CT acquisition using PET detectors and electronics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The emergence of positron emission tomography/computerized tomography (PET/CT) multimodality imaging has provided the ability to sequentially obtain anatomic and functional information using adjacent PET and CT scanners without having to move the patient from the bed. To avoid the need for successive PET and CT scans, we have investigated the possibility of acquiring both the anatomic and functional images using the same detection system, based on PET detectors and electronics operated in photon-counting mode. The detector consisted of a high-luminosity LSO scintillator individually coupled to an avalanche photodiode (APD) to enable low-energy X-ray detection at a high-count rate. A simulator was set up to collect tomographic data using a monochromatic 60 keV source (/sup 241/Am) to irradiate a phantom made of tissue-equivalent materials. The observed spatial resolution with this nonoptimized setup was better than 2 mm, demonstrating the capability to provide fairly accurate anatomical localization in CT counting mode. The three main constituents of biological tissues (bones, water, and air) could be clearly identified in the images with a dose significantly lower than with conventional CT operated in current mode. These preliminary results demonstrate the feasibility of dual-modality PET/CT imaging based on PET detectors and electronics, and suggest that substantial dose reduction would be possible by acquiring the CT image in photon-counting mode.
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.
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 | 0.000 | 0.000 |
| 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 it