Mario Serrano-Sosa1, Christine DeLorenzo1,2, and Chuan Huang1,2,3
1Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, 2Psychiatry, Stony Brook University, Stony Brook, NY, United States, 3Radiology, Stony Brook University, Stony Brook, NY, United States
Synopsis
We developed a new PET
denoising model by utilizing a dilated CNN (dNet) architecture with PET/MRI inputs (dNetPET/MRI)
and compared it to three other deep learning models with objective imaging metrics Structural Similarity index (SSIM), Peak signal-to-noise ratio (PSNR) and mean absolute percent error (MAPE). The dNetPET/MRI performed the best across all metrics and performed significantly better than uNetPET/MRI (pSSIM=0.0218, pPSNR=0.0034, pMAPE=0.0305). Also, dNetPET performed significantly better than uNetPET (p<0.001 for all metrics). Trend-level improvements were found across all objective metrics in networks using PET/MRI compared to PET only inputs within similar networks (dNetPET/MRI vs. dNetPET and uNetPET/MRI vs. uNetPET).
Introduction
In Positron Emission Tomography (PET), reducing
counts while maintaining image quality is a task needed to be optimized.
Previous developments of deep learning techniques to denoise PET images have
utilized well-known uNet architecture1, 2; wherein more complex
networks, such as GAN and cycle-consistent GAN, work with uNet-like
architectures as their generators3, 4. Previous works have also shown that
incorporating structural MRI as an input improves performance5; this allows for the network to preserve
edges in the PET network output. Despite this success,
the field is in its infancy and work to further optimize architectures needs to
be exploited.
We have recently introduced the dilated CNN
(dNet) for PET denoising and demonstrated that it allows for better edge
preservation and improved objective image quality measures such as peak signal-to-noise
ratio (PSNR), structural similarity index (SSIM) and mean absolute percent error
(MAPE)6. In this study, we develop a new PET
denoising model by utilizing the dNet architecture with both PET and MRI inputs
(dNetPET/MRI) and compare it to three other deep learning models.Methods
dNet combines the skip
connections similar to uNet but with the notion of a dilated convolution7. Figure 1 shows the
dilated convolution kernels used in dNet. We developed dNetPET/MRI to
estimate full-count as the output and compared it to three other models: dNet
with PET only, uNet with PET/MRI and uNet with PET only inputs (dNetPET,
uNetPET/MRI, uNetPET, respectively). Comparison of these
models was evaluated through objective imaging metrics PSNR, SSIM and MAPE. Residual
learning was included into all networks.
A total of 35 18F-FDG
brain PET/MRI studies, with 8,400 total slices, were acquired and split into
training (n=7,200 slices) and testing (n=1,200 slices). Each subject was
administered between 148-185 MBq (4-5mCi) of 18F-FDG. Listmode data was
collected for 60 minutes after the injection of 18F-FDG on a Siemens
Biograph mMR PET/MRI scanner. Low-count PET data (10% count) were generated
through Poisson thinning from the full listmode file. PET images were
reconstructed using Siemens’ E7tools with ordered subset expectation
maximization (OSEM). MPRAGE images were used alongside reconstructed low-count
PET as inputs to the PET/MRI networks.Results
All four networks were successfully trained to
synthesize full-count from low-count PET images. Figure 2 shows SSIM, PSNR and
MAPE values for the independent testing set evaluated on the four networks
compared to low-count. The dNetPET/MRI performed the best across all
metrics and performed significantly better than uNetPET/MRI (pSSIM=0.0218,
pPSNR=0.0034 , pMAPE= 0.0305). Also, dNetPET
performed significantly better than uNetPET (p<0.001 for all
metrics). Trend-level improvements were found across all objective metrics in networks
using PET/MRI compared to PET only inputs within similar networks (dNetPET/MRI
vs. dNetPET and uNetPET/MRI vs. uNetPET); with
PET/MRI networks significantly outperforming PET only networks in PSNR and MAPE
(Table 1).Discussion
We have developed the
dNet architecture with structural MRI as additional input for low-count PET
denoising and compared it to the well-established uNet. Besides the proposed
network, we constructed dNetPET, uNetPET/MRI, and uNetPET.
Across all objective metrics, dNetPET/MRI outperformed all of the
other networks. Both dNet architectures (with or without MRI inputs) also
significantly outperformed their uNet counterparts, indicating that they are
superior for denoising. Typically, uNet
architectures down-sample and up-sample feature maps as they are fed into the
network, which degrades resolution and fine details. Notably, uNet processing introduces some degree of blurring from two
primary sources. The first source is the mathematical nature of the
convolution. Secondly, is the common practice of downsampling and subsequently
re-upsampling feature maps as they pass through the network. Dilated kernels
are a method to oppose this blurring effect. Our dNet, with an expanding field of view, avoids the downsampling and,
subsequent, upsampling schemes that degrade images to preserve resolution.Conclusion
This is the first work
to use dNet architecture for low-count PET denoising with structural MRI as additional
input. This network has shown to outperform uNet across various objective
imaging metrics. Although various other methods have been introduced recently
for low-count PET denoising, such as GAN, cycle-consistent GAN, etc., they
typically all use generators that have uNet-like architectures. Combining these
novel GAN’s with the expanding field of view that dilated kernels allows may improve
upon previously acquired results – further improving the field of PET/MR
denoising. Acknowledgements
No acknowledgement found.References
1. L. Xiang, Y. Qiao, D. Nie, L. An, W.
Lin, Q. Wang and D. Shen, Neurocomputing 267,
406-416 (2017).
2. J. Xu, E. Gong, J. Pauly and G. J. a.
p. a. Zaharchuk, (2017).
3. Y. Lei, X. Dong, T. Wang, K. Higgins,
T. Liu, W. J. Curran, H. Mao, J. A. Nye and X. Yang, Physics in Medicine &
Biology (2019).
4. W. Lu, J. A. Onofrey, Y. Lu, L. Shi, T.
Ma, Y. Liu and C. Liu, Physics in Medicine & Biology 64 (16), 165019 (2019).
5. K. T. Chen, E. Gong, F. B. d. C.
Macruz, J. Xu, A. Boumis, M. Khalighi, K. L. Poston, S. J. Sha, M. D. Greicius,
E. Mormino, J. M. Pauly, S. Srinivas and G. Zaharchuk, Radiology 290 (3), 649-656 (2019).
6. K. Spuhler, M. Serrano-Sosa, R.
Cattell, C. DeLorenzo and C. Huang, arXiv preprint arXiv:1910.11865 (2019).
7. L.-C. Chen, G. Papandreou, F. Schroff
and H. Adam, arXiv preprint arXiv:1706.05587 (2017).