# Artifact- and content-specific quality assessment for MRI with image rulers

arXiv: https://arxiv.org/abs/2111.03780 · HTML: https://mlsanity.com/paper/2111.03780/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 53 references that are in the map.
- First posted on arXiv 6 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015)
- [Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/1904.01112/) (2019)
- [dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs](https://mlsanity.com/paper/1904.06505/) (2019)
- [A deep learning framework for quality assessment and restoration in video endoscopy](https://mlsanity.com/paper/1904.07073/) (2019)
- [Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning](https://mlsanity.com/paper/1810.12185/) (2018)
- [Wasserstein GANs for MR Imaging: from Paired to Unpaired Training](https://mlsanity.com/paper/1910.07048/) (2019)
- [A Machine-learning framework for automatic reference-free quality assessment in MRI](https://mlsanity.com/paper/1806.09602/) (2018)
- [Blind estimation of white Gaussian noise variance in highly textured images](https://mlsanity.com/paper/1711.10792/) (2017)
- [Rank-smoothed Pairwise Learning In Perceptual Quality Assessment](https://mlsanity.com/paper/2011.10893/) (2020)
- [Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks](https://mlsanity.com/paper/1904.03639/) (2019)

## Built on by (most cited first, 8 of 8)

- [A study of why we need to reassess full reference image quality assessment with medical images](https://mlsanity.com/paper/2405.19097/) (2024)
- [A study on the adequacy of common IQA measures for medical images](https://mlsanity.com/paper/2405.19224/) (2024)
- [Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence](https://mlsanity.com/paper/2402.08749/) (2024)
- [Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-stack Attention Neural Network](https://mlsanity.com/paper/2211.04703/) (2022)
- [Estimating Head Motion from MR-Images](https://mlsanity.com/paper/2302.14490/) (2023)
- [Beyond Pixels: Medical Image Quality Assessment with Implicit Neural Representations](https://mlsanity.com/paper/2508.05168/) (2025)
- [Task-Based Assessment for Neural Networks: Evaluating Undersampled MRI Reconstructions based on Human Observer Signal Detection](https://mlsanity.com/paper/2210.12161/) (2022)
- [MMRQA: Signal-Enhanced Multimodal Large Language Models for MRI Quality Assessment](https://mlsanity.com/paper/2509.24888/) (2025)

## Abstract (arXiv)

In clinical practice MR images are often first seen by radiologists long after the scan. If image quality is inadequate either patients have to return for an additional scan, or a suboptimal interpretation is rendered. An automatic image quality assessment (IQA) would enable real-time remediation. Existing IQA works for MRI give only a general quality score, agnostic to the cause of and solution to low-quality scans. Furthermore, radiologists' image quality requirements vary with the scan type and diagnostic task. Therefore, the same score may have different implications for different scans. We propose a framework with multi-task CNN model trained with calibrated labels and inferenced with image rulers. Labels calibrated by human inputs follow a well-defined and efficient labeling task. Image rulers address varying quality standards and provide a concrete way of interpreting raw scores from the CNN. The model supports assessments of two of the most common artifacts in MRI: noise and motion. It achieves accuracies of around 90%, 6% better than the best previous method examined, and 3% better than human experts on noise assessment. Our experiments show that label calibration, image rulers, and multi-task training improve the model's performance and generalizability.