Introduction

This repository contains code to analyse a uniform phantom using the PumpIA framework. It uses the subtraction SNR method and therefore expects a repeat image, however the uniformity module will run with a single image.

It is currently not validated and is provided as is, see the license for more information.

The collection contains the following tests:

• SNR
• Uniformity

Usage

Users should make themselves familiar with the PumpIA user interface

To run the collection:

1. Clone the repository
2. Use an environment manager to install the requirements from requirements.txt or install the requirements using the command pip install -r requirements.txt when in the repository directory
3. Run the relevant script in /Testing

OR

install from PyPI using pip:

pip install pumpia-uniform-phantom

And run using the commands pumpia-uniform-phantom (making sure you are in the right environment if used for the pip install).

To use the collection:

1. Load the folder with the relevant images
2. Drag and drop the series containing the relevant images into the viewer of the Main tab
3. Generate the ROIs and run analysis
4. Correct the context if required (see below)
   • Re-run generating ROIs
5. Move any ROIs as required, this should be done through their relevant modules.
   • Re-run analysis
6. Copy the results in the relevant format. Horizontal is tab separated, vertical is new line separated.

Correcting Context

The context used for this collection is the Auto Phantom Context Manager provided with PumpIA, see the relevant documentation for this.

Modules

Subtraction SNR

Calculates SNR based on the subtraction method. The ROI size is determined from the size input as a percentage of the phantom height and width. The ROI is always centred on the phantom. The following corrections can be applied:

• Bandwidth
• Pixel Size (includes slice width)
• Number of Averages
• Number of Phase Encode Steps

A button for showing the subtraction image is included.

Uniformity

This is calculated using the integral uniformity method. The size of the ROI is determined in the same way as the SNR module.

There is the option of applying a low pass kernel convolution to the image prior to calculation, this is defaulted to on. The kernel is defined by