Nerve detection with optical spectroscopy for regional anesthesia procedures

Regional anesthesia has shown advantages over general anesthesia; lower odds of inpatient mortality and pulmonary complications during hip repair [1], faster return of bowel function [2] as well as a lower risk for delirium and nausea have been reported. Current disadvantages of regional anesthesia are the unpredictable onset time of the block as well as the risk of a failed block and, although rare, the risk for complications caused by injection of local anesthetics into critical structures such as blood vessels or nerve fascicles (see Fig. 1) [3, 4]. Accurate needle placement with respect to the various structures is therefore important [5].

Techniques like paresthesia, electrical nerve stimulation and ultrasound guidance have been introduced to improve needle placement. The elicitation of paresthesia has shown a sensitivity of only 40 % for nerve detection while for nerve stimulation up to 75 % sensitivity has been reported [6, 7]. However, the stimulation threshold does not give an exact indication of the distance between the needle and nerve. Ultrasound (US) guidance for the performance of peripheral nerve blocks improves the quality of regional anesthesia because nerve structures can be seen as multiple rounded hypoechoic areas in a hyperechoic background or vice versa [8] and the spread of local anesthetic is observed upon injection. However, the quality of the ultrasound guidance is user- and patient-dependent, requires sufficient training, and additionally ultrasound imaging has technical limitations. For example, as the US frequency decreases, degradation in the image resolution occurs [9].

Diffuse optical spectroscopy is a technique which sends white light into tissue and measures the reflected light. The reflected light has a specific spectral distribution due to the absorption and scattering of photons interacting with the tissue. Since these changes are determined by the tissue composition, the technique provides tissue discrimination. By incorporating optical fibers into needles, a method has been developed that can provide real-time tissue feedback at the tip of the needle. This spectral tissue sensing (STS) method is complementary to existing techniques that do not have this functionality. Therefore, STS could improve needle placement in regional anesthesia for accurate deposition of anesthetics. Recently, several studies have been performed aiming at nerve discrimination based on diffuse optical spectroscopy [10–12]. In the study of Brynolf et al. [10] the optical detection of the brachial plexus was studied in in vivo swine. While promising results were presented, the study was limited to detecting a “nerve target region” and performed on only two swine. Balthasar et al. [11] investigated optical detection of peripheral nerves in 20 patients. Subcutaneous fat, muscle and a nerve target region were the tissue types included and detected in the study. The results indicated that the transitions from muscle to nerve target region could be identified reliably. However, the question of nerve proximity remained unanswered. Differentiation between nerve and adipose tissue with optical spectroscopy was investigated in thyroid and parathyroid surgery by Schols et al. [12]. Discrimination between nerve and adipose tissue was possible especially when measuring the spectra in the near infrared region. However, due to the limited dataset of only including adipose tissue and nerve, no definitive conclusions could be drawn from this data regarding needle placement for regional anesthesia where the needle also traverses subcutaneous fat and muscle tissue.

In this study it was investigated whether accurate needle placement near nerve structures of the cervical nerves (C-roots) and the median nerve of human cadavers was possible when guided by STS to facilitate needle placement for regional anesthesia. Furthermore, to investigate whether a detection algorithm based on fascicular tissue of the cervical nerve can be generalized to other nerves, spectra measured at different locations near the median nerve was validated on a model trained on the spectra acquired from the cervical nerve area.

In this study the accuracy with which STS is able to discriminate nerve tissue from surrounding tissues during needle interventions for regional anesthesia was investigated. Of four tissue types spectra were collected from 19 human cadavers at 1563 different locations: 1274 spectra acquired from the cervical nerve area and 289 spectra from the median nerve area. Various classification algorithms were applied to the dataset showing that fascicular tissue of the nerve can be discriminated from surrounding tissues with sensitivity and specificity around 90 %. These results were based on the largest dataset collected to date in nerve and surrounding tissues and confirm earlier reports on nerve discrimination with smaller datasets [10–12].

The histology slides show that the nerves studied consisted mainly of fascicular bundles enclosed by loose collagenous tissue and adipose tissue of the epineurium. The outer-epineurium is surrounded by a layer of sliding fat with variable thickness. The boxplots of the physiological parameters obtained by fitting the spectra show a significant difference between fat content for tissues such as subcutaneous fat and sliding fat compared to muscle and nerve tissues. The same holds for beta-carotene which is present in fatty tissues [18]. The blood content in muscle tissue is on average higher while the blood oxygen saturation herein is lower than in the surrounding tissues. Furthermore, from the parameters related to scattering (S800, Fmie and b), the Mie slope b seems to differ most between the tissues. Collagen content shows higher values in muscle and nerve which is in line with the fact that those tissues are richer in connective tissue compared to the other tissues investigated [13]. In general, there is a significant variation in the parameters determined within each tissue, and outliers are present (indicated with ‘+’). Despite these variations and outliers, the nerve tissue can be discriminated with high accuracy from the surrounding tissues for this large data set.

