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 # Mu Map
-Repository for the code to replicate the paper of Shi et al. with our SPECT/CT Scanner.
-
-## Differences 
-|                       |                        Authors |                              Ours |
-| :-------------------- | :----------------------------: | :-------------------------------: |
-| SPECT/CT Scanner      |          GE NM/CT 850 SPECT/CT | Siemens Symbia Intevo 16 SPECT/CT |
-| Scan Arc              |                           180° |                              208° |
-| Angles                |                             60 |                                34 |
-| Energy Window Lower   |                    114-126 keV |                       110-131 keV |
-| Energy Window Upper   |                    126-155 keV |                       131-152 keV |
-| Dose                  |                         15 mCi |                                 ? |
-| Reconstruction        | OSEM (5 iterations, 4 subsets) |        Siemens Conjugate Gradient |
-| CT Energy             |                        120 kVp |                                 ? |
-| Attenuation Map Shape |                  (25-35)x64x64 |                                 ? |
-| Reconstruction Shape  |                       64x64x64 |                        51x128x128 |
-| Voxel Size            |                 6.8x6.8x6.8 mm³|                   4.8x4.8x4.8 mm³ |
-
-
-## Open Questions
+Repository containing the code of the paper `Deep Learning Approximation of Attenuation Maps for Myocardial Perfusion SPECT with an IQ·SPECT Collimator`.
+
+Using this code, it is possible to train a deep neural network to predict attenuation maps from non-attenuation-corrected reconstructions.
+Training is inspired by the description in [1] and is implemented with [PyTorch](https://pytorch.org/).
+For the optimization of hyperparameters, a random search procedure [2] is implemented.
+To compute attenuation-corrected reconstructions, the script TODO implements the post-reconstruction attenuation correction (PRAC) [3] algorithm.
+The open-source tomographic reconstruction software [STIR](https://stir.sourceforge.net/) is used for this.
+Reconstructions can be evaluated using the mean squared error or the normalized mean absolute error.
+While there is also code to evaluate polar maps, they cannot be generated here.
+For the purpose of the paper, grayscale polar maps were generated the Cedars-Sinai Cardiac Suite [4].
+
+## Results
+
+## Release ToDo's
+* Update dependency versions in pyproject.toml an requirements.txt
+* Test installation instructions
+* Add illustrations:
+  * Gif of the comparison between a predicted and an actual attenuation map
+  * polar maps
+
+## Installation
+Installation was tested with Ubuntu 20.04 and 22.04.
+1. Create and load a virtual environment `python -m venv .venv && source .venv/bin/activate`
+2. Install python requirements `pip install -r requirements.txt`
+
+### STIR
+STIR is required to project and reconstruct scans (using the PRAC algorithm).
+For its installation, this repository contains STIR as a submodule as well as an installation script.
+To install STIR run:
+1. `git submodule init && git submodule update`
+2. `cd libs && ./install.sh && cd ..`
+
+### Tesseract OCR
+Because we could not export perfusion scores from polar maps generated with the Cedars-Sinai Cardiac Suite, but only RGB images with scores written in a green font, we added a [script](/mu_map/polar_map/get_perfusion) to help extract these scores.
+This script uses [Tesseract](https://tesseract-ocr.github.io/) to automatically parse these numbers from the image.
+To install tesseract, run:
+`sudo apt install tesseract-ocr`
+
+## Usage
+This code is not intended for direct usage as data structures likely differ.
+
+## Features
+* cGAN training
+* Random search [2] for hyperparameter optimization
+* PRAC [3] algorithm
+* Evaluation of polar maps
 
 ## References
-* `Shi et al. "Deep learning-based attenuation map generation for myocardial perfusion SPECT". 2020 In: European Journal of Nuclear Medicine and Molecular Imaging 47.10`
-  * [DOI: 10.1007/s00259-020-04746-6](www.doi.org/10.1007/s00259-020-04746-6)
-
-## Install
-Install libraries for tomographic reconstruction:
-
-### NiftyRec
-* `mkdir libs`
-* `cd libs`
-* `git clone https://github.com/TomographyLab/NiftyRec`
-* `cd NiftyRec`
-* `git checkout 5329496`
-* `cp README.md README.txt` - this file is needed by cmake
-* `mkdir build`
-* `cd build`
-* `ccmake ..` - turn of the volume renderer
-* `make -j 8`
-* `make package`
-* `sudo dpkg -i NiftyRec-3.1.0-Linux-x86_64.deb`
-
-### TomoLab
-Make sure your python virtual environment is active for this.
-* `mkdir libs`
-* `cd libs`
-* `git clone https://github.com/TomographyLab/TomoLab`
-* `git checkout 86b9a58`
+1. L. Shi et al. “Deep learning-based attenuation map generation for myocardial perfusion SPECT”. In: European Journal of Nuclear Medicine and Molecular Imaging 47 (2020). doi: [10.1007/s00259-020-04746-6](https://doi.org/10.1007/s00259-020-04746-6).
+2. J. Bergstra and Y. Bengio. “Random Search for Hyper-Parameter Optimization”. In: Journal of Machine Learning Research 13 (2012).
+3. H. Liu et al. “Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation”. In: Journal of Nuclear Cardiology 29 (2021). doi: [10.1007/s12350-021-02817-1](http://doi.org/10.1007/s12350-021-02817-1).
+4. G. Germano et al. “Quantitation in gated perfusion SPECT imaging: The Cedars-Sinai approach”. In: Journal of Nuclear Cardiology 14 (2007). doi: [10.1016/j.nuclcard.2007.06.008](https://doi.org/10.1016/j.nuclcard.2007.06.008).
+