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Comparison of subject-to-template registration schemes using CT and MR radiotherapy images with brain lesions.

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Paper

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The authors' code

Shell · 49 lines · 2.6 KB · no license

  1. #!/bin/bash
  3. SELF_DIR=$(dirname "$(readlink -f "$0")")
  5. SUBJECTS_DIR=${SELF_DIR}/Nifti
  6. SEGMENTATIONS_DIR=${SELF_DIR}/Segmentations
  7. TEMPLATE_DIR=${SELF_DIR}/Template/
  8. MNI_DATA_DIR=${SELF_DIR}/MNI_data/
  9. REG_PAR_FILE_AFF=${SELF_DIR}/mirtk/mirtk-aff-dual.cfg
  10. SCHEME=Dual-Template-linear
  12. for sub_dir in ${SUBJECTS_DIR}/CoDe-B-Rad*; do
  13. sub_id=`basename ${sub_dir}`
  15. for timepoint in 'BL' '3M' '6M' '9M' '12M'; do
  16. if [[ -f "${sub_dir}/${timepoint}/CT.nii.gz" ]] && [[ -f "${sub_dir}/${timepoint}/MR.nii.gz" ]]; then
  17. out_dir=${MNI_DATA_DIR}/${sub_id}/${timepoint}
  18. if [[ ! -d ${out_dir} ]]; then
  19. mkdir -p ${out_dir}
  20. fi
  22. if [[ ! -f ${out_dir}/${SCHEME}.processed ]]; then
  23. echo -n "[Subject ${sub_id}] Affinely registering ${timepoint} CT and MR image to CT and MR templates simultaneously ... "
  24. mirtk register ${TEMPLATE_DIR}/template_sCT.nii.gz ${TEMPLATE_DIR}/template_T1w.nii.gz ${sub_dir}/${timepoint}/CT_in_MR_space.nii.gz ${sub_dir}/${timepoint}/MR.nii.gz -parin ${REG_PAR_FILE_AFF} -dofout ${out_dir}/${SCHEME}_CT_MR_to_Dual_template_aff.dof.gz -v 0
  25. echo "done"
  27. echo -n "[Subject ${sub_id}] Transforming ${timepoint} MR image to MR template ... "
  28. mirtk transform-image ${sub_dir}/${timepoint}/MR.nii.gz ${out_dir}/${SCHEME}_MR.nii.gz -target ${TEMPLATE_DIR}/template_T1w.nii.gz -dofin ${out_dir}/${SCHEME}_CT_MR_to_Dual_template_aff.dof.gz
  29. echo "done"
  31. echo -n "[Subject ${sub_id}] Transforming ${timepoint} CT image to MR template ... "
  32. mirtk transform-image ${sub_dir}/${timepoint}/CT_in_MR_space.nii.gz ${out_dir}/${SCHEME}_CT.nii.gz -target ${TEMPLATE_DIR}/template_T1w.nii.gz -dofin ${out_dir}/${SCHEME}_CT_MR_to_Dual_template_aff.dof.gz
  33. echo "done"
  35. echo -n "[Subject ${sub_id}] Transforming ${timepoint} RTDOSE_full image to MR template ... "
  36. mirtk transform-image ${sub_dir}/${timepoint}/RTDOSE_full_in_MR_space.nii.gz ${out_dir}/${SCHEME}_RTDOSE_full.nii.gz -target ${TEMPLATE_DIR}/template_T1w.nii.gz -dofin ${out_dir}/${SCHEME}_CT_MR_to_Dual_template_aff.dof.gz
  37. echo "done"
  39. echo -n "[Subject ${sub_id}] Transforming ${timepoint} MALPEM segmentation to MR template space ... "
  40. mirtk transform-image ${SEGMENTATIONS_DIR}/${sub_id}/${timepoint}/MR_MALPEM_lesion_masked.nii.gz ${out_dir}/${SCHEME}_MR_MALPEM_lesion_masked.nii.gz -target ${TEMPLATE_DIR}/template_T1w.nii.gz -dofin ${out_dir}/${SCHEME}_CT_MR_to_Dual_template_aff.dof.gz -interp NN
  41. echo "done"
  43. touch ${out_dir}/${SCHEME}.processed
  44. else
  45. echo "Scheme '${SCHEME}' data for timepoint '${timepoint}' of subject '${sub_id}' already exists. Skipping."
  46. fi
  47. fi
  48. done
  49. done

