Deep learning for temporal super-resolution 4D Flow MRI
Journal:
arXiv
Published Date:
Jan 15, 2025
Abstract
4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive technique
for volumetric, time-resolved blood flow quantification. However, apparent
trade-offs between acquisition time, image noise, and resolution limit clinical
applicability. In particular, in regions of highly transient flow, coarse
temporal resolution can hinder accurate capture of physiologically relevant
flow variations. To overcome these issues, post-processing techniques using
deep learning have shown promising results to enhance resolution post-scan
using so-called super-resolution networks. However, while super-resolution has
been focusing on spatial upsampling, temporal super-resolution remains largely
unexplored. The aim of this study was therefore to implement and evaluate a
residual network for temporal super-resolution 4D Flow MRI. To achieve this, an
existing spatial network (4DFlowNet) was re-designed for temporal upsampling,
adapting input dimensions, and optimizing internal layer structures. Training
and testing were performed using synthetic 4D Flow MRI data originating from
patient-specific in-silico models, as well as using in-vivo datasets. Overall,
excellent performance was achieved with input velocities effectively denoised
and temporally upsampled, with a mean absolute error (MAE) of 1.0 cm/s in an
unseen in-silico setting, outperforming deterministic alternatives (linear
interpolation MAE = 2.3 cm/s, sinc interpolation MAE = 2.6 cm/s). Further, the
network synthesized high-resolution temporal information from unseen
low-resolution in-vivo data, with strong correlation observed at peak flow
frames. As such, our results highlight the potential of utilizing data-driven
neural networks for temporal super-resolution 4D Flow MRI, enabling
high-frame-rate flow quantification without extending acquisition times beyond
clinically acceptable limits.
