Personalized eye protection for head CT organ‐based tube current modulation: A deep learning approach to derive 3D eyeball models from a single‐view topogram
Xiaolin Meng, Lei Zhu, Shenghao Chen, Manhua Liu, Yang WangAbstract
Background
The lens of the eye is highly radiosensitive, yet personalized shielding during head CT remains challenging due to the lack of a rapid, pre‐scan localization method.
Purpose
To develop and validate a deep learning solution that enables automated, patient‐specific eye protection by generating a precise 3D eyeball model directly from a single‐view topogram.
Methods
Our two‐stage approach combines an advanced data simulation pipeline—which generates realistic training topograms from digitally reconstructed radiographs (DRRs) using a table‐movement‐aware model and CycleGAN‐based stylization—with a dedicated generative network (EyeGen‐Net). The model was trained on 400 synthetic and validated on 100 real clinical samples.
Results
EyeGen‐Net achieved a Dice Similarity Coefficient of 0.79 ± 0.08, a Hausdorff Distance of 5.40 ± 1.57 mm, and an Average Surface Distance of 1.84 ± 0.65 mm against expert segmentations. Crucially, phantom validation demonstrated that the derived 3D model facilitates organ‐based tube current modulation (OBTCM), yielding an approximate 30% reduction in lens dose across different scanning modes without compromising diagnostic image quality.
Conclusions
This work provides a practical, automated pathway for implementing personalized radioprotection in routine head CT, aligning with the ALARA (As Low As Reasonably Achievable) principle.