Merge pull request #12 from PyTomography/development

add DOI to all paper references

Author: lukepolson

README.md

@@ -1,12 +1,34 @@
 # SPECT Point Spread Function Toolbox
 
-This toolbox provides functionality for developing and fitting PSF models to SPECT point source data; the developed PSF models can be loaded into [PyTomography](https://github.com/qurit/PyTomography) for general PSF modeling. For more information, including installation instructions and tutorials, please see the [documentation website](https://spectpsftoolbox.readthedocs.io/en/latest/). If you wish to contribute, please see the `CONTRIBUTING.md` file.
+This toolbox provides functionality for developing and fitting PSF models to SPECT point source data; the developed PSF models can be loaded into [PyTomography](https://github.com/qurit/PyTomography) for general PSF modeling.
+
+## Installation
+
+1. Clone this repository and navigate to the directory you cloned into.
+2. If using anaconda (recommended) then activate the `conda` environment you want to install the repository in.
+3. Use the command `python -m pip install -e .` to install the current `main` branch.
+
+## Usage
+
+See the [documentation](https://spectpsftoolbox.readthedocs.io/en/latest/) and in particular the associated [tutorials](https://spectpsftoolbox.readthedocs.io/en/latest/tutorials/tutorials.html) for demonstration on how to use the library.
+
+## Contributing
+
+If you wish to contribute, please see follow the [contributing guidelines](CONTRIBUTING.md) file. Contributions might include fixing bugs highlighted in the [issues](https://github.com/PyTomography/SPECTPSFToolbox/issues), as well as adding new features, such as new operators that accelerate the computational time of PSF modeling.
+
+## License
+
+The package is distributing under the MIT license. More details can be found in the [LICENSE](LICENSE).
+
+## Testing
+
+There is an automated testing script to test the functionality of the library in the [tests](/tests) folder. It requires the `pytest` package is ran using `pytest test_functionality.py`. 
 
 ## Context
 
 Gamma cameras used in SPECT imaging have finite resolution: infinitesmal point sources of radioactivity show up as finite "point spread functions" (PSF) on the camera. Sample PSFs from point sources at various distances from a camera can be seen on the left hand side of the figure below.
 
- The PSF consists of three main components: (i) the geometric component (GC) which depends on the shape and spacing of the collimator bores, (ii) the septal penetration component (SPC) which results from photons that travel through the collimator material without being attenuated, and (iii) the septal scatter component (SSC), which consists of photons that scatter within the collimator material and subsequently get detected in the scintillator. When the thickness of the SPECT collimator sufficiently matches the energy of the detected radiation, the PSF is dominated by the GC and can be sufficiently approximated using a distance dependent Gaussian function. When the energy of the photons is large relative to the thickness and hole size of the collimator material, the PSF contains significant contributions from SPC and SSC and it can no longer be approximated using simple Gaussian functions. For more information, see [chapter 16 of the greatest book of all time](https://www.wiley.com/en-in/Foundations+of+Image+Science-p-9780471153009)
+ The PSF consists of three main components: (i) the geometric component (GC) which depends on the shape and spacing of the collimator bores, (ii) the septal penetration component (SPC) which results from photons that travel through the collimator material without being attenuated, and (iii) the septal scatter component (SSC), which consists of photons that scatter within the collimator material and subsequently get detected in the scintillator. When the thickness of the SPECT collimator is large and the diameter of the collimator bores is small relative to the energy of the detected radiation, the PSF is dominated by the GC and can be reasonably approximated using a distance dependent Gaussian function. When the energy of the photons is large relative to the collimator parameters, the PSF contains significant contributions from SPC and SSC and it can no longer be approximated using simple Gaussian functions. The tails and dim background present in the PSF plots in the figure below correspond to the SPC and SSC. For more information, see [chapter 16 of the greatest book of all time](https://www.wiley.com/en-in/Foundations+of+Image+Science-p-9780471153009)
 
  The figure below shows axial slices of reconstructed Monte Carlo Ac225 SPECT data. The images highlighted as "PSF Model" correspond to application of a PSF operator developed in the library on a point source. The images highlighted as "New" are obtainable via reconstruction with [PyTomography](https://github.com/qurit/PyTomography) using the PSF operators obtained in this library; they require comprehensive PSF modeling. 
 

