Development of the Lymphatic System in the 4D XCAT Phantom for Improved Multimodality Imaging Research
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».