example_data/ holds a small real patient study (137 MB) so the viewer
can be exercised on clinical data, not only the synthetic phantom — two
breathing phases of a 4DCT, each with its own RT Structure Set:
example_data/
lung_p1_4DCT_phase_000/ 133 CT slices + 1-1.dcm (RTSTRUCT, 13 ROIs)
lung_p1_4DCT_phase_050/ 133 CT slices + 1-1.dcm (RTSTRUCT, 12 ROIs)
512 × 512, 0.977 mm in-plane, 3 mm slices, 396 mm of coverage. ROIs are
cord, both lungs, heart, esophagus, carina, lymph node (LN), tumor and
four implanted gold fiducial markers, plus a vertebra contour that exists
only on phase 000 (_c00 / _c50 suffix = breathing phase). Both series
share one Study Instance UID and one Frame of Reference, so they load as
inhale/exhale of the same study:
cargo run --release -- example_data/lung_p1_4DCT_phase_000 example_data/lung_p1_4DCT_phase_050
That is a ready-made comparison-mode and registration test case with real
respiratory motion: the tumor and markers move visibly between the phases,
and the deformable methods of the Image registration module have
something anatomically real to recover. Equivalently, load the whole
example_data/ folder as dataset A (both phases appear as two series of
one study) and right-click one phase ▶ Copy series to dataset B. It is
also the dataset the auto-segmentation was validated on
(auto-segmentation.md).
The data is patient P102 from the public 4D-Lung collection on The Cancer Imaging Archive (TCIA), a longitudinal 4D fan-beam CT / 4D cone-beam CT dataset of 20 locally advanced NSCLC patients treated with chemoradiotherapy:
https://www.cancerimagingarchive.net/collection/4d-lung/
It is redistributed here under CC BY 3.0, the license of the original collection. If you use it, cite the data and the associated publications:
Data. Hugo, G. D., Weiss, E., Sleeman, W. C., Balik, S., Keall, P. J., Lu, J., & Williamson, J. F. (2016). Data from 4D Lung Imaging of NSCLC Patients (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.ELN8YGLE
Publication. Hugo, G. D., Weiss, E., Sleeman, W. C., Balik, S., Keall, P. J., Lu, J., & Williamson, J. F. (2017). A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer. Medical Physics, 44(2), 762–771. https://doi.org/10.1002/mp.12059
TCIA. Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-6
The TCIA data is already de-identified; the copy here was additionally
rewritten to minimal, readable identifiers — patient lung_p1, and a UID
tree that is easy to read in a debugger:
| phase_000 | phase_050 | |
|---|---|---|
| CT series | 1.2.3.4.5.10 |
1.2.3.4.5.20 |
| CT slices | 1.2.3.4.5.10.<InstanceNumber> |
1.2.3.4.5.20.<InstanceNumber> |
| RTSTRUCT series / instance | 1.2.3.4.5.11 / .11.1 |
1.2.3.4.5.21 / .21.1 |
with 1.2.3.4.5.1 as the shared Study Instance UID and 1.2.3.4.5.2 as
the shared Frame of Reference UID. Everything not needed to render the
images and contours — accession number, device manufacturer and model,
software versions, acquisition dates and private tags — was dropped; pixel
data, geometry, ROI names, colors, types and contour points are untouched,
and every RTSTRUCT image reference still resolves to a slice of its own
series. The built-in anonymizer (Tools ▶ 🔏 Anonymize DICOM folder…, see
export-and-tools.md) does the same to any folder.