ch/indicator/obsan/hirnschlag (accessed on 1 August 2024).
  4. Palaniswami, M.; Yan, B. Mechanical Thrombectomy Is Now the Gold Standard for Acute Ischemic Stroke: Implications for Routine Clinical Practice. Interv. Neurol. 2015, 4, 18–29. [Google Scholar] [CrossRef] [PubMed]
  5. Bourcier, R.; Goyal, M.; Liebeskind, D.S.; Muir, K.W.; Desal, H.; Siddiqui, A.H.; Dippel, D.W.J.; Majoie, C.B.; van Zwam, W.H.; Jovin, T.G.; et al. Association of Time From Stroke Onset to Groin Puncture With Quality of Reperfusion After Mechanical Thrombectomy: A Meta-analysis of Individual Patient Data from 7 Randomized Clinical Trials. JAMA Neurol. 2019, 76, 405–411. [Google Scholar] [CrossRef]
  6. Tonetti, D.A.; Desai, S.M.; Casillo, S.; Stone, J.; Brown, M.; Jankowitz, B.; Jovin, T.G.; Gross, B.A.; Jadhav, A. Successful reperfusion, rather than number of passes, predicts clinical outcome after mechanical thrombectomy. J. Neurointerv. Surg. 2020, 12, 548–551. [Google Scholar] [CrossRef]
  7. Hashimoto, T.; Hayakawa, M.; Funatsu, N.; Yamagami, H.; Satow, T.; Takahashi, J.C.; Nagatsuka, K.; Ishibashi-Ueda, H.; Kira, J.I.; Toyoda, K. Histopathologic Analysis of Retrieved Thrombi Associated With Successful Reperfusion After Acute Stroke Thrombectomy. Stroke 2016, 47, 3035–3037. [Google Scholar] [CrossRef]
  8. Kaneko, N.; Ghovvati, M.; Komuro, Y.; Guo, L.; Khatibi, K.; Ponce Mejia, L.L.; Saber, H.; Annabi, N.; Tateshima, S. A new aspiration device equipped with a hydro-separator for acute ischemic stroke due to challenging soft and stiff clots. Interv. Neuroradiol. 2022, 28, 43–49. [Google Scholar] [CrossRef]
  9. Staessens, S.; De Meyer, S.F. Thrombus heterogeneity in ischemic stroke. Platelets 2021, 32, 331–339. [Google Scholar] [CrossRef]
  10. Boeckh-Behrens, T.; Schubert, M.; Forschler, A.; Prothmann, S.; Kreiser, K.; Zimmer, C.; Riegger, J.; Bauer, J.; Neff, F.; Kehl, V.; et al. The Impact of Histological Clot Composition in Embolic Stroke. Clin. Neuroradiol. 2016, 26, 189–197. [Google Scholar] [CrossRef]
  11. Douglas, A.; Fitzgerald, S.; Mereuta, O.M.; Rossi, R.; O’Leary, S.; Pandit, A.; McCarthy, R.; Gilvarry, M.; Holmegaard, L.; Abrahamsson, M.; et al. Platelet-rich emboli are associated with von Willebrand factor levels and have poorer revascularization outcomes. J. Neurointerv. Surg. 2020, 12, 557–562. [Google Scholar] [CrossRef]
  12. Ganeshan, R.; Nave, A.H.; Scheitz, J.F.; Schindlbeck, K.A.; Haeusler, K.G.; Nolte, C.H.; Villringer, K.; Fiebach, J.B. Assessment of thrombus length in acute ischemic stroke by post-contrast magnetic resonance angiography. J. Neurointerv. Surg. 2018, 10, 756–760. [Google Scholar] [CrossRef] [PubMed]
  13. Gunning, G.M.; McArdle, K.; Mirza, M.; Duffy, S.; Gilvarry, M.; Brouwer, P.A. Clot friction variation with fibrin content; implications for resistance to thrombectomy. J. Neurointerv. Surg. 2018, 10, 34–38. [Google Scholar] [CrossRef] [PubMed]
