Release Name: 0.3.2

Notes:
0.3.2
Matlab updates, improved DICOM support, minor enhancements, and first Docker release
* Matlab runtime version update: now uses and requires matlab 2022a
* First release of a Docker image (separate download/file)
* run_mri_reface.sh now supports a workflow where you can input a directory containing a single DICOM series, instead of a .nii image. This will be converted using dcm2niix, de-faced, converted back to dicom, and marked de-identified. This requires dcm2niix to be installed, and a python environment with pydicom and nibabel.
* Add a -faceMask option to specify an alternative set of regions to replace. This image MUST be in the voxel space of MCALT_FaceTemplate_T1.nii. Voxel value 1 = face; 2 = air behind the head potentially containing wraped face-parts; 3 = ears. Warning: Using this option may produce de-faced images that do NOT offer adequate protection from re-identification.
* For images with larger voxels (e.g. PET and 2D MRI), slightly increase the boundary around the TIV (i.e. brain) that will not be replaced
* Save the _Affine.txt earlier during processing, for de-bugging and resuming purposes.
* We now recommend niftyreg version 1.5.x or better, due to user reports of worse performance with 1.3.x.
* Minor updates to ADIR_nii2dicom

0.3.1 (internal release)
Minor improvements
* Improved intensity normalization mask for 2D FLAIR (type AFL or 2FL)
* Random noise in replacement face now uses a fixed seed for reproducibility
* If an output Affine.txt is found in the output directory, use it (resume processing from this step). Previously, we would only use affine+warp sets found together but would ignore a lone affine.