Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images

• URL: http://arxiv.org/abs/2206.06725v1
• Date: Tue, 14 Jun 2022 10:16:54 GMT
• Title: Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images
• Authors: Alessandro Sciarra, Soumick Chatterjee, Max D\"unnwald, Giuseppe Placidi, Andreas N\"urnberger, Oliver Speck and Steffen Oeltze-Jafra
• Abstract summary: Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
• Score: 54.739076152240024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. If the motion artefacts alter a correct delineation of structure and substructures of the brain, lesions, tumours and so on, the patients need to be re-scanned. Otherwise, neuro-radiologists could report an inaccurate or incorrect diagnosis. The first step right after scanning a patient is the "\textit{image quality assessment}" in order to decide if the acquired images are diagnostically acceptable. An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network has been proposed here, with the possibility to perform also the classification in different groups - by subdividing with SSIM ranges. This method predicts SSIM values of an input image in the absence of a reference ground truth image. The networks were able to detect motion artefacts, and the best performance for the regression and classification task has always been achieved with ResNet-18 with contrast augmentation. Mean and standard deviation of residuals' distribution were $\mu=-0.0009$ and $\sigma=0.0139$, respectively. Whilst for the classification task in 3, 5 and 10 classes, the best accuracies were 97, 95 and 89\%, respectively. The obtained results show that the proposed method could be a tool in supporting neuro-radiologists and radiographers in evaluating the image quality before the diagnosis.

Related papers

• Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation [3.115212915804253]
  This study presents an innovative transfer learning method for automated quality control of 3D gradient echo T1-weighted brain MRIs within a clinical data warehouse. We first intentionally corrupt images from research datasets by inducing poorer contrast, adding noise and introducing motion artefacts. Three artefact-specific models are pre-trained using these corrupted images to detect distinct types of artefacts.
  arXiv  Detail & Related papers  (2024-06-18T09:53:07Z)
• Step-Calibrated Diffusion for Biomedical Optical Image Restoration [47.191704042917394]
  Restorative Step-Calibrated Diffusion (RSCD) is an unpaired image restoration method. RSCD views the image restoration problem as completing the finishing steps of a diffusion-based image generation task. RSCD outperforms other widely used unpaired image restoration methods on both image quality and perceptual evaluation metrics.
  arXiv  Detail & Related papers  (2024-03-20T15:38:53Z)
• Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders [51.14431540035141]
  We propose a new fine-tuning strategy based on sentence sampling and positive-pair loss relaxation for improving the downstream zero-shot pathology classification performance. Our method consistently showed dramatically improved zero-shot pathology classification performance on four different chest X-ray datasets.
  arXiv  Detail & Related papers  (2022-12-14T06:04:18Z)
• Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifier [1.487444917213389]
  This paper proposed a lightweight 3D convolutional neural network (CNN) based framework for schizophrenia diagnosis using MRI images. The model achieves the highest accuracy 92.22%, sensitivity 94.44%, specificity 90%, precision 90.43%, recall 94.44%, F1-score 92.39% and G-mean 92.19% as compared to the current state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-11-05T10:27:37Z)
• Artifact- and content-specific quality assessment for MRI with image rulers [11.551528894727573]
  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. We propose a framework with multi-task CNN model trained with calibrated labels and inferenced with image rulers.
  arXiv  Detail & Related papers  (2021-11-06T02:17:12Z)
• Vision Transformers for femur fracture classification [59.99241204074268]
  The Vision Transformer (ViT) was able to correctly predict 83% of the test images. Good results were obtained in sub-fractures with the largest and richest dataset ever.
  arXiv  Detail & Related papers  (2021-08-07T10:12:42Z)
• Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution [65.25508348574974]
  We go below the MRI acceleration factors reported by all published papers that reference the original fastMRI challenge. We consider powerful deep learning based image enhancement methods to compensate for the underresolved images. The quality of the reconstructed images surpasses that of the other methods, yielding an MSE of 0.00114, a PSNR of 29.6 dB, and an SSIM of 0.956 at x16 acceleration factor.
  arXiv  Detail & Related papers  (2021-03-04T10:45:01Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency [1.7757605747890044]
  The current protocol for T2-weighted fetal brain MRI is not robust to motion. A semi-supervised deep learning method that detects slices with artifacts during the brain volume scan is proposed. The proposed method can improve model accuracy by about 6% and outperform other state-of-the-art semi-supervised learning methods.
  arXiv  Detail & Related papers  (2020-06-23T02:40:45Z)
• Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data [0.0]
  The evaluation of white matter lesion progression is an important biomarker in the follow-up of MS patients. Current automated lesion segmentation algorithms are susceptible to variability in image characteristics related to MRI scanner or protocol differences. We propose a model that improves the consistency of MS lesion segmentations in inter-scanner studies.
  arXiv  Detail & Related papers  (2020-02-03T16:56:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.