PET image reconstruction requires accurate estimates of attenuation coefficients. Metal implants corrupt both the MRI and CT images and thus are not suitable for use in image reconstruction. In particular, the metal implant appears as a large signal void in the MRI and is incorrectly estimated as having the attenuation coefficients of air. We proposed to use Bayesian deep learning to identify the location of the metal implant and use it to guide PET joint estimation of attenuation and activity. We found that the metal implant is recovered and lesion uptake near the implant agree well with our reference data.
Positron emission tomography (PET) image reconstruction requires estimating the attenuation of high energy photons as they travel through the body. This is typically done with CT scans, but these are not available on PET/MRI scanners.
Joint estimation of attenuation and activity is an algorithm to simultaneously estimate the attenuation map and PET image using only the PET emission data1. This produces an attenuation map however the solution is slow to converge, has low signal-to-noise, and suffers from cross-talk artifacts.
Deep learning has been successfully utilized to generate synthetic CT images for PET/MRI attenuation correction2. However, the synthetic CT images do not account for any bowel air in the body and are unable to account for metal implants. One potential way of identifying these regions is with the use of Bayesian deep learning. With Bayesian deep learning, data that were not present in the training data would have some uncertainty assigned to them: the larger the deviation the data from the training data, the larger the uncertainty.
In this work, we introduced a method to estimate regions of confidence in the synthetic CT image. The confidence map guided the joint estimation algorithm to estimate the attenuation and activity using the PET emission data and an initial attenuation map from MRI. To the best of our knowledge, this is the first work to integrate PET and MRI data in a fully automatic joint reconstruction framework.
An overview of the network architecture and reconstruction pipeline is shown in Figure 1. A Monte-Carlo Dropout Bayesian convolutional neural network (BCNN)3 was trained to generate synthetic CT images using Dixon in-phase, Dixon fractional fat, and Dixon fractional water MRI from a population of 10 patients. For forward inference, the BCNN was used to probabilistically generate 283 synthetic CT images for each patient. The mean and variance images were calculated from the set; the mean image was used as an initial attenuation map and the variance image was used to generate the confidence map. PET reconstruction was then performed using the joint estimation of attenuation and activity algorithm1.
We reconstructed PET images for one patient with a metal implant using three different methods: (1) joint estimation with the BCNN synthetic CT and confidence map (JE), (2) joint estimation without an MR prior (JE w/o prior), and (3) without joint estimation using the BCNN synthetic CT (DL-based pCT). We classified lesions as near the metal implant and far from the metal implant and measured the maximum standardized uptake value in each category. We used JE w/o prior as a reference for lesions near the metal implant since the attenuation from the metal dominates, and we used DL-based pCT as a reference for lesions far from the metal implant since the metal implant would have less effect.
Figure 2 shows the confidence map, initial attenuation map from the synthetic CT, reconstructed attenuation map, PET image, and MRI from a patient with a metal implant. Metal artifacts were apparent on the Dixon MRI. The initial attenuation map reasonably estimated the soft tissues and bones however the metal implant appeared like a large volume of bowel air due to the signal void in the MRI. Nevertheless, the metal artifact appeared as a region of low confidence and the joint reconstruction algorithm recovered the metal implant in the reconstructed attenuation map despite its absence in the initial attenuation map.
Figures 3 and 4 show the comparisons in PET uptake and SUVmax for lesions. We found that for lesions near the metal implant, JE w/o prior (i.e. a reconstruction using only PET data) and joint estimation with the BCNN synthetic CT and confidence map (JE) agree very well; using only DL-based pCT causes very large errors in lesions near the metal implant. For lesions far from the metal implant, JE agrees very well with DL-based pCT and JE w/o prior causes errors when the lesions are not in-plane with the implant.
[1] S. Ahn et al., “Joint estimation of activity and attenuation for PET using pragmatic MR-based prior: application to clinical TOF PET/MR whole-body data for FDG and non-FDG tracers,” Phys. Med. Biol., vol. 63, no. 4, p. 045006, 2018.
[2] A. P. Leynes et al., “Direct PseudoCT Generation for Pelvis PET/MRI Attenuation Correction using Deep Convolutional Neural Networks with Multi-parametric MRI: Zero Echo-time and Dixon Deep pseudoCT (ZeDD-CT),” J Nucl Med, p. jnumed.117.198051, Oct. 2017.
[3] Y. Gal and Z. Ghahramani, “Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,” arXiv:1506.02142 [cs, stat], Jun. 2015.