PIPET: A Pipeline to Generate PET Phantom Datasets for Reconstruction Based on Convolutional Neural Network Training

被引：0

作者：

Sanz-Sanchez, Alejandro ^{[1
]}

Garcia, Francisco B. ^{[1
]}

Mesas-Lafarga, Pablo ^{[1
]}

Prats-Climent, Joan ^{[1
]}

Rodriguez-alvarez, Maria Jose ^{[1
]}

机构：

[1] Univ Politecn Valencia UPV, Inst Instrumentac Imagen Mol I3M, Cami Vera S-N, Valencia 46022, Spain

来源：

ALGORITHMS | 2024年 / 17卷 / 11期

关键词：

PET; GATE; !text type='Python']Python[!/text; neural network; image reconstruction; SIMULATION;

D O I：

10.3390/a17110511

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

There has been a strong interest in using neural networks to solve several tasks in PET medical imaging. One of the main problems faced when using neural networks is the quality, quantity, and availability of data to train the algorithms. In order to address this issue, we have developed a pipeline that enables the generation of voxelized synthetic PET phantoms, simulates the acquisition of a PET scan, and reconstructs the image from the simulated data. In order to achieve these results, several pieces of software are used in the different steps of the pipeline. This pipeline solves the problem of generating diverse PET datasets and images of high quality for different types of phantoms and configurations. The data obtained from this pipeline can be used to train convolutional neural networks for PET reconstruction.

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页数：13

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