The huge amount of data resulting from the ac- quisition of medical images with multiple modalities can be overwhelming for storage and sharing through communication systems. Thus, efficient compression algorithms must be in- troduced to reduce the burden of storage and communication resources required by such amount of data. However, since in the medical context all details are important, the adoption of lossless image compression algorithms is paramount.
This paper proposes a novel lossless compression scheme tailored to jointly compress the modality of computerized to- mography (CT) and that of positron emission tomography (PET). Different approaches are adopted, namely image-to-image trans- lation techniques and redundancies between both images are also explored. To perform the image-to-image translation approach, we resort to lossless compression of the original CT data and apply a cross-modality image translation generative adversarial network to obtain an estimation of the corresponding PET. Then, the residue that results from the differences between the original PET and its estimation is also compressed. Thus, instead of compressing two independent image modalities, i.e., both images of the original PET-CT pair, in the proposed approach only the CT is independently encoded along with the PET residue.
The performed experiments using a publicly available PET- CT pair dataset show that the proposed scheme attains up to 8.9% compression gains for the PET data, in comparison with the naive approach, and up to 3.5% gains for the PET-CT pair.