Short acquisition time PET/MR pharmacokinetic modelling using CNNs

Standard quantification of Positron Emission Tomography (PET) data requires a long acquisition time to enable pharmacokinetic (PK) model fitting, however blood flow information from Arterial Spin Labelling (ASL) Magnetic Resonance Imaging (MRI) can be combined with simultaneous dynamic PET data to reduce the acquisition time. Due the difficulty of fitting a PK model to noisy PET data with limited time points, such ‘fixed- R1’ techniques are constrained to a 30 min minimum acquisition, which is intolerable for many patients. In this work we apply a deep convolutional neural network (CNN) approach to combine the PET and MRI data. This permits shorter acquisition times as it avoids the noise sensitive voxelwise PK modelling and facilitates the full modelling of the relationship between blood flow and the dynamic PET data. This method is compared to three fixed- R1PK methods, and the clinically used standardised uptake value ratio (SUVR), using 60 min dynamic PET PK modelling as the gold standard. Testing on 11 subjects participating in a study of pre-clinical Alzheimer’s Disease showed that, for 30 min acquisitions, all methods which combine the PET and MRI data have comparable performance, however at shorter acquisition times the CNN approach has a significantly lower mean square error (MSE) compared to fixed- R1PK modelling (p=0.001). For both acquisition windows, SUVR had a significantly higher MSE than the CNN method (p ࣘ 0.003). This demonstrates that combining simultaneous PET and MRI data using a CNN can result in robust PET quantification within a scan time which is tolerable to patients with dementia