Bram Ruijsink

Pressure–volume loop-derived cardiac indices during dobutamine stress: a step towards understanding limitations in cardiac output in children with hypoplastic left heart syndrome☆

Background Children with a single systemic right ventricle, such as in hypoplastic left heart syndrome (HLHS), frequently experience reduced exercise capacity. Elucidating the causes could help with optimising treatment strategies. Methods Prospective data from 10 consecutive symptomatic patients with HLHS undergoing clinical cardiac magnetic resonance with catheterisation (XMR) were analysed. Mean age 8.6 years (range 3.5–11.6 years), mean time since Fontan completion 5.5 years. MR-compatible catheters were placed in the systemic right ventricle and branch pulmonary arteries to record pressures at rest, with dobutamine infusion at 10 mcg/kg/min and at 20 mcg/kg/min. Cine short-axis stacks of the ventricle were performed at each condition and used to construct pressure–volume loops. Results Compared to rest, cardiac index increased with low-dose dobutamine (p < 0.01) with no further rise at peak stress despite a further, albeit, blunted rise in heart rate (p = 0.002). A fall in stroke volume occurred (p = 0.014) despite good contractility (74% increase, p = 0.045) and a well-coupled ventriculo-arterial ratio. End-diastolic pressure and early active relaxation, markers of diastolic function, were normal at rest. However, preload fell at peak stress (p < 0.008) while pulmonary vascular resistance (PVR) was low throughout. This group of HLHS patients demonstrated a fall in SV at peak stress, coinciding with a fall in preload. Conclusions Markers of systolic and diastolic function remained normal. Failure to adequately fill the ventricle implies a ceiling of maximal flow through the Fontan circuit despite low PVR.

Free breathing whole-heart 3D CINE MRI with self-gated Cartesian trajectory

Purpose To present a method that uses a novel free-running self-gated acquisition to achieve isotropic resolution in whole heart 3D Cartesian cardiac CINE MRI. Material and methods 3D cardiac CINE MRI using navigator gating results in long acquisition times. Recently, several frameworks based on self-gated non-Cartesian trajectories have been proposed to accelerate this acquisition. However, non-Cartesian reconstructions are computationally expensive due to gridding, particularly in 3D. In this work, we propose a novel highly efficient self-gated Cartesian approach for 3D cardiac CINE MRI. Acquisition is performed using CArtesian trajectory with Spiral PRofile ordering and Tiny golden angle step for eddy current reduction (so called here CASPR-Tiger). Data is acquired continuously under free breathing (retrospective ECG gating, no preparation pulses interruption) for 4–5 min and 4D whole-heart volumes (3D + cardiac phases) with isotropic spatial resolution are reconstructed from all available data using a soft gating technique combined with temporal total variation (TV) constrained iterative SENSE reconstruction. Results For data acquired on eight healthy subjects and three patients, the reconstructed images using the proposed method had good contrast and spatio-temporal variations, correctly recovering diastolic and systolic cardiac phases. Non-significant differences (P > 0.05) were observed in cardiac functional measurements obtained with proposed 3D approach and gold standard 2D multi-slice breath-hold acquisition. Conclusion The proposed approach enables isotropic 3D whole heart Cartesian cardiac CINE MRI in 4 to 5 min free breathing acquisition.

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.

Automated CNN-Based Reconstruction of Short-Axis Cardiac MR Sequence from Real-Time Image Data

We present a methodology for reconstructing full-cycle respiratory and cardiac gated short-axis cine MR sequences from real-time MR data. For patients who are too ill or otherwise incapable of consistent breath holds, real-time MR sequences are the preferred means of acquiring cardiac images, but suffer from inferior image quality compared to standard short-axis sequences and lack cardiac ECG gating. To construct a sequence from real-time images which, as close as possible, replicates the characteristics of short-axis series, the phase of the cardiac cycle must be estimated for each image and the left ventricle identified, to be used as a landmark for slice re-alignment. Our method employs CNN-based deep learning to segment the left ventricle in the real-time sequence, which is then used to estimate the pool volume and thus the position of each image in the cardiac cycle. We then use manifold learning to account for the respiratory cycle so as to select images of the best quality at expiration. From these images a selection is made to automatically reconstruct a single cardiac cycle, and the images and segmentations are then aligned. The aligned pool segmentations can then be used to calculate volume over time and thus volume-based 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.

