Cine and 4D flow cardiac MRI are two important non-invasive MR imaging techniques to assess cardiac function and diagnose cardiovascular diseases. Cine MRI offers great soft tissue detail which... Show moreCine and 4D flow cardiac MRI are two important non-invasive MR imaging techniques to assess cardiac function and diagnose cardiovascular diseases. Cine MRI offers great soft tissue detail which allows clinical experts to evaluate structure and function of the heart. 4D flow MRI further has the ability of three-dimensional time-resolved acquisition of blood flow velocity, which can be used to derive intra-cardiac hemodynamic parameters. In this thesis, we developed deep learning-based approaches to analyze cine and 4D flow cardiac MRI. This thesis proposes deep learning based methods for quantifying cardiac MRI. The described methods can be applied for cine MR image quality classification and ventricle segmentation without any human interactions. Investigating combining and fusing magnitude and velocity images can be helpful for left ventricle segmentation in 4D flow MRI, which is not fully explored yet. Moreover, we proposed a network to predict the blood flow pattern from the cine MRI. By combining visualization of the blood flow and myocardial motion in the routinely acquired standard CMR exams, the method can be potentially used in clinical studies. Show less
Background: There has been a rapid increase in the number of Artificial Intelligence (AI) studies of cardiac MRI (CMR) segmentation aiming to automate image analysis. However, advancement and... Show moreBackground: There has been a rapid increase in the number of Artificial Intelligence (AI) studies of cardiac MRI (CMR) segmentation aiming to automate image analysis. However, advancement and clinical translation in this field depend on researchers presenting their work in a transparent and reproducible manner. This systematic review aimed to evaluate the quality of reporting in AI studies involving CMR segmentation. Methods: MEDLINE and EMBASE were searched for AI CMR segmentation studies in April 2022. Any fully automated AI method for segmentation of cardiac chambers, myocardium or scar on CMR was considered for inclusion. For each study, compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was assessed. The CLAIM criteria were grouped into study, dataset, model and performance description domains. Results: 209 studies published between 2012 and 2022 were included in the analysis. Studies were mainly published in technical journals (58%), with the majority (57%) published since 2019. Studies were from 37 different countries, with most from China (26%), the United States (18%) and the United Kingdom (11%). Short axis CMR images were most frequently used (70%), with the left ventricle the most commonly segmented cardiac structure (49%). Median compliance of studies with CLAIM was 67% (IQR 59-73%). Median compliance was highest for the model description domain (100%, IQR 80-100%) and lower for the study (71%, IQR 63-86%), dataset (63%, IQR 50-67%) and performance (60%, IQR 50-70%) description domains. Conclusion: This systematic review highlights important gaps in the literature of CMR studies using AI. We identified key items missing-most strikingly poor description of patients included in the training and validation of AI models and inadequate model failure analysis-that limit the transparency, reproducibility and hence validity of published AI studies. This review may support closer adherence to established frameworks for reporting standards and presents recommendations for improving the quality of reporting in this field. Show less
Toemen, L.; Santos, S.; Roest, A.A.; Jelic, G.; Lugt, A. van der; Felix, J.F.; ... ; Jaddoe, V.W.V. 2020
BACKGROUND: Adiposity is associated with larger left ventricular mass in children and adults. The role of body fat distribution in these associations is not clear. We examined the associations of... Show moreBACKGROUND: Adiposity is associated with larger left ventricular mass in children and adults. The role of body fat distribution in these associations is not clear. We examined the associations of body fat distribution and overweight with cardiac measures obtained by cardiac magnetic resonance imaging in school--age children.METHODS AND RESULTS: In a population--based cohort study including 2836 children, 10 years of age, we used anthropometric measures, dual--energy X--ray absorptiometry, and magnetic resonance imaging to collect information on body mass index, lean mass index, fat mass index, and abdominal visceral adipose tissue index. Indexes were standardized by height. Cardiac measures included right and left ventricular end--diastolic volume, left ventricular mass, and mass--to--volume ratio as a marker for concentricity. All body fat measures were positively associated with right and left ventricular end--diastolic volumes and left ventricular mass, with the strongest associations for lean mass index (all P<0.05). Obese children had a 1.12 standard deviation score (95% CI, 0.94-1.30) larger left ventricular mass and a 0.35 standard deviation score (95% CI, 0.14- 0.57) higher left ventricular mass--to--volume ratio than normal weight children. Conditional on body mass index, higher lean mass index was associated with higher right and left ventricular end--diastolic volume and left ventricular mass, whereas higher fat mass measures were inversely associated with these cardiac measures (all P<0.05).CONCLUSIONS: Higher childhood body mass index is associated with a larger right and left ventricular size. This association is influenced by higher lean mass. In childhood, lean mass may be a stronger determinant of heart growth than fat mass. Fat mass may influence cardiac structures at older ages. Show less
Purpose Cardiac motion tracking enables quantitative evaluation of myocardial strain, which is clinically interesting in cardiovascular disease research. However, motion tracking is difficult to... Show morePurpose Cardiac motion tracking enables quantitative evaluation of myocardial strain, which is clinically interesting in cardiovascular disease research. However, motion tracking is difficult to perform manually. In this paper, we aim to develop and compare two fully automated motion tracking methods for the steady state free precession (SSFP) cine magnetic resonance imaging (MRI), and explore their use in real clinical scenario with different patient groups. Methods We proposed two automated cardiac motion tracking method: (a) a traditional registration-based method, named full cardiac cycle registration, which simultaneously tracks all cine frames within a full cardiac cycle by joint registration of all frames; and (b) a modern convolutional neural network (CNN)-based method, named Groupwise MotionNet, which enhances the temporal coherence by fusing motion along a continuous time scale. Both methods were evaluated on the healthy volunteer data from the MICCAI 2011 STACOM Challenge, as well as on patient data including hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI). Results The full cardiac cycle registration method achieved an average end-point error (EPE) 2.89 +/- 1.57 mm for cardiac motion tracking, with computation time of around 9 min per short-axis cine MRI (size 128 x 128, 30 cardiac phases). In comparison, the Groupwise MotionNet achieved an average EPE of 0.94 +/- 1.59 mm, taking < 1 s for a full cardiac phases. Further experiments showed that registration method had stable performance, independent of patient cohort and MRI machine, while the CNN-based method relied on the training data to deliver consistently accurate results. Conclusion Both registration-based and CNN-based method can track the cardiac motion from SSFP cine MRI in a fully automated manner, while taking temporal coherence into account. The registration method is generic, robust, but relatively slow; the CNN-based method trained with heterogeneous data was able to achieve high tracking accuracy with real-time performance. Show less
Tao, Q.; Lelieveldt, B.P.F.; Geest, R.J. van der 2020
OBJECTIVE. The recent advancement of deep learning techniques has profoundly impacted research on quantitative cardiac MRI analysis. The purpose of this article is to introduce the concept of deep... Show moreOBJECTIVE. The recent advancement of deep learning techniques has profoundly impacted research on quantitative cardiac MRI analysis. The purpose of this article is to introduce the concept of deep learning, review its current applications on quantitative cardiac MRI, and discuss its limitations and challenges.CONCLUSION. Deep learning has shown state-of-the-art performance on quantitative analysis of multiple cardiac MRI sequences and holds great promise for future use in clinical practice and scientific research. Show less
