## 4 Discussion  
In this study we assessed the potential of a DL-powered SRR algorithm for improved T2w imaging of the hippocampus in patients with (epileptic) seizures and suspected hippocampal pathology. We showed that DL-powered SSR can enhance imaging performance by introducing flexibility in T2w hippocampal imaging. Specifically, it either improves image quality for non-accelerated imaging or preserves acceptable quality in accelerated imaging yet at the benefit of a reduced scan time. The hippocampus exhibits an exceptionally intricate and delicate anatomy, requiring high-quality visualization to effectively evaluate any potential pathological changes. Specifically, the anatomy of the hippocampus on imaging can be described as follows: The Ammon horn of the hippocampus (cornu ammonia), with the T2w hyperintense pyramidal cell layers CA1 to CA4, surrounds the T2w hyperintense central dentate gyrus. The cornu ammonia is covered by white matter, seen as the T2w hypotontense alveus that ends medially as the T2w hypointense fimbria. The Ammon horn of the hippocampus is in continuity with the subiculum and the subsequent entorhinal cortex of the parahippocampal gyrus. Between the dentate gyrus and the cornu ammonia CA1-CA4 and the subiculum, the T2w hypointense dark band of white matter of the stratum radiatum, lacunosum, and moleculare (SRLM) is located, thus forming a typical spiral appearance [3, 4, 6, 28]. Thus, the hippocampal internal architecture (HIA) is based on this laminar structure of the body of the hippocampus, with a spiral appearance in the coronal plane due to the adjacent layers of gray and white matter in the Ammon's horn [1, 3]. This hypointense band is only 1 mm thick in coronal images, which corroborates the necessity of acquiring high resolution and high quality T2w TSE images for clear visualization [3]. Importantly, changes in the HIA are key indicators of hippocampal sclerosis, making its clear visualization crucial for epilepsy patients. Advancing hippocampal imaging for clinical purposes has not yet gained widespread attention. While literature on high-resolution imaging of the hippocampus for research purposes exists, these studies often focus on different imaging sequences, higher field strengths (e.g., 7T MRI), and significantly different requirements [4, 29, 30]. In research, protocols and scan times do not need to be as short as those required in clinical practice, meaning acceleration and efficiency are less critical. However, in a clinical setting, protocols must balance speed, high resolution, and sufficient signal-to-noise ratio to enable accurate diagnosis and the detection of potential pathologies. Despite its importance, there is a noticeable lack of studies specifically addressing the optimization of T2w imaging of the hippocampus at 3T for clinical applications. Regarding the use of AI, a recent study by [PERSON] et al. is of interest. The authors analyzed the diagnostic performance and image quality of three MRI protocols for temporal lobe epilepsy (TLE) in 117 patients. They found that 1.5 mm slice thickness MRI, with deep learning-based reconstruction (1.5-mm MRI + DLR), significantly improved pooled sensitivity (91.2% vs. 72.1%) and depiction of hippocampal abnormalities compared to routine 3 mm MRI, although with lower specificity (76.5% vs. 89.2%). Furthermore, 1.5 mm MRI + DLR provided higher accuracy (91.2% vs. 73.1%), as well as superior image quality, SNR, and CNR than did 1.5 mm MRI without DLR. The study concluded that 1.5 mm MRI + DLR enhances TLE diagnosis, particularly for hippocampal evaluation, due to improved lesion conspicuity and image quality [31]. Largely corroborating these findings, we also present an elegant deep learning-based solution that enhances T2w hippocampal imaging at 3T, achieving improved image quality without extending scan time or compromising other technical MRI parameters. However, in contrast to the approach by [PERSON] et al.