Abstract
This study evaluated the feasibility of using only diagnostically relevant magnetic resonance (MR) images together with deep learning for positron emission tomography (PET)/MR attenuation correction (deepMRAC) in the pelvis. Such an approach could eliminate dedicated MRAC sequences that have limited diagnostic utility but can substantially lengthen acquisition times for multibed position scans. We used axial T2 and T1 LAVA Flex magnetic resonance imaging images that were acquired for diagnostic purposes as inputs to a 3D deep convolutional neural network. The network was trained to produce a discretized (air, water, fat, and bone) substitute computed tomography (CT) (CTsub). Discretized (CTref-discrete) and continuously valued (CTref) reference CT images were created to serve as ground truth for network training and attenuation correction, respectively. Training was performed with data from 12 subjects. CTsub, CTref, and the system MRAC were used for PET/MR attenuation correction, and quantitative PET values of the resulting images were compared in 6 test subjects. Overall, the network produced CTsub with Dice coefficients of 0.79 ± 0.03 for cortical bone, 0.98 ± 0.01 for soft tissue (fat: 0.94 ± 0.0; water: 0.88 ± 0.02), and 0.49 ± 0.17 for bowel gas when compared with CTref-discrete. The root mean square error of the whole PET image was 4.9% by using deepMRAC and 11.6% by using the system MRAC. In evaluating 16 soft tissue lesions, the distribution of errors for maximum standardized uptake value was significantly narrower using deepMRAC (−1.0% ± 1.3%) than using system MRAC method (0.0% ± 6.4%) according to the Brown–Forsy the test (P < .05). These results indicate that improved PET/MR attenuation correction can be achieved in the pelvis using only diagnostically relevant MR images.
Highlights
Accurate correction for photon attenuation remains a challenge for quantitative positron emission tomography (PET)/magnetic resonance (MR) imaging
Because PET/MR imaging was conducted after a PET/computed tomography (CT) scan, PET imaging was conducted an average of 135 Ϯ 25 minutes after radiotracer injection
Sensitivity Analysis Because deepMRAC produces a discretized CT instead of a continuously valued CT for attenuation correction, we evaluated the effect of using a discrete CT instead of a continuously valued CT for PET attenuation correction
Summary
Accurate correction for photon attenuation remains a challenge for quantitative positron emission tomography (PET)/magnetic resonance (MR) imaging. Owing to the absence of transmission imaging during PET/MR imaging, either through computed tomography (CT) or a transmission source, MR-based attenuation correction (MRAC) methods are needed to estimate the pixelwise photon attenuation coefficients for quantitative PET reconstruction. Many MRAC techniques have been developed over the past decade [1, 2], with dual-echo chemical shift-encoded (2point Dixon) imaging being used in most commercial PET/MR scanners. In Dixon-based MRAC, a single acquisition yields images that are separated into water and fat components and assigned Hounsfield units (HU) for air, fat, lung, and water [3, 4]. While ignoring bone in MRAC appears to have little impact on the diagnostic accuracy of PET/MR imaging [6], it can lead to quantitative PET errors exceeding 20%, depending on the location [7]
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