AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans. Academic Article uri icon

Overview

abstract

  • PURPOSE: To develop a deep learning approach that enables ultra-low-dose, 1% of the standard clinical dosage (3 MBq/kg), ultrafast whole-body PET reconstruction in cancer imaging. MATERIALS AND METHODS: In this Health Insurance Portability and Accountability Act-compliant study, serial fluorine 18-labeled fluorodeoxyglucose PET/MRI scans of pediatric patients with lymphoma were retrospectively collected from two cross-continental medical centers between July 2015 and March 2020. Global similarity between baseline and follow-up scans was used to develop Masked-LMCTrans, a longitudinal multimodality coattentional convolutional neural network (CNN) transformer that provides interaction and joint reasoning between serial PET/MRI scans from the same patient. Image quality of the reconstructed ultra-low-dose PET was evaluated in comparison with a simulated standard 1% PET image. The performance of Masked-LMCTrans was compared with that of CNNs with pure convolution operations (classic U-Net family), and the effect of different CNN encoders on feature representation was assessed. Statistical differences in the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and visual information fidelity (VIF) were assessed by two-sample testing with the Wilcoxon signed rank t test. RESULTS: The study included 21 patients (mean age, 15 years ± 7 [SD]; 12 female) in the primary cohort and 10 patients (mean age, 13 years ± 4; six female) in the external test cohort. Masked-LMCTrans-reconstructed follow-up PET images demonstrated significantly less noise and more detailed structure compared with simulated 1% extremely ultra-low-dose PET images. SSIM, PSNR, and VIF were significantly higher for Masked-LMCTrans-reconstructed PET (P < .001), with improvements of 15.8%, 23.4%, and 186%, respectively. CONCLUSION: Masked-LMCTrans achieved high image quality reconstruction of 1% low-dose whole-body PET images.Keywords: Pediatrics, PET, Convolutional Neural Network (CNN), Dose Reduction Supplemental material is available for this article. © RSNA, 2023.

authors

  • Wang, Yan-Ran
  • Qu, Liangqiong
  • Sheybani, Natasha Diba
  • Luo, Xiaolong
  • Wang, Jiangshan
  • Hawk, Kristina Elizabeth
  • Theruvath, Ashok Joseph
  • Gatidis, Sergios
  • Xiao, Xuerong
  • Pribnow, Allison
  • Rubin, Daniel
  • Daldrup-Link, Heike E

publication date

  • May 3, 2023

Identity

PubMed Central ID

  • PMC10245181

Scopus Document Identifier

  • 85161398596

Digital Object Identifier (DOI)

  • 10.1148/ryai.220246

PubMed ID

  • 37293349

Additional Document Info

volume

  • 5

issue

  • 3