Statistical Motion Mask and Sliding Registration

Eiben, Björn; Tran, Huong Elena; Menten, Martin J.; Oelfke, Uwe; Hawkes, David J.; McClelland, Jamie R.

doi:10.1007/978-3-319-92258-4_2

Cited by 6 publications

(12 citation statements)

References 13 publications

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“…When combining the two fields (external and internal) however, gaps and overlaps that occur at the boundary of the two regions, for example, due to sliding boundaries have to be corrected 17 (⑦). For this, we use a similar method as previously proposed for the XCAT phantoms 18,19 .…”

Section: Methodsmentioning

confidence: 99%

Technical note: Towards more realistic 4DCT(MRI) numerical lung phantoms

et al. 2023

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BackgroundNumerical 4D phantoms, together with associated ground truth motion, offer a flexible and comprehensive data set for realistic simulations in radiotherapy and radiology in target sites affected by respiratory motion.PurposeWe present an openly available upgrade to previously reported methods for generating realistic 4DCT lung numerical phantoms, which now incorporate respiratory ribcage motion and improved lung density representation throughout the breathing cycle.MethodsDensity information of reference CTs, toget her with motion from multiple breathing cycle 4DMRIs have been combined to generate synthetic 4DCTs (4DCT(MRI)s). Inter‐subject correspondence between the CT and MRI anatomy was first established via deformable image registration (DIR) of binary masks of the lungs and ribcage. Ribcage and lung motions were extracted independently from the 4DMRIs using DIR and applied to the corresponding locations in the CT after post‐processing to preserve sliding organ motion. In addition, based on the Jacobian determinant of the resulting deformation vector fields, lung densities were scaled on a voxel‐wise basis to more accurately represent changes in local lung density. For validating this process, synthetic 4DCTs, referred to as 4DCT(CT)s, were compared to the originating 4DCTs using motion extracted from the latter, and the dosimetric impact of the new features of ribcage motion and density correction were analyzed using pencil beam scanned proton 4D dose calculations.ResultsLung density scaling led to a reduction of maximum mean lung Hounsfield units (HU) differences from 45 to 12 HU when comparing simulated 4DCT(CT)s to their originating 4DCTs. Comparing 4D dose distributions calculated on the enhanced 4DCT(CT)s to those on the original 4DCTs yielded 2%/2 mm gamma pass rates above 97% with an average improvement of 1.4% compared to previously reported phantoms.ConclusionsA previously reported 4DCT(MRI) workflow has been successfully improved and the resulting numerical phantoms exhibit more accurate lung density representations and realistic ribcage motion.

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Section: Methodsmentioning

confidence: 99%

Technical note: Towards more realistic 4DCT(MRI) numerical lung phantoms

et al. 2023

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“…Deformable image registration was applied to the motion images of each dataset to obtain motion measurements of the internal anatomy. We used an extension of the open-source software NiftyReg 4 4 https://github.com/KCL-BMEIS/niftyreg which can account for sliding motion (Eiben et al 2018 ). NiftyReg is based on the fast free-form deformation algorithm with the cubic B-splines transformation model defined on a control point grid (Modat et al 2010 ).…”

Section: Methodsmentioning

confidence: 99%

“…NiftyReg is based on the fast free-form deformation algorithm with the cubic B-splines transformation model defined on a control point grid (Modat et al 2010 ). Full details of the modifications that allow for sliding motion can be found in Eiben et al ( 2018 ), and only a brief summary is given here. The source (moving) image is segmented into two regions that can move independently and hence slide past each other, with a separate transformation used for each region.…”

Section: Methodsmentioning

confidence: 99%

“…The interpolated surrogate signals’ values of the test set were used as input for each motion model to yield motion estimates. The model estimated the CPD m for both sliding regions, from which we calculated the deformation vector fields (DVF) defined at each pixel location (Eiben et al 2018 ). Then, we computed the deformation field error (DFE) defined as the L2-norm difference between the model estimated DVF and the DVF provided by image registration.…”

