CT synthesis from CBCT using a sequence-aware contrastive generative network.

Computerized tomography (CT) synthesis from cone-beam computerized tomography (CBCT) is a key step in adaptive radiotherapy. It uses a synthetic CT to calculate the dose to correct and adjust the radiotherapy plan in a timely manner. The cycle-consistent adversarial network (Cycle GAN) is commonly used in CT synthesis tasks but it has some defects: (a) the premise of the cycle consistency loss is that the conversion between domains is bijective, but the CBCT and CT conversion does not fully satisfy the bijective relationship, and (b) it does not take advantage of the complementary information between multiple sets of CBCTs for the same patient.

Geometry-Consistent Adversarial Registration Model for Unsupervised Multi-Modal Medical Image Registration.

Deformable multi-modal medical image registration aligns the anatomical structures of different modalities to the same coordinate system through a spatial transformation. Due to the difficulties of collecting ground-truth registration labels, existing methods often adopt the unsupervised multi-modal image registration setting. However, it is hard to design satisfactory metrics to measure the similarity of multi-modal images, which heavily limits the multi-modal registration performance.