Fabrication and characterization of polymer gel for MRI phantom with embedded lesion particles
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) is a medical imaging technique used in radiology to visualize the detailed internal structure and body soft tissues in complete 3D image. MRI performs best when optimal imaging parameters such as contrast, signal to noise ratio (SNR), spatial resolution and total scan time are utilized. However, due to a variety of imaging parameters that differ with the manufacturer, a calibration medium that allows the control of these parameters is necessary. Therefore, a phantom that behaves similar to human soft tissue is developed to replace a real human. Polymer gel is novel material that has great potential in the medical imaging. Since very few have focused on examining the behavior of polymer lesions, the motivation of this study is to develop a polymer gel phantom, especially for liver, with embedded lesions. Both the phantom and lesions should be capable of reflecting T1 and T2 relaxation values through various characterization processes. In this paper, phantom and lesion particles were fabricated with carrageenan as a gelling agent by physical aggregation. Agar was used as supplementary gelling agent and T2 modifier and Gd-DTPA as T1 modifier. The polymer gel samples were fabricated by varying the concentrations of the gelling agent, and T1 and T2 modifiers. The lesion particles were obtained by extracting molten polymer gel solution in chilled oil bath to obtain spherical shape. The polymer gel properties including density, elastic modulus, dielectric constant and optical properties were measured to compare with human tissue values for long period of time.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".