Limited View and Sparse Photoacoustic Tomography for Neuroimaging with Deep Learning
This work addresses image quality degradation in PAT for neuroimaging, offering a computationally efficient solution for real-time rendering, but it is incremental as it builds on existing deep learning techniques.
The paper tackled image reconstruction artifacts in limited-view and sparse-sensor photoacoustic tomography (PAT) for neuroimaging by proposing PixelDL, a deep learning method combining pixelwise interpolation with a CNN, which achieved comparable performance to iterative methods and outperformed other CNN-based approaches in simulations.
Photoacoustic tomography (PAT) is a nonionizing imaging modality capable of acquiring high contrast and resolution images of optical absorption at depths greater than traditional optical imaging techniques. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their view of the imaging target, which result in significant image reconstruction artifacts degrading image quality. Iterative reconstruction methods can be used to reduce artifacts but are computationally expensive. In this work, we propose a novel deep learning approach termed pixelwise deep learning (PixelDL) that first employs pixelwise interpolation governed by the physics of photoacoustic wave propagation and then uses a convolution neural network to directly reconstruct an image. Simulated photoacoustic data from synthetic vasculature phantom and mouse-brain vasculature were used for training and testing, respectively. Results demonstrated that PixelDL achieved comparable performance to iterative methods and outperformed other CNN-based approaches for correcting artifacts. PixelDL is a computationally efficient approach that enables for realtime PAT rendering and for improved image quality, quantification, and interpretation.