## PUMA 1.0 🐾 - One Image multiple perspectives 🎭
[](https://pypi.org/project/pumaz/) [](https://www.gnu.org/licenses/gpl-3.0) [&color=9400D3&style=flat-square&logo=python)](https://pypi.org/project/pumaz/) [&color=9400D3&style=flat-square&logo=python)](https://pypi.org/project/pumaz/) <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
[](#contributors-)
<!-- ALL-CONTRIBUTORS-BADGE:END -->
**🌈 PET Reimagined: From Monochrome to a Palette of Possibilities with PUMA 1.0**
Step into the vibrant new world of PET imaging with PUMA 1.0 🌟, where traditional monochrome scans are transformed into a dynamic spectrum of diagnostic data. 🎨
With PUMA 1.0, experience the power of multiplexing: a process that fuses multiple tracer images into a single, multicolored composite that reveals not just the presence of disease but its multifaceted physiological context. 🔄 The multiplexed approach provides a more comprehensive view, helping clinicians uncover nuanced insights about tumors and their microenvironments. 🔍
Whether you're in a high-tech lab or a remote clinic, PUMA 1.0 delivers high-quality, multiplexed PET scans that are as rich in detail as they are in color. 🏥 It’s not just an upgrade—it’s a whole new way of seeing PET data, turning every image into a detailed map of insight and opportunity. 🗺️
## 🎉 Key Features
### 🚀 Versatile and Powerful
Run PUMA 1.0 on any device, any OS, from x86 to ARM64 (hello, Apple Silicon fans!). Whether you’re on a high-end GPU or a humble CPU, PUMA adapts to your needs without breaking a sweat.
### 🔍 Precision Meets Speed
Harness the power of AI with MOOSE-driven segmentations and ‘greedy’ library’s wizardry for diffeomorphic image registration. PUMA 1.0 nails the perfect balance of sharp accuracy and zippy performance.
### 🎨 Art of Imaging
Why settle for ordinary when you can visualize in vibrant RGB? Each color shines a spotlight on a different tracer, turning complex data into a vivid, easy-to-interpret display. With processing times ranging from just 5 to 12 minutes, you're all set for a speedy yet thorough diagnostic journey.
## 🚀 Why PUMA?
https://github.com/ENHANCE-PET/PUMA/assets/48599863/03368642-a288-44cb-8eaf-4833380a26c8
## Requirements ✅
Before stepping into the future with PUMA 1.0, here's what you need for an optimal experience:
- **Operating System**: Windows, Mac, or Linux - PUMA 1.0 is versatile and works across all these platforms.
- **Memory**: Make sure your system has enough memory (8-16 GB) to run the tasks smoothly.
- **GPU**: You would benefit from a cuda-enabled GPU (NVIDIA), 8 GB or more, or MPS (Apple silicon) - if you like quick results ⚡️. But if you don't fret not, PUMA runs a cpu-tuned version if there is no GPU.
- **Python**: PUMA 1.0 operates the best with Python 3.10.
Once these specifications are met, you're all set to experience PUMA 1.0's capabilities.
## Installation Guide 🛠️
Installation is a breeze on Windows, Linux, and MacOS. Follow the steps below to start your journey with PUMA 1.0.
### For Linux and MacOS 🐧🍏
1. Create a Python environment named 'puma-env' or as per your preference.
```bash
python3 -m venv puma-env
```
2. Activate the environment.
```bash
source puma-env/bin/activate # for Linux
source puma-env/bin/activate # for MacOS
```
3. Install PUMA 1.0.
```bash
pip install pumaz # for Linux and MacOS
```
4. Apple M1 Specific Installation: If you are installing PUMA on an Apple Silicon device (e.g., Apple M1), follow this step. Do not do this if you are installing PUMA on Linux!
```bash
pip install git+https://github.com/LalithShiyam/pytorch-mps.git
```
Congratulations! You're all set to start using PUMA 1.0.
### For Windows 🪟
1. Create a Python environment, e.g., 'puma-env'.
```bash
python -m venv puma-env
```
2. Activate the environment.
```bash
.\puma-env\Scripts\activate
```
3. Install PUMA 1.0.
```bash
pip install pumaz
```
You're now ready to experience the precision and speed of PUMA 1.0.
