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Medical Imaging Solutions

Cutting-edge medical imaging software and visualization solutions that improve diagnostic accuracy, streamline workflows, and enhance patient care through advanced technology.

Advanced Medical Visualization

We develop sophisticated medical imaging software that transforms complex data from CT, MRI, ultrasound, and other imaging modalities into actionable insights. Our solutions leverage the latest in 3D rendering, artificial intelligence, and machine learning technologies.

Our Medical Imaging Capabilities:

  • • DICOM-compliant medical imaging viewers
  • • 3D volumetric rendering and visualization
  • • AI-assisted diagnostic tools and image analysis
  • • Medical image segmentation and quantitative analysis
  • • Web-based medical imaging platforms
  • • VR/AR solutions for medical education and planning
  • • Teleradiology and remote diagnostics platforms

Regulatory Compliance & Security

Our medical imaging solutions are built with regulatory requirements in mind. We develop software that complies with HIPAA, GDPR, and FDA regulations while implementing stringent security measures to protect sensitive patient data and maintain privacy.

RF NeRF Visualization System

Our cutting-edge RF NeRF Visualization System provides a comprehensive platform for visualizing Radio Frequency (RF) signal data in 3D space using advanced techniques like Neural Radiance Fields (NeRF), Neural Gaussian Splats, and Neural Correspondence Fields. The platform features a web-based frontend for interactive visualization and a robust backend for data management, processing, and job handling—with performance optimization through CUDA Python for GPU acceleration.

Key Features:

  • • Multiple Visualization Techniques: Point Clouds, NeRF volumes, Gaussian Splats, and Correspondence Fields for diverse data representation
  • • Interactive 3D Frontend: Built with React and Three.js for responsive user experience
  • • GPU Acceleration: Leverages CUDA, CuPy, and Numba for significant performance improvements
  • • Data Management: Supports various RF and medical imaging data formats (DICOM, NIfTI, etc.)

Technical Implementation

Below is an excerpt from our CUDA-accelerated Neural Radiance Field renderer, which enables real-time visualization of complex 3D medical imaging data. The implementation leverages GPU parallel processing for ray generation and volumetric rendering.

CUDA NeRF Renderer Implementation

High-performance GPU-accelerated renderer using CUDA kernels for medical imaging visualization

Python
1import torch
2import cupy as cp
3import numpy as np
4from numba import cuda
5import math
6from typing import Dict, List, Tuple, Optional
7
8class CUDANeRFRenderer:
9    """
10    CUDA-accelerated volumetric renderer for RF-NeRF model.
11    Provides GPU-optimized ray marching and volumetric rendering.
12    """
13    
14    def __init__(
15        self,
16        model,                    # PyTorch NeRF model
17        num_samples: int = 64,    # Number of samples along each ray
18        near_bound: float = 0.1,  # Near boundary for sampling
19        far_bound: float = 10.0,  # Far boundary for sampling
20        chunk_size: int = 32768,  # Chunk size for batched inference

Innovation in Medical Imaging

  • AI-Driven Diagnostics: Machine learning algorithms that assist radiologists in identifying anomalies in medical scans, improving accuracy and reducing interpretation time.
  • Surgical Planning Systems: Interactive 3D visualization tools that help surgeons plan complex procedures with greater precision and confidence.
  • Research Applications: Custom imaging software for research institutions conducting advanced studies in medical imaging and analysis.
  • Patient Education: Interactive visualization tools that help patients better understand their conditions and treatment options.
  • RF-Medical Integration: Novel techniques combining RF signal data with traditional medical imaging for enhanced diagnostics and monitoring.
Technology Stack

Frontend: React, Three.js
Backend: Node.js, Express.js, MongoDB, Redis
Processing Engine: PyTorch, CUDA Python, NumPy, CuPy
Supported Formats: DICOM, NIfTI, CSV, NPY, IQ, MAT

Performance Highlights
  • • 10-15x faster rendering compared to CPU-only implementation
  • • Real-time visualization of 1GB+ medical imaging datasets
  • • Optimized for NVIDIA RTX/Quadro GPUs used in medical workstations
  • • Multi-GPU support for handling complex volumetric data

Experiance the 3D SDR Platform