A Deep Neural Network for Parametric Image Reconstruction on A Large-Axial Field of View PET

Abstract Purpose: The long axial field-of-view (AFOV) PET scanners with ultra-high sensitivity provide new opportunities for enhanced parametric imaging but suffer from the dramatically increased volume and complexity of dynamic data. This study reconstructed a high-quality direct Patlak Ki image from five frames sinograms without input function by a deep learning framework based on DeepPET to explore the potential of artificial intelligence reducing the acquisition time and the dependence of input function in parametric imaging.Methods: This study is implemented on a large AFOV PET/CT scanner (Biograph Vision Quadra) and twenty patients were recruited with 18 F-Fluorodeoxyglucose ( 18 F-FDG) dynamic scans. During training and testing of the proposed deep learning framework, the last five frames (25 min, 40-65 min post-injection) sinograms were set as input and the reconstructed Patlak Ki images by a nested EM algorithm on the vendor were set as ground truth. To evaluate the image quality of predicted Ki images, mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) were calculated. Meanwhile, a linear regression process was applied between predicted and true Ki means on avid malignant lesions and tumor volume of interests (VOIs).Results: In testing phase, the proposed method achieved excellent MSE of less than 0.003, high SSIM, and PSNR of ~0.98 and ~38 dB, respectively. Moreover, there had a high correlation (DeepPET: = 0.7525, self-attention DeepPET: =0.8382) between predicted Ki and traditionally reconstructed Patlak Ki means over eleven lesions.Conclusions: The results show that the deep learning based method produced high-quality parametric images from small frames of projection data without input function. It has much potential to address the dilemma that the long scan time and dependency on input function that still hampers the clinical translation of dynamic PET.