Magnetic resonance imaging has become an important diagnostic tool in the differential diagnosis of lesions for evaluation of cardiovascular disorders. In magnetic resonance tagging (MRt), tissue elements are magnetically labeled so that their positions can be tracked as a function of time. Thus, MRt evaluates heart wall motion both qualitatively and quantitatively. We present herein the case of a 12-year-old boy who had chest pain, dyspnea on effort and murmur. On cardiac computed tomography, there was focal thickening of the left ventricular posterior wall, similar to a mass. MRt indicated active displacement and deformation of the tags at the level of the hypertrophic myocardium during systole, as with normal myocardium. Thus, the tagged images supported the diagnosis of focal hypertrophic cardiomyopathy (HCM). In view of these results, MRt should be considered as a useful technique for differentiating between a mass-like focal lesion such as neoplasm and HCM.

Download full-text PDF

Source
http://dx.doi.org/10.1111/ped.12499DOI Listing

Publication Analysis

Top Keywords

magnetic resonance
12
resonance tagging
8
diagnosis focal
8
focal hypertrophic
8
hypertrophic cardiomyopathy
8
tagging diagnosis
4
focal
4
cardiomyopathy child
4
child magnetic
4
resonance imaging
4

Similar Publications

An increasing number of treatment guidelines recommend rapid initiation of antiretroviral therapy (ART) after the diagnosis of human immunodeficiency virus (HIV) infection. However, data on the association between rapid ART initiation and alterations in brain structure and function remain limited in people with HIV (PWH). A cross-sectional analysis was conducted on HIV-positive men who have sex with men (MSM) undergoing ART.

View Article and Find Full Text PDF

The sequence-structure-function relationship of intrinsic ERα disorder.

Nature

January 2025

Case Comprehensive Cancer Center and Department of Nutrition, Case Western Reserve University School of Medicine, Cleveland, OH, USA.

The oestrogen receptor (ER or ERα), a nuclear hormone receptor that drives most breast cancer, is commonly activated by phosphorylation at serine 118 within its intrinsically disordered N-terminal transactivation domain. Although this modification enables oestrogen-independent ER function, its mechanism has remained unclear despite ongoing clinical trials of kinase inhibitors targeting this region. By integration of small-angle X-ray scattering and nuclear magnetic resonance spectroscopy with functional studies, we show that serine 118 phosphorylation triggers an unexpected expansion of the disordered domain and disrupts specific hydrophobic clustering between two aromatic-rich regions.

View Article and Find Full Text PDF

The extraction of coal seams with high gas content and low permeability presents significant challenges, particularly due to the extended period required for gas extraction to meet safety standards and the inherently low extraction efficiency. Hydraulic fracturing technology, widely employed in the permeability enhancement of soft and low-permeability coal seams, serves as a key intervention. This study focuses on the high-rank raw coal from the No.

View Article and Find Full Text PDF

In magnetic resonance imaging of the brain, an imaging-preprocessing step removes the skull and other non-brain tissue from the images. But methods for such a skull-stripping process often struggle with large data heterogeneity across medical sites and with dynamic changes in tissue contrast across lifespans. Here we report a skull-stripping model for magnetic resonance images that generalizes across lifespans by leveraging personalized priors from brain atlases.

View Article and Find Full Text PDF

Task relevant autoencoding enhances machine learning for human neuroscience.

Sci Rep

January 2025

Department of Cognitive Sciences, University of California, 2201 Social & Behavioral Sciences Gateway, Irvine, CA, 92697, USA.

In human neuroscience, machine learning can help reveal lower-dimensional neural representations relevant to subjects' behavior. However, state-of-the-art models typically require large datasets to train, and so are prone to overfitting on human neuroimaging data that often possess few samples but many input dimensions. Here, we capitalized on the fact that the features we seek in human neuroscience are precisely those relevant to subjects' behavior rather than noise or other irrelevant factors.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!