{"ID":2872385,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.09894","arxiv_id":"2509.09894","title":"Physics-Aware Neural Operators for Direct Inversion in 3D Photoacoustic Tomography","abstract":"Learning physics-constrained inverse operators-rather than post-processing physics-based reconstructions-is a broadly applicable strategy for problems with expensive forward models. We demonstrate this principle in three-dimensional photoacoustic computed tomography (3D PACT), where current systems demand dense transducer arrays and prolonged scans, restricting clinical translation. We introduce PANO (PACT imaging neural operator), an end-to-end physics-aware neural operator-a deep learning architecture that generalizes across input sampling densities without retraining-that directly learns the inverse mapping from raw sensor measurements to a 3D volumetric image. Unlike two-step methods that reconstruct then denoise, PANO performs direct inversion in a single pass, jointly embedding physics and data priors. It employs spherical discrete-continuous convolutions to respect hemispherical sensor geometry and Helmholtz equation constraints to ensure physical consistency. PANO reconstructs high-quality images from both simulated and real data across diverse sparse acquisition settings, achieves real-time inference and outperforms the widely-used UBP algorithm by approximately 33 percentage points in cosine similarity on simulated data and 14 percentage points on real phantom data. These results establish a pathway toward more accessible 3D PACT systems for preclinical research, and motivate future in-vivo validation for clinical translation.","short_abstract":"Learning physics-constrained inverse operators-rather than post-processing physics-based reconstructions-is a broadly applicable strategy for problems with expensive forward models. We demonstrate this principle in three-dimensional photoacoustic computed tomography (3D PACT), where current systems demand dense transdu...","url_abs":"https://arxiv.org/abs/2509.09894","url_pdf":"https://arxiv.org/pdf/2509.09894v2","authors":"[\"Jiayun Wang\",\"Yousuf Aborahama\",\"Arya Khokhar\",\"Yang Zhang\",\"Chuwei Wang\",\"Karteekeya Sastry\",\"Julius Berner\",\"Yilin Luo\",\"Boris Bonev\",\"Zongyi Li\",\"Kamyar Azizzadenesheli\",\"Lihong V. Wang\",\"Anima Anandkumar\"]","published":"2025-09-11T23:12:55Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.LG\"]","methods":"[]","has_code":false}