Search

Intracranial aneurysms are common, but only a minority rupture and cause subarachnoid haemorrhage, presenting a dilemma regarding which to treat. Vessel wall imaging (VWI) is a contrast-enhanced magnetic resonance imaging (MRI) technique used to identify unstable aneurysms. The pathological basis of MR enhancement of aneurysms is the subject of debate. This review synthesises the literature to determine the pathological basis of VWI enhancement. PubMed and Embase searches were performed for studies reporting VWI of intracranial aneurysms and their correlated histological analysis. The risk of bias was assessed. Calculations of interdependence, univariate and multivariate analysis were performed. Of 228 publications identified, 7 met the eligibility criteria. Individual aneurysm data were extracted for 72 out of a total of 81 aneurysms. Univariate analysis showed macrophage markers (CD68 and MPO, p = 0.001 and p = 0.002), endothelial cell markers (CD34 and CD31, p = 0.007 and p = 0.003), glycans (Alcian blue, p = 0.003) and wall thickness (p = 0.030) were positively associated with enhancement. Aneurysm enhancement therefore appears to be associated with inflammatory infiltrate and neovascularisation. However, all these markers are correlated with each other, and the literature is limited in terms of the numbers of aneurysms analysed and the parameters considered. The data are therefore insufficient to determine if these associations are independent of each other or of aneurysm size, wall thickness and rupture status. Thus, the cause of aneurysm-wall enhancement currently remains unknown.

Haptoglobin is the body’s first line of defence against the toxicity of extracellular haemoglobin released following a subarachnoid haemorrhage (SAH). We investigated the haptoglobin response after SAH in cerebrospinal fluid (CSF) and serum. Paired CSF and serum samples from 19 controls and 92 SAH patients were assayed as follows: ultra-performance liquid chromatography for CSF haemoglobin and haptoglobin, immunoassay for serum haptoglobin and multiplexed CSF cytokines, and colorimetry for albumin. There was marked CSF haptoglobin deficiency: 99% of extracellular haemoglobin was unbound. The quotients for both CSF/serum albumin (qAlb) and haptoglobin (qHp) were used to compute the CSF haptoglobin index (qHp/qAlb). CSF from SAH patients had a significantly lower haptoglobin index compared to controls, especially in Haptoglobin-1 allele carriers. Serum haptoglobin levels increased after SAH and were correlated with CSF cytokine levels. Haptoglobin variables were not associated with long-term clinical outcomes post-SAH. We conclude that: (1) intrathecal haptoglobin consumption occurs after SAH, more so in haptoglobin-1 allele carriers; (2) serum haptoglobin is upregulated after SAH, in keeping with the liver acute phase response to central inflammation; (3) haptoglobin in the CSF is so low that any variation is too small for this to affect long-term outcomes, emphasising the potential for therapeutic haptoglobin supplementation.

The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200⁻3500 cm−1. An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.