This checkpoint is a 512x512 2D diffusion prior trained on chest X-ray domain images and used as the image prior inside DDNM for CT projection super-resolution. It is not a standalone CT volume reconstruction model. In the full pipeline, a low-resolution CT volume is first converted into 2D projection images; DDNM then uses this diffusion prior to enhance each projection; the enhanced projections are subsequently used by the 3D reconstruction stage.

Intended Use

2D CT projection super-resolution through DDNM.
Zero-shot 3D CT super-resolution pipelines where projection-domain priors are used before volume reconstruction.
Research use and reproduction of the projection-prior stage.

Training Data

The diffusion prior was trained on chest X-ray domain images from:

CheX-ray14
CheXpert

The model is used as a natural/medical X-ray image prior for projection-domain restoration. It was not trained directly on the target CT volumes used for downstream evaluation.

Architecture and Sampling Settings

The checkpoint follows an improved-diffusion / guided-diffusion style UNet configuration:

yaml

image_size: 512
in_channels: 3
out_channels: 3
num_channels: 256
num_res_blocks: 2
attention_resolutions: "32,16,8"
num_heads: 4
num_head_channels: 64
dropout: 0.0
learn_sigma: false
use_scale_shift_norm: true
use_fp16: true
resblock_updown: true
beta_schedule: linear
beta_start: 0.0001
beta_end: 0.02
num_diffusion_timesteps: 1000

DDNM projection SR settings used in the release wrapper:

Scale	degradation	eta	sigma_y	sampling steps
4x	sr_averagepooling	0.990	0.0010	50
8x	sr_averagepooling	0.990	0.0025	50

Files

Upload the DDNM/SIDE checkpoint as:

ema_0.9999_620000.pt

The GitHub wrapper expects this file by default.

Usage

bash

pip install huggingface_hub

python ddnm_inference/run_ddnm_projection_sr.py \
  --hf-model-repo Hyun-Jic/ddnm-xray512-ct-projection-prior \
  --hf-model-file ema_0.9999_620000.pt \
  --ddnm-root /path/to/DDNM \
  --input-npy examples/mela_0050/mela_0050_projection_4x_128x128.npy \
  --gt-pickle /path/to/MELA_GT_512_rmbed/mela_0050_rmbed.pickle \
  --case-id mela_0050 \
  --scale 4

Limitations

The model is a 2D projection prior, not a complete 3D CT reconstructor.
Output quality depends on the DDNM degradation operator, projection normalization, and downstream reconstruction.
Clinical use is not intended without additional validation.

Notes

The checkpoint is large and should be stored on Hugging Face rather than in the GitHub repository.