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"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

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 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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Copyright © 2019 by the authors. Published version © 2019 by Springer Lecture Notes in Computer Science. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

 

BibTeX entry

@inproceedings{Yousefi:2019,
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}",
address = "{Shenzhen, China}",
series = "{Lecture Notes in Computer Science}",
month = "{October}",
year = "{2019}",
}

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