## 4 Discussion
that employed deep learning to reduce noise from images, thus boosting image quality [31], our SRR algorithm goes further by aiming to reduce ringing artifacts and upscale image resolution. Specifically, in our study, we demonstrated that for standard CS reconstruction, a three-fold acceleration of the CS sequence (from CS1 to CS3) resulted in a noticeable decline in image quality. At very high resolutions, such as those achieved here with voxel sizes of 0.6 mm, even slight imperfections become immediately apparent, further degrading image quality. Maintaining a sufficient signal within each voxel is essential for constructing reliable images. As a result, even minor accelerations pose significant challenges, as they inevitably reduce the signal-to-noise ratio, impacting the overall image quality. However, incorporating the SRR algorithm into the CS3 sequence restored performance to a level comparable to that of the non-accelerated CS1 sequence, while achieving a considerable reduction in scan time of 63%. Alternatively, imaging performance could be further enhanced by applying the SRR algorithm to the CS1 sequence. Visualization of the hippocampal internal architecture and pes hippocampi was improved. Although a slight increase in artifacts was noted--likely attributable to the reconstruction process--the CS1-SRR-s sequence still provided overall superior image quality. Thus, integrating the SRR algorithm into clinical practice offers dual benefits, i.e., reduced scan time and enhanced image quality. While this study did not directly assess diagnostic outcomes, future validation should examine SRR's impact on diagnostic performance, particularly since improved image quality demonstrably enhances both diagnostic reliability in hippocampal MRI, as also exemplarily demonstrated by [PERSON] et al., as well as segmentation accuracy. Hippocampal segmentation, increasingly used not only for research but also for clinical applications (e.g., Alzheimer's diagnostics; epilepsy evaluation; and potentially, radiotherapy planning), is essential for quantifying biomarkers like atrophy [32]. Beyond increasing the sensitivity for detecting subtle lesions in epilepsy, advanced imaging techniques achieving higher resolution or reduced noise have thus been shown to improve segmentation accuracy [33]. These gains stem from superior visualization of internal architecture (e.g., strata radiatum) and pathology (e.g., sclerosis), confirming that image fidelity improvements intrinsically support clinical decision making. Dedicated studies with larger cohorts of confirmed hippocampal pathology are warranted to validate these relationships, including segmentation quality and accuracy for SRR, specifically. Beyond optimizing sequence parameters and incorporating AI to enhance image quality, moving to higher field strength has been suggested as a powerful means of bringing hippocampal imaging to a new level. Specifically, the advent of 7T MRI for clinical purposesoffers unprecedented potential. With a hippocampal sublayer thickness ranging from 0.1-0.5 mm, the resolution required to visualize these structures approaches the practical limits of in vivo full FOV MR imaging at lower field strengths, such as 1.5 or 3T [34]. The increased signal-to-noise ratio and spatial resolution afforded by 7T MRI allow for more detailed visualization of these delicate sublayers, providing new opportunities for advanced neuroimaging studies and improved understanding of hippocampal function and pathology [4; 35]. Whether 3T MRI enhanced by AI can keep up with 7T remains to be seen and should be investigated in future studies. Our study exhibits the following limitations: Firstly, this was a single-center study performed on a limited number of subjects. Secondly, our study employed hardware and software exclusively from a single vendor, limiting the generalizability of results to other platforms.