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Myocardium

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Comparison of post reconstruction- and reconstruction-based deep learning denoising methods in cardiac SPECT.

Biomedical physics & engineering express
. The quality of myocardial perfusion SPECT (MPS) images is often hampered by low count statistics. Poor image quality might hinder reporting the studies and in the worst case lead to erroneous diagnosis. Deep learning (DL)-based methods can be used ...

MRI Deep Learning-Based Automatic Segmentation of Interventricular Septum for Black-Blood Myocardial T2* Measurement in Thalassemia.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The T2* value of interventricular septum is routinely reported for grading myocardial iron load in thalassemia major, and automatic segmentation of septum could shorten analysis time and reduce interobserver variability.

Automated assessment of cardiac pathologies on cardiac MRI using T1-mapping and late gadolinium phase sensitive inversion recovery sequences with deep learning.

BMC medical imaging
BACKGROUND: A deep learning (DL) model that automatically detects cardiac pathologies on cardiac MRI may help streamline the diagnostic workflow. To develop a DL model to detect cardiac pathologies on cardiac MRI T1-mapping and late gadolinium phase ...

Strong Diagnostic Performance of Single Energy 256-row Multidetector Computed Tomography with Deep Learning Image Reconstruction in the Assessment of Myocardial Fibrosis.

Internal medicine (Tokyo, Japan)
Objective Although magnetic resonance imaging (MRI) is the gold standard for evaluating abnormal myocardial fibrosis and extracellular volume (ECV) of the left ventricular myocardium (LVM), a similar evaluation has recently become possible using comp...

Deep learning approach for automated segmentation of myocardium using bone scintigraphy single-photon emission computed tomography/computed tomography in patients with suspected cardiac amyloidosis.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: We employed deep learning to automatically detect myocardial bone-seeking uptakeĀ as a marker of transthyretin cardiac amyloid cardiomyopathy (ATTR-CM) in patients undergoing 99mTc-pyrophosphate (PYP) or hydroxydiphosphonate (HDP) single-p...

Quality assurance of late gadolinium enhancement cardiac magnetic resonance images: a deep learning classifier for confidence in the presence or absence of abnormality with potential to prompt real-time image optimization.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Late gadolinium enhancement (LGE) of the myocardium has significant diagnostic and prognostic implications, with even small areas of enhancement being important. Distinguishing between definitely normal and definitely abnormal LGE images ...

Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue.

Computers in biology and medicine
BACKGROUND: Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between biological s...