Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.

ABSTRACT:

Purpose

We aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols.

Methods

Eighty simultaneous [¹⁸F]florbetaben PET/MRI studies were acquired, split equally between two sites (site 1: Signa PET/MRI, GE Healthcare, 39 participants, 67 ± 8 years, 23 females; site 2: mMR, Siemens Healthineers, 64 ± 11 years, 23 females) with different MRI protocols. Twenty minutes of list-mode PET data (90-110 min post-injection) were reconstructed as ground-truth. Ultra-low-count data obtained from undersampling by a factor of 100 (site 1) or the first minute of PET acquisition (site 2) were reconstructed for ultra-low-dose/ultra-short-time

SUBMITTER: Chen KT 

PROVIDER: S-EPMC7680289 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature