Enhanced Surgical Guidance with Multimodal Spectroscopic Imaging

Dear Colleagues,

Multimodal imaging utilizes spectroscopic techniques to enhance surgical guidance and improve patient results. It incorporates multiple imaging modalities to offer comprehensive, real-time information about the surgical field and helps surgeons to make more informed decisions during procedures. For complex treatments, traditional surgical methods may not be sufficient because they mostly depend on preoperative imaging and the surgeon's experience. By providing a real-time, high-resolution and multi-dimensional view of tissues, multimodal imaging overcomes these drawbacks. Via the integration of functional and anatomical data, this method enhances accuracy, offers real-time feedback during surgery, and eventually reduces problems while accelerating recovery. Optical coherence tomography (OCT) provides high-resolution cross-sectional tissue images, fluorescence imaging highlights specific structures with fluorescent markers, and Raman spectroscopy provides molecular fingerprints based on vibrational energy levels and photoacoustic imaging, which combines optical and ultrasound methods to produce high-contrast images of biological tissues; these are all examples of spectroscopic techniques that are integrated into multimodal imaging in order to utilize their unique strengths.

OCT uses light waves to take pictures with a micrometer resolution inside optical scattering materials, such as biological tissue. In fluorescence imaging, certain biological components are exposed to light wavelengths through the application of dyes that attach to them. Raman Spectroscopy uses laser light to interact with molecular vibrations and provides comprehensive information on the chemical composition of an object. Photoacoustic imaging creates high-resolution pictures of tissue structures by using ultrasonic signals created by pulsed lasers. The integration of machine learning (ML) and artificial intelligence (AI) is an important trend in multimodal imaging. ML and AI assess large, complicated datasets from several imaging modalities to provide predictive insights and improve the interpretation of images.

The development of safer and more effective contrast agents for enhanced imaging, the adoption of personalized medicine that adapts imaging techniques to individual patient needs, and the acceleration of AI-enhanced diagnostics for faster and more accurate image analysis all point to a promising future for multimodal imaging in surgical guidance. The challenges that persist in multimodal imaging include technical complexity, such as the incorporation of multiple systems, which can be costly. To overcome these issues, standardized protocols and cost-effective technologies are required. AI and complex data processing algorithms are required to analyze the vast amount of imaging data, which is also referred to as data overload. Working closely with regulatory agencies and conducting exhaustive clinical trials are necessary for navigating past regulatory obstacles, such as ensuring that medical regulations are followed. However, these technologies have the potential to greatly enhance surgical precision and patient outcomes.

List of Interested topics include, but are not limited to, the following:

• Enhancing patient outcomes through multimodal imaging in surgery.
• Employing spectroscopic techniques for improved surgical navigation.
• Strategies to overcome surgical challenges with multimodal imaging.
• Applications of multimodal imaging in complex surgical procedures.
• Current trends and future directions in precision surgery enabled by multimodal imaging.
• Exploring the impact of multimodal imaging innovations in surgical technology.
• Role of Optical Coherence Tomography (OCT) in biomedical imaging.
• Advancements in multimodal imaging and AI for transforming the landscape of medical diagnosis.
• Challenges and opportunities for safer and more effective pediatric surgeries.
• Analysis of emerging trends in multimodal imaging, from 3D reconstruction to virtual reality visualization.

Dr. Solihin Iwan Mahmud
Prof. Dr. Kuo-Ping Lin
Guest Editors

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• spectroscopy
• image-guided surgery
• surgical guidance
• medical imaging
• multimodal imaging
• imaging