# 3D-QCNet -- A Pipeline for Automated Artifact Detection in Diffusion MRI images

arXiv: https://arxiv.org/abs/2103.05285 · HTML: https://mlsanity.com/paper/2103.05285/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [QC-Automator: Deep Learning-based Automated Quality Control for Diffusion MR Images](https://mlsanity.com/paper/1911.06816/) (2019)

## Built on by (most cited first, 3 of 3)

- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [Automated quality assessment using appearance-based simulations and hippocampus segmentation on low-field paediatric brain MR images](https://mlsanity.com/paper/2410.06161/) (2024)
- [Unsupervised dMRI Artifact Detection via Angular Resolution Enhancement and Cycle Consistency Learning](https://mlsanity.com/paper/2409.15883/) (2024)

## Abstract (arXiv)

Artifacts are a common occurrence in Diffusion MRI (dMRI) scans. Identifying and removing them is essential to ensure the accuracy and viability of any post processing carried out on these scans. This makes QC (quality control) a crucial first step prior to any analysis of dMRI data. Several QC methods for artifact detection exist, however they suffer from problems like requiring manual intervention and the inability to generalize across different artifacts and datasets. In this paper, we propose an automated deep learning (DL) pipeline that utilizes a 3D-Densenet architecture to train a model on diffusion volumes for automatic artifact detection. Our method is applied on a vast dataset consisting of 9000 volumes sourced from 7 large clinical datasets. These datasets comprise scans from multiple scanners with different gradient directions, high and low b values, single shell and multi shell acquisitions. Additionally, they represent diverse subject demographics like the presence or absence of pathologies. Our QC method is found to accurately generalize across this heterogenous data by correctly detecting 92% artifacts on average across our test set. This consistent performance over diverse datasets underlines the generalizability of our method, which currently is a significant barrier hindering the widespread adoption of automated QC techniques. For these reasons, we believe that 3D-QCNet can be integrated in diffusion pipelines to effectively automate the arduous and time-intensive process of artifact detection.