Internal Motion Estimation during Free-Breathing via External/Internal Correlation Model

SummaryObjectives: A major problem associated with the irradiation of thoracic and abdominal tumors is respiratory motion. In clinical practice, motion compensation approaches are frequently steered by low-dimensional breathing signals (e.g., spirometry) and patient-specific correspondence models, which are used to estimate the sought internal motion given a signal measurement. Recently, the use of multidimensional signals derived from range images of the moving skin surface has been proposed to better account for complex motion patterns. In this work, a simulation study is carried out to investigate the motion estimation accuracy of such multidimensional signals and the influence of noise, the signal dimensionality, and different sampling patterns (points, lines, regions).Methods: A diffeomorphic correspondence modeling framework is employed to relate multidimensional breathing signals derived from simulated range images to internal motion patterns represented by diffeomorphic non-linear transformations. Furthermore, an automatic approach for the selection of optimal signal combinations/patterns within this framework is presented.Results: This simulation study focuses on lung motion estimation and is based on 28 4D CT data sets. The results show that the use of multidimensional signals instead of one-dimensional signals significantly improves the motion estimation accuracy, which is, however, highly affected by noise. Only small differences exist between different multidimensional sampling patterns (lines and regions). Automatically determined optimal combinations of points and lines do not lead to accuracy improvements compared to results obtained by using all points or lines.Conclusions: Our results show the potential of multidimensional breathing signals derived from range images for the model-based estimation of respiratory motion in radiation therapy.

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Region adaptive PET gating using internal motion estimation

2011 IEEE Nuclear Science Symposium Conference Record ◽

10.1109/nssmic.2011.6152526 ◽

2011 ◽

Cited By ~ 1

Author(s):

Jung Woo Yu ◽

Sang-Keun Woo ◽

Yong Jin Lee ◽

Jin Su Kim ◽

Kyo Chul Lee ◽

...

Keyword(s):

Motion Estimation ◽

Internal Motion

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Undersampled free breathing cardiac perfusion MRI reconstruction without motion estimation

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro ◽

10.1109/isbi.2011.5872363 ◽

2011 ◽

Cited By ~ 1

Author(s):

Ganesh Adluru ◽

Liyong Chen ◽

Edward V.R. DiBella

Keyword(s):

Motion Estimation ◽

Free Breathing ◽

Perfusion Mri ◽

Cardiac Perfusion ◽

Mri Reconstruction

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Internal motion prediction using kernel density estimation and general canonical correlation model

2011 IEEE Nuclear Science Symposium Conference Record ◽

10.1109/nssmic.2011.6153713 ◽

2011 ◽

Cited By ~ 1

Author(s):

M. Alnowami ◽

E. Lewis ◽

K. Wells

Keyword(s):

Density Estimation ◽

Kernel Density Estimation ◽

Canonical Correlation ◽

Kernel Density ◽

Internal Motion ◽

Motion Prediction ◽

Correlation Model

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Internal Motion Estimation by Internal-external Motion Modeling for Lung Cancer Radiotherapy

Scientific Reports ◽

10.1038/s41598-018-22023-3 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 1

Author(s):

Haibin Chen ◽

Zichun Zhong ◽

Yiwei Yang ◽

Jiawei Chen ◽

Linghong Zhou ◽

...

Keyword(s):

Lung Cancer ◽

Motion Estimation ◽

Internal Motion ◽

Motion Modeling ◽

Cancer Radiotherapy

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Low-Dose PET Imaging of Tumors in Lung and Liver Regions Using Internal Motion Estimation

Diagnostics ◽

10.3390/diagnostics11112138 ◽

2021 ◽

Vol 11 (11) ◽

pp. 2138

Author(s):

Sang-Keun Woo ◽

Byung-Chul Kim ◽

Eun Kyoung Ryu ◽

In Ok Ko ◽

Yong Jin Lee

Keyword(s):

Motion Estimation ◽

Pet Imaging ◽

Molecular Sieves ◽

Motion Correction ◽

Low Dose ◽

Internal Motion ◽

High Dose ◽

Activity Group ◽

Quantification Analysis ◽

Low Activity

Motion estimation and compensation are necessary for improvement of tumor quantification analysis in positron emission tomography (PET) images. The aim of this study was to propose adaptive PET imaging with internal motion estimation and correction using regional artificial evaluation of tumors injected with low-dose and high-dose radiopharmaceuticals. In order to assess internal motion, molecular sieves imitating tumors were loaded with 18F and inserted into the lung and liver regions in rats. All models were classified into two groups, based on the injected radiopharmaceutical activity, to compare the effect of tumor intensity. The PET study was performed with injection of F-18 fluorodeoxyglucose (18F-FDG). Respiratory gating was carried out by external trigger device. Count, signal to noise ratio (SNR), contrast and full width at half maximum (FWHM) were measured in artificial tumors in gated images. Motion correction was executed by affine transformation with estimated internal motion data. Monitoring data were different from estimated motion. Contrast in the low-activity group was 3.57, 4.08 and 6.19, while in the high-activity group it was 10.01, 8.36 and 6.97 for static, 4 bin and 8 bin images, respectively. The results of the lung target in 4 bin and the liver target in 8 bin showed improvement in FWHM and contrast with sufficient SNR. After motion correction, FWHM was improved in both regions (lung: 24.56%, liver: 10.77%). Moreover, with the low dose of radiopharmaceuticals the PET image visualized specific accumulated radiopharmaceutical areas in the liver. Therefore, low activity in PET images should undergo motion correction before quantification analysis using PET data. We could improve quantitative tumor evaluation by considering organ region and tumor intensity.

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