  14. Novotny, J.; Oberdieck, P.; Titova, A.; Pelisek, J.; Chandraratne, S.; Nicol, P.; Hapfelmeier, A.; Joner, M.; Maegdefessel, L.; Poppert, H.; et al. Thrombus NET content is associated with clinical outcome in stroke and myocardial infarction. Neurology 2020, 94, e2346–e2360. [Google Scholar] [CrossRef]
  15. Haggenmüller, B.; Kreiser, K.; Sollmann, N.; Huber, M.; Vogele, D.; Schmidt, S.A.; Beer, M.; Schmitz, B.; Ozpeynirci, Y.; Rosskopf, J.; et al. Pictorial Review on Imaging Findings in Cerebral CTP in Patients with Acute Stroke and Its Mimics: A Primer for General Radiologists. Diagnostics 2023, 13, 447. [Google Scholar] [CrossRef]
  16. Santos, E.M.; Marquering, H.A.; den Blanken, M.D.; Berkhemer, O.A.; Boers, A.M.; Yoo, A.J.; Beenen, L.F.; Treurniet, K.M.; Wismans, C.; van Noort, K.; et al. Thrombus Permeability Is Associated With Improved Functional Outcome and Recanalization in Patients With Ischemic Stroke. Stroke 2016, 47, 732–741. [Google Scholar] [CrossRef]
  17. Berndt, M.; Muck, F.; Maegerlein, C.; Wunderlich, S.; Zimmer, C.; Wirth, S.; Monch, S.; Kaesmacher, J.; Friedrich, B.; Boeckh-Behrens, T. Introduction of CTA-index as Simplified Measuring Method for Thrombus Perviousness. Clin. Neuroradiol. 2021, 31, 773–781. [Google Scholar] [CrossRef]
  18. Borggrefe, J.; Kottlors, J.; Mirza, M.; Neuhaus, V.F.; Abdullayev, N.; Maus, V.; Kabbasch, C.; Maintz, D.; Mpotsaris, A. Differentiation of Clot Composition Using Conventional and Dual-Energy Computed Tomography. Clin. Neuroradiol. 2018, 28, 515–522. [Google Scholar] [CrossRef]
  19. Dutra, B.G.; Tolhuisen, M.L.; Alves, H.; Treurniet, K.M.; Kappelhof, M.; Yoo, A.J.; Jansen, I.G.H.; Dippel, D.W.J.; van Zwam, W.H.; van Oostenbrugge, R.J.; et al. Thrombus Imaging Characteristics and Outcomes in Acute Ischemic Stroke Patients Undergoing Endovascular Treatment. Stroke 2019, 50, 2057–2064. [Google Scholar] [CrossRef] [PubMed]
  20. Santos, E.M.M.; Arrarte Terreros, N.; Kappelhof, M.; Borst, J.; Boers, A.M.M.; Lingsma, H.F.; Berkhemer, O.A.; Dippel, D.W.J.; Majoie, C.B.; Marquering, H.A.; et al. Associations of thrombus perviousness derived from entire thrombus segmentation with functional outcome in patients with acute ischemic stroke. J. Biomech. 2021, 128, 110700. [Google Scholar] [CrossRef]
  21. Santos, E.M.M.; Dankbaar, J.W.; Treurniet, K.M.; Horsch, A.D.; Roos, Y.B.; Kappelle, L.J.; Niessen, W.J.; Majoie, C.B.; Velthuis, B.; Marquering, H.A.; et al. Permeable Thrombi Are Associated With Higher Intravenous Recombinant Tissue-Type Plasminogen Activator Treatment Success in Patients With Acute Ischemic Stroke. Stroke 2016, 47, 2058–2065. [Google Scholar] [CrossRef]
  22. Borst, J.; Berkhemer, O.A.; Santos, E.M.M.; Yoo, A.J.; den Blanken, M.; Roos, Y.B.W.E.M.; van Bavel, E.; van Zwam, W.H.; van Oostenbrugge, R.J.; Lingsma, H.F.; et al. Value of Thrombus CT Characteristics in Patients with Acute Ischemic Stroke. Am. J. Neuroradiol. 2017, 38, 1758–1764. [Google Scholar] [CrossRef] [PubMed]
