Non-Contrast Ultrasound Image Analysis for Spatial and Temporal Distribution of Blood Flow After Spinal Cord Injury

doi:10.21203/rs.3.rs-3363873/v1

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Non-Contrast Ultrasound Image Analysis for Spatial and Temporal Distribution of Blood Flow After Spinal Cord Injury

https://doi.org/10.21203/rs.3.rs-3363873/v1

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published 06 Jan, 2024

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Ultrasound technology can provide high-resolution imaging of blood flow following spinal cord injury (SCI). Blood flow imaging may improve critical care management of SCI, yet is limited clinically by the invasive nature of contrast agent injection required for high-resolution, continuous monitoring. In this study, we aim to establish non-contrast ultrasound as a clinically translatable imaging technique for spinal cord blood flow via comparison to contrast-based methods and by measuring the spatial distribution of blood flow after SCI. A rodent model of contusion SCI at the T12 spinal level was carried out using three different impact forces. We compared images of spinal cord blood flow taken using both non-contrast and contrast-enhanced ultrasound. Subsequently, we processed the images as a function of distance from injury, yielding the distribution of blood flow through space after SCI, and found the following. (1) Both non-contrast and contrast-enhanced imaging methods resulted in similar blood flow distributions (Spearman’s ρ = 0.55, p < 0.0001). (2) We found an area of decreased flow at the injury epicenter, or umbra (p < 0.0001). Unexpectedly, we found increased flow at the periphery, or penumbra (rostral, p < 0.05; caudal, p < 0.01), following SCI. However, distal flow remained unchanged, in what is presumably unaffected tissue. (3) Finally, tracking blood flow in the injury zones over time revealed interesting dynamic changes. After an initial decrease, blood flow in the penumbra increased during the first 10 minutes after injury, while blood flow in the umbra and distal tissue remained constant over time. These results demonstrate the viability of non-contrast ultrasound as a clinical monitoring tool. Furthermore, our surprising observations of increased flow in the injury periphery pose interesting new questions about how the spinal cord vasculature reacts to SCI, with potentially increased significance of the penumbra.

Health sciences/Neurology/Neurological disorders/Spinal cord diseases

Physical sciences/Engineering/Biomedical engineering

Biological sciences/Biological techniques/Imaging/Ultrasound

Biological sciences/Physiology/Circulation/Blood flow

Traumatic spinal cord injury (SCI) significantly reduces patient quality of life. Following the initial impact, a secondary phase develops consisting of inflammation, swelling, and ischemia as the body responds to the primary insult.[1]–[6] This phase develops over approximately one week, and patient management during this first week is critical to mitigate the severity of damage.[4], [5] Clinical guidelines currently recommend maintaining mean arterial pressure at 85–90 mmHg to counter ischemia in the cord.[4], [5], [7], [8] However, this standard, on its own, is not enough, as it was developed based on small clinical series without measuring blood flow at the site of injury and does not account for patient and injury heterogeneity.[9] Several studies have measured blood flow in the spinal cord after injury and have noted an area of low to no perfusion at the umbra, or injury epicenter.[10]–[15] In these studies the penumbra, or periphery, has mostly been shown to have decreased perfusion compared to baseline, but with lesser magnitude change compared to the umbra. Some older studies, on the other hand, have noted some areas of increased blood flow after SCI, but it is unclear whether this is due to variability in injury severity, blood pressure, or measurement technique.[16] In human injuries, blood flow is more variable, and hyperperfusion has also been noted after SCI.[17] Despite attempts to establish patterns of blood flow after SCI, the importance of the different zones around the injury to patient neurological outcomes remains unclear. To better characterize these zones, we set out to establish a technique for continuous blood flow monitoring after SCI, with the ultimate goal of clinical monitoring in the intensive care unit.

Ultrasound offers a potential solution to monitor blood flow in the spinal cord at the site of injury. Ultrasound of the spinal cord is traditionally limited by the vertebrae; however, the decompressive laminectomy performed after SCI removes the bony elements compressing the cord and allows for imaging.[1], [4] Both contrast-enhanced and non-contrast ultrasound have been used to measure spinal cord blood flow. Non-contrast ultrasound uses Doppler-based methods to detect movement of blood within vessels.[18] Contrast-based methods rely on intravascular injection of microbubble contrast, which can be detected using contrast harmonic imaging modalities. The microbubble signal can be measured through time, and blood flow can be quantified through a variety of techniques.[19]–[21] Specifically, more recent work has used ultrasound localization microscopy to obtain extremely high-resolution images of rat spinal cord blood flow after injury.[22] This technique relies on high frame rate recordings, which can localize individual microbubbles in blood vessels and reconstruct velocity fields from their movement paths.

Non-contrast methods have been mostly forgone in favor of contrast-enhanced imaging in measurement of spinal cord blood flow. Traditionally, non-contrast methods are only able to detect flow in blood vessels exceeding a certain size and flow velocity (i.e., arteries and arterioles), and motion artifact can render it difficult to detect small vessels with low flow velocities.[18], [23] Consequently, non-contrast methods are unable to measure capillary perfusion, which represents how much blood is effectively delivered to the tissue.[10] Additionally, non-contrast ultrasound is limited to measuring flow through vessels within the imaging plane.[18] This poses a challenge as capillaries within the plane may receive blood from a larger vessel not in the plane. Contrast-based methods can detect tissue perfusion through these capillaries in addition to the larger vessels, accurately reflecting the amount of blood being delivered to tissue.[24] Non-contrast methods, however, would miss the out-of-plane vessel, resulting in a deceptively low measure of blood flow despite adequate capillary perfusion. Given these challenges, non-contrast ultrasound has been underutilized, and further exploration of its strengths is warranted.

Contrast-enhanced imaging, however, does have its own limitations. The clinical use of contrast-based techniques for patient monitoring is limited by the need for intravenous infusion of microbubbles. Although most studies have shown microbubble contrast to be relatively safe, adverse events can occur, such as allergic reactions and anaphylaxis.[25]–[27] Furthermore, microbubbles are usually administered via bolus dosing, and there are likely to be further contraindications to continuous infusion, a key requirement for long-term monitoring of patients with SCI. Non-contrast ultrasound does not require any infusion. Moreover, new non-contrast techniques, such as Superb Microvascular Imaging (SMI)[23] and functional ultrasound,[28] in combination with high-frequency ultrasound transducers, have overcome the barrier of measuring flow in small vessels with much lower flow velocity than was previously possible. Additionally, the anatomy of the spinal cord is particularly suitable for non-contrast imaging. The main perfusing vessels are the anterior spinal artery and its off-shooting sulcal arteries.[29], [30] All these vessels lie within the mid-sagittal plane of the cord and can be captured using a single, carefully positioned imaging plane (Fig. 1B),[31], [32] reducing the potential effects from out-of-plane vessels mentioned above.

