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

Abstract

AbstractBackgroundThe 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.PurposeThis 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.MethodsDifferent 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.ResultsCompared 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.ConclusionFor 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.