  23. Mokin, M.; Waqas, M.; Fifi, J.; De Leacy, R.; Fiorella, D.; Levy, E.I.; Snyder, K.; Hanel, R.; Woodward, K.; Chaudry, I.; et al. Clot perviousness is associated with first pass success of aspiration thrombectomy in the COMPASS trial. J. Neurointerv. Surg. 2021, 13, 509–514. [Google Scholar] [CrossRef]
  24. Ye, G.; Cao, R.; Lu, J.; Qi, P.; Hu, S.; Chen, K.; Tan, T.; Chen, J.; Wang, D. Histological composition behind CT-based thrombus density and perviousness in acute ischemic stroke. Clin. Neurol. Neurosurg. 2021, 207, 106804. [Google Scholar] [CrossRef]
  25. Waqas, M.; Li, W.Z.; Patel, T.R.; Chin, F.; Tutino, V.M.; Dossani, R.H.; Ren, Z.G.; Guerrero, W.R.; Borlongan, C.V.; Pressman, E.; et al. Clot imaging characteristics predict first pass effect of aspiration-first approach to thrombectomy. Interv. Neuroradiol. 2022, 28, 152–159. [Google Scholar] [CrossRef] [PubMed]
  26. Bilgic, A.B.; Gocmen, R.; Arsava, E.M.; Topcuoglu, M.A. The Effect of Clot Volume and Permeability on Response to Intravenous Tissue Plasminogen Activator in Acute Ischemic Stroke. J. Stroke Cerebrovasc. Dis. 2020, 29, 104541. [Google Scholar] [CrossRef] [PubMed]
  27. Byun, J.S.; Nicholson, P.; Hilditch, C.A.; Chun On Tsang, A.; Mendes Pereira, V.; Krings, T.; Fang, Y.; Brinjikji, W. Thrombus perviousness is not associated with first-pass revascularization using stent retrievers. Interv. Neuroradiol. 2019, 25, 285–290. [Google Scholar] [CrossRef]
  28. Bertalan, G.; Duparc, R.; Krepuska, M.; Toth, D.; Madjidyar, J.; Thurner, P.; Schubert, T.; Kulcsar, Z. Dynamic Perviousness Predicts Revascularization Success in Acute Ischemic Stroke. Diagnostics 2024, 14, 535. [Google Scholar] [CrossRef] [PubMed]
  29. Garyfallidis, E.; Brett, M.; Amirbekian, B.; Rokem, A.; van der Walt, S.; Descoteaux, M.; Nimmo-Smith, I.; Contributors, D. Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinform. 2014, 8, 8. [Google Scholar] [CrossRef]
  30. Fedorov, A.; Beichel, R.; Kalpathy-Cramer, J.; Finet, J.; Fillion-Robin, J.C.; Pujol, S.; Bauer, C.; Jennings, D.; Fennessy, F.; Sonka, M.; et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging 2012, 30, 1323–1341. [Google Scholar] [CrossRef]
  31. Benson, J.C.; Fitzgerald, S.T.; Kadirvel, R.; Johnson, C.; Dai, D.Y.; Karen, D.; Kallmes, D.F.; Brinjikji, W. Clot permeability and histopathology: Is a clot’s perviousness on CT imaging correlated with its histologic composition? J. Neurointerv. Surg. 2020, 12, 38–42. [Google Scholar] [CrossRef]
  32. Benson, J.C.; Brinjikji, W.; Messina, S.A.; Lanzino, G.; Kallmes, D.F. Cervical internal carotid artery tortuosity: A morphologic analysis of patients with acute ischemic stroke. Interv. Neuroradiol. 2020, 26, 216–221. [Google Scholar] [CrossRef]
  33. Hund, H.; Boodt, N.; Arrarte Terreros, N.; Taha, A.; Marquering, H.A.; van Es, A.; Bokkers, R.P.H.; Lycklama, A.N.G.J.; Majoie, C.; Dippel, D.W.J.; et al. Quantitative thrombus characteristics on thin-slice computed tomography improve prediction of thrombus histopathology: Results of the MR CLEAN Registry. Eur. Radiol. 2022, 32, 7811–7823. [Google Scholar] [CrossRef] [PubMed]
