Introduction

This repository contains Supporting Material with Python and R code as well as data processing tutorials to accompany the following publication:

              **"How to quantify and minimise error in coral skeletal density estimates using X-ray µCT"**

                                  (link to paper and DOI once published).
                                  
                                  Corresponding author: Leonardo Bertini 
                                  
                                  e-mail :  l.bertini@nhm.ac.uk  | l.bertini@bristol.ac.uk

                The code is distributed under the MIT license https://en.wikipedia.org/wiki/MIT_License

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This project was supported by:

• 4D-Reef, a Marie Skłodowska-Curie Innovative Training Network funded by European Union Horizon 2020 research and innovation programme
• The Natural History Museum Science Investment Fund.
• SYNTHESIS+ museum initiative transnational access.

This work was a joint effort across people from the following institutions:

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Below, you'll find a brief description of all the directories in this repository, which might have their own 'README' files with further info.

AvizoTutorials

Instructions for X-ray volume operations using Avizo® covering :

• How to align replicate X-ray µCT scans

• How to segment X-ray volumes and shrinkwrap them quickly

• How to export 16-bit single-binned histogram datasets for large X-ray volumes

• How to extract volume metrics (e.g., Volume and SurfaceArea for example)

• How to create a '.VolMetrics' file containing information from a respective scan

PhantomExtraction

Python code for extracting greyscale values from an X-ray stack containing a phantom disc with embedded density standard materials (i.e., 'greyscale probing'):

• SemiAutomated_Extraction_Phantom.py: Extraction across the stack by prompting user interaction to mark density standard materials embedded in or attached around a radiology phantom.

• Standard_Extract.py: Automated extraction of phantom density inserts using computer vision to detect regular circular features across the phantom stack.

CoralWeightTests

Python code for density calibration and weight tests on X-ray µCT volumes that have been previously shrinkwraped (Refer to AvizoTutorials) :

• Phantom_Fittings.py: Fitting different calibration curves to PhantomExtract results and performing weight tests.

• WeightTest_DiagnosticFigures.py: Producing diagnostic figures to visualize different calibration fits and how µCT histogram and phantom density standards overlap.

• ResultsAggregator.py: A wrapper to aggregate and bind weight test results across the ProjectRoot. This also appends volume and area measurements to resulting dataframes.

HistogramsReplicateScans

Python code for generating figures of X-ray histograms of replicate scans done under varying settings

• Histogram_overlays.py: Plotting histogram overlays with kurtosis and skewness measurements.

R_ScriptsMainFiguresAndStats

R code to produce all base figures used in the publication. Statistical analyses therein. Code written in R version 4.2.2. Scripts are named based on the figure they generate and are self-explanatory.

Data

Data used in the paper and figures.