Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.

BACKGROUND: Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. METHODS: Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). RESULTS: By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement is 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric is 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability. CONCLUSIONS: We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures.

Compressive manifold learning: estimating one-dimensional respiratory motion directly from undersampled k-space data.

PURPOSE: To present and validate a manifold learning (ML)-based method that estimates the respiratory signal directly from undersampled k-space data and that can be applied for respiratory self-gated liver MRI. METHODS: ML methods embed high-dimensional space data in a low-dimensional space while preserving their characteristic properties. These methods have been used to estimate one-dimensional respiratory motion (low-dimensional manifold) from a set of high-dimensional free-breathing abdominal MR images. These approaches require MR images to be reconstructed first from the acquired undersampled data. Recently, the concept of compressive manifold learning (CML) has been introduced that combines compressed sensing with ML by learning low-dimensional manifolds directly from a partial set of compressed measurements, provided that the sampling satisfies the restricted isometry property. We propose to use the CML concept to extract the respiratory signal directly from undersampled k-space data. RESULTS: Simulation results from free-breathing abdominal MR data show that CML can accurately estimate respiratory motion from highly retrospectively undersampled k-space (up to 25-fold acceleration under ideal assumptions). Prospective free-breathing golden-angle radial two-dimensional (2D) acquisitions further demonstrate the feasibility of the CML method for respiratory self-gating acquisition, estimating the respiratory motion from up to 15-fold accelerated MR data. CONCLUSION: The proposed method performs accurate respiratory signal estimation from highly undersampled k-space data and can be used for respiratory self-navigated 2D liver MRI.

Whole-heart coronary MRA with 3D affine motion correction using 3D image-based navigation.

PURPOSE: Robust motion correction is necessary to minimize respiratory motion artefacts in coronary MR angiography (CMRA). The state-of-the-art method uses a 1D feet-head translational motion correction approach, and data acquisition is limited to a small window in the respiratory cycle, which prolongs the scan by a factor of 2-3. The purpose of this work was to implement 3D affine motion correction for Cartesian whole-heart CMRA using a 3D navigator (3D-NAV) to allow for data acquisition throughout the whole respiratory cycle. METHODS: 3D affine transformations for different respiratory states (bins) were estimated by using 3D-NAV image acquisitions which were acquired during the startup profiles of a steady-state free precession sequence. The calculated 3D affine transformations were applied to the corresponding high-resolution Cartesian image acquisition which had been similarly binned, to correct for respiratory motion between bins. RESULTS: Quantitative and qualitative comparisons showed no statistical difference between images acquired with the proposed method and the reference method using a diaphragmatic navigator with a narrow gating window. CONCLUSION: We demonstrate that 3D-NAV and 3D affine correction can be used to acquire Cartesian whole-heart 3D coronary artery images with 100% scan efficiency with similar image quality as with the state-of-the-art gated and corrected method with approximately 50% scan efficiency.

Comparing NER Approaches on French Clinical Text, with Easy-to-Reuse Pipelines.

The task of Named Entity Recognition (NER) is central for leveraging the content of clinical texts in observational studies. Indeed, texts contain a large part of the information available in Electronic Health Records (EHRs). However, clinical texts are highly heterogeneous between healthcare services and institutions, between countries and languages, making it hard to predict how existing tools may perform on a particular corpus. We compared four NER approaches on three French corpora and share our benchmarking pipeline in an open and easy-to-reuse manner, using the medkit Python library. We include in our pipelines fine-tuning operations with either one or several of the considered corpora. Our results illustrate the expected superiority of language models over a dictionary-based approach, and question the necessity of refining models already trained on biomedical texts. Beyond benchmarking, we believe sharing reusable and customizable pipelines for comparing fast-evolving Natural Language Processing (NLP) tools is a valuable contribution, since clinical texts themselves can hardly be shared for privacy concerns.

Motion corrected compressed sensing for free-breathing dynamic cardiac MRI.

Compressed sensing (CS) has been demonstrated to accelerate MRI acquisitions by reconstructing sparse images of good quality from highly undersampled data. Motion during MR scans can cause inconsistencies in k-space data, resulting in strong motion artifacts in the reconstructed images. For CS to be useful in these applications, motion correction techniques need to be combined with the undersampled reconstruction. Recently, joint motion correction and CS approaches have been proposed to partially correct for effects of motion. However, the main limitation of these approaches is that they can only correct for affine deformations. In this work, we propose a novel motion corrected CS framework for free-breathing dynamic cardiac MRI that incorporates a general motion correction formulation directly into the CS reconstruction. This framework can correct for arbitrary affine or nonrigid motion in the CS reconstructed cardiac images, while simultaneously benefiting from highly accelerated MR acquisition. The application of this approach is demonstrated both in simulations and in vivo data for 2D respiratory self-gated free-breathing cardiac CINE MRI, using a golden angle radial acquisition. Results show that this approach allows for the reconstruction of respiratory motion corrected cardiac CINE images with similar quality to breath-held acquisitions.

Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies.

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

Evaluating reinforcement learning agents for anatomical landmark detection.

Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans. An artificial RL agent learns to identify the optimal path to the landmark by interacting with an environment, in our case 3D images. Furthermore, we investigate the use of fixed- and multi-scale search strategies with novel hierarchical action steps in a coarse-to-fine manner. Several deep Q-network (DQN) architectures are evaluated for detecting multiple landmarks using three different medical imaging datasets: fetal head ultrasound (US), adult brain and cardiac magnetic resonance imaging (MRI). The performance of our agents surpasses state-of-the-art supervised and RL methods. Our experiments also show that multi-scale search strategies perform significantly better than fixed-scale agents in images with large field of view and noisy background such as in cardiac MRI. Moreover, the novel hierarchical steps can significantly speed up the searching process by a factor of 4-5 times.

Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests.

The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.

Retrospective Rigid Motion Correction in k-Space for Segmented Radial MRI.

Motion occurring during magnetic resonance imaging acquisition is a major factor of image quality degradation. Self-navigation can help reduce artefacts by estimating motion from the acquired data to enable motion correction. Popular self-navigation techniques rely on the availability of a fully-sampled motion-free reference to register the motion corrupted data with. In the proposed technique, rigid motion parameters are derived using the inherent correlation between radial segments in k-space. The registration is performed exclusively in k-space using the Phase Correlation Method, a popular registration technique in computer vision. Robust and accurate registration has been carried out from radial segments composed of as few as 32 profiles. Successful self-navigation has been performed on 2-D dynamic brain scans corrupted with continuous motion for six volunteers. Retrospective motion correction using the derived self-navigation parameters resulted in significant improvement of image quality compared to the conventional sliding window. This work also demonstrates the benefits of using a bit-reversed ordering scheme to limit undesirable effects specific to retrospective motion correction on radial trajectories. This method provides a fast and efficient mean of measuring rigid motion directly in k-space from dynamic radial data under continuous motion.