Sinogram to image: direct reconstruction of photoacoustic tomography image using a hybrid deep learning approach

Photoacoustic computed tomography (PACT) is an advanced biomedical imaging modality that integrates optical and ultrasound imaging techniques. A central goal in PACT is to estimate the initial pressure rise from raw photoacoustic data (sinogram), a task complicated by the need for accurate inverse modeling, sensitivity to noise, and hardware limitations. Conventional reconstruction methods typically rely on multi-step processing and require careful parameter tuning to achieve high-quality results. While deep learning has proven effective in addressing challenges like noise suppression and computational efficiency, its use in PACT has largely been limited to image-domain or sensor data (sinogram)-domain enhancement, rather than direct image reconstruction from sensor data (sinograms) - a direction more extensively explored in computed tomography (CT) and magnetic resonance imaging (MRI). In this study, we propose a novel, data-driven methodology that formulates PACT image reconstruction as a supervised learning problem. Our approach employs a hybrid fully connected network followed by a Swin-UNETR to learn a direct mapping from raw sinograms (sensor data) to reconstruct images, effectively eliminating the need for conventional, handcrafted reconstruction steps. The proposed method achieved high image quality, with peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of 33.878 dB and 0.975, respectively.