4D-CT imaging of a volume influenced by respiratory motion on multi-slice CT
Bibliographic record
Abstract
We propose a new scanning protocol for generating 4D-CT image data sets influenced by respiratory motion. A cine scanning protocol is used during data acquisition, and two registration methods are used to sort images into temporal phases. A volume is imaged in multiple acquisitions of 1 or 2 cm length along the cranial-caudal direction. In each acquisition, the scans are continuously acquired for a time interval greater than or equal to the average respiratory cycle plus the duration of the data for an image reconstruction. The x ray is turned off during CT table translation and the acquisition is repeated until the prescribed volume is completely scanned. The scanning for 20 cm coverage takes about 1 min with an eight-slice CT or 2 mins with a four-slice CT. After data acquisition, the CT data are registered into respiratory phases based on either an internal anatomical match or an external respiratory signal. The internal approach registers the data according to correlation of anatomy in the CT images between two adjacent locations in consecutive respiratory cycles. We have demonstrated the technique with ROIs placed in the region of diaphragm. The external approach registers the image data according to an externally recorded respiratory signal generated by the Real-Time Position Management (RPM) Respiratory Gating System (Varian Medical Systems, Palo Alto, CA). Compared with previously reported prospective or retrospective imaging of the respiratory motion with a single-slice or multi-slice CT, the 4D-CT method proposed here provides (1) a shorter scan time of three to six times faster than the single-slice CT with prospective gating; (2) a shorter scan time of two to four times improvement over a previously reported multi-slice CT implementation, and (3) images over all phases of a breathing cycle. We have applied the scanning and registration methods on phantom, animal and patients, and initial results suggest the applicability of both the scanning and the registration methods.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".