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

Sahar Yousefi, L. Hirschler, M. van der Plas, Mohamed Elmahdi, Hessam Sokooti, Mathias J.P. van Osch and Marius Staring


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 multilevel 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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BibTeX entry

author = "{Sahar Yousefi and L. Hirschler and M. van der Plas and Mohamed Elmahdi and Hessam Sokooti and Mathias J.P. van Osch and Marius Staring}",
title = "{Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network}",
booktitle = "{Machine Learning for Medical Image Reconstruction, MICCAI workshop}",
editor = "{Florian Knoll and Andreas Maier and Daniel Rueckert and Jong Chul Ye}",
address = "{Shenzhen, China}",
series = "{Lecture Notes in Computer Science}",
volume = "{11905}",
pages = "{25 - 35}",
month = "{October}",
year = "{2019}",

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