Data Organization

This document describes how to prepare data and run all the scripts for generating a 3D reconstruction of histology and derived maps using 7T and 9.4T MRI. Please refer to the paper for overall flow of the approach.

** This document describes a generic setup; if you are working on PATCH|Lab PMACS, see the specific instructions.

For the purposes of this document we will use variable ${id} as the example subject identifier, and ${block} as the example block identifier.

Set up directory structure

At the root of your directory tree, create the following directories:

input                    : Input MRI and histology data organized here
manifest                 : Manifest files summarizing the data/parameters
manual                   : Manual steps are performed here
scripts                  : Clone this repo here
work                     : All the work directories are created here

Preparing the 7T MRI

Perform the 3D printing process

Organize the images generated during 3D printing as follows:

input/${id}/mold_mri/mtl7t.nii.gz          
input/${id}/mold_mri/contour_image.nii.gz 
input/${id}/mold_mri/slitmold.nii.gz 
input/${id}/mold_mri/holderrotation.nii.gz 

Preparing the 9.4T MRI

Convert the 9.4T MRI to NIFTI format

Place the image here:

input/${id}/hires_mri/${id}_mri_hires.nii.gz

Preparing the blockface images

The blockface images are .jpg files and all blockface images for each block should be placed in the folder
```
input/${id}/blockface/${block}/
```
with the following naming convention:
```
${id}_${block}_05_08.jpg
```
where the first number is the section number (each section is 500um) and the second number is the slide number (each slide is 50um). Numbering starts at 1. Slide 10 in each section is the Nissl slide, and typically slide 9 is the Tau slide. Typical blockface image is 3000x4000 pixels in size and looks like this:

Preparing the histology data

Organizing the histology data is the hardest part of the process. Here we describe what must be provided for the recon.sh scripts, but some of these data will require their own scripts to be run. Each histology slide will have a unique name. Name is arbitrary, but ideally it would contain the specimen, block, section and stain in the name. For example:
```
S01_HR1a_NISSL_05
```
Here we refer to this name as ${slide}
Each slide should have an input directory
```
input/${id}/histo_proc/${slide}
```

This directory contains the following files:

preproc/${slide}_metadata.json                  # Slide metadata
preproc/${slide}_rgb_40um.nii.gz                # Medium-resolution RGB image of slide
preproc/${slide}_thumbnail.tiff                 # Smallish (1000pix) thumbnail

For Nissl slides, it should contain these files:

preproc/${slide}_deepcluster.nii.gz             # DeepCluster output for slide

For tau slides, it should contain these files:

density/${slide}_Tau_tangles_densitymap.nii.gz  # Tau burden map from WildCat

To see how these files are generated, see Histology Processing

Additional details on these files are provided below.

Slide metadata file

preproc/${slide}_metadata.json contains slide metadata, as generated by OpenSlide. Here is an example file:

{
  "level_downsamples": [1.0, 2.0, 4.0, 8.000258431321877], 
  "spacing": [0.0004, 0.0004], 
  "dimensions": [61916, 71904], 
  "level_dimensions": [[61916, 71904], [30958, 35952], [15479, 17976], [7739, 8988]], 
  "level_count": 4
}

Other Image Files

preproc/${slide}_rgb_40um.nii.gz is a 3-component NIFTI image with red, green, and blue channels. It has the pixel size of 40 microns, so 1000-2000 pixels across.
preproc/${slide}_thumbnail.tiff is a thumbnail generated by OpenSlide and is 1000 pixels across in either width or height.
preproc/${slide}_deepcluster.nii.gz is a 20-channel image that is generated by running the Nissl DeepCluster processing pipeline. It’s dimensions are 1/256 of the raw histology slide. Every pixel represents a 20-feature representation of the 256x256 patch from the raw Nissl image.
density/${slide}_Tau_tangles_densitymap.nii.gz is a scalar density map derived by running the WildCat classifier on the whole-slide image.

`