In circular scan photoacoustic tomography (PAT), the axial resolution is spatially invariant and is limited by the bandwidth of the detector. However, the tangential resolution is spatially variant and is dependent on the aperture size of the detector. In particular, the tangential resolution improves with the decreasing aperture size. However, using a detector with a smaller aperture reduces the sensitivity of the transducer. Thus, large aperture size detectors are widely preferred in circular scan PAT imaging systems. Although several techniques have been proposed to improve the tangential resolution, they have inherent limitations such as high cost and the need for customized detectors. Herein, we propose a novel deep learning architecture to counter the spatially variant tangential resolution in circular scanning PAT imaging systems. We used a fully dense U-Net based convolutional neural network architecture along with 9 residual blocks to improve the tangential resolution of the PAT images. The network was trained on the simulated datasets and its performance was verified by experimental in vivo imaging. Results show that the proposed deep learning network improves the tangential resolution by eight folds, without compromising the structural similarity and quality of image.

中文翻译：

---

提高光声断层扫描切向分辨率的深度学习方法


在圆形扫描光声断层扫描 (PAT) 中，轴向分辨率在空间上不变，并且受到探测器带宽的限制。然而，切向分辨率在空间上是变化的并且取决于探测器的孔径尺寸。特别是，切向分辨率随着孔径尺寸的减小而提高。然而，使用孔径较小的检测器会降低换能器的灵敏度。因此，大孔径探测器在圆形扫描 PAT 成像系统中受到广泛青睐。尽管已经提出了几种技术来提高切向分辨率，但它们具有固有的局限性，例如成本高和需要定制探测器。在此，我们提出了一种新颖的深度学习架构来应对圆形扫描 PAT 成像系统中空间变化的切向分辨率。我们使用基于 U-Net 的全密集卷积神经网络架构以及 9 个残差块来提高 PAT 图像的切向分辨率。该网络在模拟数据集上进行了训练，并通过体内成像实验验证了其性能。结果表明，所提出的深度学习网络将切向分辨率提高了八倍，并且不影响图像的结构相似性和质量。