Therefore, in order to improve the clinical utility of ultrasound-based monitoring of spinal cord perfusion, we seek to establish non-contrast ultrasound as a translatable blood flow imaging modality for patients with SCI. To accomplish this, we will first demonstrate functional similarity of non-contrast ultrasound to contrast methods in the spinal cord by comparing post-injury images taken with each modality in the same imaging plane. We expect there to be a good correlation between non-contrast and contrast methods. As a result, once validated, non-contrast methods will present a viable alternative for blood flow measurement in SCI. Furthermore, we hypothesize that non-contrast imaging will show similar blood flow patterns to the reported literature. To verify this, we will analyze ultrasound images obtained without contrast and show their biological relevance by demonstrating the spatial extent of injury at different severities. We expect these results to generally agree with the literature, showing a decrease in flow at the injury center. Finally, we will demonstrate the ability of non-contrast ultrasound to monitor blood flow changes over time with high temporal resolution. We expect that monitoring these patterns over time will reveal dynamic changes to spinal cord blood flow within and beyond the primary injury zone.

2.1. Animals

All animal experiments were conducted in compliance with the National Institutes of Health guide for the care and use of laboratory animals (NIH Publications No. 8023, revised 1978). The experimental protocol was approved by the Johns Hopkins University Animal Care and Use Committee. All methods were performed following the ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments). A total of 29 female Sprague-Dawley rats (11 weeks, Charles River Laboratories, Wilmington, MA, USA) were used for this study. The choice of sex and age were based on standard models found in the literature.[33] All rats underwent a T11-T13 laminectomy to expose the spinal cord. For the injury severity experiment, 24 rats underwent recordings before injury and at 15 minutes post-injury using 5-second video recordings to capture multiple cardiac cycles. They received injuries of 100 kDyn (mild, n = 10), 175 kDyn (moderate, n = 5), or 250 kDyn (severe, n = 9). Seven of the injury severity rats additionally underwent contrast harmonic imaging with contrast injection for the contrast comparison experiment (see Section 2.4). Continuous monitoring was performed on five additional rats as follows. In this experiment, pre-injury recording was performed continuously for 15 minutes, and post-injury recording lasted 30 minutes. Rats that underwent continuous monitoring were all injured at 250 kDyn (severe). This information is summarized in Supplementary Table 1.

2.2. Laminectomy and spinal cord impact

After induction of anesthesia with 3% isoflurane in room air, rats were maintained at 1.8-2% isoflurane. The inhaled concentration was titrated based on animal physiologic monitoring, including through respiratory rate and paw-pinch reflex. The T11-T13 vertebrae (Fig. 1A) were identified by palpation of the last floating rib. These levels were chosen due to their large size, enabling a three-level laminectomy to span the maximal possible tissue, maximizing the spatial distribution available for hemodynamic study. An incision was made above the chosen vertebrae, and the paraspinal muscles were cut away from the T10-L1 vertebrae. The T10 and L1 spinous processes were clamped using a stereotaxic frame with spine clamps (David Kopf Instruments, Tujunga, CA, USA). Light traction was applied to position the spine with minimal lateral curvature. Bone cutters[15] were used to cut the lamina and expose the spinal cord. After pre-injury imaging, the spinal cord was contused using an Infinite Horizons IH-0400 spinal cord impactor (PSI Impactors, Fairfax Station, VA, USA) at the level of T12.

2.3. Non-contrast imaging

Imaging was performed using an Aplio i800 ultrasound machine with an i22LH8 probe (Canon USA, Melville, USA). Blood flow was visualized using SMI (Doppler frequency 12 MHz, 39 fps), due to the ability to accurately detect low flow vessels. The probe was positioned over the midsagittal plane of the spinal cord (Fig. 1B) using a 3D-printed probe holder, a ball-and-socket joint, and the stereotaxic frame. Small translational and rotational adjustments were made until the vasculature through the entire length of the exposed spinal cord was clearly visible. Pre-injury imaging was performed (Fig. 1C) and the stereotaxic coordinates were noted. The stereotaxic arm was rotated away from the cord during impact and returned to the exact same coordinates for post-injury imaging (Fig. 1D). Velocity maps were extracted from the images using a color-to-velocity index transform.[34] For the contrast comparison and injury severity experiments, each 5-second duration velocity map was averaged over time to obtain a single image for each pre- and post-injury recording.

2.4. Contrast-enhanced recording

After performing the non-contrast imaging described above, rats allocated for contrast-based imaging underwent injection of a 400 µL bolus of Lumason microbubble contrast (Bracco, Milan, Italy) through a tail vein catheter. The spinal cord was recorded using contrast harmonic imaging for 150 seconds. This imaging was performed immediately after the SMI pre-injury recording with no change in the probe or spine position to enable accurate comparison of the modalities. After a 30-minute washout, SCI was induced as before and recording with each modality was performed again. Time between contrast-enhanced and non-contrast recordings was 1–2 minutes, during which any change in blood flow was assumed to be minimal.

2.5. Processing contrast-enhanced images

To generate the contrast-enhanced image (Fig. 2), the value of each pixel in the contrast harmonic recording (Fig. 2A) was plotted over time. The signal was processed using a simple moving average filter 32 samples in length for noise reduction. The filtered signal, starting from the time of maximum signal intensity, was then fitted to a first order exponential decay model. The magnitude of the decay constant of the model (Fig. 2B) was used as the surrogate for flow with units of 1/s.[21] The higher the decay constant magnitude, the faster the contrast agent is cleared from the cord, indicating greater perfusion. This parameter was chosen as opposed to peak height or other parameters as it is more robust to error introduced by differences in the contrast bolus amount.[19]–[21] Certain pixels did not receive any microbubble signal and thus were assigned a value of 0 1/s. These pixels were chosen as those that did not reach a maximum intensity value of 0.015 AU or that did not achieve peak flow until 75 s into recording.

The resulting images contained speckle noise, potentially due to occasional clumps of microbubbles detected in the tissue, artificially increasing the peak intensity in the first-order model (Fig. 2B) and resulting in an elevated decay constant. To counteract this, the images were processed using a morphological opening operator with the MATLAB offset disk-shaped structuring element (5-pixel radius, 0.05 1/s height), yielding the final image (Fig. 2C).