## Usage Guide 📚
Start your journey with PUMA 1.0 by using our straightforward command-line tool. It requires the directory path containing different tracer images, and each image should be stored in separate folders. Here's how you can get started:
```bash
pumaz \
-d <path_to_image_dir> # Directory path containing the images to be analyzed
-ir <regions_to_ignore> # Regions to ignore: arms, legs, head, none
-m # Optional: Enable multiplexed RGB image output
-cs <color_selection> # Optional: Custom color selection for RGB output (requires -m)
-c2d <convert_back_to_dicom> # Optional: Once set, the generated nifti images will be converted back to DICOM
```
- `<path_to_image_dir>` refers to the parent directory containing different tracer images in their respective sub-directories.
- `-ir` specifies the regions to be ignored during registration. If you don't want to ignore any regions, use `none`. If you want to ignore the arms, legs, or head during registration, pass the corresponding regions delimited by a `,`. For example: `-ir head,arms` to ignore the head and arms.
- `-m` will activate the output of a multiplexed RGB image of the combined tracer images.
- `-cs`, when passed along with `-m`, PUMA will ask you to provide a custom order of color channels for the corresponding tracer images. That way, you can freely decide which tracer image is associated with which channel.
- `-c2d`, when set the generated aligned nifti images will be converted back to DICOM.
For assistance or additional information, you can always type:
```bash
pumaz -h
```
### Example usage:
Apply PUMA to images in a directory, ignoring arms and legs, with multiplexed RGB output and custom colors:
```bash
pumaz -d /path/to/images -ir arms,legs -m -cs -c2d
```
## Directory Structure and Naming Conventions for PUMA 📂🏷️
PUMA 1.0 requires your data to be structured in a certain way. It supports DICOM directories and NIFTI files. For NIFTI files, users need to ensure that the files are named with the correct modality tag at the start.
### Required Directory Structure 🌳
Here is the directory structure that PUMA 1.0 expects:
```
📁 Parent_Directory
│
└───📂 Tracer1 # can be named anything
│ │
│ └───📁 PET_DICOM_Directory or 🗃️ PT_xxxx.nii.gz # If it's DICOM, the folder name can be anything, but if nifti use a prefix 'PT' for PET
│ │
│ └───📁 CT_DICOM_Directory or 🗃️ CT_xxxx.nii.gz # If it's DICOM, the folder name can be anything, but if nifti use a prefix 'CT' for CT
│
└───📂 Tracer2
│ │
│ └───📁 PET_DICOM_Directory or 🗃️ PT_xxxx.nii.gz
│ │
│ └───📁 CT_DICOM_Directory or 🗃️ CT_xxxx.nii.gz
...
└───📂 Tracer3
│
└───📁 PET_DICOM_Directory or 🗃️ PT_xxxx.nii.gz
│
└───📁 CT_DICOM_Directory or 🗃️ CT_xxxx.nii.gz
```
### Naming Conventions 🏷️
- For DICOM directories, no specific naming is required.
- For NIFTI files, the file should start with the DICOM modality tag (e.g., 'PT_' or 'CT_') followed by the desired name. For example, 'PT_MySample.nii.gz'.
Note: All the PET and CT images related to a tracer should be placed in the same directory named after the tracer.
## 🚀 Benchmarks
- [Apple Intel 4 Cores | Device: CPU | Archictecture: x86_64 | ~25 min](https://github.com/Keyn34/PUMA/blob/master/Images/Apple-intel-4-core.png)
- [Apple M1 Ultra 20 Cores | Device: CPU | Archictecture: ARM | ~12 min](https://github.com/Keyn34/PUMA/blob/master/Images/Apple-M1-Ultra-20-Cores.png)
- [Linux Server 128 Cores | Nvidia A100 GPU | Architecture: x86_64 | ~10 min](https://github.com/Keyn34/PUMA/blob/master/Images/Linux-Server-Nvidia-A100-128-Cores.png)
## A Note on QIMP Python Packages: The 'Z' Factor 📚🚀
All of our Python packages here at QIMP carry a special signature – a distinctive 'Z' at the end of their names. The 'Z' is more than just a letter to us; it's a symbol of our forward-thinking approach and commitment to continuous innovation.
