Variable Lung Density Consideration in Attenuation Correction of Whole-Body PET/MRI
Bibliographic record
Abstract
UNLABELLED: Present attenuation-correction algorithms in whole-body PET/MRI do not consider variations in lung density, either within or between patients; this may adversely affect accurate quantification. In this work, a technique to incorporate patient-specific lung density information into MRI-based attenuation maps is developed and compared with an approach that assumes uniform lung density. METHODS: Five beagles were scanned with (18)F-FDG PET/CT and MRI. The relationship between MRI and CT signal in the lungs was established, allowing the prediction of attenuation coefficients from MRI. MR images were segmented into air, lung, and soft tissue and converted into attenuation maps, some with constant lung density and some with patient-specific lung densities. The resulting PET images were compared by both global metrics of quantitative fidelity (accuracy, precision, and root mean squared error) and locally with relative error in volumes of interest. RESULTS: A linear relationship was established between MRI and CT signal in the lungs. Constant lung density attenuation maps did not perform as well as patient-specific lung density attenuation maps, regardless of what constant density was chosen. In particular, when attenuation maps with patient-specific lung density were used, precision, accuracy, and root mean square error improved in lung tissue. In volumes of interest placed in the lungs, relative error was significantly reduced from a minimum of 12% to less than 5%. The benefit extended to tissues adjacent to the lungs but became less important as distance from the lungs increased. CONCLUSION: A means of using MRI to infer patient-specific attenuation coefficients in the lungs was developed and applied to augment whole-body MRI-based attenuation maps. This technique has been shown to improve the quantitative fidelity of PET images in the lungs and nearby tissues, compared with an approach that assumes uniform lung density.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".