2.6. Pixelwise processing to obtain the spatial distribution

Both the contrast and non-contrast images were processed using pixelwise analysis to yield the spatial distribution. This analysis was inspired by Soubeyrand et al,[14] with higher spatial resolution and the addition of distance as a numeric variable. The 0.1 mm wide distance bins resulted in ~ 200 regions of interest, compared to the 7 previously reported.[14] In this pipeline (Fig. 3A), injuries were labeled as a line perpendicular to the long axis of the spinal cord. Additionally, the upper and lower borders of the spinal cord were drawn manually to exclude any signal from the anterior and posterior spinal arteries. The minimum Euclidean distance was then calculated from each pixel in the images to the injury line. Pixels in the rostral portion of the image (left of injury) were chosen to have negative distance and pixels in the caudal portion (right of injury) were chosen to have positive distance.

Pixels were then binned according to distance into bins 0.1 mm wide and the average area-adjusted flow parameter was calculated for each image. The flow parameter was velocity index (arbitrary units, AU) for the SMI images and decay constant (1/s) for the contrast-enhanced images. The area-adjusted parameter ($I$, Eq. 1) was introduced by Scholbach and Scholbach[35] and represents the product of average pixel brightness ($v$) with fractional area of colored pixels (${A}_{px}$) over total bin area (${A}_{ROI}$). We have found this parameter to be more sensitive to small changes in blood flow when compared to pixel brightness alone. It also includes normalization of the flow parameter to the bin area, which was necessary as not all distance bins had the same total number of pixels. Plotting this parameter against distance yielded the spatial distribution of blood flow (Fig. 3B).

$$I=v*\frac{{A}_{px}\left[c{m}^{2}\right]}{{A}_{ROI}\left[c{m}^{2}\right]}$$

Equation 1

2.7. Labeling of injury zones

We used the output of the pixelwise analysis of non-contrast images to determine locations of different zones of injury. The MATLAB findpeaks function was used on the post-injury spatial distributions with the minimum peak prominence parameter set at 0.03 AU. The two closest peaks on either side of 0 mm distance were chosen as the rostral and caudal penumbras. The distance where minimum flow between the two peaks was observed was labeled as the injury epicenter, or umbra. If the minimum flow was detected at more than one distance bin, the average distance was used to label the epicenter. This epicenter value did not always match the manual injury label, perhaps due to image analysis revealing details not visible to the human eye. Distal (presumably unaffected) regions were then labeled as all points rostral to the rostral penumbra and caudal to the caudal penumbra. Single points were chosen for the umbra and penumbras to emphasize the differences in flow, but it may be advantageous in different applications to define these points as regions as well. Finally, penumbra prominence was defined as the difference in blood flow between each penumbra and the umbra.

Blood flow for each injury zone was then calculated as the average area-adjusted velocity index at the locations corresponding to that zone. Finally, we found the injury rostral-caudal extent as the distance between the two half-maximum points on either side of the umbra. These points are defined by the location at which the flow is equal to the average between the penumbra and umbra values. Pre-injury parameters (umbra and penumbra blood flow, penumbra prominence) were similarly determined as stated above from the pre-injury distribution using the locations from the post-injury distribution.

2.8. Flow over time

To obtain recordings of blood flow over time, the extended-length (15 min pre-injury, 30 min post-injury) continuous recordings were obtained as described by continuous monitoring in Section 2.1. Instead of taking the average velocity map over time, they were analyzed frame-by-frame using the analysis described in Sections 2.6 and 2.7. The locations of the umbra and penumbra occasionally had jump discontinuities in time due to errors in their labeled position. Since biologically, the injury regions would not be expected to exhibit these types of discontinuities, their locations were median filtered with a filter size of 40 frames. Each flow parameter could then be plotted against time to see its evolution.

2.9. Statistical analysis

GraphPad Prism version 9.5.1 and MATLAB R2022b were used for all statistical analyses and plots. To determine the correlation between contrast-enhanced and non-contrast ultrasound, Spearman’s correlation coefficient (ρ) was calculated for the values of each modality at each distance bin. Average correlation among multiple rats was calculated by using Fisher’s z transformation.[36], [37] This method of averaging correlations was chosen over a single, multi-rat correlation as there was greater inter-rat variability between the contrast-enhanced methods than non-contrast. This error likely arose from variations in the small volume (400 µL) injection of concentrated microbubbles. To determine effects of injury severity, a two-way repeated measures analysis of variance (ANOVA) was used with factors of pre- vs post-injury timepoints and injury severity. If the ANOVA detected a statistically significant difference, the Šidák multiple comparisons test was used to determine differences between injury severity groups. Significance was set at p < 0.05.

Graphical abstract and Fig. 1 were created using BioRender.com.

3.1. Similarity of the non-contrast distribution to contrast-enhanced ultrasound

First, we set out to determine the validity of non-contrast ultrasound measurements. As mentioned before, non-contrast measurements can capture flow through vessels, but may miss perfusion of tissue from out-of-plane vessels. Therefore, non-contrast (Fig. 4A) and contrast-enhanced images (Fig. 4B) were taken in seven rats in the same imaging plane, both before and after injury. Each image was processed using the technique detailed in Fig. 3 to yield the distributions in Fig. 4C. The resulting blood flow distributions were then compared as a function of distance. In Fig. 4D, an example plot between the non-contrast and contrast-enhanced blood flow measures shows correlation between the two measures (ρ = 0.77, p < 0.0001). When compared among different rats, the estimated monotonic relationship between the two changed, yet overall, the correlation was maintained when averaged among all the rats (Fig. 4E, ρ = 0.55, p < 0.0001). One rat in Fig. 4E showed no significant correlation between the two modalities. Excluding this rat, average correlation increased to ρ = 0.67, p < 0.0001.

3.2. Interpretable blood flow parameters

We next show that non-contrast ultrasound can detect blood flow changes after SCI. Figure 3B shows a representative distribution of blood flow 15 minutes after spinal cord injury, with corresponding pre-injury flow also shown. At the epicenter of the injury, when compared to pre-injury, there was a relative paucity of blood flow. As the distance from injury increased in both directions, blood flow increased to a level above baseline in tissue that was affected by injury but not completely destroyed. Distal to the injury, the blood flow returned to pre-injury values in presumably unaffected tissue.

For simpler analysis, we extracted a set of interpretable parameters from the distribution (Fig. 5). Based on the shape of the distribution, we measured the blood flow at the umbra and both the rostral and caudal penumbras as described in Sections 2.6 and 2.7. Additionally, we measured the average blood flow of the distal tissue in each direction. We then also measured the prominence of each penumbra as shown in Fig. 5, which was calculated as the difference in blood flow between the penumbra peak and umbra trough.

3.3. Parameters pre- and post-Injury and dependence on injury force

We next aimed to use these parameters to quantify blood flow changes 15 minutes after spinal cord injury. The first comparison to this effect was the pre- to post-injury difference in the parameters. The second question we hoped to answer was how these changes are further affected by severity of the injury. Figure 6 summarizes the findings. It should be noted that the comparison from pre- to post-injury was performed pairwise, as each measurement was performed on each rat. The comparison between injury severities, however, was not pairwise, as different rats were injured at different impact forces.

