Harmonized low‐dose computed tomographic protocols for quantitative lung imaging using dose modulation and advanced reconstructions
Bibliographic record
Abstract
BACKGROUND: Quantitative computed tomography (QCT) lung imaging is employed in many multi-center studies. Standardized protocols have used fixed volumetric CT dose index (CTDIvol) adjusted for body mass index to minimize dose while accounting for participant size. Dose modulation and iterative/deep-learning reconstruction (IR/DLR) offer new opportunities for QCT standardization for a multi-center protocol. PURPOSE: To develop harmonized reduced dose lung QCT protocols implementing dose modulation and IR/DLR in the context of the Precision Intervention for Severe Asthma (PrecISE) multi-center study. METHODS: A low-dose protocol was first developed on one state-of-the-art scanner having similar quantitative characteristics to a widely used standard-dose protocol as a reference for establishing harmonized protocols for a range of CT systems across four major manufacturers and ten sites. An anthropomorphic chest phantom with outer chest plates (LUNGMAN Chest Phantom, Kyoto; 43.5 cm left-right, 22.9 cm anterior-posterior) and custom inserts containing differently attenuating materials was imaged using varying dose modulation and IR/DLR settings. Hounsfield Unit (HU), standard deviations (SD), and coefficient of variation (CoV) in multiple density standards, including standardized foams, lung tissue, air, and water were compared for measures of accuracy, noise, and precision. The in-plane and z-direction modulation transfer functions (MTF) were also derived from a cubic insert. Purpose-built segmentation software (Pulmonary Analysis Software Suite, PASS) assured sampling of similar regions of interest. Final protocols included dose modulation-IR/DLR combinations yielding target low-dose CTDIvol, which minimized HU mean differences and SD, and maximized MTF compared to the reference-standard. RESULTS: The low-dose protocols achieved a mean CTDIvol reduction of 54% ± 7% (range 42%-70%) compared with the current standard-dose (SPIROMICS and MESALung). Compared to the reference-standard, mean HU difference was 12.0 ± 9.2 HU (range 0.4-28.5 HU) for air and 1.9 ± 1.3 HU (range 0.0-4.5 HU) for water inserts across the other nine low-dose protocols, and HU SD was lower in nine of ten low-dose protocols compared to standard-dose. HU CoV for all 10 low-dose protocols were near 0 for air and ranged 2.3-33.4 for water. MTF measurements were 2.71-4.22 and 4.02-7.13 cycles/cm for 50% and 20% cutoffs, respectively, compared with 3.17-3.50 and 4.68-5.54 cycles/cm for standard-dose. CONCLUSION: We provide harmonized low-dose QCT protocols using manufacturers' current dose modulation and IR/DLR techniques to reduce radiation dose by up to 70% and broadly maintain measurement accuracy and precision suitable for multi-center studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".