Morphological three-dimensional analysis of papillary muscles in borderline left ventricles

OBJECTIVE Mitral valve anatomy has a significant impact on potential surgical options for patients with hypoplastic or borderline left ventricle. Papillary muscle morphology is a major component regarding this aspect. The purpose of this study was to use cardiac magnetic resonance to describe the differences in papillary muscle anatomy between normal, borderline, and hypoplastic left ventricles. METHODS We carried out a retrospective, observational cardiac magnetic resonance study of children (median age 5.36 years) with normal (n=30), borderline (n=22), or hypoplastic (n=13) left ventricles. Borderline and hypoplastic cases had undergone an initial hybrid procedure. Morphological features of the papillary muscles, location, and arrangement were analysed and compared across groups. RESULTS All normal ventricles had two papillary muscles with narrow pedicles; however, 18% of borderline and 46% of hypoplastic cases had a single papillary muscle, usually the inferomedial type. In addition, in borderline or hypoplastic ventricles, the supporting pedicle occasionally displayed a wide insertion along the ventricular wall. The length ratio of the superolateral support was significantly different between groups (normal: 0.46±0.08; borderline: 0.39±0.07; hypoplastic: 0.36±0.1; p=0.009). No significant difference, however, was found when analysing the inferomedial type (0.42±0.09; 0.38±0.07; 0.39±0.22, p=0.39). The angle subtended between supports was also similar among groups (113°±17°; 111°±51° and 114°±57°; p=0.99). A total of eight children with borderline left ventricle underwent biventricular repair. There were no significant differentiating features for papillary muscle morphology in this subgroup. CONCLUSIONS The superolateral support can be shorter or absent in borderline or hypoplastic left ventricle cases. The papillary muscle pedicles in these patients often show a broad insertion. These changes have important implications on surgical options and should be described routinely.

Right ventricular function and adaption after hemi-Fontan completion in children with hypoplastic left heart syndrome

Background The systemic right ventricle (RV) in patients with hypoplastic left heart syndrome (HLHS) is prone to failure. The complex geometry and contraction pattern of the systemic RV makes identification of patients at risk for failure challenging, especially in the light of radical changes in loading conditions during staged palliation. MRI allows for accurate quantification of cardiac volumes and provides assessment of the whole heart without restrictions in imaging planes. Recent advances in cardiac MRI analysis software allows for quantification of regional deformation from conventional balanced steady state free precession cine images. We sought to investigate changes in ventricular function before and after the hemi-Fontan (HF).

LA structural remodeling is predicted by arterial stiffening independently of conventional risk factors

Background Left atrium (LA) size and function are powerful biomarkers of cardiovascular outcomes in many diseases. We sought to determine if the expected age-associated increase in arterial stiffness (AS) and left ventricular (LV)-LA afterload leads to corresponding effects on LA function and this can be measured with cardiovascular magnetic resonance (CMR). Additionally, we investigated the significance of these markers in asymptomatic individuals with cardiovascular risk factors (CRF).

Routine 3D SSFP cine imaging for improved analysis of myocardial volumetry and deformation

Background Conventional 2 dimensional (2D) cine MRI of myocardial motion is limited in its ability to describe the complex 3 dimensional (3D) motion of the heart due to sparse coverage and inconsistent breath-holding. So far, clinical feasibility of 3D cine imaging has been limited due to the long acquisition times and requirements for acceleration techniques that are not routinely available. We demonstrate the use navigator-gated 3D cine acquisition that can be performed with routine acceleration techniques within the time constraints of clinical MRI scans. We compared the accuracy, reproducibility and image quality of 3D steady-state-free-precession (SSFP) cine imaging of the heart with conventional 2D SSFP cine imaging for the assessment of cardiac volumes, global and regional myocardial function.