Flow at the site of the umbra was significantly decreased post-injury when compared to pre-injury (Fig. 6A). This is expected based on our qualitative analysis in Section 3.2. The umbra flow trended towards decreasing with respect to increasing injury severity, although this effect did not achieve statistical significance. Conversely, the injury extent increased with respect to severity, achieving significance in the comparison between mild and severe injuries (Fig. 6E).

The penumbras both rostrally and caudally showed increased flow after injury compared to baseline (Fig. 6B,F). The rostral penumbra in the severe injury case also showed a significantly higher flow than in the moderate injury case only. The caudal penumbra, however, showed no difference between the injury severities. As expected, this change was not reflected in the distal flow in each location, as neither the rostral nor caudal distal tissue showed any change from pre- to post-injury (Fig. 6C,G).

The final parameter analyzed was the prominence of each penumbra, or the difference between the penumbra and umbra flow (Fig. 6D,H). This parameter showed the greatest difference between pre- and post-injury groups in both the rostral and caudal cases. Furthermore, in the rostral penumbra, there was a significant difference between the severe injury group and both the mild and moderate groups. Although the trend was not significant in the caudal group, the mean prominence slightly increased from mild to moderate and moderate to severe.

3.4. Tracking of Regions through Time

Finally, we aimed to highlight a particular advantage of using non-contrast ultrasound over contrast-enhanced techniques: high temporal resolution of blood flow recordings. Figure 7A shows the timeline, in which we recorded blood flow continuously for 15 minutes pre-injury and 30 minutes post-injury. Using the spatial distribution, we can plot blood flow as a function of both time and space (Fig. 7B). We see in this representative plot that flow in the umbra as well as in the healthy tissue remained constant after initial post-injury changes. However, both penumbra peaks seemed to be delayed in their formation, starting off at either lower or equal value to the pre-injury baseline.

This phenomenon was observed across all five rats in both the rostral and caudal tissue (Fig. 7C,D). The flow in the umbra was abolished immediately after injury and did not recover within the first 30 minutes. Flow in the healthy tissue did not significantly change at any point in the experiment. In both penumbras, however, the average flow dropped below pre-injury values at first, before climbing to a plateau 5–10 minutes after the injury. Additionally, the variance of each penumbra was greater than the other flows, as reflected by the error bars, indicating that this may be a more sensitive parameter to small changes in the injury and/or animal physiology.

In the following section, we will discuss the methodology for determining the spatial distribution of blood flow. We will follow this with an evaluation of the comparison of our non-contrast technique to contrast-enhanced ultrasound. We will further explore the blood flow distributions, with emphasis on the somewhat unexpected increase in blood flow in the umbra. Finally, we will discuss the ability of non-contrast ultrasound to continuously measure spinal cord blood flow and subsequent clinical applications.

4.1. Methodology to Determine the Spatial Distribution of Blood Flow

The method for calculating the spatial distribution of spinal cord blood flow was chosen as a simple way to visualize the extent of injury. The inspiration for the calculation came from Soubeyrand and colleagues,[14], [15] who used contrast-enhanced ultrasound to quantify blood flow. By drawing seven regions of interest distributed rostro-caudally, they showed a decrease in blood flow at the injury epicenter. We expanded upon this method through a more rigorous definition of distance from injury and by increasing the spatial resolution of the distribution through smaller distance bins. We also chose to investigate the rostral-caudal distribution, as that enabled measurement of both affected and healthy tissue. The bin size of 0.1 mm was chosen to balance increased spatial resolution with high enough sampling of pixels (on the order of 4000 pixels per bin). We discuss the shape of the distribution in detail in Section 4.3, but briefly, the distribution shows a central decrease in blood flow (umbra) with peripheral increase in blood flow in each direction (penumbra). It should be noted that the umbra is not always centered at exactly the 0 mm distance, likely due to error introduced by manual drawing of the injury location (Fig. 5). In addition to the sagittal view, it may be interesting in future work to study axial images to investigate perfusion from the smaller posterior spinal arteries.[29]

4.2. Comparison to Contrast-Enhanced Imaging

Blood flow to tissue as measured by ultrasound can be split into two categories: flow through relatively large vessels and perfusion through capillaries and the extracellular space.[24] The main limitation of SMI is that it only images vasculature and misses capillary perfusion. As mentioned in the introduction, this means that areas that lack flow on non-contrast images may be perfused by an out-of-plane vessel, resulting in artificially low measures of blood flow. Newer non-contrast techniques, such as functional ultrasound, partially overcome this limitation through innovative beam-forming techniques and high sampling rates in the range of kHz.[28] However, in the case of the spinal cord, the layout of the main sulcal arteries within the mid-sagittal plane (Fig. 1B) allows us to overcome this problem even with a simple linear, clinically available probe. Conversely, generalizability of this analysis to other organ systems may be limited if their vasculature does not conveniently fall in a single plane. For example, the brain’s vasculature is morphologically more complex than that of the spinal cord, and the vasculature cannot be easily measured with one imaging plane.[38] To counter this, it may be useful to utilize a 3-dimensional image and distribution to map the blood flow over time (4D/real-time 3D ultrasound).[18], [39] Alternately, functional ultrasound may be able to measure capillary perfusion and may be a more viable non-contrast method to use clinically in situations with non-planar blood flow.[40]

Our results show that there is a correlation between our contrast-enhanced and SMI images (average ρ = 0.55). When performed on multiple rats, the average of correlations was calculated using Fisher’s z transformation as opposed to a single correlation calculated on data from all rats. This transformation was used as the contrast-enhanced values seemed to vary between rats more than the non-contrast values. Such variability could have been due to inconsistencies in the contrast injection technique, despite using an image generation method (i.e., time constant analysis) that was more robust to injection amount errors.[20], [21] Such errors were introduced due to the need for rapid bolus injection of a small amount (400 µL) of concentrated contrast at high rate, resulting in relatively variable peak contrast concentrations. Future studies could use a different contrast technique, such as the destruction-replenishment method[15], [21] or non-linear methods to address this issue.[24]

Non-contrast and contrast-enhanced measurements were also performed as close to each other as possible (1–2 minutes). This permitted the assumption that minimal blood flow change would occur over that time scale, although this may not be true in the penumbra, as shown in Fig. 7. Notably, one of the rats showed no correlation between the two modalities (Fig. 4E), perhaps due to poor positioning of the probe resulting in the imaging plane not being optimally aligned to the midsagittal plane of the rat. Removing this rat from the analysis resulted in an average correlation of 0.67. We included this rat to show the limitations of our system and to emphasize the importance of careful probe positioning. In human patients, this may also prove to be less limiting, as larger overall vessels permit greater errors in the imaging plane. Overall, this demonstrates that careful non-contrast measurements may function as an adequate substitute for contrast-based methods, especially when contrast-based methods are unavailable or untenable, such as long-term or repeated clinical measurements.

