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Record W2006548362 · doi:10.1118/1.4794504

Distribution of lung tissue hysteresis during free breathing

2013· article· en· W2006548362 on OpenAlexaff
B White, Tianyu Zhao, James Lamb, Sara Wuenschel, Jeffrey D. Bradley, Issam El Naqa, Daniel A. Low

Bibliographic record

VenueMedical Physics · 2013
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsMcGill University
FundersNational Cancer InstituteNational Institutes of Health
KeywordsBreathingDistribution (mathematics)MedicineLungBiomedical engineeringNuclear medicineMathematicsInternal medicineAnesthesiaMathematical analysis

Abstract

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PURPOSE: To characterize and quantify free breathing lung tissue motion distributions. METHODS: Forty seven patient data sets were acquired using a 4DCT protocol consisting of 25 ciné scans at abutting couch positions on a 16-slice scanner. The tidal volume of each scan was measured by simultaneously acquiring spirometry and an abdominal pneumatic bellows. The concept of a characteristic breath was developed to manage otherwise natural breathing pattern variations. The characteristic breath was found by first dividing the breathing traces into individual breaths, from maximum exhalation to maximum exhalation. A linear breathing drift model was assumed and the drift removed for each breath. Breaths that exceeded one standard deviation in period or amplitude were removed from further analysis. A characteristic breath was defined by normalizing each breath to a common amplitude, aligning the peak inhalation times for all of the breaths, and determining the average time at each tidal volume, keeping inhalation and exhalation separate. Breathing motion trajectories were computed using a previously published five-dimensional lung tissue trajectory model which expresses the position of internal lung tissue, X, as: X(v,f:X0)=X0+α(X0)v+β(X0)f, where X0 is the internal lung tissue position at zero tidal volume and zero airflow, the scalar values v and f are the measured tidal volume and airflow, respectively, and the vectors α and β are fitted free parameters. In order to characterize the motion patterns, the trajectory elongations were examined throughout the subject's lungs. Elongation was defined here by generating a rectangular bounding box with one side parallel to the α vector and the box oriented in the plane defined by the α and β motion vectors. Hysteresis motion was defined as the ratio of the box dimensions aligned orthogonal to and parallel to the α vector. The 15th and 85th percentile of the elongation were used to characterize tissue trajectory hysteresis. RESULTS: The 15th and 85th percentile bounding box elongations were 0.090 ± 0.005 and 0.083 ± 0.013 in the upper left lung and 0.187 ± 0.037 and 0.203 ± 0.053, in the lower left lung. The 15th and 85th percentiles for the upper right lung were 0.092 ± 0.006 and 0.085 ± 0.013, and 0.184 ± 0.038, and 0.196 ± 0.043 in the lower right lung. Both percentiles were calculated for tidal volume displacements between 5 and 15 mm. In the left lung, the average elongations in the upper and lower lung were ζ=0.120 ± 0.064 and ζ=0.090 ± 0.055, respectively. The average elongations in the upper and lower right lung were ζ=0.107 ± 0.060 and ζ=0.082 ± 0.048, respectively. The elongation varied smoothly throughout the lungs. CONCLUSIONS: The hysteresis motion was relatively small compared to the volume-filling motion, contributing between 8% and 20% of the overall motion. Statistically significant differences were observed in the range of hysteresis contribution for upper and lower lung regions. The characteristic breath process provided an excellent method for defining an average breath. The characteristic breath had continuous tidal volume and airflow characteristics when the breath was continuously repeated,useful for generating patterns representative of realistic motion for breathing motion studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.256
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2013
Admission routes1
Has abstractyes

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