UltraDiff: Differentiable Ray Tracing in Ultrasound for Shape Optimization

Key points

• Physically-based differentiable rendering enables gradient-based optimization of scene parameters by matching rendered images to measurements, but has so far mainly focused on light transport.1
• We extend this paradigm to medical ultrasound, where image formation resembles transient rendering: echoes are binned by time-of-flight rather than projected onto an image plane.1
• UltraDiff formulates ultrasound image formation as a path-space integral, gated by travel time between the transducer and tissue interfaces, and derives a Monte Carlo estimator of both the forward model and its gradients with respect to scene parameters.1
• We demonstrate this on an inverse geometry estimation: starting from a sphere, an SDF is optimized until simulated echoes match measured ones, recovering vertebral surfaces from simulated B-mode sweeps and from a real robotic acquisition of a spine phantom.1

Sources (1)

• [1]UltraDiff: Differentiable Ray Tracing in Ultrasound for Shape Optimization
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 08:16 AM

  We present UltraDiff, a modular framework for differentiable ultrasound ray tracing.

  Physically-based differentiable rendering enables gradient-based optimization of scene parameters by matching rendered images to measurements, but has so far mainly focused on light transport.

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