4.3. Effect of injury on the blood flow distribution

Using SMI images, we have consistently noted a pattern of blood flow following injury with decreased flow at the injury epicenter, or umbra. Immediately peripheral to the umbra, we see increased flow, which we define as the penumbra. Distal tissue in each direction seems relatively unaffected. Qualitatively, this concept is illustrated in Fig. 5, with statistical analysis in Fig. 6. It should be noted that the penumbra height distributions in Fig. 6B,F have large variance, indicating that penumbra hyperperfusion is not necessarily consistent. The representative rat in Fig. 4C illustrates this concept, as the rostral penumbra in this rat was not as drastically hyperperfused, especially in the contrast-enhanced distribution. However, across all rats, the pre- to post-injury pairwise comparison (Fig. 6B,D,F,H) was robust, supporting the concept that after injury, penumbra flow tends to increase in general.

The presence of increased flow in the penumbra areas is perhaps the most surprising phenomenon that we have noted in this study. Many of the studies that have used contrast-enhanced imaging have only shown decreased perfusion around the injury site, without peripheral hyperperfusion.[10]–[14], [22] The authors in most of these studies did not generate spatial distributions in the same way that we have demonstrated here, and therefore may have missed the injury zones revealed by this analysis. Moreover, Soubeyrand et al[14] generated a similar distribution to our study, albeit with only seven different sites through space, and showed only decreased perfusion around the injury. On the other hand, some older studies have noted increased perfusion after spinal cord injury, which has been variably attributed to low injury severity, increased blood pressure, or sampling site.[16]

Our results, with a hyperperfused penumbra, shed more light on the perfusion distribution than has been previously observed. We believe the pattern observed here may have a biological explanation. One potential interpretation is increased flow to the penumbra within larger arterioles which does not make it to the capillaries. Since there is disruption to the blood spinal cord barrier after SCI,[41] blood collects in the extracellular space and does not adequately perfuse tissue. In that case, we might expect the discrepancy between contrast-enhanced and non-contrast ultrasound to change depending on the location around the injury. Although we did not observe this with our data, it may be worthwhile to perform a more targeted study to quantify differences between flow and perfusion in the different injury zones. An ultrasound imaging technique that can simultaneously record both types of vasculature would be ideally suited for this, such as the singular value decomposition of non-linear Doppler sequences developed by Bruce et al.[10], [24]

Gallagher et al have also defined the penumbra as an area of decreased flow in humans with the use of laser speckle contrast imaging.[17], [42] However, in one of their figures, it appears that there may be a hyperperfused penumbra visible, and they also describe hyperperfusion and patchy perfusion modes of injury. Perhaps this phenomenon is due to the higher variability of SCI encountered in human patients outside the controlled lab setting. As a result, blood flow distributions may be more variable as well, and the penumbra may be less spatially defined. Our results also suggest the possibility of regions with both increased and decreased blood flow, and further study of more heterogeneous modes of injury may shed light on this phenomenon.

4.4. Injury Severity Effects

Although our technique was highly effective at detecting post-injury effects when compared to pre-injury, it faced limitations in delineating the effects of injury severity (Fig. 6). Other groups have demonstrated statistically significant correlations between injury severity, measured through impact force and behavioral testing, and contrast-derived flow parameters.[10], [22] We see some statistically significant differences as well, but the magnitudes of changes are not large overall. The prominence parameter (Fig. 6D,H) showed the greatest dependence on injury severity, which is expected as it is affected by changes in both the umbra and penumbra flow.

Additionally, we performed no functional or histological analyses to verify the results. Instead, we show these findings as proof-of-concept, demonstrating that this clinically available non-contrast ultrasound system can detect effects of injury force on blood flow. To make stronger conclusions on the effects of injury severity, this experiment could be repeated with behavioral and histological analysis. Some differences between the rostral and caudal penumbras were also observed. Although this phenomenon could be explained anatomically, as arteries supplying the anterior spinal vasculature enter at specific spinal levels, we believe a more targeted study is needed before drawing conclusions.

4.5. Tracking through time

By tracking blood flow in the first 30 minutes after injury (Fig. 7), we have shown that umbral blood flow is annihilated after a severe SCI, distal blood flow appears relatively unaffected, and the penumbra seems to develop within the first 5–10 minutes after injury. One potential explanation for this may be that capillary perfusion in both the umbra and penumbra is compromised by the injury. This explanation is the consensus of the literature discussed in Section 4.3. In the umbra, if the larger vasculature is also compromised, flow would not be able to recover. However, in the penumbra, the decrease in perfusion, combined with blood spinal cord barrier disruption,[41] would result in a local buildup of CO₂ causing vasodilation.[30], [43] As a result, arteriolar flow, as measured by non-contrast imaging, may increase to this site. However, there would not be a corresponding perfusion increase through the compromised capillaries, as measured by contrast-enhanced ultrasound. This hypothesis may explain the difference between contrast-enhanced and non-contrast measurements described in Section 4.3, but requires further experiments to confirm.

The progression of blood flow has been captured in the first hour after injury using contrast-enhanced ultrasound.[14] The authors of that study found a general decrease in blood flow in all regions of the spinal cord in the hour after injury, although this was also seen in the uninjured group, likely indicating a non-injury cause of the decrease. Otherwise, our results agree with this study, showing relative decrease in perfusion at the epicenter when compared to healthy tissue. Again, they did not note an increase at the penumbra, further highlighting the potential difference between flow and perfusion. Furthermore, they were only able to measure flow every five minutes,[44] a far lower sampling rate than our rate of 39 Hz. Newer contrast-enhanced techniques[10], [22], [24] should potentially overcome this weakness, enabling simultaneous high temporal resolution tracking and future investigations of the difference between perfusion and flow on a temporal scale.

