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oai:figshare.com:article/27812643 2024-11-19T13:51:20Z category_30190 portal_549 item_type_2 month_year_11_2024
10.5522/04/27812643.v1 https://figshare.com/articles/media/Animations_and_captions_relating_to_the_registration_of_healthy_volunteer_free-breathing_dynamic_lung_OE-MRI_data_Supplementary_thesis_material_/27812643 https://ndownloader.figshare.com/files/50576076 https://ndownloader.figshare.com/files/50576073 https://ndownloader.figshare.com/files/50576082 https://ndownloader.figshare.com/files/50576079 https://ndownloader.figshare.com/files/50576085 https://ndownloader.figshare.com/files/50576091 https://ndownloader.figshare.com/files/50576088 Needleman, Sarah Sarah Needleman <![CDATA[Animations and captions relating to the registration of healthy volunteer free-breathing dynamic lung OE-MRI data. Supplementary thesis material.]]> Medical physics OE-MRI registration dynamic 2024-11-19 2024-11-19 Media 2024 University College London

Animations to illustrate the performance of different registration software applied to dynamic free-breathing lung oxygen-enhanced MRI (OE-MRI) data. NiftyReg and ANTs registration software were implemented.

See the Animations Captions PDF document for detailed descriptions of the registered image series.


NiftyReg registration:

M. Modat et al. “Fast free-form deformation using graphics processing units". In: Computer Methods and Programs in Biomedicine 98.3 (2010), pp. 278-284. ISSN: 01692607. DOI: 10.1016/j.cmpb.2009.09.002.

M. Modat et al. “Parametric non-rigid registration using a stationary velocity field". In: 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. 2012, pp. 145-150. DOI: 10.1109/MMBIA.2012.6164745.

Details of the NiftyReg registration implementation:

Multi-resolution levels

3

Control point grid spacing

3 voxels, 14.1 mm final spacing

Similarity metric

Locally normalised cross-correlation

Penalty terms and weights

Bending energy, 5 x10-3

Linear elastic energy, 1 x 10-2

Transformation parameterisation

Stationary velocity field

Maximum number of iterations per resolution level

600, 300, 150



ANTs registration:

B. B. Avants et al. “Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain." In: Medical image analysis 12.1 (2008), pp. 26-41. ISSN: 1361-8423 (Electronic). DOI: 10.1016/j.media.2007.06.004.

B. B. Avants et al. “A reproducible evaluation of ANTs similarity metric performance in brain image registration". In: NeuroImage 54.3 (2011), pp. 2033-2044. ISSN: 1053-8119. DOI: https://doi.org/10.1016/j.neuroimage.2010.09.025.

N. Tustison and B. Avants. “Explicit B-spline regularization in diffeomorphic image registration". In: Frontiers in Neuroinformatics 7 (2013). DOI: 10.3389/fninf.2013.00039.

B. B. Avants et al. “The Insight ToolKit image registration frame-work". In: Frontiers in Neuroinformatics 8 (2014). DOI: 10.3389/fninf.2014.00044.

Details of the ANTs registration implementation:

Multi-resolution levels

4

Smoothing parameters

2 x 2 x 0 x 0 mm

Gradient step

0.1

Update field variance in voxel space

3

Total field variance in voxel space

0

Update field knot spacing

26 mm

Similarity metric

Cross-correlation

Metric radius

2

Metric weight

1

Convergence parameter

500 x 200 x 100 x 50



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