Performance Evaluation of a High-Sensitivity Organ-Targeted Positron Emission Tomography (PET) System for Small Lesion Detection and Quantitative Imaging
Bibliographic record
Abstract
In this study, we evaluate the imaging performance of the Radialis PET camera, a high-sensitivity organ-targeted Positron Emission Tomography (PET) system, using standardized and custom tests previously used for Positron Emission Mammography (PEM) systems. We assess the imaging characteristics related to standardized uptake value (SUV) and detectability of small lesions, including spatial resolution, linearity, uniformity, and recovery coefficients. In-plane spatial resolution of 2.3 mm ± 0.1 mm, spatial accuracy of 0.1 mm, and uniformity measured with flood field and NEMA NU-4 phantom of 11.7% and 8.3%, respectively are reported. Recovery coefficients were measured to be 0.21 for the 1 mm hot rod and up to 0.89 for the 5 mm hot rod of NEMA NU-4 phantom, indicating the ability of Radialis PET camera to accurately reconstruct activity in tumors as small as 5 mm.Radialis PET camera provides an improved contrast recovery and spill-over ratio compared to other organ-dedicated PET systems with similar spatial resolution. We relate this improvement to optimized count rate performance and image reconstruction workflow. Presented clinical images demonstrate the imaging capabilities of the system under different conditions, such as reduced 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (18F-FDG) activity and time-delayed acquisitions. SUV measurements in clinical images show that the Radialis PET camera can provide accurate quantitative assessment for different types of cancer, including invasive lobular carcinoma with low metabolic activity.Due to the improved accuracy of tumor activity evaluation, the Radialis PET camera may be well-suited for emerging clinical applications, such as image-guided assessment of response to neoadjuvant systemic treatment (NST) in lesions smaller than 2 cm. The study also highlights the importance of recovery coefficient as a primary performance metric when targeting PET systems for accurate lesion size and radiotracer uptake assessment.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".