Machine learning-based prediction of conversion coefficients for I-123 metaiodobenzylguanidine heart-to-mediastinum ratio.

ABSTRACT:

Purpose

We developed a method of standardizing the heart-to-mediastinal ratio in ¹²³I-labeled meta-iodobenzylguanidine (MIBG) images using a conversion coefficient derived from a dedicated phantom. This study aimed to create a machine-learning (ML) model to estimate conversion coefficients without using a phantom.

Methods

210 Monte Carlo (MC) simulations of ¹²³I-MIBG images to obtain conversion coefficients using collimators that differed in terms of hole diameter, septal thickness, and length. Simulated conversion coefficients and collimator parameters were prepared as training datasets, then a gradient-boosting ML was trained to estimate conversion coefficients from collimator parameters. Conversion coefficients derived by ML were compared with those

SUBMITTER: Okuda K 

PROVIDER: S-EPMC10372132 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature