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. Author manuscript; available in PMC: 2023 Jun 16.
Published in final edited form as: Phys Med Biol. 2022 Jun 16;67(12):10.1088/1361-6560/ac749a. doi: 10.1088/1361-6560/ac749a

Figure 2.

Figure 2.

Training strategy for the DL-VIF deep CNN. Motion-free MDCT datasets were used as base instances for the generation of motion corrupted CBCT data. The motion-corrupted data were reconstructed without any motion compensation and with motion compensation using the same motion pattern used to generate the motion corrupted dataset, providing a ground truth for ideal motion compensation. Conventional VIF values were computed for each motion-corrupted volume, using the ideal compensation as reference, to serve as training labels for DL-VIF training. Note that at inference time (see dashed box), only the motion corrupted image is needed, making DL-VIF a true reference-free metric.