Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose

Abstract

The increasing frequency of computed tomography (CT) examinations has sparked development of dose reduction techniques to reduce the radiation dose to patients. Optimal dose while maintaining image quality can be achieved through accurate and realistic dose estimates. Unfortunately, existing dosimetric measures are either prohibitively slow or heavily reliant on absorbed dose within a cylindrical phantom, thereby ignoring the impact of patient anatomy and organ radiosensitivity on effective dose. We propose a novel deep learning-based patient-specific CT organ dose estimation method namely, multimodal contrastive learning with Scout images (Scout-MCL). Our proposed Scout-MCL gives accurate and realistic dose estimates in real-time and prospectively, by learning from multi-modal information leveraging image (lateral and frontal scouts) and profile (patient body size). Additionally, the incorporation of an accurately modeled tube current modulation (TCM) enables Scout-MCL to learn realistic dose variations. We evaluate our proposed method on a scout-CT paired scan dataset and show its effectiveness on predicting diverse TCM doses.