Journal article
AAPM Truth-based CT (TrueCT) reconstruction grand challenge
Medical physics (Lancaster), Vol.52(4), pp.1978-1990
04/2025
DOI: 10.1002/mp.17619
PMCID: PMC11973969
PMID: 39807653
Abstract
This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction.
To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases.
Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions. The organs were modeled based on clinical CT images of real patients. The emphysematous regions were modeled using segmentations from patient CT cases in the COPDGene Phase I dataset. For the lung and liver lesion cases, 1-6 malignant lesions were created and inserted into the human models, with lesion diameters ranging from 5.6 to 21.9 mm for lung lesions and 3.9 to 14.9 mm for liver lesions. The contrast defined between the liver lesions and liver parenchyma was 82 ± 12 HU, ranging from 50 to 110 HU. Similarly, the contrast between the lung lesions and the lung parenchyma was defined as 781 ± 11 HU, ranging from 725 to 805 HU. For the emphysematous regions, the defined HU values were -950 ± 17 HU ranging from -918 to -979 HU. The developed human models were imaged with a validated CT simulator. The resulting CT sinograms were shared with the participants. The participants reconstructed CT images from the sinograms and sent back their reconstructed images. The reconstructed images were then scored by comparing the results against the corresponding ground truth values. The scores included both task-generic (root mean square error [RMSE] and structural similarity matrix [SSIM]), and task-specific (detectability index [d'] and lesion volume accuracy) metrics. For the cases with multiple lesions, the measured metric was averaged across all the lesions. To combine the metrics with each other, each metric was normalized to a range of 0 to 1 per disease type, with "0" and "1" being the worst and best measured values across all cases of the disease type for all received reconstructions.
The True-CT challenge attracted 52 participants, out of which 5 successfully completed the challenge and submitted the requested 200 reconstructions. Across all participants and disease types, SSIM absolute values ranged from 0.22 to 0.90, RMSE from 77.6 to 490.5 HU, d' from 0.1 to 64.6, and volume accuracy ranged from 1.2 to 753.1 mm
. The overall scores demonstrated that participant "A" had the best performance in all categories, except for the metrics of d' for lung lesions and RMSE for liver lesions. Participant "A" had an average normalized score of 0.41 ± 0.22, 0.48 ± 0.32, and 0.42 ± 0.33 for the emphysema, lung lesion, and liver lesion cases, respectively.
The True-CT challenge successfully enabled objective assessment of CT reconstructions with the unique advantage of access to a diverse population of diseased human models with known ground truth. This study highlights the significant potential of virtual imaging trials in objective assessment of medical imaging technologies.
Details
- Title: Subtitle
- AAPM Truth-based CT (TrueCT) reconstruction grand challenge
- Creators
- Ehsan Abadi - Duke UniversityW Paul Segars - Duke UniversityNicholas Felice - Duke UniversitySaman Sotoudeh-Paima - Duke UniversityEric A Hoffman - University of IowaXiao Wang - Oak Ridge National LaboratoryWei Wang - Institute of Applied MathematicsDarin Clark - Duke UniversitySiqi Ye - Stanford UniversityGiavanna Jadick - University of ChicagoMilo Fryling - Duke UniversityDonald P Frush - Duke UniversityEhsan Samei - Duke University
- Resource Type
- Journal article
- Publication Details
- Medical physics (Lancaster), Vol.52(4), pp.1978-1990
- DOI
- 10.1002/mp.17619
- PMID
- 39807653
- PMCID
- PMC11973969
- NLM abbreviation
- Med Phys
- ISSN
- 0094-2405
- eISSN
- 2473-4209
- Publisher
- WILEY
- Grant note
- P41EB028744 / NIH HHS R01HL155293 / NIH HHS R01EB001838 / NIH HHS
- Language
- English
- Electronic publication date
- 01/14/2025
- Date published
- 04/2025
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
- Record Identifier
- 9984774235602771
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