---
language: en
library_name: pytorch
tags:
- medical-imaging
- computed-tomography
- diffusion
- image-reconstruction
---

# UPath-X2CT

Pretrained weights for **Geometry-Aware Biplanar Chest CT Reconstruction with
Residual Latent Diffusion**. [Code, fixed splits, training and evaluation](https://github.com/hekaiwang/UPath-X2CT).

## Released model

The current release provides one complete model checkpoint:
**`upath_x2ct_final.pt`**. It embeds the frozen normalized autoencoder, coarse
conditioning network, validation-selected step-22,000 residual diffusion,
training-only projection normalization, CT-to-HU scale/offset, and projector
calibration. `manifest.json` records its SHA256, size and source provenance.

EMA weights are materialized as ordinary PyTorch state dictionaries, preserving
all tensor values and dtypes. No optimizer state or training data is included.
The code trains all three stages and exports the same single-file format.
There is no separate coarse checkpoint or standalone coarse result table in the
current release. Earlier revisions retain the previous release layout.

## Use

Clone and install the linked GitHub repository, then run:

```bash
python scripts/download_weights.py --output weights
python scripts/predict.py --weights weights/upath_x2ct_final.pt --input projections.npz \
  --case-id example --output runs/example
```

Input is calibrated 512×512 AP and lateral **CT-derived DRRs**, with explicit
camera matrices in centered LPS coordinates. Inference does not require target
CT or masks. Output is a 128³ CT mean in HU plus sample standard deviation,
using K=8, 50 DDIM steps, eta=0. See the code repository for exact input geometry.

## Training and evaluation

Training: LIDC-IDRI, 817 patients / 824 CT volumes. Validation: 91 patients /
91 volumes. Held-out LIDC: 102 patients / 103 volumes. External tests: MSD Lung
(63) and CT-RATE (425); neither was used for fitting. Seed: 20260724.

Historical final-model results (PSNR dB / SSIM percent / whole-volume MAE HU):

| Cohort | PSNR | SSIM | MAE |
|---|---:|---:|---:|
| LIDC-IDRI | 26.35 | 81.45 | 50.00 |
| MSD Lung | 24.98 | 76.43 | 63.14 |
| CT-RATE | 24.14 | 71.84 | 70.99 |

These are patient-level means from the original evaluation. Release-time replay
measurements and numerical drift are documented in the GitHub repository.
Standalone coarse results could not be reconciled with the local replay; the
author reports a precision-related reproduction issue, and some paper results
were produced on other servers. The release checks have not established the
cause. Bundling the internal components does not resolve this issue or promise
exact reproduction of historical final-model predictions.

## Intended use and limitations

Research on calibrated biplanar DRR-to-CT reconstruction. Performance on real
clinical radiographs and diagnostic replacement are not established. Sampling
standard deviation is not calibrated clinical confidence. No raw patient images
or private clinical datasets are included.

The associated source code retains Apache-2.0. Dataset, ImageNet initialization
and dependency terms remain separate; this card does not assign a new license
to those upstream assets. See THIRD_PARTY_NOTICES.md in the code repository.