🎉 adds various final changes #4 (PR#19 ready to be merged):

* imports #13 (comment) #12,
* imports #13 (comment) #12,
* __init__.py #13 (comment) #12,
* relative imports #13 (comment) #12,
* removes src/Readme and episodes/data/Untitled.txt #4 > #19 (comment)
* adds ITK-SNAP visualisation coordinate system #4

Author: mxochicale

.gitignore

@@ -9,6 +9,9 @@ episodes/*html
 site/*
 !site/README.md
 
+## Temporal files for ITK-SNAP registration
+**Untitled.txt
+
 # History files
 .Rhistory
 .Rapp.history

README.md

@@ -23,14 +23,15 @@ git clone git@github.com:HealthBioscienceIDEAS/Medical-Image-Registration-Short-
 ```
 
 ## Maintainers
+This project exists thanks to all mainteners. Sort list by last name (alphabetically). 
 * [Dave Cash](https://github.com/davecash75)
 * [Jamie Mcclelland](https://github.com/jamie-mcclelland)
 
 ## Contributors
-This project exists thanks to all the people who contribute.
+This project exists thanks to all the people who contribute. Sort list by last name (alphabetically). 
 * [Dave Cash](https://github.com/davecash75)
-* [Jamie Mcclelland](https://github.com/jamie-mcclelland)
 * [Milan Malfait](https://github.com/milanmlft)
+* [Jamie Mcclelland](https://github.com/jamie-mcclelland)
 * [Zakaria Senousy](https://github.com/zsenousy)
 * [Stephen Thompson](https://github.com/thompson318)
 * [Miguel Xochicale](https://github.com/mxochicale)

episodes/data/README.md

@@ -1,4 +1,4 @@
-# Demo data
+# Medical Data Imaging 
 
 ## CT-vs-PET-Ventilation-Imaging
 CT Ventilation as a Functional Imaging Modality for Lung Cancer Radiotherapy from [TCIA](https://www.cancerimagingarchive.net/collection/ct-vs-pet-ventilation-imaging/).
@@ -39,7 +39,12 @@ Data paths look like these:
 ```
 
 
-### Data is converted using:
+### Citations & Data Usage Policy
+Data Citation Required: Users must abide by [the TCIA Data Usage Policy and Restrictions](https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/). 
+Attribution must include the following citation, including the Digital Object Identifier:
+> Eslick, E. M., Kipritidis, J., Gradinscak, D., Stevens, M. J., Bailey, D. L., Harris, B., Booth, J. T., & Keall, P. J. (2022). CT Ventilation as a functional imaging modality for lung cancer radiotherapy (CT-vs-PET-Ventilation-Imaging) (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/3ppx-7s22
+
+### Data is converted from DICOM to nifti using `dicom2nifti`:
 ```
 from pathlib import Path 
 import dicom2nifti 
@@ -50,5 +55,5 @@ dicom2nifti.convert_directory(dicoms, ".", compression=True, reorient=True)
 
 ## Demons Image Registration Exercise Data
 
-Please check `data` folder inside `episodes` for accessing `cine_MR_img_1.png`, `cine_MR_img_2.png`, and `cine_MR_img_3.png` that are used for demonReg.py and `exampleSolution3.py`.
+Please check [`data`](https://github.com/HealthBioscienceIDEAS/Medical-Image-Registration-Short-Course/tree/main/episodes/data) folder inside `episodes` for accessing `cine_MR_img_1.png`, `cine_MR_img_2.png`, and `cine_MR_img_3.png` that are used for [`demonReg.py`](https://github.com/HealthBioscienceIDEAS/Medical-Image-Registration-Short-Course/blob/main/src/mirc/utils/demonsReg.py) and [`exampleSolution3.py`](https://github.com/HealthBioscienceIDEAS/Medical-Image-Registration-Short-Course/blob/main/src/mirc/examples/exampleSolution3.py).
 

episodes/data/Untitled.txt

@@ -25,6 +25,20 @@ Open terminal, activate `mirVE` environment and run python
 conda activate mirVE
 python
 ```
+:::::::::::::::::::::::::::::::::::::: spoiler
+
+## How to avoid `ModuleNotFoundError: No module named VARIABLE`?
+
+You need to export relative imports, using the following notation for either linux or windows (might be similar to mac)
+
+```bash
+#WINDOWS
+set PYTHONPATH=%PYTHONPATH%;C:\path\to\your\project\
+#LINUX
+export PYTHONPATH="${PYTHONPATH}:/path/to/your/project/"
+```
+
+::::::::::::::::::::::::::::::::::::::
 
