Literature DB >> 23910710

PET motion compensation for radiation therapy using a CT-based mid-position motion model: methodology and clinical evaluation.

Matthijs F Kruis1, Jeroen B van de Kamer, Antonetta C Houweling, Jan-Jakob Sonke, José S A Belderbos, Marcel van Herk.   

Abstract

PURPOSE: Four-dimensional positron emission tomography (4D PET) imaging of the thorax produces sharper images with reduced motion artifacts. Current radiation therapy planning systems, however, do not facilitate 4D plan optimization. When images are acquired in a 2-minute time slot, the signal-to-noise ratio of each 4D frame is low, compromising image quality. The purpose of this study was to implement and evaluate the construction of mid-position 3D PET scans, with motion compensated using a 4D computed tomography (CT)-derived motion model. METHODS AND MATERIALS: All voxels of 4D PET were registered to the time-averaged position by using a motion model derived from the 4D CT frames. After the registration the scans were summed, resulting in a motion-compensated 3D mid-position PET scan. The method was tested with a phantom dataset as well as data from 27 lung cancer patients.
RESULTS: PET motion compensation using a CT-based motion model improved image quality of both phantoms and patients in terms of increased maximum SUV (SUV(max)) values and decreased apparent volumes. In homogenous phantom data, a strong relationship was found between the amplitude-to-diameter ratio and the effects of the method. In heterogeneous patient data, the effect correlated better with the motion amplitude. In case of large amplitudes, motion compensation may increase SUV(max) up to 25% and reduce the diameter of the 50% SUV(max) volume by 10%.
CONCLUSIONS: 4D CT-based motion-compensated mid-position PET scans provide improved quantitative data in terms of uptake values and volumes at the time-averaged position, thereby facilitating more accurate radiation therapy treatment planning of pulmonary lesions.
Copyright © 2013 Elsevier Inc. All rights reserved.

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Year:  2013        PMID: 23910710     DOI: 10.1016/j.ijrobp.2013.06.007

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  8 in total

1.  Assessing and accounting for the impact of respiratory motion on FDG uptake and viable volume for liver lesions in free-breathing PET using respiration-suspended PET images as reference.

Authors:  Guang Li; C Ross Schmidtlein; Irene A Burger; Carole A Ridge; Stephen B Solomon; John L Humm
Journal:  Med Phys       Date:  2014-09       Impact factor: 4.071

Review 2.  The developing role of FDG PET imaging for prognostication and radiotherapy target volume delineation in non-small cell lung cancer.

Authors:  Tom Konert; Jeroen B van de Kamer; Jan-Jakob Sonke; Wouter V Vogel
Journal:  J Thorac Dis       Date:  2018-08       Impact factor: 2.895

3.  Motion-compensated FDG PET/CT for oesophageal cancer.

Authors:  Francine E M Voncken; Erik Vegt; Johanna W van Sandick; Jolanda M van Dieren; Cecile Grootscholten; Annemarieke Bartels-Rutten; Steven L Takken; Jan-Jakob Sonke; Jeroen B van de Kamer; Berthe M P Aleman
Journal:  Strahlenther Onkol       Date:  2021-04-07       Impact factor: 3.621

Review 4.  Challenges in the target volume definition of lung cancer radiotherapy.

Authors:  Susan Mercieca; José S A Belderbos; Marcel van Herk
Journal:  Transl Lung Cancer Res       Date:  2021-04

5.  Clinical evaluation of respiration-induced attenuation uncertainties in pulmonary 3D PET/CT.

Authors:  Matthijs F Kruis; Jeroen B van de Kamer; Wouter V Vogel; José Sa Belderbos; Jan-Jakob Sonke; Marcel van Herk
Journal:  EJNMMI Phys       Date:  2015-02-24

6.  Comparison of biological target volume metrics based on FDG PET-CT and 4DCT for primary non-small-cell lung cancer.

Authors:  Yingjie Zhang; Jianbin Li; Yili Duan; Wei Wang; Fengxiang Li; Qian Shao; Min Xu
Journal:  Oncotarget       Date:  2017-07-01

7.  Robust, independent and relevant prognostic 18F-fluorodeoxyglucose positron emission tomography radiomics features in non-small cell lung cancer: Are there any?

Authors:  Tom Konert; Sarah Everitt; Matthew D La Fontaine; Jeroen B van de Kamer; Michael P MacManus; Wouter V Vogel; Jason Callahan; Jan-Jakob Sonke
Journal:  PLoS One       Date:  2020-02-25       Impact factor: 3.240

8.  A Novel Radiomics-Based Tumor Volume Segmentation Algorithm for Lung Tumors in FDG-PET/CT after 3D Motion Correction-A Technical Feasibility and Stability Study.

Authors:  Lena Bundschuh; Vesna Prokic; Matthias Guckenberger; Stephanie Tanadini-Lang; Markus Essler; Ralph A Bundschuh
Journal:  Diagnostics (Basel)       Date:  2022-02-23
  8 in total

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