compute_MNI_data_Dual_Template_linear.sh, no license · at the source

Overview

Authors: Anna Bangiri1,2, Stefanie Thust1,2,3,4, Paul S Morgan1,2,3, Stefan Pszczolkowski1,3
ORCID iDs: Stefanie Thust
  1. School of Medicine, University of Nottingham, Nottingham, United Kingdom
  2. Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
  3. Nottingham NIHR Biomedical Research Centre, University of Nottingham, Nottingham, United Kingdom
  4. Department of Translational Neuroscience and Stroke, UCL Queen Square Institute of Neurology, London, United Kingdom
Institutions: Nottingham University Hospitals NHS Trust (United Kingdom); University of Nottingham (United Kingdom); Nottingham Biomedical Research Centre (United Kingdom); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom)
Journal: Frontiers in neuroscience, volume 20, article 1858490
Dates: received 17 April 2026; accepted 6 July 2026; published online 28 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1858490 · PMID 42582330 · PMCID PMC13457462 · OpenAlex W7171566683
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), human (organism)
Methods: Statistics, Machine learning
Keywords: CT, MRI, radiosurgery, radiotherapy, registration, voxel-based analysis
Topic: Advanced Radiotherapy Techniques (Radiation, Physics and Astronomy), according to OpenAlex
Funding: Wellcome Trust (223508/Z/21/Z)
Citations: not cited yet (Europe PMC); 44 references in the paper

Abstract

Introduction: Voxel-based analyses have been used more widely in radiotherapy in recent years. The purpose of this study is to compare eight different methods of registering images on to a template space for such analyses. A novel way, using both CT and MR data, is proposed.

Methods: CT and MR brain images from 85 participants in the CoDe-B-Rad study (NCT06466720) were registered on an age-specific template. The registration schemes used included sCT-Template (linear and non-linear); MR-Template (linear, non-linear, masked, and enantiomorphic); and Dual-Template (linear and non-linear). The registrations were compared qualitatively and quantitatively against the template MR images with scores ranging from 1 (lowest) to 5 (highest) and quantitatively (via Jaccard, ASD, and HD95).

Results: Qualitatively, the best registration scheme was the Dual-Template-non-linear registration, with 60 participants scoring above 4. The second best was the MR-Template-enantiomorphic, with 53 participants scoring above 4. Quantitatively, the Dual-Template-non-linear method outperformed on the mean (±SD) for the ASD and HD95, with 0.918 (±0.1774) and 2.965 (±0.5392) respectively. For ASD the difference compared to other methods was significant (p = 0.03). The MR-Template-masked outperformed on the Jaccard mean (±SD), median (IQR), and ASD, achieving values of 0.567 (±0.0557), 0.555 (0.055), and 0.808 (0.162) respectively. The Dual-Template-non-linear and MR-Template-masked and non-linear had the same result for the median HD95: 2.639. The performance of all linear schemes was inadequate both quantitatively and qualitatively. All non-linear registration schemes had issues with distortions of tissue and landmarks, however, for the Dual-Template scheme these were minimal.

Conclusion: The Dual-Template-non-linear registration scheme is a new way of registering lesioned brain images for use with voxel-wise techniques in radiotherapy, which utilises both CT and MR image data. The scheme provides fidelity of the underlying soft tissue as well as the surrounding skull, minimising anatomical and dosimetric distortions.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

OSF 5upq8

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Shell (10)
Size: 19 files, 10 scripts
Software Heritage: not checked
Found in: “Data availability statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
10 files
At the source: doi.org/10.17605/OSF.IO/5UPQ8

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 10 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The defaced image sets, along with the raw numerical data for the analysis and the scripts have been shared in a publicly available depository, available at: https://doi.org/10.17605/OSF.IO/5UPQ8.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 6 keywords, 1 funder, 37 references.

Cite

This paper

Bangiri, A., Thust, S., Morgan, P. S., & Pszczolkowski, S. (2026). Comparison of subject-to-template registration schemes using CT and MR radiotherapy images with brain lesions. Frontiers in neuroscience, 20, 1858490. https://doi.org/10.3389/fnins.2026.1858490

BibTeX

@article{bangiri2026comparison,
author = {Bangiri, Anna and Thust, Stefanie and Morgan, Paul S and Pszczolkowski, Stefan},
title = {{Comparison of subject-to-template registration schemes using CT and MR radiotherapy images with brain lesions}},
journal = {Frontiers in neuroscience},
year = {2026},
month = jul,
volume = {20},
pages = {1858490},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1858490},
url = {https://doi.org/10.3389/fnins.2026.1858490},
pmid = {42582330},
pmcid = {PMC13457462}
}

RIS

TY - JOUR
AU - Bangiri, Anna
AU - Thust, Stefanie
AU - Morgan, Paul S
AU - Pszczolkowski, Stefan
TI - Comparison of subject-to-template registration schemes using CT and MR radiotherapy images with brain lesions
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/07/28
VL - 20
SP - 1858490
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1858490
UR - https://doi.org/10.3389/fnins.2026.1858490
LA - en
ER -

CSL-JSON

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