Development of the Lymphatic System in the 4D XCAT Phantom for Improved Multimodality Imaging Research
Bibliographic record
Abstract
113 Objectives: Non-Hodgkin’s lymphoma classically presents with lymphadenopathy and bulky lymph node conglomerates. FDG PET/CT scans are used to stage and assess treatment response. Total metabolic tumour volume (TMTV) quantification has shown promising results for predicting therapy response and overall survival of lymphoma patients. However, TMTV accuracy can be impacted by the selected reconstruction parameters and segmentation method. Conventionally, the NEMA Image Quality phantom is used to evaluate image characteristics, but does not represent realistic patient anatomy or tumour properties. Simulated phantoms, such as the 4D extended cardiac-torso (XCAT) phantom used for multimodality imaging research, allows for more realistic patient modelling. The XCAT phantom defines the activity and attenuation for a simulated patient, which includes a complete set of organs, muscle, bone, soft tissue, while also accounting for age, sex, and body mass index (BMI), which allows phantom studies to be performed at a population scale. However, the XCAT phantom does not currently include the lymphatic system, critical for evaluating bulky nodal malignancies in lymphoma. The aim of this study was to incorporate a full lymphatic system into the XCAT phantom, and to generate realistic simulated PET/CT images via guidance from lymphoma patient studies, to enable lymphoma PET/CT optimization studies for improved image quality and quantification. Methods: A template lymphatic system model based on anatomical data from the Visible Human Project of the National Library of Medicine was used to define 276 lymph nodes and corresponding vessels using non-uniform rational basis spline (NURBS) surfaces. The multichannel large deformation diffeomorphic metric mapping (MC-LDDMM) method was used to propagate from the template phantom to different XCAT anatomies. This allows for the lymphatic system to be investigated on patients with different genders, weight, sizes, age, and other anatomical differences. Lymph node properties were modified using the Rhinoceros 3D viewing software. To determine typical activity concentrations observed in lymphoma, FDG PET/CT images of 5 patients from a cohort of PMBCL positive scans were analyzed using MIM (MIM Software, USA). The XCAT general parameter script was used to input organ concentrations and generate binary files with uptake and attenuation information. The phantom was used as the input to a MATLAB-based PET simulation and reconstruction tool (Ashrafinia et al., 2017) generating simulated PET/CT images for a GE Discovery RX scanner, reconstructed with OSEM (2 iterations, 24 subsets). Results: The lymphatic system was added to the XCAT phantom with the capability to select male/female anatomy or patient size. Lymph nodes can be scaled, asymmetrically stretched, and translated within the intuitive Rhinoceros interface, to allow for realistic simulation of different lymph node pathologies. Bulky, heterogeneous PMBCL tumours were generated in the mediastinum using expanded lymph nodes. Simulated PET images from the XCAT phantom were optimized to represent FDG PET/CT images of PMBCL patients and was assessed to be realistic by an experienced nuclear medicine clinician. Conclusions: An upgraded XCAT phantom with a fully-simulated lymphatic system was created. Realistic simulated PET/CT images were generated using the phantom with uptake values measured from real patient scans. Made publicly available, the XCAT phantom with the new lymphatic system has the potential of enabling studies to optimize image quality and quantitation, towards improved assessment of lymphoma including predictive modeling (e.g. improved TMTV and radiomics research).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".