**7. Conclusions**

In this paper, we propose a new super-resolution method for brain MR images with a significantly smaller number of training images. Our method is GAN-based superresolution with two essential proposed techniques: the SPS and ASD. These proposed techniques succeeded in generating super-resolution images from the training of only about 30 brain MR images. The images generated in this way showed an overall improvement in image quality and an increase in the resolution of critical diagnostic regions, which helped to improve the disease diagnostic performance of the CNN-based classifier built on these images.

**Author Contributions:** Conceptualization, methodology, K.I., H.I.; software, K.I.; validation, K.I., H.I., K.O.; resources, data curation, K.O.; writing—original draft preparation, K.I.; writing—review and editing, K.I., H.I., K.O.; supervision, H.I., K.O.; project administration, H.I. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported in part by the Ministry of Education, Science, Sports and Culture of Japan (JSPS KAKENHI), Grant-in-Aid for Scientific Research (C), 21K12656, 2021–2023.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** We used a brain MR image dataset published by the OpenNeuro and Alzheimer's Disease Neuroimaging Initiative (ADNI) project.

**Acknowledgments:** The MRI data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

**Conflicts of Interest:** The authors declare no conflict of interest.
