Cardiac Function

Bogaert, Jan

doi:10.1007/174_2011_335

Cited by 4 publications

(12 citation statements)

References 288 publications

(251 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RV often has the highest Hausdorff distances, the lowest Dice scores, the lowest correlation values, and the largest biases. To further underline this observation, we recorded in Table V the percentage of patients for which the predicted EF is less than 5% away from the ground-truth (5% is often considered as an acceptable error margin [57]). While the top six methods accurately predict the LV ejection fraction for ≈ 87% of the patients, that number drastically goes down to ≈ 59% for the RV.…”

Section: Rvmentioning

confidence: 99%

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Bernard

Lalande

Zotti

et al. 2018

IEEE Trans. Med. Imaging

1,290

834

View full text Add to dashboard Cite

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

show abstract

Section: Rvmentioning

confidence: 99%

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Bernard

Lalande

Zotti

et al. 2018

IEEE Trans. Med. Imaging

1,290

834

View full text Add to dashboard Cite

show abstract

“…(Am J Vet Res 2017;78:1163-1170) locities in a region of interest multiplied by the area in each phase of the cardiac cycle represents the flow volume per cardiac cycle. 7,10 A basic challenge in cardiac MRI is the acquisition of high-quality images with minimal blood flow artifacts. In contrast to adult human cardiac MRI, but similar to pediatric cardiac MRI, anesthesia is required in veterinary patients for basic immobilization and also for the induction of breath holding.…”

Section: Conclusion and Clinical Relevancementioning

confidence: 99%

“…1 Authors have used calculations of SV based on morphological MRI as a criterion-referenced standard to test new ultrasonography 2-4 and CT 5 techniques. Two-dimensional PCA is a validated and established technique for use in human cardiac MRI 6,7 ; the technique allows measurement of cardiac output, shunt fraction, collateral flow around obstructions, and flow differences in the left and right pulmonary arteries. 8 Use of 2-D PCA also provides information regarding stenotic flow, similar to information provided by Doppler examinations.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Comparison of left and right ventricular stroke volume of dogs calculated on the basis of morphology and blood flow determined by use of cardiac magnetic resonance imaging

Dennler

Richter

Matos

et al. 2017

ajvr

View full text Add to dashboard Cite

OBJECTIVE To compare stroke volume (SV) calculated on the basis of cardiac morphology determined by MRI and results of phase-contrast angiography (PCA) of ventricular inflow and outflow in dogs. ANIMALS 10 healthy Beagles. PROCEDURES Cardiac MRI was performed twice on each Beagle. Cine gradient echo sequences of both ventricles in short-axis planes were used for morphological quantification of SVs by assessment of myocardial contours. From the long-axis plane, SVs in 4-chamber and left ventricular 2-chamber views were acquired at end diastole and end systole. For calculation of SV on the basis of blood flow, PCA was performed for cardiac valves. RESULTS Mean ± SD values for SV quantified on the basis of blood flow were similar in all valves (aortic, 17.8 ± 4.1 mL; pulmonary, 17.2 ± 5.4 mL; mitral, 17.2 ± 3.9 mL; and tricuspid, 16.9 ± 5.1 mL). Morphological quantification of SV in the short-axis plane yielded significant differences between left (13.4 ± 2.7 mL) and right (8.6 ± 2.4 mL) sides. Morphological quantification of left ventricular SV in the long-axis plane (15.2 ± 3.3 mL and 20.7 ± 3.8 mL in the 4- and 2-chamber views) yielded variable results, which differed significantly from values for flow-based quantification, except for values for the morphological 4-chamber view and PCA for the atrioventricular valves, for which no significant differences were identified. CONCLUSIONS AND CLINICAL RELEVANCE In contrast to quantification based on blood flow, calculation on the basis of morphology for the short-axis plane significantly underestimated SV, probably because of through-plane motion and complex right ventricular anatomy.

show abstract

“…The parameters which are used to express the regional myocardial function of the left ventricle are myocardial wall thickness, systolic myocardial wall thickening, circumferential and longitudinal myocardial wall motion or shortening. 2 In our study, we selected wall motion to be a qualitative and quantitative studied parameter to express the normal and impaired myocardial regional function. The aim of this study were to analyze the inter-segmental myocardial regional wall motion integration pattern and to prove that the integration pattern of normal myocardial regional wall motion can be used as a supporting tool with visual estimation method to depict the impaired regional wall motion myocardial segment.…”

Section: Conclusion the Circumferential Regional Wall Motion Integramentioning

confidence: 99%

Impaired Regional Myocardial Function Detection Using the Standard Inter-Segmental Integration SINE Wave Curve On Magnetic Resonance Imaging

Ngam-Maung¹,

Chaothawee²

2012

bkkmedj

View full text Add to dashboard Cite

Cardiac Function

Cited by 4 publications

References 288 publications

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Comparison of left and right ventricular stroke volume of dogs calculated on the basis of morphology and blood flow determined by use of cardiac magnetic resonance imaging

Impaired Regional Myocardial Function Detection Using the Standard Inter-Segmental Integration SINE Wave Curve On Magnetic Resonance Imaging

Contact Info

Product

Resources

About