## 4 Discussion  
The CycleGAN-generated images ranged from small infarcts, such as lacunar infarcts, to large infarcts, such as cardiogenic cerebral emboli. Infact images were also generated from brain slices at different heights; infarcts along the lateral ventricles and cerebellum were also generated. Most of the generated infarct images had high signal intensities within the infarcted region, and infarcts with low signal intensity were not often generated. The signal intensities of the infarcts used in the study tended to be high. In some of the generated images, the signal intensities of the entire image increased, and no infarct was generated. We extracted the regions of cerebral infarction using the U-Net. Then, we compared the accuracy and sensitivity of extracting infarcted regions when only actual cases of stroke patients were used for U-Net training and when CycleGAN-generated images and actual cases were used. The DI for extraction accuracy was 0.473 and 0.553 when using only real cases and CycleGAN-generated images, respectively. The extraction accuracy improved when using generated images for training. One of the reasons for the improvement in the extraction accuracy is that the infarcted region characteristics extracted by U-Net changed when the CycleGAN-generated images were used together. Most of the CycleGAN-generated images had small and high-contrast infarcted regions. U-Net was trained using these images, and the infarcted regions with a difference of more than 80 from the healthy region were extracted, and the DI was 0.707. However, if the infarcted region was large and the signal value within the infarcted region was uneven, only the high-signal region was extracted and underestimated in some cases. The sensitivity for detecting infarcts was 0.940 when training on real cases only and 0.920 when using CycleGAN-generated images. The combined use of CycleGAN-generated images decreased the sensitivity because one small infarct was missed that had been detected when only real cases were used. The sensitivity differed depending on the size of the infarcted area and the difference in pixel values between the normal parenchyma and the infarcted area, both in real cases alone and when CycleGAN-generated images were used. The sensitivity of infarcts with diameters >10 mm and a pixel difference >50 was 0.976. However, infarcts with sizes of <10 mm or faint infarcts with pixel intensity differences of <50 were not extracted in some cases, and the sensitivity was 0.750. It is challenging to recognize contrast with the surrounding normal parenchyma in cases of small or low-contrast infarcts. Furthermore, when the number of FPs was compared, the number of FPs per case was 3.750 when only real cases were used and 1.234 when CycleGAN-generated images were used, a reduction of approximately 67% in the number of FPs. FPs were caused by a slight signal increase in the brain parenchyma and linear high-signal artifacts in the peripheral areas of the brain parenchyma due to DWI distortion. In many cases, a region with a relatively high signal compared to the surrounding area in the healthy brain parenchyma was mistakenly identified as the infarcted area. When the CycleGAN-generated images were combined, a high-signal area in the brain parenchyma was rarely identified as the wrong infarcted area. The CycleGAN introduced in this study generated many patterns that resembled actual infarcts. Therefore, data could be collected to more clearly separate the characteristics of FPs and infarct patterns, and U-Net, trained on infarct patterns generated by CycleGAN and actual infarcts, could distinguish between infarcts and FPs accurately. Although it is necessary to improve the detection of small and low-contrast infarcts, including those detected when training was performed only in real cases, the combined use of CycleGAN-generated images is highly effective in removing FPs. This method can be said to be effective overall.