Komninos Charalampos, Pissas Theodoros, Flores Blanca, Bloch Edward, Vercauteren Tom, Ourselin Sébastien, Da Cruz Lyndon, Bergeles Christos
School of Biomedical Engineering & Imaging Sciences, King's College London, SE1 7EU, London, UK.
Moorfields Eye Hospital, EC1V 2PD, London, UK.
Biomed Opt Express. 2024 Jan 16;15(2):772-788. doi: 10.1364/BOE.501743. eCollection 2024 Feb 1.
Regenerative therapies show promise in reversing sight loss caused by degenerative eye diseases. Their precise subretinal delivery can be facilitated by robotic systems alongside with Intra-operative Optical Coherence Tomography (iOCT). However, iOCT's real-time retinal layer information is compromised by inferior image quality. To address this limitation, we introduce an unpaired video super-resolution methodology for iOCT quality enhancement. A recurrent network is proposed to leverage temporal information from iOCT sequences, and spatial information from pre-operatively acquired OCT images. Additionally, a patchwise contrastive loss enables unpaired super-resolution. Extensive quantitative analysis demonstrates that our approach outperforms existing state-of-the-art iOCT super-resolution models. Furthermore, ablation studies showcase the importance of temporal aggregation and contrastive loss in elevating iOCT quality. A qualitative study involving expert clinicians also confirms this improvement. The comprehensive evaluation demonstrates our method's potential to enhance the iOCT image quality, thereby facilitating successful guidance for regenerative therapies.
再生疗法在逆转由退行性眼病引起的视力丧失方面显示出前景。机器人系统与术中光学相干断层扫描（iOCT）一起可以促进其精确的视网膜下递送。然而，iOCT的实时视网膜层信息因图像质量较差而受到影响。为了解决这一限制，我们引入了一种用于增强iOCT质量的无配对视频超分辨率方法。提出了一种循环网络，以利用来自iOCT序列的时间信息和术前获取的OCT图像的空间信息。此外，逐块对比损失实现了无配对超分辨率。广泛的定量分析表明，我们的方法优于现有的最先进的iOCT超分辨率模型。此外，消融研究展示了时间聚合和对比损失在提高iOCT质量方面的重要性。一项涉及专家临床医生的定性研究也证实了这种改进。综合评估表明，我们的方法有潜力提高iOCT图像质量，从而为再生疗法提供成功的指导。