 This document explains the implementation of a 2D image registration algorithm using the Demons algorithm. The implementation includes visualisation updates within a single window containing three panes for easier comparison of source, target, and warped images.
 
@@ -460,4 +474,4 @@ demonsReg(cine_MR_img_1, cine_MR_img_2)
 <video width="800" height="450" controls>
     <source src="fig/final_results_vid.mp4" type="video/mp4">
     Your browser does not support the video tag.
-</video>
+</video>

episodes/excthreesteps.Rmd

@@ -508,7 +508,10 @@ system (i.e. the values increase as you move to the right and anterior of the pa
 Therefore, the world coordinates decrease as the voxel indices increase.
 
 #### ITK-SNAP visualisation coordinate system
-TOADD 
+ITK-SNAP provides the option, `Tools>Reorient Image`, to set image orientation using three-letter code (**RAI** code), describing the image rows, columns, and slice map to the anatomical coordinates. 
+For example, the code **ASL** means that image rows (X) run from **A**nterior to **P**osterior, image columns (Y) run from **S**uperior to **I**nferior, and images slices (Z) run from **L**eft to **R**ight. 
+
+![Figure. ITK-SNAP Reorient image](fig/itk-snap-tools-reorient-image.png)
 
 ## Modifying `NifTi` images and headers with Nibabel
 
@@ -916,7 +919,7 @@ For manual image registration in ITK-SNAP go to Tools > image registration
 
 ![Figure. ITK-SNAP main window](fig/itk-snap-image-manual-registration.png)
 
-* Automatic registration (affine)
+* Automatic registration (affine)   
 You can use [niftyreg](https://nipype.readthedocs.io/en/latest/api/generated/nipype.interfaces.niftyreg.reg.html), [Reg_aladin](http://cmictig.cs.ucl.ac.uk/wiki/index.php/Reg_aladin) or [DL methods](https://github.com/Project-MONAI/tutorials/tree/main/3d_registration).
 
 ## References 

episodes/fig/itk-snap-tools-reorient-image.png

@@ -0,0 +1,3 @@
+"""mirc"""
+
+__version__ = "0.0.1"

episodes/itksnap.Rmd

@@ -1,42 +1,29 @@
 """
 example solution for the image registration exercises 3 for module MPHY0025 (IPMI)
 
-Jamie McClelland
-UCL
+Author: Jamie McClelland UCL
+Contributors: Zakaria Senousy and Miguel Xochicale UCL-ARC
 """
-#%%
-# set MATPLOTLIB to use qt backend - this will display figures in separate windows and
-# is required for the animation to display correctly
 import matplotlib.pyplot as plt
-import demonsReg
+from src.mirc.utils.demonsReg import demonsReg
+from src.mirc.utils.utils3 import dispImage, dispDefField, calcJacobian
+import numpy as np
+import skimage.io
 #%matplotlib qt
 
-#%%
-# Load in the three 2D images required for these exercises using the imread function from the
-# scikit-image python library
-import skimage.io
 cine_MR_img_1 = skimage.io.imread('cine_MR_1.png') 
 cine_MR_img_2 = skimage.io.imread('cine_MR_2.png')
 cine_MR_img_3 = skimage.io.imread('cine_MR_3.png')
 
