## 4 Discussion  
This study developed a Bayesian estimation-based image reconstruction method as a useful approach under the constraints anticipated in BNCT-SPECT. We compared this new method with the existing ML-EM method. Specifically, we created a numerical simulation model that replicated the projection angle expected in BNCT-SPECT. This model encompassed the entire process, from the projection data calculations to the image reconstruction, using both the proposed and ML-EM methods. Performance comparisons were conducted using error indices such as the MAE and SSIM.  
Considering the statistical errors in the measurements, that is, both projection angle and measurement time constraints in BNCT-SPECT, when performing projections with \(\Delta\theta=30\) [degrees] and using the proposed method for image reconstruction, the MAE for the entire estimated image was 0.0568 \(\pm\) 0.0021, and for normal cells alone was 0.3154 \(\pm\) 0.0005. Both values exhibited minimal error. Of note is that these errors were calculated from the standard errors of data obtained through 50 numerical experiments. Therefore, it can be concluded that, \"under the conditions anticipated for BNCT-SPECT, performing a total of four projections with a projection angle \(\Delta\theta=30\) [degrees] and using the proposed method for image reconstruction is optimal\". However, in this case, when defining the relative error of the estimated image as the MAE of normal cells divided by the true pixel value of normal cells (=0.31), the value was approximately 16%, which is considerably large, and the T/N ratio remained around 1.7. Thus, process improvements are necessary for the practical implementation of BNCT-SPECT.  
By combining these findings, we determined that the proposed Bayesian estimation method offers promising prospects as a valuable new image reconstruction method under BNCT-SPECT conditions. However, in the practical application stage of BNCT-SPECT, the decision to continue treatment can be based on the T/N ratio of reconstructed images. Supposing that the threshold for BNCT applicability is set at a T/N ratio of 2.0, which is similar to the baseline value before treatment, the current method may incorrectly classify patients who are genuinely suitable for treatment as ineligible. Therefore, prioritizing the development of the new method is essential to improve the accuracy of the T/N ratio.  
Although the proposed image reconstruction method for BNCT-SPECT demonstrated promising results, several potential challenges for clinical application should be considered. One concern is the extended acquisition time required for high-quality projection data, which may increase patient discomfort, particularly when patients are required to remain immobile for long periods. The limited view-angle approach may also lead to incomplete tumor visualization, especially for tumors with complex shapes and distributions, thereby affecting treatment accuracy.  
At present, this method may be most suitable for patients with small, well-localized tumors, where the precision of reconstruction compensates for the limited projection data. Future work should explore optimization strategies through simulated calculations of more complex tumor scenarios to widen its application to a broader range of clinical cases.