Attention:The NSF Public Access Repository (PAR) system and access will be unavailable from 11:00 PM ET on Thursday, October 15th until 12:00 AM ET on Friday, October 16th due to maintenance. We apologize for the inconvenience.


Title: Hyperspectral imaging in neurosurgery: a review of systems, computational methods, and clinical applications
Significance: Accurate identification between pathologic (e.g., tumors) and healthy brain tissue is a critical need in neurosurgery. However, conventional surgical adjuncts have significant limitations toward achieving this goal (e.g., image guidance based on pre-operative imaging becomes inaccurate up to 3 cm as surgery proceeds). Hyperspectral imaging (HSI) has emerged as a potential powerful surgical adjunct to enable surgeons to accurately distinguish pathologic from normal tissues. Aim: We review HSI techniques in neurosurgery; categorize, explain, and summarize their technical and clinical details; and present some promising directions for future work. Approach: We performed a literature search on HSI methods in neurosurgery focusing on their hardware and implementation details; classification, estimation, and band selection methods; publicly available labeled and unlabeled data; image processing and augmented reality visualization systems; and clinical study conclusions. Results: We present a detailed review of HSI results in neurosurgery with a discussion of over 25 imaging systems, 45 clinical studies, and 60 computational methods. We first provide a short overview of HSI and the main branches of neurosurgery. Then, we describe in detail the imaging systems, computational methods, and clinical results for HSI using reflectance or fluorescence. Clinical implementations of HSI yield promising results in estimating perfusion and mapping brain function, classifying tumors and healthy tissues (e.g., in fluorescence-guided tumor surgery, detecting infiltrating margins not visible with conventional systems), and detecting epileptogenic regions. Finally, we discuss the advantages and disadvantages of HSI approaches and interesting research directions as a means to encourage future development. Conclusions: We describe a number of HSI applications across every major branch of neurosurgery. We believe these results demonstrate the potential of HSI as a powerful neurosurgical adjunct as more work continues to enable rapid acquisition with smaller footprints, greater spectral and spatial resolutions, and improved detection.  more » « less
Award ID(s):
1730574
PAR ID:
10580673
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Journal of Biomedical Optics
Date Published:
Journal Name:
Journal of biomedical optics
ISSN:
1083-3668
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Advanced stage glioma is the most aggressive form of malignant brain tumors with a short survival time. Real-time pathology assisted, or image guided surgical procedures that eliminate tumors promise to improve the clinical outcome and prolong the lives of patients. Our work is focused on the development of a rapid and sensitive assay for intraoperative diagnostics of glioma and identification of optical markers essential for differentiation between tumors and healthy brain tissues. We utilized fluorescence lifetime imaging (FLIM) of endogenous fluorophores related to metabolism of the glioma from freshly excised brains tissues. Macroscopic time-resolved fluorescence images of three intracranial animal glioma models and surgical samples of patients’ glioblastoma together with the white matter have been collected. Several established and new algorithms were applied to identify the imaging markers of the tumors. We found that fluorescence lifetime parameters characteristic of the glioma provided background for differentiation between the tumors and intact brain tissues. All three rat tumor models demonstrated substantial differences between the malignant and normal tissue. Similarly, tumors from patients demonstrated statistically significant differences from the peritumoral white matter without infiltration. While the data and the analysis presented in this paper are preliminary and further investigation with a larger number of samples is required, the proposed approach based on the macroscopic FLIM has a high potential for diagnostics of glioma and evaluation of the surgical margins of gliomas. 
    more » « less
  2. null (Ed.)
