FReSCO: Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring using deep artifact suppression and segmentation

03/25/2022
by   Olivier Jaubert, et al.
5

Purpose: Real-time monitoring of cardiac output (CO) requires low latency reconstruction and segmentation of real-time phase contrast MR (PCMR), which has previously been difficult to perform. Here we propose a deep learning framework for 'Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring' (FReSCO). Methods: Deep artifact suppression and segmentation U-Nets were independently trained. Breath hold spiral PCMR data (n=516) was synthetically undersampled using a variable density spiral sampling pattern and gridded to create aliased data for training of the artifact suppression U-net. A subset of the data (n=96) was segmented and used to train the segmentation U-net. Real-time spiral PCMR was prospectively acquired and then reconstructed and segmented using the trained models (FReSCO) at low latency at the scanner in 10 healthy subjects during rest, exercise and recovery periods. CO obtained via FReSCO was compared to a reference rest CO and rest and exercise Compressed Sensing (CS) CO. Results: FReSCO was demonstrated prospectively at the scanner. Beat-to-beat heartrate, stroke volume and CO could be visualized with a mean latency of 622ms. No significant differences were noted when compared to reference at rest (Bias = -0.21+-0.50 L/min, p=0.246) or CS at peak exercise (Bias=0.12+-0.48 L/min, p=0.458). Conclusion: FReSCO was successfully demonstrated for real-time monitoring of CO during exercise and could provide a convenient tool for assessment of the hemodynamic response to a range of stressors.

READ FULL TEXT

Authors

page 12

page 13

page 14

page 15

page 18

page 19

03/14/2018

Real-time Cardiovascular MR with Spatio-temporal De-aliasing using Deep Learning - Proof of Concept in Congenital Heart Disease

PURPOSE: Real-time assessment of ventricular volumes requires high accel...
03/14/2018

Spatio-temporal Deep De-aliasing for Prospective Assessment of Real-time Ventricular Volumes

PURPOSE: Real-time assessment of ventricular volumes requires high accel...
08/12/2020

Real-Time Cardiac Cine MRI with Residual Convolutional Recurrent Neural Network

Real-time cardiac cine MRI does not require ECG gating in the data acqui...
07/18/2020

ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation

Real-time cine magnetic resonance imaging (MRI) plays an increasingly im...
03/30/2022

L^3U-net: Low-Latency Lightweight U-net Based Image Segmentation Model for Parallel CNN Processors

In this research, we propose a tiny image segmentation model, L^3U-net, ...
09/15/2019

MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation

Cardiac magnetic resonance imaging (MRI) is an essential tool for MRI-gu...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.