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Journal articleDewey M, Siebes M, Kachelrieß M, et al., 2020,
Clinical quantitative cardiac imaging for the assessment of myocardial ischaemia
, Nature Reviews Cardiology, Vol: 17, Pages: 427-450, ISSN: 1759-5002Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality.
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Journal articleKariholu U, Montaldo P, Markati T, et al., 2020,
Therapeutic hypothermia for mild neonatal encephalopathy: A systematic review and meta-analysis
, Archives of Disease in Childhood. Fetal and Neonatal Edition, Vol: 105, Pages: 225-228, ISSN: 1359-2998Objectives To examine if therapeutic hypothermia reduces the composite outcome of death, moderate or severe disability at 18 months or more after mild neonatal encephalopathy (NE).Data source MEDLINE, Cochrane database, Scopus and ISI Web of Knowledge databases, using ‘hypoxic ischaemic encephalopathy’, ‘newborn’ and ‘hypothermia’, and ‘clinical trials’ as medical subject headings and terms. Manual search of the reference lists of all eligible articles and major review articles and additional data from the corresponding authors of selected articles.Study selection Randomised and quasirandomised controlled trials comparing therapeutic hypothermia with usual care.Data extraction Safety and efficacy data extracted independently by two reviewers and analysed.Results We included the data on 117 babies with mild NE inadvertently recruited to five cooling trials (two whole-body cooling and three selective head cooling) of moderate and severe NE, in the meta-analysis. Adverse outcomes occurred in 11/56 (19.6%) of the cooled babies and 12/61 (19.7%) of the usual care babies (risk ratio 1.11 (95% CIs 0.55 to 2.25)).Conclusions Current evidence is insufficient to recommend routine therapeutic hypothermia for babies with mild encephalopathy and significant benefits or harm cannot be excluded.
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Journal articleTarroni G, Bai W, Oktay O, et al., 2020,
Large-scale quality control of cardiac imaging in population studies: application to UK Biobank
, Scientific Reports, Vol: 10, ISSN: 2045-2322In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment isunfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) imagesto the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics(heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factorsincluding acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage(i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of thestacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slicemotion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastoliccardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involvedin UKBB CMR acquisition and for the ones who use the dataset for research purposes.
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Conference paperChen C, Ouyang C, Tarroni G, et al., 2020,
Unsupervised multi-modal style transfer for cardiac MR segmentation
, MICCAI STACOM Workshop, Publisher: Springer International Publishing, Pages: 209-219, ISSN: 0302-9743In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire. Our framework mainly consists of two neural networks: a multi-modal image translation network for style transfer and a cascaded segmentation network for image segmentation. The multi-modal image translation network generates realistic and diverse synthetic LGE images conditioned on a single annotated bSSFP image, forming a synthetic LGE training set. This set is then utilized to fine-tune the segmentation network pre-trained on labelled bSSFP images, achieving the goal of unsupervised LGE image segmentation. In particular, the proposed cascaded segmentation network is able to produce accurate segmentation by taking both shape prior and image appearance into account, achieving an average Dice score of 0.92 for the left ventricle, 0.83 for the myocardium, and 0.88 for the right ventricle on the test set.
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Conference paperLu P, Qiu H, Qin C, et al., 2020,
Going Deeper into Cardiac Motion Analysis to Model Fine Spatio-Temporal Features
, 24th Conference on Medical Image Understanding and Analysis (MIUA), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 294-306, ISSN: 1865-0929- Author Web Link
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Conference paperWang S, Tarroni G, Qin C, et al., 2020,
Deep Generative Model-Based Quality Control for Cardiac MRI Segmentation.
