  Accurate quantification of cerebral blood flow (CBF) is essential for the
diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid
stenosis, aneurysms, and stroke. Positron emission tomography (PET) is
currently regarded as the gold standard for the measurement of CBF in the human
brain. PET imaging, however, is not widely available because of its prohibitive
costs, use of ionizing radiation, and logistical challenges, which require a
co-localized cyclotron to deliver the 2 min half-life Oxygen-15 radioisotope.
Magnetic resonance imaging (MRI), in contrast, is more readily available and
does not involve ionizing radiation. In this study, we propose a multi-task
learning framework for brain MRI-to-PET translation and disease diagnosis. The
proposed framework comprises two prime networks: (1) an attention-based 3D
encoder-decoder convolutional neural network (CNN) that synthesizes
high-quality PET CBF maps from multi-contrast MRI images, and (2) a multi-scale
3D CNN that identifies the brain disease corresponding to the input MRI images.
Our multi-task framework yields promising results on the task of MRI-to-PET
translation, achieving an average structural similarity index (SSIM) of 0.94
and peak signal-to-noise ratio (PSNR) of 38dB on a cohort of 120 subjects. In
addition, we show that integrating multiple MRI modalities can improve the
clinical diagnosis of brain diseases.
