PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning

Sangwon Lee, Jin Ho Jung, Dongwoo Kim, Hyun Keong Lim, Mi Ae Park, Garam Kim, Minjae So, Sun Kook Yoo, Byoung Seok Ye, Yong Choi, Mijin Yun

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose This study was to develop a convolutional neural network (CNN) model with a residual learning framework to predict the full-time 18F-florbetaben (18F-FBB) PET/CT images from corresponding short-time scans. Methods In this retrospective study, we enrolled 22 cognitively normal subjects, 20 patients with mild cognitive impairment, and 42 patients with Alzheimer disease. Twenty minutes of list-mode PET/CT data were acquired and reconstructed as the ground-truth images. The short-time scans were made in either 1, 2, 3, 4, or 5 minutes. The CNN with a residual learning framework was implemented to predict the ground-truth images of 18F-FBB PET/CT using short-time scans with either a single-slice or a 3-slice input layer. Model performance was evaluated by quantitative and qualitative analyses. Additionally, we quantified the amyloid load in the ground-truth and predicted images using the SUV ratio. Results On quantitative analyses, with increasing scan time, the normalized root-mean-squared error and the SUV ratio differences between predicted and ground-truth images gradually decreased, and the peak signal-to-noise ratio increased. On qualitative analysis, the predicted images from the 3-slice CNN model showed better image quality than those from the single-slice model. The 3-slice CNN model with a short-time scan of at least 2 minutes achieved comparable, quantitative prediction of full-time 18F-FBB PET/CT images, with adequate to excellent image quality. Conclusions The 3-slice CNN model with a residual learning framework is promising for the prediction of full-time 18F-FBB PET/CT images from short-time scans.

Original languageEnglish (US)
Pages (from-to)e133-e140
JournalClinical nuclear medicine
Volume46
Issue number3
DOIs
StatePublished - 2021
Externally publishedYes

Keywords

  • Alzheimer disease
  • PET/CT
  • amyloid imaging
  • deep learning

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning'. Together they form a unique fingerprint.

Cite this