# AI use cases for Voxenra (l5769389/voxenra)

Repo: [l5769389/voxenra](https://github.com/l5769389/voxenra) | Use cases: 3 | Page: https://dedupedai.com/use-cases/l5769389-voxenra/

Voxenra is a cross-platform desktop DICOM viewer for CT, MR and PET/CT that covers image review, MPR and 3D views, PET/CT fusion, measurement and segmentation, and local AI auto-segmentation through TotalSegmentator.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Segment CT organs locally with AI and refine the result: https://dedupedai.com/use-cases/l5769389-voxenra/ai-organ-segmentation-ct-review/
- Measure lesions on CT or MR and export a PDF or CSV report: https://dedupedai.com/use-cases/l5769389-voxenra/measure-lesions-export-report/
- Review PET/CT fusion and align it manually: https://dedupedai.com/use-cases/l5769389-voxenra/pet-ct-fusion-review/

## How to segment CT organs locally with AI and refine the result with Voxenra

Repo: [l5769389/voxenra](https://github.com/l5769389/voxenra) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Run TotalSegmentator on a CT study inside Voxenra, then check and correct the organ masks before saving.

**Who it is for:** A radiology researcher or imaging analyst who has a CT study and needs organ masks quickly without sending data to a cloud service.

**The problem:** Manual organ contouring on CT takes a long time, and automatic masks still need checking before they can be used.

**The outcome:** Organ masks for the selected structures overlaid on the MPR views, refined with brush or eraser, and saved in the workspace or exported as SEG or NRRD.

### Steps

1. Clone the repo from https://github.com/l5769389/voxenra and enter the folder.
2. Install TotalSegmentator and its CT fast model separately, then open Settings → AI and check that the environment is detected.
3. Run `uv run voxenra`, import your CT study, and open it in MPR or 4D.
4. Search for and tick the organs you need (the default is liver, spleen and both kidneys), then start the local inference on the current phase.
5. Review the overlay in the three planes, correct errors with the brush or eraser, and use undo where needed.
6. Save the workspace, or export the segmentation as SEG or NRRD.

### Prompt to paste into your agent

```text
Work in the repo l5769389/voxenra. Run `uv run voxenra`, open the CT study at <path to CT study>, and run TotalSegmentator on the liver and spleen. Report the saved file path and confirm the masks appear in the axial, coronal and sagittal views.
```

### Caveat

The README says the demo is a synthetic phantom and does not show model accuracy or compute time, so check AI masks against the images before relying on them.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/l5769389-voxenra/ai-organ-segmentation-ct-review/

## How to measure lesions on CT or MR and export a PDF or CSV report with Voxenra

Repo: [l5769389/voxenra](https://github.com/l5769389/voxenra) | Difficulty: Beginner | Time: 30 minutes | Basis: From the README

Draw length, angle and ROI measurements on images, then export the results as CSV, PDF or DICOM SR.

**Who it is for:** A clinical researcher or radiologist who needs written measurements from a study for a case review or a research record.

**The problem:** Measurements scattered across viewer screenshots are hard to reproduce and hard to share.

**The outcome:** A list of measurements with area, perimeter and intensity statistics, exported as CSV or PDF with a matched reference image, or as DICOM SR.

### Steps

1. Clone the repo from https://github.com/l5769389/voxenra and enter the folder.
2. Run `uv run voxenra` and import the study by file, folder, archive or PACS query.
3. Open the series in two-dimensional view and draw length, angle, curve or ROI measurements.
4. Check each measurement in the list, rename or lock items, and fix any mistakes with undo.
5. Export the results as CSV, PDF or DICOM SR from the measurement export options.

### Prompt to paste into your agent

```text
Work in the repo l5769389/voxenra. Start it with `uv run voxenra`, open <path to study>, and measure the lesion with a length tool. Export the results to <output folder> as CSV and confirm the file lists each measurement with its value.
```

### Caveat

Physical size measurements need valid pixel spacing in the image data, and the README says HU analysis is not available for colour images.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/l5769389-voxenra/measure-lesions-export-report/

## How to review PET/CT fusion and align it manually with Voxenra

Repo: [l5769389/voxenra](https://github.com/l5769389/voxenra) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Fuse PET and CT in slices and 3D, adjust the blend, and register the two volumes by hand.

**Who it is for:** A nuclear medicine or imaging researcher who needs to check where PET activity sits on the CT anatomy.

**The problem:** PET and CT are acquired separately, so the two datasets can be misaligned and need a fusion view with manual correction.

**The outcome:** A fused view of CT, PET and MIP with a rigidly registered PET volume, adjustable colour table and blend ratio, and a 3D fusion view.

### Steps

1. Clone the repo from https://github.com/l5769389/voxenra and enter the folder.
2. Run `uv run voxenra` and import the paired CT and PET series.
3. Open the PET/CT fusion view and choose the three-plane slice mode.
4. Adjust the colour table and the fusion ratio, then apply a manual rigid registration until the anatomy lines up.
5. Switch to the fused 3D view and control the CT and PET display separately.

### Prompt to paste into your agent

```text
Work in the repo l5769389/voxenra. Run `uv run voxenra`, import the PET and CT series at <path to PET/CT study>, and open the fusion view. Report whether the PET overlay aligns with CT anatomy at the slice you chose, and state the blend ratio used.
```

### Caveat

The README says PET quantification depends on the image metadata, and dynamic or gated PET is not supported.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/l5769389-voxenra/pet-ct-fusion-review/