Automated reference-free detection of motion artifacts in magnetic resonance images

Küstner, Thomas; Liebgott, Annika; Mauch, Lukas; Martirosian, Petros; Bamberg, Fabian; Nikolaou, Konstantin; Yang, Bin; Schick, Fritz; Gatidis, Sergios

doi:10.1007/s10334-017-0650-z

Cited by 83 publications

(76 citation statements)

References 29 publications

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“…In contrast, DL uses multiresolution image features learned from the image data. The high performance achieved using our model supports the emerging role of DL in image quality assessment as reported in recent studies for automated detection of motion artifacts in head and abdomen MRI, 14 automated image quality evaluation of T 2 -weighted liver MRI, 15 and detection of motion artifacts in brain MRI 16 (Table 3).…”

Section: Discussionsupporting

confidence: 82%

“…The major advantage of DCNNs is their ability to self‐learn the image features that in traditional algorithms are hand‐engineered . Recent studies have demonstrated the feasibility of DCNNs for automated assessment of image quality of MR images acquired from a single site . It is unclear whether the results can be successfully generalized to multicenter datasets.…”

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confidence: 99%

“…13 Recent studies have demonstrated the feasibility of DCNNs for automated assessment of image quality of MR images acquired from a single site. [14][15][16] It is unclear whether the results can be successfully generalized to multicenter datasets.…”

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confidence: 99%

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Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks

Sujit

Coronado

Kamali

et al. 2019

Magnetic Resonance Imaging

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Background Deep learning (DL) is a promising methodology for automatic detection of abnormalities in brain MRI. Purpose To automatically evaluate the quality of multicenter structural brain MRI images using an ensemble DL model based on deep convolutional neural networks (DCNNs). Study Type Retrospective. Population The study included 1064 brain images of autism patients and healthy controls from the Autism Brain Imaging Data Exchange (ABIDE) database. MRI data from 110 multiple sclerosis patients from the CombiRx study were included for independent testing. Sequence T1‐weighted MR brain images acquired at 3T. Assessment The ABIDE data were separated into training (60%), validation (20%), and testing (20%) sets. The ensemble DL model combined the results from three cascaded networks trained separately on the three MRI image planes (axial, coronal, and sagittal). Each cascaded network consists of a DCNN followed by a fully connected network. The quality of image slices from each plane was evaluated by the DCNN and the resultant image scores were combined into a volumewise quality rating using the fully connected network. The DL predicted ratings were compared with manual quality evaluation by two experts. Statistical Tests Receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitivity, specificity, accuracy, and positive (PPV) and negative (NPV) predictive values. Results The AUC, sensitivity, specificity, accuracy, PPV, and NPV for image quality evaluation of the ABIDE test set using the ensemble model were 0.90, 0.77, 0.85, 0.84, 0.42, and 0.96, respectively. On the CombiRx set the same model achieved performance of 0.71, 0.41, 0.84, 0.73, 0.48, and 0.80. Data Conclusion This study demonstrated the high accuracy of DL in evaluating image quality of structural brain MRI in multicenter studies. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1260–1267.

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Section: Discussionsupporting

confidence: 82%

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confidence: 99%

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Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks

Sujit

Coronado

Kamali

et al. 2019

Magnetic Resonance Imaging

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“…Deep learning methods has also been applied to MR artifact detection, e.g. poor quality spectra in MRSI [188]; detection and removal of ghosting artifacts in MR spectroscopy [189]; and automated reference-free detection of patient motion artifacts in MRI [190].…”

Section: Image Restoration (Denoising Artifact Detection)mentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,587

883

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What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging. Artificial neural networksArtificial neural networks (ANNs) is one of the most famous machine learning models, introduced already in the 1950s, and actively studied since [41, Chapter 1.2]. 11 Roughly, a neural network consists of a number of connected computational units, called neurons, arranged in layers. There's an input layer where data enters the network, followed by one or more hidden layers transforming the data as it flows through, before ending at an output layer that produces the neural network's predictions. The network is trained to output useful predictions by identifying patterns in a set of labeled training data, fed through the network while the outputs are compared with the actual labels by an objective function. During training the network's parametersthe strength of each neuron-is tuned until the patterns identified by the network result in good 10 https://www.deeplearningbook.org/ 11 The loose connection between artificial neural networks and neural networks in the brain is often mentioned, but quite over-blown considering the complexity of biological neural networks. However, there is some interesting recent work connecting neuroscience and artificial neural networks, indicating an increase in the cross-fertilization between the two fields [43,44,45].3 predictions for the training data. Once the patterns are learned, the network can be used to make predictions on new, unseen data, i.e. generalize to new data.It h...

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“…Their algorithm used training data from fruit images and artificial motion data. Also, Kustner et al [6] proposed a patch-based motion artefact detection method for brain and abdomen MR images, but they made their tests on a small dataset of 16 MR images with significant motion artefacts. Motion artefacts in CMR imaging are largely caused by ECG mistriggering and pose a significantly different problem.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Using K-Space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Öksüz

Ruijsink

Puyol‐Antón

et al. 2018

Lecture Notes in Computer Science

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Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and timeconsuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.

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Automated reference-free detection of motion artifacts in magnetic resonance images

Cited by 83 publications

References 29 publications

Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks

Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks

An overview of deep learning in medical imaging focusing on MRI

Deep Learning Using K-Space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

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