Development of lesion and organ negative cast modelling technique for quality assurance and optimization of nuclear medicine images
Bibliographic record
Abstract
Nuclear medicine imaging allows for a wide variety of data acquisition and image generation methods in the clinical setting. Imaging phantoms are routinely used to evaluate and optimize image quality and quantitative accuracy of features, but few phantoms realistically model the anatomy or heterogeneity of target regions within patient images, such as tumours that are commonly observed in oncology. We developed a negative cast modelling (NCM) technique which enables applications such as non-standard shape tumour phantoms, organ phantoms for radiation dosimetry, and quality control phantoms with small lesions. Tumour templates were derived from segmented PET images of primary mediastinal B-cell lymphoma (PMBCL) patients. Lesion segmentations were saved and 3D-printed. Negatives were developed using silicone-based molding materials, and final models cast using a composition of liquid plastic, pigment, and PET radiotracer. Images of lesions were acquired using the GE DMI PET/CT scanner, and image features were quantified. Mean absolute error (MAE) for tumour volume between the original template and casted models is 13.8%, indicating that the method is reasonably accurate. The high viscosity of the liquid plastic used in the casting process establishes non-uniform tumour models, which is very useful in practice for evaluating image features related to heterogeneity. PET images using the NCM method is determined to be highly realistic by an experienced nuclear medicine physician, due to the non-standard shapes that can be established within the tumours. The NCM method has potential to enable more realistic phantom studies within nuclear medicine imaging. The cost for the lymphoma tumour phantom study is less than $400 USD, making it feasible for large-scale studies. Radioactive substances are used to diagnose and treat disease. Special testing devices called phantoms are used to ensure that the scanners used to detect the radioactive substances work properly. These are usually simple shapes, such as spheres, which do not accurately represent the complexity of real human anatomy. In this study, we developed a technique that uses medical images from patients to create phantoms with more realistic shapes, such as irregular tumors, salivary glands, and very small lesions. These phantoms are made using a low-cost molding and casting process and can be reused. By improving how scanners are tested and optimized, this approach may lead to better diagnosis and treatment for conditions such as cancer. Fedrigo et al. present a technique to create realistic nuclear medicine phantoms from patient-derived anatomical contours using negative cast modelling (NCM). This method enables non-spherical tumor models, organ dosimetry replicas, and small-lesion phantoms, which supports advanced quality assurance and protocol optimization in nuclear medicine.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".