High Resolution Isotropic 3D Cine imaging with Automated Segmentation using Concatenated 2D Real-time Imaging and Deep Learning
Journal:
arXiv
Published Date:
Jun 27, 2025
Abstract
Background: Conventional cardiovascular magnetic resonance (CMR) in
paediatric and congenital heart disease uses 2D, breath-hold, balanced steady
state free precession (bSSFP) cine imaging for assessment of function and
cardiac-gated, respiratory-navigated, static 3D bSSFP whole-heart imaging for
anatomical assessment. Our aim is to concatenate a stack 2D free-breathing
real-time cines and use Deep Learning (DL) to create an isotropic a fully
segmented 3D cine dataset from these images. Methods: Four DL models were
trained on open-source data that performed: a) Interslice contrast correction;
b) Interslice respiratory motion correction; c) Super-resolution (slice
direction); and d) Segmentation of right and left atria and ventricles (RA, LA,
RV, and LV), thoracic aorta (Ao) and pulmonary arteries (PA). In 10 patients
undergoing routine cardiovascular examination, our method was validated on
prospectively acquired sagittal stacks of real-time cine images. Quantitative
metrics (ventricular volumes and vessel diameters) and image quality of the 3D
cines were compared to conventional breath hold cine and whole heart imaging.
Results: All real-time data were successfully transformed into 3D cines with a
total post-processing time of <1 min in all cases. There were no significant
biases in any LV or RV metrics with reasonable limits of agreement and
correlation. There is also reasonable agreement for all vessel diameters,
although there was a small but significant overestimation of RPA diameter.
Conclusion: We have demonstrated the potential of creating a 3D-cine data from
concatenated 2D real-time cine images using a series of DL models. Our method
has short acquisition and reconstruction times with fully segmented data being
available within 2 minutes. The good agreement with conventional imaging
suggests that our method could help to significantly speed up CMR in clinical
practice.
