We present a deep learning-based computational algorithm for inversion of
circular Radon transforms in the partial radial setup, arising in photoacoustic
tomography. We first demonstrate that the truncated singular value
decomposition-based method, which is the only traditional algorithm available
to solve this problem, leads to severe artifacts which renders the
reconstructed field as unusable. With the objective of overcoming this
computational bottleneck, we train a ResBlock based U-Net to recover the
inferred field that directly operates on the measured data. Numerical results
with augmented Shepp-Logan phantoms, in the presence of noisy full and limited
view data, demonstrate the superiority of the proposed algorithm.

我们提出了一种基于深度学习的计算算法来破解局部径向设定下的圆形 Radon 变换，该变换在光声断层扫描中出现。我们首先证明了截断奇异值分解方法，这是唯一可用于解决此问题的传统算法，会导致严重的伪影从而使重建场景没有用武之地。为了克服这个计算瓶颈，我们训练了一个基于 ResBlock 的 U-Net，以直接通过测量数据恢复推断场景。在噪声全视图和有限视图数据存在的情况下，使用增强 Shepp-Logan 幻影的数值结果，证明了所提出算法的优越性。

学习偏径向设置下圆形 Radon 变换的端到端反演

Learning end-to-end inversion of circular Radon transforms in the  partial radial setup

Recently, a lot of single stage detectors using multi-scale features have
been actively proposed. They are much faster than two stage detectors that use
region proposal networks (RPN) without much degradation in the detection
performances. However, the feature maps in the lower layers close to the input
which are responsible for detecting small objects in a single stage detector
have a problem of insufficient representation power because they are too
shallow. There is also a structural contradiction that the feature maps have to
deliver low-level information to next layers as well as contain high-level
abstraction for prediction. In this paper, we propose a method to enrich the
representation power of feature maps using Resblock and deconvolution layers.
In addition, a unified prediction module is applied to generalize output
results and boost earlier layers' representation power for prediction. The
proposed method enables more precise prediction, which achieved higher score
than SSD on PASCAL VOC and MS COCO. In addition, it maintains the advantage of
fast computation of a single stage detector, which requires much less
computation than other detectors with similar performance. Code is available at
this https URL

该论文提出了一种方法，使用 Resblock 和反卷积层丰富特征图的表示能力并应用统一预测模块来提高早期层的表示能力，从而在 PASCAL VOC 和 MS COCO 上实现了比 SSD 更高的得分，并保持了单级检测器的快速计算的优势。