# 3D CT Reconstruction on CT datasets engine, foot, jaw, pepper, teapot noise level synthetic (P1000)

Source: [3D CT Reconstruction on CT datasets engine, foot, jaw, pepper, teapot noise level synthetic (P1000)](<https://www.sota2.com/research/sota/3d-ct-reconstruction-on-ct-datasets-engine-foot-jaw-pepper-teapot-noise-level-synthetic-p1000>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-04T17:01:33.209Z

Evaluation of 3D CT Reconstruction performance on a synthetic dataset containing engine, foot, jaw, pepper, and teapot phantoms, tested specifically at P1000 noise level, using metrics like 2D MS-SSIM and 3D PSNR.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | 3D CT Reconstruction |
| Dataset (including any stated split) | CT datasets engine, foot, jaw, pepper, teapot noise level synthetic (P1000) |
| Metric | 2D MS-SSIM |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 4 of 4 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [2D MS-SSIM](<https://www.sota2.com/research/sota/3d-ct-reconstruction-on-ct-datasets-engine-foot-jaw-pepper-teapot-noise-level-synthetic-p1000.md?metric=2D%20MS-SSIM>): Higher is better; unit: PERCENT.

- [3D PSNR](<https://www.sota2.com/research/sota/3d-ct-reconstruction-on-ct-datasets-engine-foot-jaw-pepper-teapot-noise-level-synthetic-p1000.md?metric=3D%20PSNR>): Higher is better; unit: SCALAR.

## Recorded results

| Method | 2D MS-SSIM | Result date | Report paper |
| --- | --- | --- | --- |
| 3D FoJ | 83.8 | Not recorded | 69a66c582d603e6de3e3d179 |
| 3D-TV | 82.9 | Not recorded | 69a66c582d603e6de3e3d179 |
| Filter2Noise | 69.5 | Not recorded | 69a66c582d603e6de3e3d179 |
| R2-Gaussian | 65.2 | Not recorded | 69a66c582d603e6de3e3d179 |

### 3D FoJ

2D MS-SSIM: 83.8. Result ID: 69a8655e6dbb7075b9501876.

[Reporting paper](<https://www.sota2.com/research/paper/69a66c582d603e6de3e3d179.md>)

[Method origin paper](<https://www.sota2.com/research/paper/69a66c582d603e6de3e3d179.md>)

Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction.

### 3D-TV

2D MS-SSIM: 82.9. Result ID: 69a8655e6dbb7075b9501873.

[Reporting paper](<https://www.sota2.com/research/paper/69a66c582d603e6de3e3d179.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa54751e209131ce98bc03.md>)

Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction.

### Filter2Noise

2D MS-SSIM: 69.5. Result ID: 69a8655e6dbb7075b9501875.

[Reporting paper](<https://www.sota2.com/research/paper/69a66c582d603e6de3e3d179.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa781f1e209131cea76df6.md>)

Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction.

### R2-Gaussian

2D MS-SSIM: 65.2. Result ID: 69a8655e6dbb7075b9501874.

[Reporting paper](<https://www.sota2.com/research/paper/69a66c582d603e6de3e3d179.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa756f1e209131cea67e07.md>)

Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction.

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