Section: Methodsmentioning

confidence: 99%

“…Both sagittal and coronal slices were used to assess the ability to model motion in all 3 spatial directions. The 2D motion at each time point was estimated using a deformable image registration algorithm that can preserve sliding motion (Eiben et al 2018 ), and used to build and evaluate the models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of MRI-derived surrogate signals to model respiratory motion

Tran

Eiben

Wetscherek

et al. 2020

Biomed. Phys. Eng. Express

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An MR-Linac can provide motion information of tumour and organs-at-risk before, during, and after beam delivery. However, MR imaging cannot provide real-time high-quality volumetric images which capture breath-to-breath variability of respiratory motion. Surrogate-driven motion models relate the motion of the internal anatomy to surrogate signals, thus can estimate the 3D internal motion from these signals. Internal surrogate signals based on patient anatomy can be extracted from 2D cine-MR images, which can be acquired on an MR-Linac during treatment, to build and drive motion models. In this paper we investigate different MRI-derived surrogate signals, including signals generated by applying principal component analysis to the image intensities, or control point displacements derived from deformable registration of the 2D cine-MR images. We assessed the suitability of the signals to build models that can estimate the motion of the internal anatomy, including sliding motion and breath-to-breath variability. We quantitatively evaluated the models by estimating the 2D motion in sagittal and coronal slices of 8 lung cancer patients, and comparing them to motion measurements obtained from image registration. For sagittal slices, using the first and second principal components on the control point displacements as surrogate signals resulted in the highest model accuracy, with a mean error over patients around 0.80 mm which was lower than the in-plane resolution. For coronal slices, all investigated signals except the skin signal produced mean errors over patients around 1 mm. These results demonstrate that surrogate signals derived from 2D cine-MR images, including those generated by applying principal component analysis to the image intensities or control point displacements, can accurately model the motion of the internal anatomy within a single sagittal or coronal slice. This implies the signals should also be suitable for modelling the 3D respiratory motion of the internal anatomy.

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Consistent and invertible deformation vector fields for a breathing anthropomorphic phantom: a post-processing framework for the XCAT phantom

et al. 2020

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Breathing motion is challenging for radiotherapy planning and delivery. This requires advanced four-dimensional (4D) imaging and motion mitigation strategies and associated validation tools with known deformations. Numerical phantoms such as the XCAT provide reproducible and realistic data for simulation-based validation. However, the XCAT generates partially inconsistent and non-invertible deformations where tumours remain rigid and structures can move through each other. We address these limitations by post-processing the XCAT deformation vector fields (DVF) to generate a breathing phantom with realistic motion and quantifiable deformation. An open-source post-processing framework was developed that corrects and inverts the XCAT-DVFs while preserving sliding motion between organs. Those post-processed DVFs are used to warp the first XCAT-generated image to consecutive time points providing a 4D phantom with a tumour that moves consistently with the anatomy, the ability to scale lung density as well as consistent and invertible DVFs. For a regularly breathing case, the inverse consistency of the DVFs was verified and the tumour motion was compared to the original XCAT. The generated phantom and DVFs were used to validate a motion-including dose reconstruction (MIDR) method using isocenter shifts to emulate rigid motion. Differences between the reconstructed doses with and without lung density scaling were evaluated. The post-processing framework produced DVFs with a maximum 95 t h -percentile inverse-consistency error of 0.02 mm. The generated phantom preserved the dominant sliding motion between the chest wall and inner organs. The tumour of the original XCAT phantom preserved its trajectory while deforming consistently with the underlying tissue. The MIDR was compared to the ground truth dose reconstruction illustrating its limitations. MIDR with and without lung density scaling resulted in small dose differences up to 1 Gy (prescription 54 Gy). The proposed open-source post-processing framework overcomes important limitations of the original XCAT phantom and makes it applicable to a wider range of validation applications within radiotherapy.

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Statistical Motion Mask and Sliding Registration

Cited by 6 publications

References 13 publications

Technical note: Towards more realistic 4DCT(MRI) numerical lung phantoms

Technical note: Towards more realistic 4DCT(MRI) numerical lung phantoms

Evaluation of MRI-derived surrogate signals to model respiratory motion

Consistent and invertible deformation vector fields for a breathing anthropomorphic phantom: a post-processing framework for the XCAT phantom

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