4.6. Clinical translatability

The true advantage of non-contrast ultrasound lies in its clinical translatability, where long-term post-SCI monitoring cannot be done with continuous injection of contrast agents. We show here proof-of-concept of translation through a high temporal resolution 30-minute recording after injury. Unlike contrast-enhanced ultrasound, there is no clinical limitation on the length of this recording, as no agent is continuously injected into the patient. This opens the door for physiologic monitoring of the spinal cord throughout the acute trauma period. Furthermore, our observation of different injury zones presents the opportunity to use interventions to optimize blood flow to specific areas, like the penumbra. Continuous blood flow monitoring has revolutionized the care of diseases such as stroke[45] and traumatic brain injury,[46] as both a prognostic marker and therapeutic guide, with interventions primarily aimed at recovering penumbra tissue. Near-infrared spectroscopy has already been used to approximate spinal cord blood flow in humans.[47]–[51] However, this alternative suffers from three main weaknesses: (1) it can only measure paraspinal muscle blood flow, a correlative of blood flow in the cord; (2) it has very low spatial resolution; and (3) it faces challenges of achieving significant penetration depth. Non-contrast ultrasound, as we have shown, does not exhibit such limitations of location and resolution, and may serve as a much-needed clinical innovation. Techniques like functional ultrasound can further improve the quality of blood flow signals recorded in the spine without contrast.[28]

It should also be noted that this study was conducted purely on female rats. Our group uses this model as the post-operative bladder expression is more easily performed in female rats. Although the literature shows minimal differences in functional outcomes between male and female rats,[52], [53] further exploration of effects of sex on spinal cord blood flow after injury is warranted. This is especially true as this technique is applied in clinical settings, where spinal cord injury pathophysiology could be more dependent on patient sex.

Traumatic spinal cord injury results in significant blood flow changes that may affect the course of recovery and overall patient outcomes. As a result, spinal cord blood flow imaging may be a useful clinical tool for physicians to use in the intensive care unit. Most blood flow imaging techniques are limited by either continuous contrast injection or inaccuracy of measurement. Here, we introduce the use of a clinically available ultrasound transducer for imaging and monitoring blood flow in rat spinal cord after injury. This technique results in spatial distributions of blood flow after injury comparable to those generated using contrast-enhanced ultrasound without the limitations of a continuous infusion. Furthermore, we have shown a new blood flow pattern after injury with a decrease in flow at the injury epicenter, or umbra, and increase in flow in the immediate periphery, or penumbra. Tracking the regions through time with high temporal resolution shows the development of the penumbra in the first 10 minutes after injury and highlights the clinical potential. We hope that our approach will set the stage for future investigations on penumbral blood flow response and restoration. Our non-contrast imaging technique will enable blood flow monitoring as a new clinical paradigm for the acute treatment of spinal cord injury.

Data/code availability Statement

All data, including images and processed files, will be made available upon request. The code used to analyze the data can be found at https://github.com/HEPIUSLAB/spatial_distribution_2023.

SCI – spinal cord injury

SMI – Superb Microvascular Imaging (non-contrast imaging modality)

Data/code availability Statement

All data, including images and processed files, will be made available upon request. The code used to analyze the data can be found at https://github.com/HEPIUSLAB/spatial_distribution_2023.

Author Contributions

Conceptualization: D.R., A.M.H., N.T., N.V.T., and A.M. Methodology: D.R., Z.S., N.K., E.B., A.M.H., A.K.M., M.B., K.J., A.D.D., C.S. Software: D.R., K.M.K.L. Validation and formal analysis: D.R. Investigation: D.R., A.M.H. Resources: N.T., N.V.T., A.M. Data curation: D.R., A.M.H. Writing – Original draft: D.R., Z.S., N.K., E.B. Writing – Review and Editing: all authors. Visualization: D.R. Supervision, project administration, and funding acquisition: N.T., N.V.T., A.M.