Our PUMA package, for example, is named as 'pumaz', pronounced "puma-see". So, why 'Z'?
Well, in the world of mathematics and science, 'Z' often represents the unknown, the variable that's yet to be discovered, or the final destination in a series. We at QIMP believe in always pushing boundaries, venturing into uncharted territories, and staying on the cutting edge of technology. The 'Z' embodies this philosophy. It represents our constant quest to uncover what lies beyond the known, to explore the undiscovered, and to bring you the future of medical imaging.
Each time you see a 'Z' in one of our package names, be reminded of the spirit of exploration and discovery that drives our work. With QIMP, you're not just installing a package; you're joining us on a journey to the frontiers of medical image processing. Here's to exploring the 'Z' dimension together! 🚀
## Contributors ✨
Thanks goes to these wonderful people ✨:
<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
<!-- prettier-ignore-start -->
<!-- markdownlint-disable -->
<table>
<tbody>
<tr>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/W7ebere"><img src="https://avatars.githubusercontent.com/u/166598214?v=4?s=100" width="100px;" alt="W7ebere"/><br /><sub><b>W7ebere</b></sub></a><br /><a href="https://github.com/LalithShiyam/PUMA/commits?author=W7ebere" title="Documentation">📖</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/mprires"><img src="https://avatars.githubusercontent.com/u/48754309?v=4?s=100" width="100px;" alt="Manuel Pires"/><br /><sub><b>Manuel Pires</b></sub></a><br /><a href="https://github.com/LalithShiyam/PUMA/commits?author=mprires" title="Code">💻</a> <a href="https://github.com/LalithShiyam/PUMA/commits?author=mprires" title="Documentation">📖</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Keyn34"><img src="https://avatars.githubusercontent.com/u/87951050?v=4?s=100" width="100px;" alt="Sebastian Gutschmayer"/><br /><sub><b>Sebastian Gutschmayer</b></sub></a><br /><a href="https://github.com/LalithShiyam/PUMA/commits?author=Keyn34" title="Code">💻</a> <a href="https://github.com/LalithShiyam/PUMA/commits?author=Keyn34" title="Documentation">📖</a></td>
</tr>
</tbody>
</table>
<!-- markdownlint-restore -->
<!-- prettier-ignore-end -->
<!-- ALL-CONTRIBUTORS-LIST:END -->
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
Raw data
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"keywords": "PET tracer alignment, diffeomorphic imaging, image processing, multiplexed tracers",
"author": "Lalith Kumar Shiyam Sundar, Sebastian Gutschmayer, Manuel Pires",
"author_email": "Lalith.shiyamsundar@meduniwien.ac.at, Sebastian.Gutschmayer@meduniwien.ac.at, Manuel.pires@meduniwien.ac.at",
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"description": "\n\n## PUMA 1.0 \ud83d\udc3e - One Image multiple perspectives \ud83c\udfad\n[](https://pypi.org/project/pumaz/) [](https://www.gnu.org/licenses/gpl-3.0) [&color=9400D3&style=flat-square&logo=python)](https://pypi.org/project/pumaz/) [&color=9400D3&style=flat-square&logo=python)](https://pypi.org/project/pumaz/) <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->\n[](#contributors-)\n<!