The spectra were measured in one-time frozen human cadavers, where the tissues were exposed so that the physician was able to validate the tissue type measured visually. Physiological parameters that are known to change from in vivo to ex vivo are the blood content and blood oxygen saturation. The settling of blood (livor mortis [23]) will affect the local blood content while the blood oxygen saturation in the exposed tissue is likely to be different from living tissue because of no blood circulation as well as the uptake of oxygen directly from the air. In what way this affects the nerve discrimination when performing the measurements in vivo is part of future investigation.

Of the various dimension reduction methods the fit method resulted in the lowest MCC. The fit method reduces the spectral information into 12 independent parameters thereby losing relevant information for discriminating nerves. Additionally the fit method, based on the analytical model derived by Farrell et al. [15], assumes the tissue to be homogenous. The histological results, however, show that within the optical probing volume of ~1 mm³ this assumption is not always fulfilled, especially near the nerve that has a more layered structure. Despite these disadvantages, the fit method provides the opportunity to link observed features in the spectra to the histological and physiological properties of the studied tissues. Trends in the boxplots of Fig. 5 can be linked to the histology shown in Fig. 2.

There is significant variation present in the measured spectra per tissue type. As a result, discrimination of the various tissues requires taking multiple parameters into account, in our case up to 40 spectral features. The danger of over fitting the data is prevented by our large spectral database collected in 19 specimens. From the classification methods, SVM performed best. SVM may be able to perform well on multi-dimensional parameter spaces because it can ignore features that do not significantly contribute to the discrimination. The fit dimension reduction allows easy interpretation of the results since the input parameters for classification are used whereas other methods post-process the parameters into scores that might not be intuitively related to the input parameters. From the fit parameters, the scattering parameters, the fat and water content as well as blood oxygen saturation contributed most to the fascicular tissue of the nerve discrimination. In general, the various classification methods resulted in similar accuracies, demonstrating the robustness of STS for tissue discrimination. This robustness allows flexibility in classification algorithm design when developing tissue discrimination based on STS into a product.

The cervical nerves as well as the median nerves investigated in this study are mostly composed of fascicular tissue with a limited amount of epineurium that consists of irregular connective tissue and adipocytes. This composition is known to be different in various nerves and changes when going from proximal to distal locations of the nerve [24]. When training the classification algorithm on the cervical nerve area only and testing it on the median nerve area, the sensitivity and specificity for discrimination were somewhat lower than for cervical nerve discrimination alone (see Tables 1, 2). Apparently, the variation in nerve tissue composition observed in the histology shown in Fig. 2 requires fine tuning of the classification algorithm. To what extent this variation in composition affects the nerve discrimination for nerves at other locations in the body has to be further investigated.

To ensure that the correct tissue type was measured, two experienced anesthesiologists with the assistance of an anatomist positioned the optical needle on the exposed tissues. The exposed setting with visual confirmation was considered to be the best gold standard, as opposed to ultrasound where the needle tip/nerve visualization is prone to errors. Special attention was given to measure pure tissue types which was sometimes challenging for thin structures such as sliding fat and when placing the needle on a nerve that could slide away. Despite these difficulties, the errors that might be introduced by these measurement inaccuracies will have limited influence on the final classification accuracy due to the large dataset we acquired.

Comparing STS (sensitivity around 90 %) with other needle guidance techniques shows a significant improvement in nerve detection compared to paresthesia (sensitivity of 40 %) [6] and nerve stimulation (sensitivity up to 75 %) [7]. Another advantage of the STS method is that the detection distance (determined by the distance between fiber ends) is on the order of a millimeter, allowing more accurate placement near the nerve compared to the other methods where, even when the needle detects nerve, the exact distance is not well known. Both improvements compared to current practice may contribute to safer loco-regional blocks in regional anesthesia.

From a clinical point of view, the most vulnerable and important structure of the nerve is the nerve fascicle itself. During the administration of loco-regional anesthesia, nerve-fascicles can be damaged directly or indirectly. For instance, direct needle trauma by cutting the fascicle, pressure of injected fluid in the fascicle, overstretching of the nerve due to fluid volume or manipulation of surrounding tissue during the procedure, or indirect trauma due to ischemia by damaging the supplying blood vessel, can occur. The cause of damage, but also the site of damage, impacts the consequences. Proximal damage causes more neurological deficit than distal damage. Also, the proximal part of the nerve is more prone to damage than the distal area. In an ideal situation, the nerves should be detected right on time, the injection of fluids should not cause damage, the spread of the anesthetic fluid is perfect and the onset time of the block is fast. Several ideal needle placements are theoretically possible. One could be just outside the nerve- structure, e.g. outer epineurium. In case of proximal nerve roots or cases of nearly mono-fascicular nerves this would be achievable. Another one could be inside the epineurium, e.g. intra-epineurally or parafascicular, but not intrafascicular. This needle position may be an option in more peripheral nerves with a lot of connective tissue and fat inside the epineurium, where the risk of damaging a fascicle is probably lower.

This study shows that employing STS at the tip of the needle is a promising technique that may improve accurate needle placement near nerve structures in regional anesthesia. In particular penetration of the needle into the nerve-fascicle could be avoided. However, before this can be implemented in real clinical practice, further validation is required where the technique is investigated in an in vivo setting where the different tissue structures are approached percutaneously. This will be part of future investigations.