Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network

TL;DR

This paper introduces a 3D CNN-based method to reconstruct dynamic perfusion and angiography MRI data from interleaved undersampled acquisitions, eliminating the need for separate scans and improving efficiency.

Contribution

The study presents a novel 3D Dense-Unet neural network that reconstructs multi-timepoint perfusion and angiographic data from combined undersampled datasets, reducing scan time.

Findings

Achieved SSIM of 97.3% for perfusion reconstruction

Achieved SSIM of 96.2% for angiographic reconstruction

Validated on 313 test datasets

Abstract

Hadamard time-encoded pseudo-continuous arterial spin labeling (te-pCASL) is a signal-to-noise ratio (SNR)-efficient MRI technique for acquiring dynamic pCASL signals that encodes the temporal information into the labeling according to a Hadamard matrix. In the decoding step, the contribution of each sub-bolus can be isolated resulting in dynamic perfusion scans. When acquiring te-ASL both with and without flow-crushing, the ASL-signal in the arteries can be isolated resulting in 4D-angiographic information. However, obtaining multi-timepoint perfusion and angiographic data requires two acquisitions. In this study, we propose a 3D Dense-Unet convolutional neural network with a multi-level loss function for reconstructing multi-timepoint perfusion and angiographic information from an interleaved $50\%$-sampled crushed and $50\%$-sampled non-crushed data, thereby negating the additional scan time. We present a framework to generate dynamic pCASL training and validation data, based on models of the intravascular and extravascular te-pCASL signals. The proposed network achieved SSIM values of $97.3 \pm 1.1$ and $96.2 \pm 11.1$ respectively for 4D perfusion and angiographic data reconstruction for 313 test data-sets.