GPU acceleration for reconstruction: a practical playbook (CT/MRI/PET)

Medical imaging reconstruction algorithms like FBP, OSEM, and iterative methods are computationally intensive. Moving them to the GPU can yield 10-50x speedups, but it requires careful architectural planning.

The Challenge of Data Transfer

One of the most common bottlenecks in GPU acceleration is the PCIe bus. When processing large volumetric datasets (CT/MRI), the time spent transferring data between host and device memory can negate the compute benefits.

We recommend a streaming architecture where data is transferred in chunks (streams in CUDA) asynchronously while the GPU processes the previous chunk. This hides the transfer latency.

Precision Matters

While single precision (FP32) is often sufficient for visualization, reconstruction often requires double precision (FP64) or mixed precision to maintain numerical stability, especially in iterative algorithms.

Key Takeaway

Always validate your GPU implementation against a CPU reference implementation with strict tolerance thresholds before optimizing for speed.

Conclusion

Porting reconstruction pipelines to GPU is complex but high-reward. By focusing on memory management and numerical validation first, teams can avoid common pitfalls.