A back‐projection‐and‐filtering‐like (BPF‐like) reconstruction method with the deep learning filtration from listmode data in TOF‐PET

The effects of back-projection variants in BPF-like TOF PET reconstruction using CNN filtration - Based on simulated and clinical brain data

BACKGROUND

The back-projection strategies such as confidence weighting (CW) and most likely annihilation position (MLAP) have been adopted into back-projection-and-filtering-like (BPF-like) deep reconstruction model and shown great potential on fast and accurate PET reconstruction. Although the two methods degenerate to an identical model at the time resolution of 0 ps, they represent two distinct approaches at the realistic time resolutions of current commercial systems. There is a lack of a systematic and fair assessment on these differences.

PURPOSE

This work aims to analyze the impact of back-projection variants on CNN-based PET image reconstruction to find the most effective back-projection model, and ultimately contribute to accurate PET reconstruction.

METHODS

Different back-projection strategies (CW and MLAP) and different angular view processing methods (view-summed and view-grouped) were considered, leading to the comparison of four back-projection variants integrated with the same CNN filtration model. Meanwhile, we investigated two strategies of physical effect compensation, either introducing pre-corrected data as the input or adding a channel of attenuation map to the CNN model. After training models separately on Monte-Carlo-simulated BrainWeb phantoms with full dose (events = 3×107), we tested them on both simulated phantoms and clinical brain scans with two dosage levels. For the performance assessment, peak signal-to-noise ratio (PSNR) and root mean square error (RMSE) were used to evaluate the pixel-wise error, structural similarity index (SSIM) to evaluate the structural similarity, and contrast recovery coefficient (CRC) in manually selected ROI to compare the region recovery.

RESULTS

Compared to two MLAP-based histo-image reconstruction models, two CW-based back-projected image methods produced clearer, sharper, and more detailed images, from both simulated and clinical data. For angular view processing methods, view-grouped histo-image improved image quality, while view-grouped cwbp-image showed no advantage except for contrast recovery. Quantitative analysis on simulated data demonstrated that the view-summed cwbp-image model achieved the best PSNR, RMSE, SSIM, while the 8-view cwbp-image model achieved the best CRC in lesions and the white matter. Additionally, the multi-channel input model including the back-projection image and attenuation map was proved to be the most efficient and simplest method for compensating for physical effects for brain data. Applying Gaussian blur to the histo-image yielded images with limited improvement. All above results hold for both the half-dose and the full-dose cases.

CONCLUSION

For brain imaging, the evaluation based on metrics PSNR, RMSE, SSIM, and CRC indicates that the view-summed CW-based back-projection variant is the most effective input for the BPF-like reconstruction model using CNN filtration, which can involve the attenuation map through an additional channel to effectively compensate for physical effects.

Deep learning-based PET image denoising and reconstruction: a review

This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection through to recent iterative PET image reconstruction algorithms, and then review deep learning methods for PET data up to the latest innovations within three main categories. The first category involves post-processing methods for PET image denoising. The second category comprises direct image reconstruction methods that learn mappings from sinograms to the reconstructed images in an end-to-end manner. The third category comprises iterative reconstruction methods that combine conventional iterative image reconstruction with neural-network enhancement. We discuss future perspectives on PET imaging and deep learning technology.

Prior information-guided reconstruction network for positron emission tomography images

Background

Deep learning has recently shown great potential in medical image reconstruction tasks. For positron emission tomography (PET) images, the direct reconstruction from raw data to radioactivity images using deep learning without any constraint may lead to the production of nonexistent structures. The aim of this study was to specifically develop and test a flexibly deep learning-based reconstruction network guided by any form of prior knowledge to achieve high quality and high reliability reconstruction.

Methods

We developed a novel prior information-guided reconstruction network (PIGRN) with a dual-channel generator and a 2-scale discriminator based on a conditional generative adversarial network (cGAN). Besides the raw data channel, an additional channel is provided in the generator for prior information (PI) to guide the training phase. The PI can be reconstructed images obtained via conventional methods, nuclear medical images from other modalities, attenuation correction maps from time-of-flight-PET (TOF-PET) data, or any other physical parameters. For this study, the reconstructed images generated by filtered back projection (FBP) were chosen as the input of the additional channel. To improve the image quality, a 2-scale discriminator was adopted which can focus on both the coarse and fine field of the reconstruction images. Experiments were carried out on both a simulation dataset and a real Sprague Dawley (SD) rat dataset.

Results

Two classic deep learning-based reconstruction networks, including U-Net and Deep-PET, were compared in our study. Compared with these two methods, our method could provide much higher quality PET image reconstruction in the study of the simulation dataset. The peak signal-to-noise ratio (PSNR) value reached 31.8498, and the structure similarity index measure (SSIM) value reached 0.9754. The real study on SD rats indicated that the proposed network also has strong generalization ability.

Conclusions

The flexible PIGRN based on cGAN for PET images combines both raw data and PI. The results of comparison experiments and a generalization experiment based on simulation and SD rat datasets demonstrated that the proposed PIGRN has the ability to improve image quality and has strong generalization ability.