-- ALL-CONTRIBUTORS-BADGE:END -->\n\n**\ud83c\udf08 PET Reimagined: From Monochrome to a Palette of Possibilities with PUMA 1.0**\n\nStep into the vibrant new world of PET imaging with PUMA 1.0 \ud83c\udf1f, where traditional monochrome scans are transformed into a dynamic spectrum of diagnostic data. \ud83c\udfa8\n\nWith PUMA 1.0, experience the power of multiplexing: a process that fuses multiple tracer images into a single, multicolored composite that reveals not just the presence of disease but its multifaceted physiological context. \ud83d\udd04 The multiplexed approach provides a more comprehensive view, helping clinicians uncover nuanced insights about tumors and their microenvironments. \ud83d\udd0d\n\nWhether you're in a high-tech lab or a remote clinic, PUMA 1.0 delivers high-quality, multiplexed PET scans that are as rich in detail as they are in color. \ud83c\udfe5 It\u2019s not just an upgrade\u2014it\u2019s a whole new way of seeing PET data, turning every image into a detailed map of insight and opportunity. \ud83d\uddfa\ufe0f\n\n## \ud83c\udf89 Key Features\n\n### \ud83d\ude80 Versatile and Powerful\nRun PUMA 1.0 on any device, any OS, from x86 to ARM64 (hello, Apple Silicon fans!). Whether you\u2019re on a high-end GPU or a humble CPU, PUMA adapts to your needs without breaking a sweat.\n\n### \ud83d\udd0d Precision Meets Speed\nHarness the power of AI with MOOSE-driven segmentations and \u2018greedy\u2019 library\u2019s wizardry for diffeomorphic image registration. PUMA 1.0 nails the perfect balance of sharp accuracy and zippy performance.\n\n### \ud83c\udfa8 Art of Imaging\nWhy settle for ordinary when you can visualize in vibrant RGB? Each color shines a spotlight on a different tracer, turning complex data into a vivid, easy-to-interpret display. With processing times ranging from just 5 to 12 minutes, you're all set for a speedy yet thorough diagnostic journey.\n\n## \ud83d\ude80 Why PUMA?\n\n\nhttps://github.com/ENHANCE-PET/PUMA/assets/48599863/03368642-a288-44cb-8eaf-4833380a26c8\n\n\n## Requirements \u2705\n\nBefore stepping into the future with PUMA 1.0, here's what you need for an optimal experience:\n\n- **Operating System**: Windows, Mac, or Linux - PUMA 1.0 is versatile and works across all these platforms.\n\n- **Memory**: Make sure your system has enough memory (8-16 GB) to run the tasks smoothly.\n\n- **GPU**: You would benefit from a cuda-enabled GPU (NVIDIA), 8 GB or more, or MPS (Apple silicon) - if you like quick results \u26a1\ufe0f. But if you don't fret not, PUMA runs a cpu-tuned version if there is no GPU.\n\n- **Python**: PUMA 1.0 operates the best with Python 3.10.\n\nOnce these specifications are met, you're all set to experience PUMA 1.0's capabilities.\n\n## Installation Guide \ud83d\udee0\ufe0f\n\nInstallation is a breeze on Windows, Linux, and MacOS. Follow the steps below to start your journey with PUMA 1.0.\n\n### For Linux and MacOS \ud83d\udc27\ud83c\udf4f\n\n1. Create a Python environment named 'puma-env' or as per your preference.\n ```bash\n python3 -m venv puma-env\n ```\n\n\n2. Activate the environment.\n ```bash\n source puma-env/bin/activate # for Linux\n source puma-env/bin/activate # for MacOS\n ```\n\n3. Install PUMA 1.0.\n ```bash\n pip install pumaz # for Linux and MacOS\n ```\n\n4. Apple M1 Specific Installation: If you are installing PUMA on an Apple Silicon device (e.g., Apple M1), follow this step. Do not do this if you are installing PUMA on Linux!\n ```bash\n pip install git+https://github.com/LalithShiyam/pytorch-mps.git\n ```\n\n\nCongratulations! You're all set to start using PUMA 1.0.\n\n### For Windows \ud83e\ude9f\n\n1. Create a Python environment, e.g., 'puma-env'.\n ```bash\n python -m venv puma-env\n ```\n\n2. Activate the environment.\n ```bash\n .\\puma-env\\Scripts\\activate\n ```\n\n3. Install PUMA 1.0.\n ```bash\n pip install pumaz\n ```\n\nYou're now ready to experience the precision and speed of PUMA 1.0.