Cardiovascular magnetic resonance imaging: what the general cardiologist should know

Musa, Tarique A; Dobson, Laura E; Plein, Sven; Greenwood, John P

doi:10.1136/heartjnl-2015-307896

Cited by 42 publications

(34 citation statements)

References 136 publications

(121 reference statements)

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“…It normalises the layer input for each training mini-batch. [2] ReLU stands for rectified linear unit. It is a type of activation function for a neuron in artificial neural networks.…”

Section: Automated Image Analysismentioning

confidence: 99%

“…Technological advances in medical imaging have led to a number of options for non-invasive investigation of CVDs, including echocardiography, computed tomography (CT), cardiovascular magnetic resonance (CMR) etc., each having its own advantages and disadvantages. Due to its good image quality, excellent soft tissue contrast and absence of ionising radiation, CMR has established itself as the non-invasive gold standard for assessing cardiac chamber volume and mass for a wide range of CVDs [2][3][4]. To derive quantitative measures such as volume and mass, clinicians have been relying on manual approaches to trace the cardiac chamber contours.…”

Section: Introductionmentioning

confidence: 99%

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Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Bai

Sinclair

Tarroni

et al. 2018

Journal of Cardiovascular Magnetic Resonance

592

560

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BackgroundCardiovascular 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.MethodsDeep 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).ResultsBy 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.ConclusionsWe show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures.Electronic supplementary materialThe online version of this article (10.1186/s12968-018-0471-x) contains supplementary material, which is available to authorized users.

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“…It normalises the layer input for each training mini-batch. [2] ReLU stands for rectified linear unit. It is a type of activation function for a neuron in artificial neural networks.…”

Section: Automated Image Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Bai

Sinclair

Tarroni

et al. 2018

Journal of Cardiovascular Magnetic Resonance

592

560

View full text Add to dashboard Cite

show abstract

“…CMR has a number of unique advantages: it provides a view of the entire heart without limitations of body habitus or imaging windows, allows free choice of imaging planes, is free of ionizing radiation and does not require contrast administration (9). CMR should therefore be considered in patients with suboptimal echocardiographic imaging or when there is a degree of uncertainty in the severity of MR, usually in the case of eccentric jets that can be underestimated by echocardiography (41,64).…”

Section: Cmrmentioning

confidence: 99%

Multimodality imaging for the quantitative assessment of mitral regurgitation

Chew

Bounford

Plein

et al. 2018

Quant. Imaging Med. Surg.

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The natural history of mitral regurgitation (MR) results in significant morbidity and mortality. Innovations in non-invasive imaging have provided new insights into the pathophysiology and quantification of MR, in addition to early detection of left ventricular (LV) dysfunction and prognostic assessment in asymptomatic patients. Transthoracic (TTE) and transesophageal (TOE) echocardiography are the mainstay for diagnosis, assessment and serial surveillance. However, the advance from 2D to 3D imaging leads to improved assessment and characterization of mitral valve (MV) disease. Cardiovascular magnetic resonance (CMR) is increasingly used for MR quantitation and can provide an alternative imaging method if echocardiography is suboptimal or inconclusive. Other techniques such as exercise echocardiography, tissue Doppler imaging and speckle-tracking echocardiography can further offer complementary information on prognosis. This review summarises the current evidence for state-of-the-art cardiovascular imaging for the investigation of MR. Whilst advanced echocardiographic techniques are superior in the evaluation of complex MV anatomy, CMR appears the most accurate technique for the quantification of MR severity. Integration of multimodality imaging for the assessment of MR utilises the advantages of each imaging technique and offers the most comprehensive assessment of MR.

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“…Cardiac MR (CMR) imaging is a non-invasive and non-ionising imaging technique that produces high image quality and excellent soft tissue contrast. Among existing imaging techniques, it has established itself as the gold standard for assessing cardiac chamber volume and mass for a wide range of cardiovascular diseases [1]. CMR imaging techniques, together with semi-automated or automated CMR segmentation algorithms [2,3,4,5,6,7,8,9,10,11], have shown a great impact on studying, understanding and diagnosing cardiovascular diseases.…”

Section: Introductionmentioning

confidence: 99%

Combining Deep Learning and Shape Priors for Bi-Ventricular Segmentation of Volumetric Cardiac Magnetic Resonance Images

Duan

Schlemper

Bai

et al. 2018

Shape in Medical Imaging

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Cardiovascular magnetic resonance imaging: what the general cardiologist should know

Abstract: This is an author produced version of a paper published in Heart. Uploaded in accordance with the publisher's self-archiving policy.eprints@whiterose

Cited by 42 publications

References 136 publications

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Multimodality imaging for the quantitative assessment of mitral regurgitation

Combining Deep Learning and Shape Priors for Bi-Ventricular Segmentation of Volumetric Cardiac Magnetic Resonance Images

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