DeepTFUS: base (run-1 reproduction)

A reproduction attempt of DeepTFUS, proposed by Srivastav et al. (arXiv:2505.12998).

This is the from-scratch baseline: 50 epochs on the paper recipe (weighted-MSE + λ·gradient-L1, no focal-position aux), base_width=16 (3.4 M params), pure-bf16, batch=4 at 256³ resolution. Given a 3D head CT and a transducer placement, predicts the resulting in-skull pressure field in <1 s on an H100 (≈ 50× faster than the k-Wave physics simulator the dataset was generated from).

⭐ Partial reproduction: matched paper on relative_l2, did not match on focal_position_error_mm (~2× worse) or max_pressure_error. This gap motivated the 5 fine-tune variants in this model collection.

Test results (n = 597 held-out CT × placement combinations)

metric	paper	base (this model)	reproduced?
`relative_l2` mean ± std	0.414 ± 0.086	0.384 ± 0.078	✅ Yes (slightly beats paper)
`relative_l2` median	0.394	0.369	✅
`focal_position_error_mm` mean ± std	2.89 ± 2.14	6.49 ± 4.58	❌ No (~2.25× worse mean)
`focal_position_error_mm` median	2.45	5.15	❌
`max_pressure_error` mean ± std	0.199 ± 0.158	0.225 ± 0.116	✅ Yes (within paper's std)
`max_pressure_error` median	0.166	0.217	(slightly above paper)
`focal_pressure_error` median	:	0.528	:
`focal_iou_fwhm` median	:	0.143	:
`inference_latency_s` (b=1, H100)	11.4 (RTX 4090)	0.233	49× faster (different HW)

Other variants and discussion

See the Collection for the 5 fine-tune variants built from this base ckpt, and the project page for the full reproduction story, interactive viewer, and discussion of trade-offs.

Usage

from huggingface_hub import hf_hub_download
import torch

ckpt = torch.load(
    hf_hub_download("masonwang025/deeptfus-base", "ckpt_best.pt"),
    map_location="cpu", weights_only=False,
)
# ckpt['model']  : state_dict for the model defined in masonwang025/deeptfus repo
# ckpt['config'] : training config (architecture knobs + train hyperparams)
# ckpt['epoch']  : 43 (best by val_rel_l2)

Model code: github.com/masonwang025/deeptfus.