paper/paper.bib

@@ -26,7 +26,7 @@ @article{simind
 pages = {257-272},
 year = {1989},
 issn = {0169-2607},
-doi = {https://doi.org/10.1016/0169-2607(89)90111-9},
+doi = {10.1016/0169-2607(89)90111-9},
 url = {https://www.sciencedirect.com/science/article/pii/0169260789901119},
 author = {Michael Ljungberg and Sven-Erik Strand},
 keywords = {Monte Carlo, SPECT, Scatter simulation, Photon transport, Pile-up, Imaging, Spectrum, Scintillation camera},
@@ -79,7 +79,8 @@ @Article{hermes_spect_ap
 keywords={image reconstruction; Kernel-method; single-photon emission computed tomography; single-photon emission computed tomography-computed tomography},
 abstract={Bone single-photon emission computed tomography (SPECT)/computed tomography (CT) imaging suffers from poor spatial resolution, but the image quality can be improved during SPECT reconstruction by using anatomical information derived from CT imaging. The purpose of this work was to compare two different anatomically guided SPECT reconstruction methods to ordered subsets expectation maximization (OSEM) which is the most commonly used reconstruction method in nuclear medicine. The comparison was done in terms of lesion quantitation and lesion detectability. Anatomically guided Bayesian reconstruction (AMAP) and kernelized ordered subset expectation maximization (KEM) algorithms were implemented and compared against OSEM. Artificial lesions with a wide range of lesion-to-background contrasts were added to normal bone SPECT/CT studies. The quantitative accuracy was assessed by the error in lesion standardized uptake values and lesion detectability by the area under the receiver operating characteristic curve generated by a non-prewhitening matched filter. AMAP and KEM provided significantly better quantitative accuracy than OSEM at all contrast levels. Accuracy was the highest when SPECT lesions were matched to a lesion on CT. Correspondingly, AMAP and KEM also had significantly better lesion detectability than OSEM at all contrast levels and reconstructions with matching CT lesions performed the best. Quantitative differences between AMAP and KEM algorithms were minor. Visually AMAP and KEM images looked similar. Anatomically guided reconstruction improves lesion quantitation and detectability markedly compared to OSEM. Differences between AMAP and KEM algorithms were small and thus probably clinically insignificant.},
 issn={0143-3636},
-url={https://journals.lww.com/nuclearmedicinecomm/Fulltext/2023/04000/Anatomically_guided_reconstruction_improves_lesion.12.aspx}
+url={https://journals.lww.com/nuclearmedicinecomm/Fulltext/2023/04000/Anatomically_guided_reconstruction_improves_lesion.12.aspx},
+doi={10.1097/MNM.0000000000001675}
 }
 
 @article{castor,
@@ -130,7 +131,8 @@ @misc{pytomography
       eprint={2309.01977},
       archivePrefix={arXiv},
       primaryClass={physics.med-ph},
-      url={https://arxiv.org/abs/2309.01977}, 
+      url={https://arxiv.org/abs/2309.01977},
+      doi={10.48550/arXiv.2309.01977} 
 }
 
 @INPROCEEDINGS{spect_proj_interp,
@@ -164,7 +166,8 @@ @article{Barrett2003FoundationsOI
   year={2003},
   volume={14},
   pages={029901},
-  url={https://api.semanticscholar.org/CorpusID:206393854}
+  url={https://api.semanticscholar.org/CorpusID:206393854},
+  doi={10.1117/1.1905634}
 }
 
 @article{ac1,
@@ -174,7 +177,8 @@ @article{ac1
   volume={59},
   number={5},
   pages={795--802},
-  year={2018}
+  year={2018},
+  doi={10.2967/jnumed.117.203539}
 }
 
 @article{ac2,
@@ -184,7 +188,8 @@ @article{ac2
   volume={61},
   number={1},
   pages={62--69},
-  year={2020}
+  year={2020},
+  doi={10.2967/jnumed.119.229229}
 }
 
 @article{ac3,
@@ -194,7 +199,8 @@ @article{ac3
   volume={62},
   number={5},
   pages={669--674},
-  year={2021}
+  year={2021},
+  doi={10.2967/jnumed.120.251017}
 }
 
 @article{ac4,
@@ -204,7 +210,8 @@ @article{ac4
   volume={11},
   number={9},
   pages={4050},
-  year={2021}
+  year={2021},
+  doi={10.7150/thno.56211}
 }
 
 @article{ac5,