====== 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




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The SLIMBRAIN database fills a crucial gap in [[multimodal intraoperative imaging research]] and sets a new [[benchmark]] for open-access [[dataset]]s 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.