Myocardial Parametric Mapping by Cardiac Magnetic Resonance Imaging in Pediatric Cardiology and Congenital Heart Disease

2022 ◽  
Author(s):  
Sruti Rao ◽  
Stephanie Y. Tseng ◽  
Amol Pednekar ◽  
Saira Siddiqui ◽  
Murat Kocaoglu ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Magnetic Relaxation ◽  
Congenital Heart ◽  
Extracellular Volume ◽  
Parametric Mapping ◽  
Disease States ◽  
Starting Point

Parametric mapping, that is, a pixel-wise map of magnetic relaxation parameters, expands the diagnostic potential of cardiac magnetic resonance by enabling quantification of myocardial tissue-specific magnetic relaxation on an absolute scale. Parametric mapping includes T 1 mapping (native and postcontrast), T 2 and T 2 * mapping, and extracellular volume measurements. The myocardial composition is altered in various disease states affecting its inherent magnetic properties and thus the myocardial relaxation times that can be directly quantified using parametric mapping. Parametric mapping helps in the diagnosis of nonfocal disease states and allows for longitudinal disease monitoring, evaluating therapeutic response (as in Thalassemia patients with iron overload undergoing chelation), and risk-stratification of certain diseases. In this review article, we describe various mapping techniques and their clinical utility in congenital heart disease. We will also review the available literature on normative values in children, the strengths, and weaknesses of these techniques. This review provides a starting point for pediatric cardiologists to understand and implement parametric mapping in their practice.

scholarly journals Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Gerhard-Paul Diller ◽  
◽  
Julius Vahle ◽  
Robert Radke ◽  
Maria Luisa Benesch Vidal ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Deep Learning ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Congenital Heart ◽  
Patient Data ◽  
Generative Adversarial Networks ◽  
Training Images

Abstract Background Deep learning algorithms are increasingly used for automatic medical imaging analysis and cardiac chamber segmentation. Especially in congenital heart disease, obtaining a sufficient number of training images and data anonymity issues remain of concern. Methods Progressive generative adversarial networks (PG-GAN) were trained on cardiac magnetic resonance imaging (MRI) frames from a nationwide prospective study to generate synthetic MRI frames. These synthetic frames were subsequently used to train segmentation networks (U-Net) and the quality of the synthetic training images, as well as the performance of the segmentation network was compared to U-Net-based solutions trained entirely on patient data. Results Cardiac MRI data from 303 patients with Tetralogy of Fallot were used for PG-GAN training. Using this model, we generated 100,000 synthetic images with a resolution of 256 × 256 pixels in 4-chamber and 2-chamber views. All synthetic samples were classified as anatomically plausible by human observers. The segmentation performance of the U-Net trained on data from 42 separate patients was statistically significantly better compared to the PG-GAN based training in an external dataset of 50 patients, however, the actual difference in segmentation quality was negligible (< 1% in absolute terms for all models). Conclusion We demonstrate the utility of PG-GANs for generating large amounts of realistically looking cardiac MRI images even in rare cardiac conditions. The generated images are not subject to data anonymity and privacy concerns and can be shared freely between institutions. Training supervised deep learning segmentation networks on this synthetic data yielded similar results compared to direct training on original patient data.

scholarly journals Knowledge-based 3D reconstruction of the right ventricle: comparison with cardiac magnetic resonance in adults with congenital heart disease

2014 ◽  
Vol 2 (4) ◽  
pp. 109-116 ◽  
Author(s):  
Aleksandra Trzebiatowska-Krzynska ◽  
Mieke Driessen ◽  
Gertjan Tj Sieswerda ◽  
Lars Wallby ◽  
Eva Swahn ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Congenital Heart ◽  
Intraclass Correlation ◽  
Imaging Protocol ◽  
Tertiary Referral Center ◽  
Knowledge Based ◽  
The Right

AimAssessment of right ventricular (RV) function is a challenge, especially in patients with congenital heart disease (CHD). The aim of the present study is to assess whether knowledge-based RV reconstruction, used in the everyday practice of an echo-lab for adult CHD in a tertiary referral center, is accurate when compared to cardiac magnetic resonance (CMR) examination.Subjects and methodsAdult patients who would undergo CMR for assessment of the RV were asked to undergo an echo of the heart for further knowledge-based reconstruction (KBR). Echocardiographic images were acquired in standard views using a predefined imaging protocol. RV volumes and ejection fraction (EF) calculated using knowledge-based technology were compared with the CMR data of the same patient.ResultsNineteen consecutive patients with congenital right heart disease were studied. Median age of the patients was 28 years (range 46 years). Reconstruction was possible in 16 out of 19 patients (85%). RV volumes assessed with this new method were smaller than with CMR. Indexed end diastolic volumes were 114±17 ml vs 121±19 ml,P<0.05 and EFs were 45±8% vs 47±9%,P<0.05 respectively. The correlation between the methods was good with an intraclass correlation of 0.84 for EDV and 0.89 for EF,Pvalue <0.001 in both cases.ConclusionKBR enables reliable measurement of RVs in patients with CHDs and can be used in clinical practice for analysis of volumes and EFs.

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