\n\n## Usage Guide \ud83d\udcda\n\nStart your journey with PUMA 1.0 by using our straightforward command-line tool. It requires the directory path containing different tracer images, and each image should be stored in separate folders. Here's how you can get started:\n\n```bash\n\n pumaz \\\n -d <path_to_image_dir> # Directory path containing the images to be analyzed\n -ir <regions_to_ignore> # Regions to ignore: arms, legs, head, none\n -m # Optional: Enable multiplexed RGB image output\n -cs <color_selection> # Optional: Custom color selection for RGB output (requires -m)\n -c2d <convert_back_to_dicom> # Optional: Once set, the generated nifti images will be converted back to DICOM\n```\n\n- `<path_to_image_dir>` refers to the parent directory containing different tracer images in their respective sub-directories.\n\n- `-ir` specifies the regions to be ignored during registration. If you don't want to ignore any regions, use `none`. If you want to ignore the arms, legs, or head during registration, pass the corresponding regions delimited by a `,`. For example: `-ir head,arms` to ignore the head and arms.\n\n- `-m` will activate the output of a multiplexed RGB image of the combined tracer images.\n\n- `-cs`, when passed along with `-m`, PUMA will ask you to provide a custom order of color channels for the corresponding tracer images. That way, you can freely decide which tracer image is associated with which channel.\n- `-c2d`, when set the generated aligned nifti images will be converted back to DICOM.\n \nFor assistance or additional information, you can always type:\n\n```bash\npumaz -h\n```\n\n\n### Example usage:\nApply PUMA to images in a directory, ignoring arms and legs, with multiplexed RGB output and custom colors:\n\n```bash\npumaz -d /path/to/images -ir arms,legs -m -cs -c2d \n```\n\n\n## Directory Structure and Naming Conventions for PUMA \ud83d\udcc2\ud83c\udff7\ufe0f\n\nPUMA 1.0 requires your data to be structured in a certain way. It supports DICOM directories and NIFTI files. For NIFTI files, users need to ensure that the files are named with the correct modality tag at the start.\n\n### Required Directory Structure \ud83c\udf33\n\nHere is the directory structure that PUMA 1.0 expects:\n\n```\n\ud83d\udcc1 Parent_Directory\n\u2502\n\u2514\u2500\u2500\u2500\ud83d\udcc2 Tracer1 # can be named anything\n\u2502 \u2502\n\u2502 \u2514\u2500\u2500\u2500\ud83d\udcc1 PET_DICOM_Directory or \ud83d\uddc3\ufe0f PT_xxxx.nii.gz # If it's DICOM, the folder name can be anything, but if nifti use a prefix 'PT' for PET\n\u2502 \u2502\n\u2502 \u2514\u2500\u2500\u2500\ud83d\udcc1 CT_DICOM_Directory or \ud83d\uddc3\ufe0f CT_xxxx.nii.gz # If it's DICOM, the folder name can be anything, but if nifti use a prefix 'CT' for CT\n\u2502\n\u2514\u2500\u2500\u2500\ud83d\udcc2 Tracer2\n\u2502 \u2502\n\u2502 \u2514\u2500\u2500\u2500\ud83d\udcc1 PET_DICOM_Directory or \ud83d\uddc3\ufe0f PT_xxxx.nii.gz\n\u2502 \u2502\n\u2502 \u2514\u2500\u2500\u2500\ud83d\udcc1 CT_DICOM_Directory or \ud83d\uddc3\ufe0f CT_xxxx.nii.gz\n...\n\n\u2514\u2500\u2500\u2500\ud83d\udcc2 Tracer3\n \u2502\n \u2514\u2500\u2500\u2500\ud83d\udcc1 PET_DICOM_Directory or \ud83d\uddc3\ufe0f PT_xxxx.nii.gz\n \u2502\n \u2514\u2500\u2500\u2500\ud83d\udcc1 CT_DICOM_Directory or \ud83d\uddc3\ufe0f CT_xxxx.nii.gz\n\n```\n\n### Naming Conventions \ud83c\udff7\ufe0f\n\n- For DICOM directories, no specific naming is required.\n- For NIFTI files, the file should start with the DICOM modality tag (e.g., 'PT_' or 'CT_') followed by the desired name. For example, 'PT_MySample.nii.gz'.\n\nNote: All the PET and CT images related to a tracer should be placed in the same directory named after the tracer.