Deep Learning Based Approach to Quantification of PET Tracer Uptake in Small Tumors

Laura Dal Toso*, Elisabeth Pfaehler, Ronald Boellaard, Julia A. Schnabel, Paul K. Marsden

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

In Positron Emission Tomography (PET), quantification of tumor radiotracer uptake is mainly performed using standardised uptake value and related methods. However, the accuracy of these metrics is limited by the poor spatial resolution and noise properties of PET images. Therefore, there is a great need for new methods that allow for accurate and reproducible quantification of tumor radiotracer uptake, particularly for small regions. In this work, we propose a deep learning approach to improve quantification of PET tracer uptake in small tumors using a 3D convolutional neural network. The network was trained on simulated images that present 3D shapes with typical tumor tracer uptake distributions (‘ground truth distributions’), and the corresponding set of simulated PET images. The network was tested on unseen simulated PET images and was shown to robustly estimate the original radiotracer uptake, yielding improved images both in terms of shape and activity distribution. The same network was successful when applied to 3D tumors acquired from physical phantom PET scans.

Original languageEnglish
Title of host publicationMachine Learning for Medical Image Reconstruction - 2nd International Workshop, MLMIR 2019, held in Conjunction with MICCAI 2019, Proceedings
EditorsFlorian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye
PublisherSpringer
Pages181-192
Number of pages12
ISBN (Print)9783030338428
DOIs
Publication statusPublished - 1 Jan 2019
Event2nd International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2019 held in Conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 17 Oct 201917 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11905 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2019 held in Conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period17/10/201917/10/2019

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