Spatiotemporal motion estimation for respiratory‐correlated imaging of the lungs

Vandemeulebroucke, Jef; Rit, Simon; Kybic, Jan; Clarysse, Patrick; Sarrut, David

doi:10.1118/1.3523619

Cited by 139 publications

(128 citation statements)

References 51 publications

(1 reference statement)

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“…Thus, 36 pairs of nonrigid registrations were performed for the total four data sets, resulting in 108 registrations using our approach and the normalized cross correlation (NCC) and MI methods. The deformation registration accuracy was evaluated using the target registration error (TRE) (Vandemeulebroucke et al 2011, Vandemeulebroucke et al 2012 calculated as the 3D Euclidean distance between the manually marked landmarks in the maximum exhalation image and the ones estimated by exploiting the registration approach to the corresponding location in the inhale image (Castillo et al 2010). Figure 11 illustrates the box-and-whisker plots of TREs for 108 registrations.…”

Section: Real Data Experimentsmentioning

confidence: 99%

3D nonrigid medical image registration using a new information theoretic measure

Li¹,

Yang²,

Coatrieux³

et al. 2015

Phys. Med. Biol.

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Section: Real Data Experimentsmentioning

confidence: 99%

3D nonrigid medical image registration using a new information theoretic measure

Li¹,

Yang²,

Coatrieux³

et al. 2015

Phys. Med. Biol.

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“…These datasets were collected from different sources. Datasets 1-5 were acquired using a helical 4D CT system located at the Lon Brard Cancer Center [8]. Datasets 6-8 were acquired on a cine 4D CT system located in the MD Anderson Cancer Center [9].…”

Section: A Methodologymentioning

confidence: 99%

A comparison between adaptive kernel density estimation and Gaussian Mixture Regression for real-time tumour motion prediction from external surface motion

Tahavori

Alnowami

Wells

2012

2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC)

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Abstract-In this present study, tumour (3D) locations are predicted via external surface motion, extracted from abdomen/thoracic surface measurements that can be used to enhance dose targeting in external beam radiotherapy. Canonical Correlation Analysis (CCA) is applied to the surface and tumour motion data to maximise the correlation between them. This correlation is exploited for motion prediction [1]. Nine dynamic CT datasets were used to extract the surface and tumour motion and to create the Canonical Correlation model (CCM). Gaussian Mixture Regression (GMR) and Adaptive Kernel Density Estimation (AKDE) were trained on these nine datasets to predict the respiratory signal by updating the surface motion and CCM. A leave-one-out method was used to evaluate and compare the performance of GMR and AKDE in predicting the tumour motion.

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“…The model describes tumor motion due to respiration over the entire breathing cycle and is driven by an external surface surrogate used for breathing phase identification. Breathing motion models can also be extracted from planning cine‐CT (26) or four‐dimensional (4D) CT (27) and then adapted to interfraction baseline variations based on daily CBCT imaging (28) …”

Section: Introductionmentioning

confidence: 99%

An image‐based method to synchronize cone‐beam CT and optical surface tracking

Fassi

Schaerer

Riboldi

et al. 2015

J Applied Clin Med Phys

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The integration of in‐room X‐ray imaging and optical surface tracking has gained increasing importance in the field of image guided radiotherapy (IGRT). An essential step for this integration consists of temporally synchronizing the acquisition of X‐ray projections and surface data. We present an image‐based method for the synchronization of cone‐beam computed tomography (CBCT) and optical surface systems, which does not require the use of additional hardware. The method is based on optically tracking the motion of a component of the CBCT/gantry unit, which rotates during the acquisition of the CBCT scan. A calibration procedure was implemented to relate the position of the rotating component identified by the optical system with the time elapsed since the beginning of the CBCT scan, thus obtaining the temporal correspondence between the acquisition of X‐ray projections and surface data. The accuracy of the proposed synchronization method was evaluated on a motorized moving phantom, performing eight simultaneous acquisitions with an Elekta Synergy CBCT machine and the AlignRT optical device. The median time difference between the sinusoidal peaks of phantom motion signals extracted from the synchronized CBCT and AlignRT systems ranged between ‐3.1 and 12.9 msec, with a maximum interquartile range of 14.4 msec. The method was also applied to clinical data acquired from seven lung cancer patients, demonstrating the potential of the proposed approach in estimating the individual and daily variations in respiratory parameters and motion correlation of internal and external structures. The presented synchronization method can be particularly useful for tumor tracking applications in extracranial radiation treatments, especially in the field of patient‐specific breathing models, based on the correlation between internal tumor motion and external surface surrogates.PACS number: 87

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Spatiotemporal motion estimation for respiratory‐correlated imaging of the lungs

Cited by 139 publications

References 51 publications

3D nonrigid medical image registration using a new information theoretic measure

3D nonrigid medical image registration using a new information theoretic measure

A comparison between adaptive kernel density estimation and Gaussian Mixture Regression for real-time tumour motion prediction from external surface motion

An image‐based method to synchronize cone‐beam CT and optical surface tracking

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