RadOptima AI

RadOptima AI is a real-time medical imaging system designed to address one of the most critical challenges in modern radiology:

How can we achieve high-quality diagnostic imaging while minimizing radiation exposure and preserving clinical trust?

---

Core Problem

Modern medical imaging faces three major gaps:

• Radiation vs. Quality Trade-off
  Low-dose CT scans are safer but produce noisy, unreliable images.

• Real-Time Visualization Limitations
  High-quality 3D rendering is computationally expensive and often not interactive.

• AI Trust Gap
  Deep learning models can hallucinate or obscure critical details, making clinicians hesitant to rely on them.

---

Project Goal

RadOptima AI aims to:

Enable radiologists to interactively apply AI-powered denoising to low-dose CT scans in real time—without losing access to the original data.

Instead of replacing the raw scan, the system introduces a “human-in-the-loop” approach, where users can:

• Compare raw vs. AI-enhanced data
• Focus enhancement only on regions of interest
• Maintain full control over diagnostic interpretation

---

Key Features

Neural Lens (Core Innovation)

A real-time focus-aware AI system:

• Applies denoising only where the user is looking
• Preserves raw data outside the region
• Enables instant verification of AI outputs

Real-Time 3D Volume Rendering

• GPU raymarching with OpenGL
• Interactive slicing
• Transfer function editing for tissue isolation

AI-Powered Denoising

• 3D deep learning model for low-dose CT enhancement
• Runs on-demand inference on sub-volumes
• Designed to minimize hallucination risks

Dual-View Comparison

• Split-screen or lens-based visualization
• Raw vs AI-enhanced data in real time
• Supports clinical trust and validation

---

System Architecture

RadOptima AI follows a dual-stage + interactive loop architecture:

1. Core Engine (C++ / CUDA)

• High-performance volume rendering
• GPU memory management
• CUDA–OpenGL interoperability (zero-copy)

2. AI & Application Layer (Python)

• PyTorch inference engine
• DICOM loading and preprocessing
• UI and interaction logic

3. Interactive Loop (Key Innovation)

Instead of:

Input → AI → Output

The system operates as:

User Interaction → ROI Selection → AI Inference → Instant Visual Feedback

---

This enables:

• Real-time updates
• Reduced computation
• Focus-aware enhancement

---

Technical Highlights

• Zero-Copy GPU Pipeline
  Direct memory sharing between PyTorch and OpenGL using CUDA

• Raymarching Renderer
  Physically-based volumetric rendering using GLSL

• Hounsfield Unit Intelligence
  Native CT intensity handling for accurate tissue visualization

• On-Demand Inference
  Processes only small voxel regions (e.g., 64³) for efficiency

---

Target Users

• Radiologists – Faster and safer diagnostic workflows
• Medical Students / Residents – Better 3D anatomical understanding
• Researchers / Engineers – AI + graphics + GPU systems integration

---

AI Model Credit & Citation

The denoising model used in this project is based on prior research in medical imaging.

If you use or reference this system, please cite their paper and model:

E. Eulig, B. Ommer, and M. Kachelrieß,
“Benchmarking deep learning-based low-dose CT image denoising algorithms,”
Medical Physics, vol. 51, no. 12, pp. 8776–8788, Dec. 2024.

---

Tech Stack

• Languages: Python 3.11, C++17
• Graphics: OpenGL (GLSL)
• Compute: CUDA 12+
• AI: PyTorch, NumPy
• Interop: PyBind11

---

Status

Actively in Development on UI Improvements