    Cancer affects one in three people worldwide. Surgery remains the primary curative option for localized cancers, but good prognoses require complete removal of primary tumors and timely recognition of metastases. To expand surgical capabilities and enhance patient outcomes, we developed a six-channel color/near-infrared image sensor inspired by the mantis shrimp visual system that enabled near-infrared fluorescence image guidance during surgery. The mantis shrimp’s unique eye, which maximizes the number of photons contributing to and the amount of information contained in each glimpse of its surroundings, is recapitulated in our single-chip imaging system that integrates arrays of vertically stacked silicon photodetectors and pixelated spectral filters. To provide information about tumor location unavailable from a single instrument, we tuned three color channels to permit an intuitive perspective of the surgical procedure and three near-infrared channels to permit multifunctional imaging of optical probes highlighting cancerous tissue. In nude athymic mice bearing human prostate tumors, our image sensor enabled simultaneous detection of two tumor-targeted fluorophores, distinguishing diseased from healthy tissue in an estimated 92% of cases. It also permitted extraction of near-infrared structured illumination enabling the mapping of the three-dimensional topography of tumors and surgical sites to within 1.2-mm error. In the operating room, during surgical resection in 18 patients with breast cancer, our image sensor further enabled sentinel lymph node mapping using clinically approved near-infrared fluorophores. The flexibility and performance afforded by this simple and compact architecture highlights the benefits of biologically inspired sensors in image-guided surgery. 
    more » « less
  3. The lack of tactile feedback in robot‐assisted minimally invasive surgery (RMIS) limits surgeons’ ability to palpate tissues, a critical technique for locating abnormalities such as tumors. To address this challenge, we introduceTacScope, a novel, vision‐based tactile sensor leveraging the magnification properties of a spherical‐surface elastomer to provide tactile feedback for advanced clinical applications.TacScopefeatures a robust, low‐cost, and easyly fabricate design, enabling seamless integration into surgical robotic setups. It reconstructs high‐resolution 3D geometry from variations in particle‐density distribution across its elastomer surface, requiring only a single image for calibration. The curved elastomer membrane alters particle‐density distribution under contact pressures, enabling detection of both surface‐level and subsurface tissue abnormalities. Unlike conventional vision‐based tactile sensors,TacScopeis compact and tailored for surgical devices. We validated our prototype first on rigid tissue phantoms for tumor detection and shape classification, and extended evaluation to soft‐tissue phantoms under a simulated operative conditions.TacScopeachieved 100% accuracy detecting artificial rigid tumors at depths up to 5 mm and over 90% accuracy classifying four tumor shapes up to 6 mm. It further achieved over 96% accuracy detecting artificial soft tumors 2 mm beneath the surface, confirming its potential for safer, more precise minimally invasive surgery. 
    more » « less
  4. This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i.e., caused by brain tumors). In contrast to previous learning-based registration methods that have little or no control over appearance-changes, our model introduces a new regularization that can effectively suppress the negative effects of appearance changing areas. In particular, we develop a piecewise regularization on the tangent space of diffeomorphic transformations (also known as initial velocity fields) via learned segmentation maps of abnormal regions. The geometric transformation and appearance changes are treated as joint tasks that are mutually beneficial. Our model MetaMorph is more robust and accurate when searching for an optimal registration solution under the guidance of segmentation, which in turn improves the segmentation performance by providing appropriately augmented training labels. We validate MetaMorph on real 3D human brain tumor magnetic resonance imaging (MRI) scans. Experimental results show that our model outperforms the state-of-the-art learning-based registration models. The proposed MetaMorph has great potential in various image-guided clinical interventions, e.g., real-time image-guided navigation systems for tumor removal surgery. 
    more » « less
  5. This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i.e., caused by brain tumors). In contrast to previous learning-based registration methods that have little or no control over appearance-changes, our model introduces a new regularization that can effectively suppress the negative effects of appearance changing areas. In particular, we develop a piecewise regularization on the tangent space of diffeomorphic transformations (also known as initial velocity fields) via learned segmentation maps of abnormal regions. The geometric transformation and appearance changes are treated as joint tasks that are mutually beneficial. Our model MetaMorph is more robust and accurate when searching for an optimal registration solution under the guidance of segmentation, which in turn improves the segmentation performance by providing appropriately augmented training labels. We validate MetaMorph on real 3D human brain tumor magnetic resonance imaging (MRI) scans. Experimental results show that our model outperforms the state-of-the-art learning-based registration models. The proposed MetaMorph has great potential in various image-guided clinical interventions, e.g., real-time image-guided navigation systems for tumor removal surgery. 
    more » « less