\n\n## \ud83d\ude80 Benchmarks\n\n- [Apple Intel 4 Cores | Device: CPU | Archictecture: x86_64 | ~25 min](https://github.com/Keyn34/PUMA/blob/master/Images/Apple-intel-4-core.png)\n- [Apple M1 Ultra 20 Cores | Device: CPU | Archictecture: ARM | ~12 min](https://github.com/Keyn34/PUMA/blob/master/Images/Apple-M1-Ultra-20-Cores.png)\n- [Linux Server 128 Cores | Nvidia A100 GPU | Architecture: x86_64 | ~10 min](https://github.com/Keyn34/PUMA/blob/master/Images/Linux-Server-Nvidia-A100-128-Cores.png)\n\n## A Note on QIMP Python Packages: The 'Z' Factor \ud83d\udcda\ud83d\ude80\n\nAll of our Python packages here at QIMP carry a special signature \u2013 a distinctive 'Z' at the end of their names. The 'Z' is more than just a letter to us; it's a symbol of our forward-thinking approach and commitment to continuous innovation.\n\nOur PUMA package, for example, is named as 'pumaz', pronounced \"puma-see\". So, why 'Z'?\n\nWell, in the world of mathematics and science, 'Z' often represents the unknown, the variable that's yet to be discovered, or the final destination in a series. We at QIMP believe in always pushing boundaries, venturing into uncharted territories, and staying on the cutting edge of technology. The 'Z' embodies this philosophy. It represents our constant quest to uncover what lies beyond the known, to explore the undiscovered, and to bring you the future of medical imaging.\n\nEach time you see a 'Z' in one of our package names, be reminded of the spirit of exploration and discovery that drives our work. With QIMP, you're not just installing a package; you're joining us on a journey to the frontiers of medical image processing. Here's to exploring the 'Z' dimension together! \ud83d\ude80\n \n\n## Contributors \u2728\n\nThanks goes to these wonderful people \u2728:\n\n<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->\n<!-- prettier-ignore-start -->\n<!-- markdownlint-disable -->\n<table>\n <tbody>\n <tr>\n <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/W7ebere\"><img src=\"https://avatars.githubusercontent.com/u/166598214?v=4?s=100\" width=\"100px;\" alt=\"W7ebere\"/><br /><sub><b>W7ebere</b></sub></a><br /><a href=\"https://github.com/LalithShiyam/PUMA/commits?author=W7ebere\" title=\"Documentation\">\ud83d\udcd6</a></td>\n <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/mprires\"><img src=\"https://avatars.githubusercontent.com/u/48754309?v=4?s=100\" width=\"100px;\" alt=\"Manuel Pires\"/><br /><sub><b>Manuel Pires</b></sub></a><br /><a href=\"https://github.com/LalithShiyam/PUMA/commits?author=mprires\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/LalithShiyam/PUMA/commits?author=mprires\" title=\"Documentation\">\ud83d\udcd6</a></td>\n <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/Keyn34\"><img src=\"https://avatars.githubusercontent.com/u/87951050?v=4?s=100\" width=\"100px;\" alt=\"Sebastian Gutschmayer\"/><br /><sub><b>Sebastian Gutschmayer</b></sub></a><br /><a href=\"https://github.com/LalithShiyam/PUMA/commits?author=Keyn34\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/LalithShiyam/PUMA/commits?author=Keyn34\" title=\"Documentation\">\ud83d\udcd6</a></td>\n </tr>\n </tbody>\n</table>\n\n<!-- markdownlint-restore -->\n<!-- prettier-ignore-end -->\n\n<!-- ALL-CONTRIBUTORS-LIST:END -->\n\nThis project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!\n",
"bugtrack_url": null,
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"summary": "PUMA (PET Universal Multi-tracer Aligner) is a robust and efficient tool for aligning images from different PET tracers. It leverages advanced diffeomorphic imaging techniques to offer high-precision alignment for multiplexed tracer images. PUMA aims to significantly enhance the accuracy and reproducibility of PET image studies.",
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