  34. Berndt, M.; Friedrich, B.; Maegerlein, C.; Moench, S.; Hedderich, D.; Lehm, M.; Zimmer, C.; Straeter, A.; Poppert, H.; Wunderlich, S.; et al. Thrombus Permeability in Admission Computed Tomographic Imaging Indicates Stroke Pathogenesis Based on Thrombus Histology. Stroke 2018, 49, 2674–2682. [Google Scholar] [CrossRef]
  35. Patel, T.R.; Fricano, S.; Waqas, M.; Tso, M.; Dmytriw, A.A.; Mokin, M.; Kolega, J.; Tomaszewski, J.; Levy, E.I.; Davies, J.M.; et al. Increased Perviousness on CT for Acute Ischemic Stroke is Associated with Fibrin/Platelet-Rich Clots. Am. J. Neuroradiol. 2021, 42, 57–64. [Google Scholar] [CrossRef] [PubMed]
  36. Wei, L.M.; Zhu, Y.Q.; Deng, J.S.; Li, Y.H.; Li, M.H.; Lu, H.T.; Zhao, Y.W. Visualization of Thrombus Enhancement on Thin-Slab Maximum Intensity Projection of CT Angiography: An Imaging Sign for Predicting Stroke Source and Thrombus Compositions. Radiology 2021, 298, 374–381. [Google Scholar] [CrossRef] [PubMed]
  37. Santos, E.M.M.; d’Esterre, C.D.; Treurniet, K.M.; Niessen, W.J.; Najm, M.; Goyal, M.; Demchuk, A.M.; Majoie, C.B.; Menon, B.K.; Marquering, H.A.; et al. Added value of multiphase CTA imaging for thrombus perviousness assessment. Neuroradiology 2018, 60, 71–79. [Google Scholar] [CrossRef]
  38. Kappelhof, M.; Tolhuisen, M.L.; Treurniet, K.M.; Dutra, B.G.; Alves, H.; Zhang, G.; Brown, S.; Muir, K.W.; Davalos, A.; Roos, Y.; et al. Endovascular Treatment Effect Diminishes With Increasing Thrombus Perviousness: Pooled Data From 7 Trials on Acute Ischemic Stroke. Stroke 2021, 52, 3633–3641. [Google Scholar] [CrossRef]
  39. Chen, Z.; Shi, F.; Gong, X.; Zhang, R.; Zhong, W.; Zhang, R.; Zhou, Y.; Lou, M. Thrombus Permeability on Dynamic CTA Predicts Good Outcome after Reperfusion Therapy. AJNR Am. J. Neuroradiol. 2018, 39, 1854–1859. [Google Scholar] [CrossRef]
  40. Murphy, A.; Symons, S.P.; Hopyan, J.; Aviv, R.I. Factors Influencing Clinically Meaningful Recanalization after IV-rtPA in Acute Ischemic Stroke. Am. J. Neuroradiol. 2013, 34, 146–152. [Google Scholar] [CrossRef]
  41. Yuki, I.; Kan, I.; Vinters, H.V.; Kim, R.H.; Golshan, A.; Vinuela, F.A.; Sayre, J.W.; Murayanna, Y.; Vinuela, F. The Impact of Thromboemboli Histology on the Performance of a Mechanical Thrombectomy Device. Am. J. Neuroradiol. 2012, 33, 643–648. [Google Scholar] [CrossRef]
  42. Choi, M.H.; Park, G.H.; Lee, J.S.; Lee, S.E.; Lee, S.J.; Kim, J.H.; Hong, J.M. Erythrocyte Fraction Within Retrieved Thrombi Contributes to Thrombolytic Response in Acute Ischemic Stroke. Stroke 2018, 49, 652–659. [Google Scholar] [CrossRef] [PubMed]
  43. Moftakhar, P.; English, J.D.; Cooke, D.L.; Kim, W.T.; Stout, C.; Smith, W.S.; Dowd, C.F.; Higashida, R.T.; Halbach, V.V.; Hetts, S.W. Density of thrombus on admission CT predicts revascularization efficacy in large vessel occlusion acute ischemic stroke. Stroke 2013, 44, 243–245. [Google Scholar] [CrossRef] [PubMed]
  44. Vidmar, J.; Sersa, I.; Kralj, E.; Popovic, P. Unsuccessful percutaneous mechanical thrombectomy in fibrin-rich high-risk pulmonary thromboembolism. Thromb. J. 2015, 13, 30. [Google Scholar] [CrossRef] [PubMed]
