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High Resolution Isotropic ‘Pseudo’ 3D Cine imaging with Automated Segmentation using Concatenated 2D Real-time Imaging and Deep Learning
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DOI：10.1016/j.jocmr.2026.102780.png)
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Abstract
En 中文
Conventional cardiovascular magnetic resonance (CMR) in pediatric and congenital heart disease uses 2D, breath-hold (BH), balanced steady state free precession (bSSFP) cine imaging for assessment of function, in addition to cardiac-gated, respiratory-navigated, static 3D bSSFP whole-heart imaging for anatomical assessment. Our aim is to concatenate a stack of 2D free-breathing real-time cines and use Deep Learning (DL) to create an isotropic fully segmented ‘pseudo’ 3D-cine dataset from these images.
Keywords:
2D
two-dimensional
3D
three-dimensional
3D-cine
three-dimensional cine
4CH
four-chamber
Ao
aorta
BH
breath-hold
bSSFP
balanced steady-state free precession
CHD
congenital heart disease
CMR
cardiovascular magnetic resonance
CNN
convolutional neural network
CNR
contrast-to-noise ratio
CPU
central processing unit
DAS
deep artefact suppression
DL
deep learning
EDV
end-diastolic volume
EF
ejection fraction
ES
edge sharpness
ESV
end-systolic volume
GAN
generative adversarial network
GMAE
gradient mean absolute error
GPU
graphics processing unit
LA
left atrium
LPA
left pulmonary artery
LV
left ventricle
MAE
mean absolute error
ML
machine learning
MPA
main pulmonary artery
MPR
multi-planar reformat
MRI
magnetic resonance imaging
MSE
mean squared error
NUFFT
non-uniform fast Fourier transform
PA
pulmonary artery
PSNR
peak signal-to-noise ratio
RA
right atrium
RPA
right pulmonary artery
RR
R-R interval
RV
right ventricle
SAX
short axis
SNR
signal-to-noise ratio
SSIM
structural similarity index measure
SVR
slice-to-volume registration
TE
echo time
TR
repetition time
UI
user interface
VCG
vectorcardiogram
WH
whole-heart
Free-breathing
Real-time
Deep-Learning
3D-cine
Congenital heart disease
Non-contrast
Pediatric cardiology
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