IIT Madras · HTIC — 2026
Sub-10 ms bone morphing during surgery, with no preoperative CT — the patient's femur and tibia reconstructed from points swept with a probe on the table.
In progress — paper in preparation, MICCAI 2026 workshop track
Image slot
GPUNeuroMorph — neural signed distance field morphing a femur surface
1600×1000 · pipeline diagram or a render of the morphed femur/tibia surface
Role
Research engineer — algorithm design, CUDA implementation, benchmarking
Year
2026
Stack
- CUDA
- Holoscan SDK
- PyTorch
- C++
- Neural SDF
- Python
The problem
Total knee arthroplasty navigation normally begins with a preoperative CT. The patient is scanned, the bone is segmented, and the surgical plan is built against that model. The scan costs time, money, and radiation dose, and it fixes the plan to anatomy captured days before the incision. The alternative is to build the model in theatre, from points the surgeon sweeps across exposed bone — but that only works if the morphing is fast enough to feel instantaneous and accurate enough to plan a cut against. Those two requirements pull directly against each other, and the usual answer is to give up some of one.
Approach
Represent the bone as a neural signed distance field — a multiresolution hash grid feeding a small MLP — so the surface is continuous and queryable at any resolution rather than frozen into a mesh at authoring time.
Warp in field space using Gaussian radial basis functions rather than displacing vertices. The surface stays watertight under deformations large enough to break a mesh-based approach.
Register the sparse probed points against the statistical model with Coherent Point Drift over hierarchical Gaussian mixtures, keeping the correspondence search well below the quadratic cost of the naive formulation.
Schedule the entire path as a Holoscan SDK operator graph so the intraoperative loop never round-trips to the host — the data stays resident on the GPU from probe input to rendered surface.
Results
- GPU latency, full morph
~7 ms
GPU latency, full morph
- femur surface error
0.47 mm
femur surface error
- tibia surface error
0.33 mm
tibia surface error
- preoperative CT scans required
zero
preoperative CT scans required
Internal benchmarks measured on the lab rig. The work is ongoing and has not yet been peer-reviewed — figures are preliminary and will be restated when the paper lands.
Image slot
GPUNeuroMorph system architecture diagram
1600×1000
Image slot
Surface error heat map across the femur
1600×1000
What it came to
The result that mattered was not the accuracy number on its own, it was the accuracy number holding at seven milliseconds. Anything slower than a frame and the surgeon feels the system thinking; anything looser than half a millimetre and the plan is not worth building on. Removing the preoperative CT removes a scan, a wait, and a dose from the patient's path to surgery.