Adversarial and Implicit Neural Representation Frameworks for 3D Cranial Synthetic CT Reconstruction from Biplanar X-Rays

Abstract

Objective:

This study compared two deep learning architectures, a generative adversarial network (GAN) and a convolutional neural network combined with implicit neural representations (CNN–INR), for generating cranial synthetic computed tomography (sCT) volumes from biplanar radiographs.

Methods:

Three cranial CT datasets comprising 235 subjects were used for training and evaluation. The GAN model used a dual-view generator and 3D PatchGAN discriminator, whereas the CNN–INR learned a coordinate-to-intensity mapping conditioned on radiographic features. Digitally reconstructed radiographs (DRRs) generated from non-contrast CTs provided standardized 2 D inputs. Both models were trained for 170 epochs under identical preprocessing, normalization, and optimization conditions. Quantitative evaluation employed the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity (CS), complemented by qualitative assessment of anatomical fidelity.

Results:

The GAN achieved higher SSIM and PSNR in both internal (0.739 ± 0.016; 16.70 ± 0.40 dB) and external (0.730 ± 0.012; 15.61 ± 0.27 dB) validations compared with the CNN–INR (0.686 ± 0.014; 16.41 ± 0.27 dB and 0.673 ± 0.012; 15.23 ± 0.16 dB, respectively). CS values were similar across models. Qualitatively, GAN-generated sCTs exhibited greater resemblance to ground-truth CTs, while CNN–INR reconstructions showed smoother spatial transitions.

Conclusions:

Both architectures demonstrated feasibility for volumetric reconstruction from limited radiographic projections. The GAN’s adversarial training enhanced perceptual realism and structural fidelity, whereas the CNN–INR maintained spatial continuity. Although neither model produced clinically viable sCTs, both represent promising approaches for future development of data-efficient tomographic reconstruction.