  45. Boodt, N.; Snouckaert van Schauburg, P.R.W.; Hund, H.M.; Fereidoonnezhad, B.; McGarry, J.P.; Akyildiz, A.C.; van Es, A.; De Meyer, S.F.; Dippel, D.W.J.; Lingsma, H.F.; et al. Mechanical Characterization of Thrombi Retrieved With Endovascular Thrombectomy in Patients With Acute Ischemic Stroke. Stroke 2021, 52, 2510–2517. [Google Scholar] [CrossRef]
  46. Ciasca, G.; Papi, M.; Di Claudio, S.; Chiarpotto, M.; Palmieri, V.; Maulucci, G.; Nocca, G.; Rossi, C.; De Spirito, M. Mapping viscoelastic properties of healthy and pathological red blood cells at the nanoscale level. Nanoscale 2015, 7, 17030–17037. [Google Scholar] [CrossRef]
  47. Anagnostakou, V.; Toth, D.; Bertalan, G.; Müller, S.; Reimann, R.R.; Epshtein, M.; Madjidyar, J.; Thurner, P.; Schubert, T.; Wegener, S.; et al. Dynamic Perviousness Has Predictive Value for Clot Fibrin Content in Acute Ischemic Stroke. Diagnostics 2024, 14, 1387. [Google Scholar] [CrossRef]
Sci 06 00064 g001
Figure 1. Representative non-contrast CT (NCCT) and CT perfusion (CTP) images of our clinical AIS protocol with corresponding segmentation in 2D.
Figure 1. Representative non-contrast CT (NCCT) and CT perfusion (CTP) images of our clinical AIS protocol with corresponding segmentation in 2D.
Sci 06 00064 g001
Sci 06 00064 g002
Figure 2. The computation of (a) standard and (b) dynamic perviousness. Region of interests (ROIs) covering the entire thrombus on a 2D transversal slice were manually drawn in 3D Slicer (www.slicer.org, accessed on 1 August 2024) [30] by considering the hyperdense artery sign. The time-resolved contrast agent uptake (CAU) curve in (b) was determined by computing the mean thrombus density within the ROI for each imaging time point of the CTP time series.
Figure 2. The computation of (a) standard and (b) dynamic perviousness. Region of interests (ROIs) covering the entire thrombus on a 2D transversal slice were manually drawn in 3D Slicer (www.slicer.org, accessed on 1 August 2024) [30] by considering the hyperdense artery sign. The time-resolved contrast agent uptake (CAU) curve in (b) was determined by computing the mean thrombus density within the ROI for each imaging time point of the CTP time series.
Sci 06 00064 g002
Sci 06 00064 g003
Figure 3. Statistical analysis of standard perviousness (a) and dynamic perviousness (b–d). (a) Standard perviousness, using only two imaging time points (CTA and NCCT), showed no significant association with the clinical outcome. (b) Thrombi with good outcomes after reperfusion therapy showed a tendency for higher contrast agent uptake than thrombi with poor outcomes (p = 0.07). (c,d) CAU in thrombi with good clinical outcomes was approximately 20% faster (p = 0.04) than in thrombi with poor clinical outcomes (c), while the corresponding attenuation increase rate per second (d) was approximately 36% higher (p = 0.02). p-values were computed with the two-sided Wilcoxon–Mann–Whitney test in Matlab.