X. Jia, R. G. Kowalski, D. M. Sciubba, and R. G. Geocadin, “Critical care of traumatic spinal cord injury,” J Intensive Care Med, vol. 28, no. 1, pp. 12–23, Jan. 2013, doi: 10.1177/0885066611403270.
C. S. Ahuja et al., “Traumatic Spinal Cord Injury—Repair and Regeneration,” Neurosurgery, vol. 80, no. 3S, pp. S9–S22, Mar. 2017, doi: 10.1093/neuros/nyw080.
A. Anjum et al., “Spinal cord injury: Pathophysiology, multimolecular interactions, and underlying recovery mechanisms,” International Journal of Molecular Sciences, vol. 21, no. 20. Int J Mol Sci, pp. 1–35, Oct. 02, 2020. doi: 10.3390/ijms21207533.
B. C. Walters et al., “Guidelines for the management of acute cervical spine and spinal cord injuries: 2013 update,” Neurosurgery, vol. 60, no. SUPPL. 1, pp. 82–91, Aug. 2013, doi: 10.1227/01.neu.0000430319.32247.7f.
M. N. Hadley, “Blood pressure management after acute spinal cord injury,” Neurosurgery, vol. 50, no. 3 SUPPL., pp. 58–62, 2002, doi: 10.1097/00006123-200203001-00012.
A. M. Hersh et al., “Advancements in the treatment of traumatic spinal cord injury during military conflicts,” Neurosurg Focus, vol. 53, no. 3, p. E15, Sep. 2022, doi: 10.3171/2022.6.FOCUS22262.
N. Theodore et al., “Cerebrospinal Fluid Drainage in Patients with Acute Spinal Cord Injury: A Multi-Center Randomized Controlled Trial,” World Neurosurg, p. 140326, Jun. 2023, doi: 10.1016/j.wneu.2023.06.078.
Y. Tsehay et al., “Advances in monitoring for acute spinal cord injury: a narrative review of current literature,” Spine Journal, vol. 22, no. 8, pp. 1372–1387, 2022, doi: 10.1016/j.spinee.2022.03.012.
C. Weber-Levine et al., “Multimodal interventions to optimize spinal cord perfusion in patients with acute traumatic spinal cord injuries: a systematic review,” J Neurosurg Spine, vol. 37, no. 5, pp. 729–739, Nov. 2022, doi: 10.3171/2022.4.SPINE211434.
M. Bruce, D. DeWees, J. N. Harmon, L. Cates, Z. Z. Khaing, and C. P. Hofstetter, “Blood Flow Changes Associated with Spinal Cord Injury Assessed by Non-linear Doppler Contrast-Enhanced Ultrasound,” Ultrasound Med Biol, vol. 48, no. 8, pp. 1410–1419, Aug. 2022, doi: 10.1016/j.ultrasmedbio.2022.03.004.
Z. Z. Khaing, L. N. Cates, J. E. Hyde, R. Hammond, M. Bruce, and C. P. Hofstetter, “Transcutaneous contrast-enhanced ultrasound imaging of the posttraumatic spinal cord,” Spinal Cord, vol. 58, no. 6, pp. 695–704, 2020, doi: 10.1038/s41393-020-0415-9.
Z. Z. Khaing et al., “Contrast-enhanced ultrasound to visualize hemodynamic changes after rodent spinal cord injury,” J Neurosurg Spine, vol. 29, no. 3, pp. 306–313, Sep. 2018, doi: 10.3171/2018.1.SPINE171202.
Z. Z. Khaing et al., “Contrast-Enhanced Ultrasound for Assessment of Local Hemodynamic Changes Following a Rodent Contusion Spinal Cord Injury,” in Military Medicine, Oxford Academic, Jan. 2020, pp. 470–475. doi: 10.1093/milmed/usz296.
M. Soubeyrand, E. Laemmel, A. Dubory, E. Vicaut, C. Court, and J. Duranteau, “Real-Time and Spatial Quantification Using Contrast-Enhanced Ultrasonography of Spinal Cord Perfusion During Experimental Spinal Cord Injury,” Spine (Phila Pa 1976), vol. 37, no. 22, pp. E1376–E1382, Oct. 2012, doi: 10.1097/BRS.0b013e318269790f.
A. Dubory et al., “Contrast enhanced ultrasound imaging for assessment of spinal cord blood flow in experimental spinal cord injury,” Journal of Visualized Experiments, vol. 2015, no. 99, p. e52536, May 2015, doi: 10.3791/52536.
C. H. Tator and M. G. Fehlings, “Review of the secondary injury theory of acute spinal cord trauma with emphasis on vascular mechanisms,” J Neurosurg, vol. 75, no. 1, pp. 15–26, Jul. 1991, doi: 10.3171/jns.1991.75.1.0015.
M. J. Gallagher, F. R. A. Hogg, A. Zoumprouli, M. C. Papadopoulos, and S. Saadoun, “Spinal Cord Blood Flow in Patients with Acute Spinal Cord Injuries,” J Neurotrauma, vol. 36, no. 6, pp. 919–929, 2019, doi: 10.1089/neu.2018.5961.
M. Soubeyrand, A. Badner, R. Vawda, Y. S. Chung, and M. G. Fehlings, “Very high resolution ultrasound imaging for real-time quantitative visualization of vascular disruption after spinal cord injury,” J Neurotrauma, vol. 31, no. 21, pp. 1767–1775, Nov. 2014, doi: 10.1089/neu.2013.3319.
C. Greis, “Quantitative evaluation of microvascular blood flow by contrast-enhanced ultrasound (CEUS),” Clin Hemorheol Microcirc, vol. 49, no. 1–4, pp. 137–149, 2011, doi: 10.3233/CH-2011-1464.
M.-X. Tang et al., “Quantitative contrast-enhanced ultrasound imaging: a review of sources of variability,” Interface Focus, vol. 1, no. 4, pp. 520–539, Aug. 2011, doi: 10.1098/rsfs.2011.0026.
S. Turco et al., “Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning,” Ultrasound Med Biol, vol. 46, no. 3, pp. 518–543, Mar. 2020, doi: 10.1016/j.ultrasmedbio.2019.11.008.
B. Beliard et al., “Ultrafast Doppler imaging and ultrasound localization microscopy reveal the complexity of vascular rearrangement in chronic spinal lesion,” Sci Rep, vol. 12, no. 1, pp. 1–14, 2022, doi: 10.1038/s41598-022-10250-8.
F. Aghabaglou et al., “Ultrasound monitoring of microcirculation: An original study from the laboratory bench to the clinic,” Microcirculation, Jun. 2022, doi: 10.1111/micc.12770.
M. Bruce et al., “High-Frequency Nonlinear Doppler Contrast-Enhanced Ultrasound Imaging of Blood Flow,” IEEE Trans Ultrason Ferroelectr Freq Control, vol. 67, no. 9, pp. 1776–1784, 2020, doi: 10.1109/TUFFC.2020.2986486.
G. ter Haar, “Safety and bio-effects of ultrasound contrast agents,” Med Biol Eng Comput, vol. 47, no. 8, pp. 893–900, 2009, doi: 10.1007/s11517-009-0507-3.
C. Hu, Y. Feng, P. Huang, and J. Jin, “Adverse reactions after the use of SonoVue contrast agent: Characteristics and nursing care experience,” Medicine, vol. 98, no. 44, p. e17745, 2019, doi: 10.1097/MD.0000000000017745.
C. Tang et al., “Safety of Sulfur Hexafluoride Microbubbles in Sonography of Abdominal and Superficial Organs: Retrospective Analysis of 30,222 Cases,” Journal of Ultrasound in Medicine, vol. 36, no. 3, pp. 531–538, 2017, doi: 10.7863/ultra.15.11075.
J. Claron et al., “Large-scale functional ultrasound imaging of the spinal cord reveals in-depth spatiotemporal responses of spinal nociceptive circuits in both normal and inflammatory states,” Pain, vol. 162, no. 4, pp. 1047–1059, 2021, doi: 10.1097/j.pain.0000000000002078.
A. K. Thron, Vascular Anatomy of the Spinal Cord. Vienna: Springer Vienna, 1988. doi: 10.1007/978-3-7091-6947-6.
N. L. Martirosyan, J. E. S. Feuerstein, N. Theodore, D. D. Cavalcanti, R. F. Spetzler, and M. C. Preul, “Blood supply and vascular reactivity of the spinal cord under normal and pathological conditions: A review,” Journal of Neurosurgery: Spine, vol. 15, no. 3. American Association of Neurological Surgeons, pp. 238–251, Sep. 01, 2011. doi: 10.3171/2011.4.SPINE10543.
