### Convolution Patterns in EMT: A Projection-Based Approach
In 1959, experiments conducted by Hubel and Wiesel on the visual cortices of cats revealed that specific cortical neurons were susceptible to edges or stripes of specific orientations, laying the biological foundation for the subsequent development of convolutional neural networks [35]. Inspired by the cognitive processes of animal vision, the CNN eliminates the one-to-one connections between specific pixels, adopting a convolutional kernel to implement a weight-sharing mechanism. This change uses the high correlation between adjacent pixels in images, reducing the number of network parameters while preserving the characteristics of pixel connectivity. For the convolution of EMT detection signals, it is necessary to examine the characteristics of EMT in order to select the appropriate convolution method for them. In 1917, the Radon transform laid the theoretical foundation for tomographic imaging [36]. Tomographic imaging is a technique that reconstructs internal structures by collecting projection data that pass through an object from different angles. The concept of projection is significant in the tomographic imaging process. Physically, it refers to the measure of changes in the detected signal values due to the presence of the object when an excitation signal passes through the object in a particular direction (linear or nonlinear). Each projection contains information as seen from a specific angle but is insufficient to reconstruct the structure of the object being measured independently. These projection data are collected and used to reconstruct the internal image of the object through mathematical algorithms. The core of tomographic imaging lies in integrating these projection data from different angles to reconstruct a complete internal structure. Figure 1 describes the sensitivity regions of projection \(p_{1-3}\), projection \(p_{1-4}\), and projection \(p_{5-7}\) in an eight-coil EMT system. Taking projection \(p_{1-3}\) as an example, the notation refers to the configuration where coil 1 serves as the excitation source and coil 3 serves as the detector. From Figure 1, it can be seen that projection \(p_{1-3}\) has an overlapping sensitivity region with projection \(p_{1-4}\). However, projections \(p_{1-3}\) and \(p_{5-7}\) hardly have overlapping sensitivity regions. From this, we can infer that it may be inappropriate to convolve projection \(p_{1-3}\) with projection \(p_{5-7}\) together, as they are not adjacent in physical space, so such an approach would fail to provide commonalities and be prone to extracting erroneous features. Thus, the 1D-CNNs and 2D-CNNs in [34] were reanalyzed. Because the input signals of 1D-CNNs and 2D-CNNs are distributed through signal acquisition, there is no second design. Between the two, 1D-CNNs use more adjacent projections to participate in convolution than 2D-CNNs (the use of projection by 2D-CNNs is also chaotic), which is consistent with the better image reconstruction effect of 1D-CNNs compared to that of 2D-CNNs. However, careful observation shows that 1D-CNNs do not make good use of all adjacent projections to participate in convolution, and non-adjacent projections participate in convolution. This may be the physical interpretation of the process. In summary, there is limited research on deep learning for 3D ET, especially EMT. Compared to the classic 2D image reconstruction algorithms for 3D image reconstruction, deep learning offers an end-to-end approach, a more direct method for 3D image reconstruction. Convolutional neural networks extract standard features by exploiting the spatial correlations of adjacent elements. If this process could be integrated with the intrinsic relationships between the projections of EMT itself, it would likely result in a convolution pattern that is more suitable for EMT applications. However, research on this topic is scarce, and it merits further investigation. For this reason, this paper proposes a rotational convolution pattern, 'Conv-P', which utilizes adjacent projections for convolution, applicable to 3D EMT.