Paper Info
Title
Short Acquisition Time PET/MR Pharmacokinetic Modelling Using CNNs.
Abstract
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-R-1' techniques are constrained to a 30min 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-R-1 PK 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 30min 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-R-1 PK 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.
Year
DOI
Venue
2018
10.1007/978-3-030-00928-1_6
Lecture Notes in Computer Science
Field
DocType
Volume
Pattern recognition,Convolutional neural network,Pharmacokinetics,Computer science,Scan time,Mean squared error,Positron emission tomography,Artificial intelligence,Magnetic resonance imaging
Conference
11070
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
4
9
Name
Order
Citations
PageRank
Catherine J. Scott100.34
Jieqing Jiao211.09
Cardoso M. Jorge36413.70
Kerstin Kläser402.03
Andrew Melbourne524.08
P. J. Markiewicz6184.20
J M Schott711710.92
Brian F. Hutton89814.33
Sébastien Ourselin92499237.61