Abstract We present FieldGPU, a CUDA-accelerated reimplementation of the Field II ultrasound simulation framework, accessible as a Python package. FieldGPU leverages GPU parallelism to improve scalability and reduce simulation times for large transducer arrays and high-quality simulation scenarios. It supports most core Field II features, including transmit and receive simulation and custom transducer configurations with rectangular aperture elements. Comparison benchmarks performed between Field II and FieldGPU demonstrate that FieldGPU achieves up to 1100 times performance increase compared to Field II for the largest tested simulation with 10 million scatterers. Our simulation method significantly reduces simulation times and provides a modern Python interface that facilitates the direct translation of Field II simulation scripts to FieldGPU.
Focused wave propagation simulation generated using FieldGPU
Technical Approach
GPU Acceleration
CUDA-based parallel processing distributes spatial impulse response calculations across GPU threads, utilizing the massively parallel architecture for significant performance improvements over CPU implementations.
Python Integration
Native Python bindings enable integration with scientific computing workflows, machine learning frameworks, and data analysis pipelines commonly used in medical imaging research.
Scalable Architecture
Performance improvements scale with simulation complexity, enabling large-scale studies previously limited by computational constraints in traditional CPU-based implementations.
Performance Evaluation
Test Configuration
- Hardware: NVIDIA RTX 3090, AMD Threadripper 3970X, 128 GB RAM
- Transducer: 64-element linear array, 5 MHz center frequency
- Sampling: 100 MHz sampling rate, 1540 m/s speed of sound
- Function: calc_scat_multi performance comparison
Comprehensive benchmarks were conducted comparing FieldGPU with the original Field II implementation across varying scatterer counts and multiple functions. For the function calc_scat_multi FieldGPU achieves maximum speedups of over 1,300×:
| Scatterers | Field II (s) | FieldGPU (s) | Speedup |
|---|---|---|---|
| 100 | 0.099 | 0.0024 | 41× |
| 1,000 | 0.673 | 0.0050 | 135× |
| 10,000 | 6.68 | 0.022 | 304× |
| 100,000 | 69.5 | 0.086 | 810× |
| 1,000,000 | 644 | 0.51 | 1,265× |
| 10,000,000 | 5,876 | 4.31 | 1,364× |
Discussion
This work demonstrates that GPU acceleration can significantly improve the computational efficiency of ultrasound simulation workflows. The performance improvements achieved (average 650× speedup, maximum 1,364×) enable simulation scenarios that were previously unfeasible, particularly for studies involving large numbers of scatterers or complex transducer geometries.
While FieldGPU currently supports rectangular elements and maintains compatibility with most standard Field II workflows, the Python interface facilitates integration with modern scientific computing and machine learning frameworks.
Citation
Klitzner, F., Göbl, R., Hennersperger, C., & Wörz, S. (2025 September).
FieldGPU: A GPU-based Version of Field II with Python Bindings for Large Scale Simulations and Complex Transducer Configurations.
In 2025 IEEE International Ultrasonics Symposium (IUS) (pp. 1-4). IEEE.