 
-#%%
-import numpy as np
-# ***************
-# ADD CODE HERE TO CONVERT ALL THE IMAGES TO DOUBLE DATA TYPE AND TO REORIENTATE THEM
-# INTO 'STANDARD ORIENTATION'
 cine_MR_img_1 = np.double(cine_MR_img_1)
 cine_MR_img_2 = np.double(cine_MR_img_2)
 cine_MR_img_3 = np.double(cine_MR_img_3)
 
 cine_MR_img_1 = np.flip(cine_MR_img_1.T, 1)
 cine_MR_img_2 = np.flip(cine_MR_img_2.T, 1)
 cine_MR_img_3 = np.flip(cine_MR_img_3.T, 1)
-# ***************
 
-from utils3 import dispImage
-# ***************
-# ADD CODE HERE TO DISPLAY EACH IMAGE IN A SEPARATE FIGURE
 plt.figure()
 dispImage(cine_MR_img_1)
 plt.figure()
@@ -47,38 +34,25 @@
 dispImage(cine_MR_img_1 - cine_MR_img_2)
 plt.figure()
 dispImage(cine_MR_img_1 - cine_MR_img_3)
-# ***************
 
-#%%
 plt.close('all')
 
-from demonsReg import demonsReg
 
-#%%
 [warped_image_def, def_field_def] = demonsReg(cine_MR_img_1, cine_MR_img_2)
-
-#%%
 [warped_image, def_field] = demonsReg(cine_MR_img_1, cine_MR_img_2, sigma_elastic=0)
-
-#%%
 [warped_image, def_field] = demonsReg(cine_MR_img_1, cine_MR_img_2, sigma_fluid=0)
 
-from utils3 import dispDefField
 plt.figure()
 dispImage(warped_image_def)
 plt.figure()
 dispDefField(def_field_def, spacing=2)
 
-#%%
 [warped_image, def_field] = demonsReg(cine_MR_img_1, cine_MR_img_2, num_lev=1)
 
-#%%
 [warped_image, def_field] = demonsReg(cine_MR_img_1, cine_MR_img_2, num_lev=6)
 
-#%%
 [warped_image_add, def_field_add] = demonsReg(cine_MR_img_1, cine_MR_img_2, sigma_elastic=0.5)
 
-from utils3 import calcJacobian
 [J, J_Mat] = calcJacobian(def_field_add)
 plt.figure()
 dispImage(J)
@@ -87,9 +61,7 @@
 plt.figure()
 dispImage(J<=0)
 
-#%%
 [warped_image_comp, def_field_comp] = demonsReg(cine_MR_img_1, cine_MR_img_2, sigma_elastic=0.5, use_composition=True)
-from utils3 import calcJacobian
 [J, J_Mat] = calcJacobian(def_field_comp)
 plt.figure()
 dispImage(J)
@@ -101,24 +73,18 @@
 dispImage(warped_image_add)
 
 
-#%%
 [warped_image, def_field] = demonsReg(cine_MR_img_1, cine_MR_img_2, check_MSD=False, disp_freq=50)
 
-#%%
 [warped_image, def_field] = demonsReg(cine_MR_img_1, cine_MR_img_2, check_MSD=False, disp_freq=50, max_it=500)
 
-#%%
 [warped_image, def_field] = demonsReg(cine_MR_img_1, cine_MR_img_2, use_target_grad=True)
 
 plt.figure()
 dispImage(warped_image_def)
 plt.figure()
 dispDefField(def_field_def, spacing=2)
 
-#%%
 [warped_image, def_field] = demonsReg(cine_MR_img_1, cine_MR_img_3, sigma_elastic=0, sigma_fluid=5, use_composition=True, num_lev=6, disp_freq=0)
-#%%
 [warped_image, def_field] = demonsReg(cine_MR_img_3, cine_MR_img_1, sigma_elastic=0.6, sigma_fluid=0.5, use_composition=True, num_lev=6, disp_freq=0)
 
 
-# %%