Differentiable rendering (DR) is an emerging field that facilitates the calculation and propagation of gradients of 3D objects through images. In contrast to deep neural networks (DNNs), which generally lack the understanding of 3D objects that form the image, DR mitigates the need for extensive 3D data collection and annotation, thereby offering a higher success rate across a range of applications. In this work, we introduce a new approach in positron emission tomography (PET) image reconstruction, leveraging inverse rendering (IR) techniques which aim to reconstruct the highest resolution image from the measured sinograms, outperforming other algorithms based on DNNs or maximum likelihood expectation maximization (MLEM).