  Arterial spin labeling (ASL) allows to quantify the cerebral blood flow (CBF)
by magnetic labeling of the arterial blood water. ASL is increasingly used in
clinical studies due to its noninvasiveness, repeatability and benefits in
quantification. However, ASL suffers from an inherently low-signal-to-noise
ratio (SNR) requiring repeated measurements of control/spin-labeled (C/L) pairs
to achieve a reasonable image quality, which in return increases motion
sensitivity. This leads to clinically prolonged scanning times increasing the
risk of motion artifacts. Thus, there is an immense need of advanced imaging
and processing techniques in ASL. In this paper, we propose a novel deep
learning based approach to improve the perfusion-weighted image quality
obtained from a subset of all available pairwise C/L subtractions.
Specifically, we train a deep fully convolutional network (FCN) to learn a
mapping from noisy perfusion-weighted image and its subtraction (residual) from
the clean image. Additionally, we incorporate the CBF estimation model in the
loss function during training, which enables the network to produce high
quality images while simultaneously enforcing the CBF estimates to be as close
as reference CBF values. Extensive experiments on synthetic and clinical ASL
datasets demonstrate the effectiveness of our method in terms of improved ASL
image quality, accurate CBF parameter estimation and considerably small
computation time during testing.
