用于增强肿瘤分割的对比剂注射前至注射后乳腺MRI合成
Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
AI总结：
本研究利用GAN将对比剂注射前乳腺MRI合成为注射后DCE-MRI，并引入SAMe指标评估合成数据，证明合成数据能通过数据增强有效提升3D乳腺肿瘤分割模型的鲁棒性。
摘要：
尽管对比剂在动态对比增强MRI（DCE-MRI）中对肿瘤检测和治疗有益，但其使用会带来一系列问题，包括有创性、生物蓄积以及引发肾源性系统性纤维化的风险。本研究探索了利用生成对抗网络（GAN），将对比剂注射前T1加权脂肪饱和乳腺MRI转换为对应的首次DCE-MRI序列，以生成合成对比增强的可行性。此外，我们引入了缩放聚合度量（SAMe），旨在以有原则的方式定量评估合成数据的质量，并作为选择最优生成模型的基础。我们使用定量图像质量指标评估生成的DCE-MRI数据，并将其应用于3D乳腺肿瘤分割的下游任务。结果表明，通过数据增强，对比剂注射后DCE-MRI的合成在提升乳腺肿瘤分割模型鲁棒性方面具有潜力。代码可在https://github.com/RichardObi/pre_post_synthesis获取。
英文摘要：
Despite its benefits for tumour detection and treatment, the administration of contrast agents in dynamic contrast-enhanced MRI (DCE-MRI) is associated with a range of issues, including their invasiveness, bioaccumulation, and a risk of nephrogenic systemic fibrosis. This study explores the feasibility of producing synthetic contrast enhancements by translating pre-contrast T1-weighted fat-saturated breast MRI to their corresponding first DCE-MRI sequence leveraging the capabilities of a generative adversarial network (GAN). Additionally, we introduce a Scaled Aggregate Measure (SAMe) designed for quantitatively evaluating the quality of synthetic data in a principled manner and serving as a basis for selecting the optimal generative model. We assess the generated DCE-MRI data using quantitative image quality metrics and apply them to the downstream task of 3D breast tumour segmentation. Our results highlight the potential of post-contrast DCE-MRI synthesis in enhancing the robustness of breast tumour segmentation models via data augmentation. Our code is available at https://github.com/RichardObi/pre_post_synthesis.
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