Multimodal Intraoperative Imaging Research
Multimodal intraoperative imaging is the integration of two or more imaging techniques during surgery to improve anatomical visualization, functional mapping, and decision-making in real time.
🔍 Definition
Multimodal intraoperative imaging combines different real-time imaging modalities during surgery to provide complementary anatomical, functional, or metabolic information about the surgical field.
🧠 Common Modalities in Neurosurgery
| Imaging Modality | Function |
|---|---|
| Intraoperative MRI (iMRI) | High-resolution anatomical images; detects residual tumor. |
| Intraoperative Ultrasound (iUS) | Real-time, dynamic visualization; guides resection. |
| Neuronavigation | GPS-like surgical guidance using pre-op MRI/CT data. |
| Fluorescence Imaging (5-ALA, ICG) | Highlights tumor tissue or vasculature. |
| Intraoperative CT (iCT) | Useful for bone, hemorrhage, and spinal alignment. |
| Hyperspectral Imaging (HSI) | Spectral differentiation of tissue types. |
| Electrophysiological Monitoring (MEP, SSEP, ECoG) | Functional mapping and safety during resection. |
| Optical Coherence Tomography (OCT) | High-resolution imaging for microstructures. |
🎯 Objectives
- Maximize tumor resection while preserving function
- Compensate for brain shift
- Detect residual tumor in real time
- Prevent complications (e.g. vascular injury)
- Enable functional preservation (e.g. motor, language)
🧪 Research Areas
- Integration platforms (coregistration of MRI + US, etc.)
- AI-based segmentation and intraoperative decision support
- Data fusion and visualization systems
- Hardware miniaturization for OR use
- Clinical outcome studies (extent of resection, survival)
📚 Example Projects
- SLIMBRAIN database – includes hyperspectral, RGB, depth images from brain surgeries
- Project HYPER-MRI – fusion of HSI with intraoperative MRI in glioma surgery
- MIT-MGH iUS Fusion Lab – AI segmentation of iUS with MRI-based navigation
The SLIMBRAIN database fills a crucial gap in multimodal intraoperative imaging research and sets a new benchmark for open-access datasets in neurosurgery. It is well-positioned to:
Foster the development of real-time ML-based surgical guidance tools.
Enable transfer learning and domain adaptation studies using rich spectral and geometric features.
Serve as a validation benchmark for new HSI sensors and fusion algorithms.
Future work should emphasize:
Cross-validation with postoperative histopathology.
Standardization of acquisition protocols and interoperability with hospital systems.
Expansion to include longitudinal follow-up and outcome-based annotations.
Conclusion
The SLIMBRAIN database is a landmark contribution to the field of surgical imaging and AI in neuro-oncology. Despite certain methodological limitations—chiefly regarding labeling consistency and histopathological correlation—its scale, multimodal depth, and open availability make it a transformative resource. Its full potential will be realized when integrated into prospective, outcome-linked ML workflows and subjected to external clinical validation.