A tumor in a scan is like a tiny bright dot on a vast dark canvas. That dot is what the doctor needs, not a perfectly smooth background. But when a neural network learns to turn a regular CT scan into something resembling a PET scan, it often tries to make everything look nice, and ends up smearing that dot.

The trouble is that tumor cells make up only a minuscule fraction of the body's total volume. If you judge quality by the whole image, the model can score high while still missing the faint glow of cancer. The authors of this new study propose adding two simple signals to the network: one shows how heterogeneous the tissue texture is, the other how strong the density contrasts are. These signals are cheap to compute from CT and help the model focus on suspicious areas.

The model is called LAFNO. It was trained on real patient data from prostate cancer cases, where tumors light up thanks to the radioactive tracer PSMA. In tests, LAFNO cut the error in estimating overall tumor activity by nearly half compared to previous methods. The accuracy of reproducing tumor texture also improved — important for downstream automated analysis.

For now, reproducing the tissue around the tumor remains unstable and depends on the tracer type. But the approach itself, hinting to the network where to look using simple physical features, could be useful in other medical imaging tasks.