Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests

Lorch, Benedikt; Vaillant, Ghislain; Baumgartner, Christian F.; Bai, Wenjia; Rueckert, Daniel; Maier, Andreas

doi:10.1155/2017/4501647

Cited by 42 publications

(26 citation statements)

References 16 publications

(18 reference statements)

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“…We compared our algorithm with a range of alternative classification techniques: K-nearest neighbours, Support Vector Machines (SVMs), Decision Trees, Random Forests, Adaboost and Naive Bayesian. The inputs to all algorithms were the cropped intensity-normalised data as described in Section 4.1 with the exception of the method proposed by Lorch et al (2017). For this method, we used hand crafted features (e.g.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Following a similar idea to Lorch et al (2017) we produce breathing artefacts by applying 2D translations to the image frames prior to generating their k-space representations. The translations follow a sinusoidal pattern.…”

Section: Methodsmentioning

confidence: 99%

“…CMR image quality has also been linked with automatic quality control of image segmentation in Robinson et al (2017). Lorch et al (2017) investigated synthetic motion artefacts and used histogram, box, line and texture features to train a random forest algorithm to detect different artefact levels. However, their algorithm was tested only on artificially corrupted synthetic data and aimed only at detecting breathing artefacts.…”

Section: Related Workmentioning

confidence: 99%

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Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

Öksüz

Ruijsink

Puyol‐Antón

et al. 2019

Medical Image Analysis

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Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. 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) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

Öksüz

Ruijsink

Puyol‐Antón

et al. 2019

Medical Image Analysis

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“…CMR image quality was also linked with automatic quality control of image segmentation in [12]. In the context of detection of CMR motion artefacts, Lorch et al [7] investigated synthetic motion artefacts. In their work, they used histogram, box, line and texture features to train a random forest algorithm to detect motion artefacts for different artefact levels.…”

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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“…Faster machine and deep learning (DL)-based methods for artifact detection in MR images have also been reported, generally resulting in per-patch or per-slice classification. 2,4,[9][10][11][12][13][14] While artifact detection on a per-patch or per-slice basis is helpful, it does not inform the technologist about whether a series needs to be rescanned. In many instances, artifacts present in select slices do not require a series rescan.…”

mentioning

confidence: 99%

A Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations

Sreekumari¹,

Shanbhag²,

Yeo

et al. 2019

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: MR imaging rescans and recalls can create large hospital revenue loss. The purpose of this study was to develop a fast, automated method for assessing rescan need in motion-corrupted brain series. MATERIALS AND METHODS:A deep learning-based approach was developed, outputting a probability for a series to be clinically useful. Comparison of this per-series probability with a threshold, which can depend on scan indication and reading radiologist, determines whether a series needs to be rescanned. The deep learning classification performance was compared with that of 4 technologists and 5 radiologists in 49 test series with low and moderate motion artifacts. These series were assumed to be scanned for 2 scan indications: screening for multiple sclerosis and stroke. RESULTS:The image-quality rating was found to be scan indication-and reading radiologist-dependent. Of the 49 test datasets, technologists created a mean ratio of rescans/recalls of (4.7 Ϯ 5.1)/(9.5 Ϯ 6.8) for MS and (8.6 Ϯ 7.7)/(1.6 Ϯ 1.9) for stroke. With thresholds adapted for scan indication and reading radiologist, deep learning created a rescan/recall ratio of (7.3 Ϯ 2.2)/(3.2 Ϯ 2.5) for MS, and (3.6 Ϯ 1.5)/(2.8 Ϯ 1.6) for stroke. Due to the large variability in the technologists' assessments, it was only the decrease in the recall rate for MS, for which the deep learning algorithm was trained, that was statistically significant (P ϭ .03). CONCLUSIONS:Fast, automated deep learning-based image-quality rating can decrease rescan and recall rates, while rendering them technologist-independent. It was estimated that decreasing rescans and recalls from the technologists' values to the values of deep learning could save hospitals $24,000/scanner/year. ABBREVIATIONS: CB ϭ clinically bad; CG ϭ clinically good; CNN ϭ convolutional neural network; DL ϭ deep learning; D0 -D5 ϭ radiologists; IQ ϭ image quality; R0 ϭ radiologist; ROC ϭ receiver operating characteristic; T1-T4 ϭ MR imaging technologists

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Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests

Cited by 42 publications

References 16 publications

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

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

A Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations

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