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Fluorescence Molecular Tomography for In Vivo Imaging of Glioblastoma Xenografts
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Versatile and comprehensive hyperspectral imaging tool for molecular neuronavigation: a case study on cerebral
Dorotea Nardini1,2, Anam Toaha1,2, Camilla Bonaudo3
1University of Florence, Department of Physics and Astronomy, Sesto Fiorentino, Italy.
Significance:
Accurate and timely characterization of brain tumors remains a major challenge in neurosurgery. Current intraoperative guidance relies on preoperative imaging modalities such as magnetic resonance imaging, positron emission tomography, or computed tomography, which are essential for surgical planning but become less reliable during surgery due to brain shift. Furthermore, postoperative tumor classification depends on histopathology, which requires weeks and can delay treatment decisions. No existing tool offers real-time, label-free, and spatially resolved biomolecular information to support both intraoperative guidance and early tissue assessment.
Aim:
We developed HyperProbe1.1 (HP1.1), a hyperspectral imaging system designed to acquire comprehensive molecular and metabolic information from brain tissue without the need for contrast agents or staining.
Approach:
HP1.1 captures reflectance images across a broad range of narrow spectral bands, enabling spatial mapping of hemoglobin, cytochrome c oxidase, and oxygen saturation. In addition, ultraviolet-excited autofluorescence imaging provides information on metabolic cofactors - nicotinamide adenine dinucleotide and flavin adenine dinucleotide - relevant for tumor characterization. The system was validated using standardized phantoms and ex vivo glioma samples.
Results:
HP1.1 demonstrated strong performance in detecting spectral features across phantoms and in distinguishing glioma tissues of different histological grades, enabling the generation of rapid and spatially resolved molecular contrast maps.
Conclusions:
By providing label-free, high-content, and rapid biomolecular imaging, HP1.1 represents a powerful platform for noninvasive tissue assessment in controlled experimental settings and paves the way for future intraoperative applications.
Insights
A new hyperspectral imaging system, HyperProbe1.1, offers real-time, label-free molecular and metabolic data for brain tumor characterization. This technology aids in surgical guidance and early tissue assessment, overcoming limitations of current methods.
Area of Science:
- Biomedical Optics
- Neurosurgical Oncology
- Molecular Imaging
Background:
- Accurate brain tumor characterization is crucial for neurosurgery but faces challenges with current imaging and histopathology methods.
- Preoperative imaging can be unreliable during surgery due to brain shift, and histopathology delays treatment decisions.
- There is a need for real-time, label-free tools for intraoperative guidance and early tissue assessment.
Purpose of the Study:
- To develop and validate a hyperspectral imaging system, HyperProbe1.1 (HP1.1), for comprehensive, label-free molecular and metabolic characterization of brain tissue.
- To enable real-time, spatially resolved biomolecular information for improved surgical guidance and tissue assessment.
Main Methods:
- HP1.1 captures reflectance and ultraviolet-excited autofluorescence images across narrow spectral bands.
- The system maps hemoglobin, cytochrome c oxidase, oxygen saturation, and metabolic cofactors like nicotinamide adenine dinucleotide and flavin adenine dinucleotide.
- Validation was performed using standardized phantoms and ex vivo glioma samples.
Main Results:
- HP1.1 successfully detected spectral features in phantoms and distinguished glioma tissues by histological grade.
- The system generated rapid, spatially resolved molecular contrast maps.
- Demonstrated strong performance in label-free tissue assessment.
Conclusions:
- HP1.1 provides high-content, rapid, label-free biomolecular imaging.
- This system is a powerful platform for noninvasive tissue assessment in experimental settings.
- HP1.1 shows promise for future intraoperative neurosurgical applications.
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