Fast Dynamic Perfusion and Angiography Reconstruction Using an End-to-End 3D Convolutional Neural Network
Sahar Yousefi (Leiden University Medical Center)
Lydiane Hirschler (Leiden University Medical Center)
Merlijn van der Plas (Leiden University Medical Center)
Mohamed S. Elmahdy (Leiden University Medical Center)
Hessam Sokooti (Leiden University Medical Center)
Matthias J P van Osch (Leiden University Medical Center)
M. Staring (TU Delft - Pattern Recognition and Bioinformatics)
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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 ± 1.1 and 96.2 ± 11.1 respectively for 4D perfusion and angiographic data reconstruction for 313 test data-sets.
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