Development and evaluation of a novel organ-specific biomechanics-guided contrast-enhanced CT volume synthesis model

Objective.Contrast-EnhancedComputedTomography (CECT) is a critical medical imaging modality, yet acquiring and annotating such datasets remains time-consuming. Generative models show potential in augmenting datasets, but existing methods mainly focus on single-organ CECT with small deformations and struggle to generate diverse data with large deformations.We aim to propose a novel biomechanics-guided CECT volume synthesizing model for generating deformed CECT volumes, and evaluate the effectiveness of deformation-augmented CECT datasets for downstream tasks.Approach. First, we develop a biomechanics-guided deformable CECT volume synthesizing framework using deformation as input to aConditionalGenerativeAdversarialNetworks, and using sequential deformations to further generate temporally consistent deformed CECT volumes. Second, we propose a module for transition region generation and contrast adjustment in CECT. Third, we trained the deformable synthesis model on liver and kidney CECT datasets and used it for dataset augmentation. The synthesized CECT volumes fidelity was verified through qualitative and quantitative tests. The augmented dataset's effectiveness was evaluated for downstream tasks, including segmentation and multi-organ deformable image registration.Main Results. For image fidelity, the meanDiceSimilarityCoefficient (DSC) andStructuralSimilarityIndexMeasure for the synthesized CECT volumes continuity are 0.838 and 0.988, higher than the real CT volumes. Our method outperforms existing approaches in comparative experiments. The specificity and sensitivity in radiologist Turing test are 47.5%and 48.0%. Comparison between deformedex vivoporcine liver CT and synthesized CECT shows the model generates realistic deformed CT. In segmentation, model on augmented datasets achieves a mean mAP@50 scores of 0.641, outperforming 0.399 without augmentation. In deformable image registration, DSC improves by 7%as the augmented training frames increases.Significance. The proposed model can synthesize deformable CECT volumes, augmenting dataset diversity and size. The synthesized CECT volumes reveal good volume continuity and perceptual similarity to real CECT. The augmented datasets can improve the performance for downstream tasks.