Esther Puyol-Antón

Fully Automated Segmentation-Based Respiratory Motion Correction of Multiplanar Cardiac Magnetic Resonance Images for Large-Scale Datasets

Cardiac magnetic resonance (CMR) can be used for quantitative analysis of heart function. However, CMR imaging typically involves acquiring 2D image planes during separate breath-holds, often resulting in misalignment of the heart between image planes in 3D. Accurate quantitative analysis requires a robust 3D reconstruction of the heart from CMR images, which is adversely affected by such motion artifacts. Therefore, we propose a fully automated method for motion correction of CMR planes using segmentations produced by fully convolutional neural networks (FCNs). FCNs are trained on 100 UK Biobank subjects to produce short-axis and long-axis segmentations, which are subsequently used in an iterative registration algorithm for correcting breath-hold induced motion artifacts. We demonstrate significant improvements in motion-correction over image-based registration, with strong correspondence to results obtained using manual segmentations. We also deploy our automatic method on 9,353 subjects in the UK Biobank database, demonstrating significant improvements in 3D plane alignment.

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

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 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) 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 1 ms. 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.

Semi-automatic Cardiac and Respiratory Gated MRI for Cardiac Assessment During Exercise

Imaging of the heart during exercise can improve detection and treatment of heart diseases but is challenging using current clinically applied cardiac MRI (cMRI) techniques. Real-time (RT) imaging strategies have recently been proposed for exercise cMRI, but respiratory motion and unreliable cardiac gating introduce significant errors in quantification of cardiac function. Self-navigated cMRI sequences are currently not routinely available in a clinical environment. We aim to establish a method for cardiac and respiratory gated cine exercise cMRI that can be applied in a clinical cMRI setting. We developed a retrospective, image-based cardiac and respiratory gating and reconstruction framework based on widely available highly accelerated dynamic imaging. From the acquired dynamic images, respiratory motion was estimated using manifold learning. Cardiac periodicity was obtained by identifying local maxima in the temporal frequency spectrum of the spatial means of the images. We then binned the dynamic images in respiratory and cardiac phases and subsequently registered and averaged them to reconstruct a respiratory and cardiac gated cine stack. We evaluated our method in healthy volunteers and patients with heart diseases and demonstrate good agreement with existing RT acquisitions (R = .82). We show that our reconstruction pipeline yields better image quality and has lower inter- and intra-observer variability compared to RT imaging. Subsequently, we demonstrate that our method is able to detect a pathological response to exercise in patients with heart diseases, illustrating its potential benefit in cardiac diagnostic and prognostic assessment.

Multiview Machine Learning Using an Atlas of Cardiac Cycle Motion

A cardiac motion atlas provides a space of reference in which the cardiac motion fields of a cohort of subjects can be directly compared. From such atlases, descriptors can be learned for subsequent diagnosis and characterization of disease. Traditionally, such atlases have been formed from imaging data acquired using a single modality. In this work we propose a framework for building a multimodal cardiac motion atlas from MR and ultrasound data and incorporate a multiview classifier to exploit the complementary information provided by the two modalities. We demonstrate that our novel framework is able to detect non ischemic dilated cardiomyopathy patients from ultrasound data alone, whilst still exploiting the MR based information from the multimodal atlas. We evaluate two different approaches based on multiview learning to implement the classifier and achieve an improvement in classification performance from 77.5% to 83.50% compared to the use of US data without the multimodal atlas.

Learning Optimal Spatial Scales for Cardiac Strain Analysis Using a Motion Atlas

Cardiac motion is inherently tied to the disease state of the heart, and as such can be used to identify the presence and extent of different cardiac pathologies. Abnormal cardiac motion can manifest at different spatial scales of the myocardium depending on the disease present. The importance of spatial scale in the analysis of cardiac motion has not previously been explicitly investigated. In this paper, a novel approach is presented for analysing myocardial strains at different spatial scales using a cardiac motion atlas to find the optimal scales for (1) predicting response to cardiac resynchronisation therapy and (2) identifying the presence of strict left bundle-branch block in a patient cohort of 34. Optimal spatial scales for the two applications were found to be \(4\%\) and \(16\%\) of left ventricular volume with accuracies of \(84.8 \pm 8.4\%\) and \(81.3 \pm 12.6\%\), respectively, using a repeated, stratified cross-validation.

Myocardial strain computed at multiple spatial scales from tagged magnetic resonance imaging: Estimating cardiac biomarkers for CRT patients

HighlightsA framework to analyse cardiac strain at multiple spatial scales using a motion atlas is proposed.Optimal scales are found for the identification of clinical biomarkers for cardiac resynchronisation therapy patients.Combining strains from multiple scales improves the accuracy of CRT response prediction. Graphical abstract Figure. No Caption available. Abstract Abnormal cardiac motion can indicate different forms of disease, which can manifest at different spatial scales in the myocardium. Many studies have sought to characterise particular motion abnormalities associated with specific diseases, and to utilise motion information to improve diagnoses. However, the importance of spatial scale in the analysis of cardiac deformation has not been extensively investigated. We build on recent work on the analysis of myocardial strains at different spatial scales using a cardiac motion atlas to find the optimal scales for estimating different cardiac biomarkers. We apply a multi‐scale strain analysis to a 43 patient cohort of cardiac resynchronisation therapy (CRT) patients using tagged magnetic resonance imaging data for (1) predicting response to CRT, (2) identifying septal flash, (3) estimating QRS duration, and (4) identifying the presence of ischaemia. A repeated, stratified cross‐validation is used to demonstrate the importance of spatial scale in our analysis, revealing different optimal spatial scales for the estimation of different biomarkers.

15 Automatic mis-triggering artefact detection for image quality assessment of cardiac MRI

Introduction High quality cardiac magnetic resonance (CMR) images are a prerequisite for high diagnostic accuracy. Analysis of bad quality image data can result in erroneous conclusions, especially in the case of automated analysis algorithms, that are currently being proposed. CMR images can contain a range of image artefacts and assessing the quality of images produced by MR scanners has long been a challenging issue. Traditionally, images are visually inspected experts, and those showing an insufficient level of quality are excluded. In this work, we propose to use a Convolutional Neural Network (CNN) model to automatically detect mis-triggering artefacts. Methods We use a deep neural network architecture to detect the mis-triggering artefacts in a large cardiac MR dataset. The input is to the network an intensity normalised 50 temporal frames of 80 × 80 CMR image, which is cropped using a Fourier transform-based region of interest extraction relying on motion patterns. The proposed network consists of five layers. The architecture of our network follows a 3D Convolutional model and consists of 6 convolutional layers and two dense layers for classification. Results We tested our algorithm on a subset of 100 cardiac MR images from UK Biobank in a 10-fold cross-validation setup. Our method achieves 0.85 accuracy and 0.81 precision for detection of the mis-triggering artefacts compared 0.67 accuracy and 0.66 precision of variance of Laplacians, which is a state of the art blurring detection method. Conclusion We have proposed a method to automatically detect low quality images with high accuracy in less than 1 ms. Our work brings fully automated evaluation of left ventricular function from CMR imaging a step closer to clinically acceptable standards, addresses a key issue for the analysis of large imaging datasets.