Figure 3. Statistical analysis of standard perviousness (a) and dynamic perviousness (b–d). (a) Standard perviousness, using only two imaging time points (CTA and NCCT), showed no significant association with the clinical outcome. (b) Thrombi with good outcomes after reperfusion therapy showed a tendency for higher contrast agent uptake than thrombi with poor outcomes (p = 0.07). (c,d) CAU in thrombi with good clinical outcomes was approximately 20% faster (p = 0.04) than in thrombi with poor clinical outcomes (c), while the corresponding attenuation increase rate per second (d) was approximately 36% higher (p = 0.02). p-values were computed with the two-sided Wilcoxon–Mann–Whitney test in Matlab.
Sci 06 00064 g003
Sci 06 00064 g004
Figure 4. Statistical analysis of the group mean time windows for the CAU peak value concentration and their associations with the clinical outcome. (a) Group mean CAU curve and corresponding tW10 (red dots), tW20 (blue dots), and tW30 (yellow dots) for mRs ≤ 2, mRs ≥ 3 and mean (including all patients), respectively. The group mean times were 9 ± 3 s, 12 ± 4 s, and 14 ± 3 s for tW10, tW20, and tW30, respectively. Thrombi with good clinical outcomes had significantly narrower time windows with the CAU peak than thrombi with poor outcomes (tW10: 7 ± 2 s vs. 10 ± 4 s, p = 0.004, tW20: 11 ± 4 s vs. 13 ± 4 s, p = 0.02, tW30: 12 ± 2 s vs. 16 ± 4 s, p = 0.004, b, c, d, respectively). p-values between mRS ≤ 2 and mRS ≤ 3 were computed with the two-sided Wilcoxon–Mann–Whitney test in Matlab.
Figure 4. Statistical analysis of the group mean time windows for the CAU peak value concentration and their associations with the clinical outcome. (a) Group mean CAU curve and corresponding tW10 (red dots), tW20 (blue dots), and tW30 (yellow dots) for mRs ≤ 2, mRs ≥ 3 and mean (including all patients), respectively. The group mean times were 9 ± 3 s, 12 ± 4 s, and 14 ± 3 s for tW10, tW20, and tW30, respectively. Thrombi with good clinical outcomes had significantly narrower time windows with the CAU peak than thrombi with poor outcomes (tW10: 7 ± 2 s vs. 10 ± 4 s, p = 0.004, tW20: 11 ± 4 s vs. 13 ± 4 s, p = 0.02, tW30: 12 ± 2 s vs. 16 ± 4 s, p = 0.004, b, c, d, respectively). p-values between mRS ≤ 2 and mRS ≤ 3 were computed with the two-sided Wilcoxon–Mann–Whitney test in Matlab.
Sci 06 00064 g004
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bertalan, G.; Krepuska, M.; Toth, D.; Madjidyar, J.; Thurner, P.; Schubert, T.; Kulcsar, Z. Dynamic Perviousness of Thrombi in Acute Ischemic Stroke Predicts Clinical Outcome after Reperfusion Therapy. Sci 2024, 6, 64. https://doi.org/10.3390/sci6040064

AMA Style

Bertalan G, Krepuska M, Toth D, Madjidyar J, Thurner P, Schubert T, Kulcsar Z. Dynamic Perviousness of Thrombi in Acute Ischemic Stroke Predicts Clinical Outcome after Reperfusion Therapy. Sci. 2024; 6(4):64. https://doi.org/10.3390/sci6040064

Chicago/Turabian Style

Bertalan, Gergely, Miklos Krepuska, Daniel Toth, Jawid Madjidyar, Patrick Thurner, Tilman Schubert, and Zsolt Kulcsar. 2024. "Dynamic Perviousness of Thrombi in Acute Ischemic Stroke Predicts Clinical Outcome after Reperfusion Therapy" Sci 6, no. 4: 64. https://doi.org/10.3390/sci6040064

Article Metrics

Back to TopTop