D. Routkevitch et al., “FlowMorph: Morphological Segmentation of Ultrasound-Monitored Spinal Cord Microcirculation,” in 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS), IEEE, Oct. 2022, pp. 610–614. doi: 10.1109/BioCAS54905.2022.9948639.
D. Routkevitch et al., “Measurement of Single-Vessel Flow Parameters for Vascular Characterization of Spinal Cord Injury,” in 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE, Apr. 2023, pp. 1–4. doi: 10.1109/NER52421.2023.10123780.
D. M. Basso, M. S. Beattie, and J. C. Bresnahan, “A Sensitive and Reliable Locomotor Rating Scale for Open Field Testing in Rats,” J Neurotrauma, vol. 12, no. 1, pp. 1–21, 1995, doi: 10.1089/neu.1995.12.1.
A. Kempski Leadingham, Kelley M; Routkevitch, Denis; Hersh, Andrew; Kerensky, Max; Abramson, Haley G; Weber-Levine, Carly; Robinson, Kayla; Jiang, Kelly; Judy, Brendan; Perdomo-Pantoja, Alexander; Theodore, Nicholas; Manbachi, “Quantification of Spinal Cord Microvascular Perfusion utilizing Ultrasound,” in IEEE International Ultrasonics Symposium, IUS (in press), 2023, pp. 1–4.
T. Scholbach and J. Scholbach, “Dynamic Sonographic Tissue Perfusion Measurement,” J Med Ultrasound, vol. 17, no. 2, pp. 71–85, 2009, doi: 10.1016/S0929-6441(09)60114-4.
L. Myers and M. J. Sirois, “Spearman Correlation Coefficients, Differences between,” in Encyclopedia of Statistical Sciences, Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. doi: 10.1002/0471667196.ess5050.pub2.
D. M. Corey, W. P. Dunlap, and M. J. Burke, “Averaging Correlations: Expected Values and Bias in Combined Pearson r s and Fisher’s z Transformations,” J Gen Psychol, vol. 125, no. 3, pp. 245–261, Jul. 1998, doi: 10.1080/00221309809595548.
S. G. Waxman, “Vascular Supply of the Brain,” in Clinical Neuroanatomy, 28e, New York, NY: McGraw-Hill Education, 2017. [Online]. Available: http://accessmedicine.mhmedical.com/content.aspx?aid=1137638254
S. H. Kwon and A. S. Gopal, “3D and 4D Ultrasound: Current Progress and Future Perspectives,” Curr Cardiovasc Imaging Rep, vol. 10, no. 12, p. 43, Dec. 2017, doi: 10.1007/s12410-017-9440-2.
E. Macé, G. Montaldo, I. Cohen, M. Baulac, M. Fink, and M. Tanter, “Functional ultrasound imaging of the brain,” Nat Methods, vol. 8, no. 8, pp. 662–664, Aug. 2011, doi: 10.1038/nmeth.1641.
L.-Y. Jin et al., “Blood–Spinal Cord Barrier in Spinal Cord Injury: A Review,” J Neurotrauma, vol. 38, no. 9, pp. 1203–1224, May 2021, doi: 10.1089/neu.2020.7413.
S. Saadoun and M. C. Papadopoulos, “Targeted Perfusion Therapy in Spinal Cord Trauma,” Neurotherapeutics, vol. 17, no. 2, pp. 511–521, Apr. 2020, doi: 10.1007/s13311-019-00820-6.
A. I. Kobrine, T. F. Doyle, and A. N. Martins, “Autoregulation of Spinal Cord Blood Flow,” Neurosurgery, vol. 22, no. PG-573-581, pp. 573–581, Jan. 1975, doi: 10.1093/neurosurgery/22.CN_suppl_1.573.
M. Soubeyrand, A. Dubory, E. Laemmel, C. Court, E. Vicaut, and J. Duranteau, “Effect of norepinephrine on spinal cord blood flow and parenchymal hemorrhage size in acute-phase experimental spinal cord injury,” European Spine Journal, vol. 23, no. 3, pp. 658–665, Mar. 2014, doi: 10.1007/s00586-013-3086-9.
A. K. Bonkhoff and C. Grefkes, “Precision medicine in stroke: Towards personalized outcome predictions using artificial intelligence,” Brain, vol. 145, no. 2. pp. 457–475, Apr. 18, 2022. doi: 10.1093/brain/awab439.
R. Chesnut et al., “A management algorithm for adult patients with both brain oxygen and intracranial pressure monitoring: the Seattle International Severe Traumatic Brain Injury Consensus Conference (SIBICC),” Intensive Care Med, vol. 46, no. 5, pp. 919–929, May 2020, doi: 10.1007/s00134-019-05900-x.
M. Luehr, F. W. Mohr, and C. D. Etz, “Indirect Neuromonitoring of the Spinal Cord by Near-Infrared Spectroscopy of the Paraspinous Thoracic and Lumbar Muscles in Aortic Surgery,” Thoracic and Cardiovascular Surgeon, vol. 64, no. 4, pp. 333–335, 2015, doi: 10.1055/s-0035-1552579.
C. D. Etz et al., “Near-infrared Spectroscopy Monitoring of the Collateral Network Prior to, During, and After Thoracoabdominal Aortic Repair: A Pilot Study,” European Journal of Vascular and Endovascular Surgery, vol. 46, no. 6, pp. 651–656, Dec. 2013, doi: 10.1016/j.ejvs.2013.08.018.
R. P. E. Boezeman et al., “Spinal near-infrared spectroscopy measurements during and after thoracoabdominal aortic aneurysm repair: A pilot study,” Annals of Thoracic Surgery, vol. 99, no. 4, pp. 1267–1274, 2015, doi: 10.1016/j.athoracsur.2014.10.032.
T. Kurita, S. Kawashima, K. Morita, and Y. Nakajima, “Spinal cord autoregulation using near-infrared spectroscopy under normal, hypovolemic, and post-fluid resuscitation conditions in a swine model: A comparison with cerebral autoregulation,” J Intensive Care, vol. 8, no. 1, pp. 1–10, Apr. 2020, doi: 10.1186/s40560-020-00443-6.
H. Radhakrishnan, A. Senapati, D. Kashyap, Y. B. Peng, and H. Liu, “Light scattering from rat nervous system measured intraoperatively by near-infrared reflectance spectroscopy,” J Biomed Opt, vol. 10, no. 5, p. 051405, 2005, doi: 10.1117/1.2098487.
T. Fukutoku et al., “Sex-Related Differences in Anxiety and Functional Recovery after Spinal Cord Injury in Mice,” J Neurotrauma, vol. 37, no. 21, pp. 2235–2243, Nov. 2020, doi: 10.1089/neu.2019.6929.
M. Emamhadi, B. Soltani, P. Babaei, H. Mashhadinezhad, and S. Ghadarjani, “Influence of Sexuality in Functional Recovery after Spinal Cord Injury in Rats.,” Arch Bone Jt Surg, vol. 4, no. 1, pp. 56–9, Jan. 2016, [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/26894220

No competing interests reported.

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Non-Contrast Ultrasound Image Analysis for Spatial and Temporal Distribution of Blood Flow After Spinal Cord Injury

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Animals

2.2. Laminectomy and spinal cord impact

2.3. Non-contrast imaging

2.4. Contrast-enhanced recording

2.5. Processing contrast-enhanced images

2.6. Pixelwise processing to obtain the spatial distribution

2.7. Labeling of injury zones

2.8. Flow over time

2.9. Statistical analysis

3. Results

3.1. Similarity of the non-contrast distribution to contrast-enhanced ultrasound

3.2. Interpretable blood flow parameters

3.3. Parameters pre- and post-Injury and dependence on injury force

3.4. Tracking of Regions through Time

4. Discussion

4.1. Methodology to Determine the Spatial Distribution of Blood Flow

4.2. Comparison to Contrast-Enhanced Imaging

4.3. Effect of injury on the blood flow distribution

4.4. Injury Severity Effects

4.5. Tracking through time

4.6. Clinical translatability

5. Conclusions

Abbreviations

Declarations

Data/code availability Statement

Author Contributions

References

Additional Declarations

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