Skip to content

Quality control

When processing data, it is considered good practice (and is also very useful!) to inspect the intermediate output files/data created after every few steps. Here are some resources that can guide you through this process.

Introduction

Introduction about quality assessment process

  1. Looking for susceptibility and pathological artifacts

  2. Checking whether motion exceeds your lab's thresholds

  3. Keeping records to make sure you have the data that you should have.

Quality Checks for fMRI Data - Lecture on quality control for neuroimaging data, especially fMRI data
  • URL
  • programming language: {python}, {matlab/octave}, {C}, ...
  • level: {beginner}
  • tags: {video} {MOOC}
  • date:
  • duration: 00:31
  • by: Andrew Jahn

Interesting paper for understanding QA

Basic quality control in routine MRI - Scientific research about the steps of QA
  • URL
  • level: {beginner}
  • tags: {video} {notebook} {fMRI} {MOOC} {blog} {website} {podcast}
  • by: Thomas Maris

Python libraries for QA

INCF Tools for quality assessment - This website has a list of QA libraries in python for different modalities with their documentation
  • URL
  • level: {beginner} / {intermediate} / {advanced}
  • tags: {video} {notebook} {fMRI} {MOOC} {blog} {website} {podcast}
  • by: INCF