Poster Keerthana Deepti Karunakaran BioMedical Engineering And Imaging Institute
Dr M. Karunakaran – Pioneering the Future of Biomedical Imaging
About the Scientist
Dr M. Karunakaran is a world‑renowned biomedical engineer whose research bridges physics, biology and data science to unlock new ways of seeing inside living organisms. With more than 60 peer‑reviewed papers in Nature, Science and IEEE Transactions on Medical Imaging, he has revolutionised the way clinicians detect disease at its earliest stages.
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Key Contributions
| Field | Achievement | Impact |
|---|---|---|
| Photoacoustic Tomography | Developed an ultra‑high‑resolution system that couples laser illumination with ultrasound detection, achieving < 50 µm depth resolution in vivo. | Enables non‑invasive imaging of tumour vasculature and oxygenation, improving early cancer diagnosis. |
| Deep Learning for Image Reconstruction | Created a generative adversarial network (GAN) that reconstructs high‑fidelity CT images from ultra‑low radiation doses (< 0.1 mSv). | Reduces patient exposure while maintaining diagnostic quality. |
| Optical Coherence Tomography (OCT) | Introduced adaptive optics into OCT, allowing imaging of retinal cells at the single‑cell level. | Facilitates early detection of degenerative eye diseases such as macular degeneration. |
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3. The Most Promising Future Development
AI‑Enhanced Multi‑Modal Imaging Platforms
Why It Stands Out
| Aspect | Explanation |
|---|---|
| Integration Across Modalities | Combines data from CT, MRI, PET, Ultrasound, and OCT in real time to provide a comprehensive view. |
| Deep Learning Fusion | Neural networks learn complementary features across modalities, improving sensitivity and specificity for https://lings.id/adrianapor subtle lesions. |
| Adaptive Imaging Protocols | AI adjusts scan parameters (contrast timing, slice thickness) on the fly based on preliminary results, reducing radiation dose or scan time. |
| Clinical Decision Support | Generates prioritized findings, risk scores, and suggested management pathways directly in the PACS interface. |
| Scalability | Modular architecture allows deployment across diverse hardware platforms, from high-end workstations to mobile devices. |
This integrated platform would transform diagnostic radiology by offering a unified, intelligent system that reduces inter‑observer variability, shortens report turnaround times, and ultimately improves patient outcomes.
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Prepared for internal review by the Radiology Informatics Working Group.
