A Novel Hyperspectral Imaging System for Intraoperative Prediction of Cerebral Hyperperfusion Syndrome after Superficial Temporal Artery-Middle Cerebral Artery Anastomosis in Patients with Moyamoya Disease

Hyperspectral and multispectral imaging in neurosurgery: a systematic literature review and meta-analysis

INTRODUCTION

The neuro-surgical community is witnessing a rising interest for surgical application of multispectral/hyperspectral imaging. Several potential technical applications of this optical imaging are reported, but the set-up is variable and so are the processing methods. We present a systematic review of the relevant literature on the topic.

MATERIALS AND METHODS

A literature search based on the PRISMA principles was performed on PubMed, SCOPUS, and Web of Science, using MESH terms and Boolean operators. Papers regarding intra-operative in-vivo application of multispectral and/or hyperspectral imaging in humans during neurosurgical procedures were included. Papers reporting technologies related to radiological applications were excluded. A meta-analysis on the performance metrics was also conducted.

RESULTS

Our search string retrieved 20 papers. The main applications of optical imaging during neurosurgery concern tumour detection and improvement of the extent of resection (15 papers) or visualization of perfusion changes during neuro-oncology or neuro-vascular surgery (5 papers). All the retrieved articles were pilot studies, proof of concepts, or case reports, with limited number of patients recruited. Sensitivity, specificity, and accuracy were promising in most of the reports, but the metanalysis showed heterogeneous approaches and results among studies.

CONCLUSIONS

The present review shows that several approaches are currently being tested to integrate hyperspectral imaging in neurosurgery, but most of the studies reported a limited pool of patients, with different approaches to data collection and analysis. Further studies on larger cohorts of patients are therefore desirable to fully explore the potential of this imaging technique.

Effect of cerebral oxygen saturation monitoring in patients undergoing superficial temporal anterior-middle cerebral artery anastomosis for ischemic Moyamoya disease: a prospective cohort study

Objective

Regional cerebral oxygen saturation (rSO2) is linked with blood pressure. This study evaluated the influence of perioperative rSO2 monitoring on the prognosis of ischemic Moyamoya disease (MMD) patients undergoing anastomosis surgery.

Methods

In this prospective cohort, patients with unilateral ischemic MMD of Suzuki stage ≥3 were included. The decision of rSO2 was made by the clinician and the patient. The rSO2 group maintained intraoperative rSO2 levels through the modulation of blood pressure, inhaled oxygen concentration, carbon dioxide in arterial blood, and red blood cell transfusion. The non-rSO2 group used conventional anesthesia practices. Perioperative mean arterial pressure (MAP), rSO2 values, neurological complications, and postoperative results were assessed.

Results

A total of 75 eligible patients were categorized into a rSO2 monitoring group (n = 30) and a non-rSO2 monitoring group (n = 45). For the rSO2 group, the preoperative rSO2 was significantly lower on the affected side (P < 0.05). After anastomosis, this value notably increased (P = 0.01). A moderate relationship was observed between perioperative rSO2 and MAP before, during, and after surgery, with correlation coefficients (r) of 0.536, 0.502, and 0.592 (P < 0.05). Post-surgery MAP levels differed between the groups, with the rSO2 group showing decreased levels compared to pre-surgery and the non-rOS2 group displaying elevated levels. Notably, the rSO2 group reported shorter hospitalizations and decreased neurological complications. Patients with a hypertension history found postoperative MAP influencing hospital stay duration.

Conclusion

Perioperative rSO2 surveillance enhanced cerebral perfusion and minimized postoperative complications in ischemic MMD patients. Thus, rSO2 monitoring is advocated for MMD patients undergoing vascular anastomosis.

Hyperspectral Imaging in Brain Tumor Surgery-Evidence of Machine Learning-Based Performance

BACKGROUND

Hyperspectral imaging (HSI) has the potential to enhance surgical tissue detection and diagnostics. Definite utilization of intraoperative HSI guidance demands validated machine learning and public datasets that currently do not exist. Moreover, current imaging conventions are dispersed, and evidence-based paradigms for neurosurgical HSI have not been declared.

METHODS

We presented the rationale and a detailed clinical paradigm for establishing microneurosurgical HSI guidance. In addition, a systematic literature review was conducted to summarize the current indications and performance of neurosurgical HSI systems, with an emphasis on machine learning-based methods.

RESULTS

The published data comprised a few case series or case reports aiming to classify tissues during glioma operations. For a multitissue classification problem, the highest overall accuracy of 80% was obtained using deep learning. Our HSI system was capable of intraoperative data acquisition and visualization with minimal disturbance to glioma surgery.

CONCLUSIONS

In a limited number of publications, neurosurgical HSI has demonstrated unique capabilities in contrast to the established imaging techniques. Multidisciplinary work is required to establish communicable HSI standards and clinical impact. Our HSI paradigm endorses systematic intraoperative HSI data collection, which aims to facilitate the related standards, medical device regulations, and value-based medical imaging systems.

Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

The study of brain science is vital to human health. The application of hyperspectral imaging in biomedical fields has grown dramatically in recent years due to their unique optical imaging method and multidimensional information acquisition. Hyperspectral imaging technology can acquire two-dimensional spatial information and one-dimensional spectral information of biological samples simultaneously, covering the ultraviolet, visible and infrared spectral ranges with high spectral resolution, which can provide diagnostic information about the physiological, morphological and biochemical components of tissues and organs. This technology also presents finer spectral features for brain imaging studies, and further provides more auxiliary information for cerebral disease research. This paper reviews the recent advance of hyperspectral imaging in cerebral diagnosis. Firstly, the experimental setup, image acquisition and pre-processing, and analysis methods of hyperspectral technology were introduced. Secondly, the latest research progress and applications of hyperspectral imaging in brain tissue metabolism, hemodynamics, and brain cancer diagnosis in recent years were summarized briefly. Finally, the limitations of the application of hyperspectral imaging in cerebral disease diagnosis field were analyzed, and the future development direction was proposed.

First-in-human pilot study of snapshot multispectral endoscopy for early detection of Barrett's-related neoplasia

SIGNIFICANCE

The early detection of dysplasia in patients with Barrett's esophagus could improve outcomes by enabling curative intervention; however, dysplasia is often inconspicuous using conventional white-light endoscopy.

AIM

We sought to determine whether multispectral imaging (MSI) could be applied in endoscopy to improve detection of dysplasia in the upper gastrointestinal (GI) tract.

APPROACH

We used a commercial fiberscope to relay imaging data from within the upper GI tract to a snapshot MSI camera capable of collecting data from nine spectral bands. The system was deployed in a pilot clinical study of 20 patients (ClinicalTrials.gov NCT03388047) to capture 727 in vivo image cubes matched with gold-standard diagnosis from histopathology. We compared the performance of seven learning-based methods for data classification, including linear discriminant analysis, k-nearest neighbor classification, and a neural network.

RESULTS

Validation of our approach using a Macbeth color chart achieved an image-based classification accuracy of 96.5%. Although our patient cohort showed significant intra- and interpatient variance, we were able to resolve disease-specific contributions to the recorded MSI data. In classification, a combined principal component analysis and k-nearest-neighbor approach performed best, achieving accuracies of 95.8%, 90.7%, and 76.1%, respectively, for squamous, non-dysplastic Barrett's esophagus and neoplasia based on majority decisions per-image.

CONCLUSIONS

MSI shows promise for disease classification in Barrett's esophagus and merits further investigation as a tool in high-definition "chip-on-tip" endoscopes.