An image registration method for voxel-wise analysis of whole-body oncological PET-CT

Whole-body positron emission tomography-computed tomography (PET-CT) imaging in oncology provides comprehensive information of each patient’s disease status. However, image interpretation of volumetric data is a complex and time-consuming task. In this work, an image registration method targeted tow...

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Veröffentlicht in:Scientific reports Jg. 12; H. 1; S. 18768 - 12
Hauptverfasser: Jönsson, Hanna, Ekström, Simon, Strand, Robin, Pedersen, Mette A., Molin, Daniel, Ahlström, Håkan, Kullberg, Joel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 05.11.2022
Nature Publishing Group
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Schlagworte:
631/114/1564
631/67/2321
Algorithms
Computed tomography
Computerized Image Processing
Datoriserad bildbehandling
Head & neck cancer
Hodgkin's lymphoma
Humanities and Social Sciences
Humans
Image processing
Image Processing, Computer-Assisted - methods
Lymphoma
multidisciplinary
Positron emission tomography
Positron Emission Tomography Computed Tomography
Positron-Emission Tomography - methods
Registration
Science
Science (multidisciplinary)
Segmentation
Tomography
Tumor Burden
Tumors
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
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Zusammenfassung:Whole-body positron emission tomography-computed tomography (PET-CT) imaging in oncology provides comprehensive information of each patient’s disease status. However, image interpretation of volumetric data is a complex and time-consuming task. In this work, an image registration method targeted towards computer-aided voxel-wise analysis of whole-body PET-CT data was developed. The method used both CT images and tissue segmentation masks in parallel to spatially align images step-by-step. To evaluate its performance, a set of baseline PET-CT images of 131 classical Hodgkin lymphoma (cHL) patients and longitudinal image series of 135 head and neck cancer (HNC) patients were registered between and within subjects according to the proposed method. Results showed that major organs and anatomical structures generally were registered correctly. Whole-body inverse consistency vector and intensity magnitude errors were on average less than 5 mm and 45 Hounsfield units respectively in both registration tasks. Image registration was feasible in time and the nearly automatic pipeline enabled efficient image processing. Metabolic tumor volumes of the cHL patients and registration-derived therapy-related tissue volume change of the HNC patients mapped to template spaces confirmed proof-of-concept. In conclusion, the method established a robust point-correspondence and enabled quantitative visualization of group-wise image features on voxel level.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-23361-z