, Publisher: Springer, Pages: 88-97 -
Journal articleSuzuki H, Venkataraman AV, Bai W, et al., 2019,
Associations of regional brain structural differences with aging, modifiable risk factors for dementia, and cognitive performance
, JAMA Network Open, Vol: 2, Pages: 1-19, ISSN: 2574-3805Importance Identifying brain regions associated with risk factors for dementia could guide mechanistic understanding of risk factors associated with Alzheimer disease (AD).Objectives To characterize volume changes in brain regions associated with aging and modifiable risk factors for dementia (MRFD) and to test whether volume differences in these regions are associated with cognitive performance.Design, Setting, and Participants This cross-sectional study used data from UK Biobank participants who underwent T1-weighted structural brain imaging from August 5, 2014, to October 14, 2016. A voxelwise linear model was applied to test for regional gray matter volume differences associated with aging and MRFD (ie, hypertension, diabetes, obesity, and frequent alcohol use). The potential clinical relevance of these associations was explored by comparing their neuroanatomical distributions with the regional brain atrophy found with AD. Mediation models for risk factors, brain volume differences, and cognitive measures were tested. The primary hypothesis was that common, overlapping regions would be found. Primary analysis was conducted on April 1, 2018.Main Outcomes and Measures Gray matter regions that showed relative atrophy associated with AD, aging, and greater numbers of MRFD.Results Among 8312 participants (mean [SD] age, 62.4 [7.4] years; 3959 [47.1%] men), aging and 4 major MRFD (ie, hypertension, diabetes, obesity, and frequent alcohol use) had independent negative associations with specific gray matter volumes. These regions overlapped neuroanatomically with those showing lower volumes in participants with AD, including the posterior cingulate cortex, the thalamus, the hippocampus, and the orbitofrontal cortex. Associations between these MRFD and spatial memory were mediated by differences in posterior cingulate cortex volume (β = 0.0014; SE = 0.0006; P = .02).Conclusions and Relevance This cross-sectional study
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Conference paperHalliday BP, Balaban G, Costa CM, et al., 2019,
Improving Arrhythmic Risk Stratification in Non-Ischemic Dilated Cardiomyopathy Through the Evaluation of Novel Scar Characteristics Using CMR
, Scientific Sessions of the American-Heart-Association, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322 -
Conference paperChen C, Biffi C, Tarroni G, et al., 2019,
Learning shape priors for robust cardiac MR segmentation from multi-view images
, International Conference on Medical Image Computing and Computer-Assisted Intervention, Publisher: Springer International Publishing, Pages: 523-531, ISSN: 0302-9743Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used.
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Journal articleBhuva AN, Bai W, Lau C, et al., 2019,
A multicenter, scan-rescan, human and machine learning CMR study to test generalizability and precision in imaging biomarker analysis
, Circulation: Cardiovascular Imaging, Vol: 12, Pages: 1-11, ISSN: 1941-9651Background:Automated analysis of cardiac structure and function using machine learning (ML) has great potential, but is currently hindered by poor generalizability. Comparison is traditionally against clinicians as a reference, ignoring inherent human inter- and intraobserver error, and ensuring that ML cannot demonstrate superiority. Measuring precision (scan:rescan reproducibility) addresses this. We compared precision of ML and humans using a multicenter, multi-disease, scan:rescan cardiovascular magnetic resonance data set.Methods:One hundred ten patients (5 disease categories, 5 institutions, 2 scanner manufacturers, and 2 field strengths) underwent scan:rescan cardiovascular magnetic resonance (96% within one week). After identification of the most precise human technique, left ventricular chamber volumes, mass, and ejection fraction were measured by an expert, a trained junior clinician, and a fully automated convolutional neural network trained on 599 independent multicenter disease cases. Scan:rescan coefficient of variation and 1000 bootstrapped 95% CIs were calculated and compared using mixed linear effects models.Results:Clinicians can be confident in detecting a 9% change in left ventricular ejection fraction, with greater than half of coefficient of variation attributable to intraobserver variation. Expert, trained junior, and automated scan:rescan precision were similar (for left ventricular ejection fraction, coefficient of variation 6.1 [5.2%–7.1%], P=0.2581; 8.3 [5.6%–10.3%], P=0.3653; 8.8 [6.1%–11.1%], P=0.8620). Automated analysis was 186× faster than humans (0.07 versus 13 minutes).Conclusions:Automated ML analysis is faster with similar precision to the most precise human techniques, even when challenged with real-world scan:rescan data. Assessment of multicenter, multi-vendor, multi-field strength scan:rescan data (available at www.thevolumesresource.com) permits a generalizable assessment of ML precision and may facili
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