To evaluate the utility of synthetic magnetic resonance imaging (MRI) of the breast in predicting the Ki-67 status in patients with oestrogen receptor (ER)-positive breast cancer. MATERIALS AND METHODS: Forty-nine patients with 50 histopathologically proven breast cancers who underwent additional synthetic MRI were enrolled in the present study. Using synthetic MRI images, T1 and T2 relaxation times and their standard deviations (SD) in the breast lesions before (T1-Pre, T2-Pre, PD-Pre, SD of T1-Pre, SD of T2-Pre, SD of PD-Pre) and after (T1-Gd, T2-Gd, PD-Gd, SD of T1-Gd, SD of T2-Gd, SD of PD-Gd) contrast agent injection were obtained. These quantitative values were compared between the low Ki-67 expression (<14%) lesions (low-proliferation group: n¼23) and high Ki-67 expression (!14%) lesions (high-proliferation group: n¼27). RESULTS: The univariate analysis showed that the SD of T1-Gd (p<0.001) and T2-Gd (p¼0.042) were significantly higher in the high-proliferation group than in the lowproliferation group. Multivariate analysis further showed that the SD of T1-Gd was a significant and independent predictor of Ki-67 expression, with an area under the receiver operating characteristic (AUROC) curve of 0.885. The sensitivity, specificity, and accuracy of the SD of T1-Gd with an optimal cutoff value of 98.5 were 77.8%, 87%, and 82%, respectively. CONCLUSION: The SD of T1-Gd obtained from synthetic MRI was useful to predict Ki-67 status.

BackgroundThe addition of synthetic MRI might improve the diagnostic performance of dynamic contrast‐enhanced MRI (DCE‐MRI) in patients with breast cancer.PurposeTo evaluate the diagnostic value of a combination of DCE‐MRI and quantitative evaluation using synthetic MRI for differentiation between benign and malignant breast masses.Study TypeRetrospective, observational.PopulationIn all, 121 patients with 131 breast masses who underwent DCE‐MRI with additional synthetic MRI were enrolled.Field Strength/Sequence3.0 Tesla, T1‐weighted DCE‐MRI and synthetic MRI acquired by a multiple‐dynamic, multiple‐echo sequence.AssessmentAll lesions were differentiated as benign or malignant using the following three diagnostic methods: DCE‐MRI type based on the Breast Imaging–Reporting and Data System; synthetic MRI type using quantitative evaluation values calculated by synthetic MRI; and a combination of the DCE‐MRI + Synthetic MRI types. The diagnostic performance of the three methods were compared.Statistical TestsUnivariate (Mann–Whitney U‐test) and multivariate (binomial logistic regression) analyses were performed, followed by receiver‐operating characteristic curve (AUC) analysis.ResultsUnivariate and multivariate analyses showed that the mean T1 relaxation time in a breast mass obtained by synthetic MRI prior to injection of contrast agent (pre‐T1) was the only significant quantitative value acquired by synthetic MRI that could independently differentiate between malignant and benign breast masses. The AUC for all enrolled breast masses assessed by DCE‐MRI + Synthetic MRI type (0.83) was significantly greater than that for the DCE‐MRI type (0.70, P < 0.05) or synthetic MRI type (0.73, P < 0.05). The AUC for category 4 masses assessed by the DCE‐MRI + Synthetic MRI type was significantly greater than that for those assessed by the DCE‐MRI type (0.74 vs. 0.50, P < 0.05).Data ConclusionA combination of synthetic MRI and DCE‐MRI improves the accuracy of diagnosis of benign and malignant breast masses, especially category 4 masses.Level of Evidence 4Technical Efficacy Stage 2J. MAGN. RESON. IMAGING 2021;53:381–391.

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Background A cardiac resting phase is used when performing free-breathing cardiac magnetic resonance examinations. Purpose The purpose of this study was to test a cardiac resting phase detection system based on neural networks in clinical practice. Material and Methods Four chamber-view cine images were obtained from 32 patients and analyzed. The rest duration, start point, and end point were compared between that determined by the experts and general operators, and a similar comparison was done between that determined by the experts and neural networks: the normalized root-mean-square error (RMSE) was also calculated. Results Unlike manual detection, the neural network was able to determine the resting phase almost simultaneously as the image was obtained. The rest duration and start point were not significantly different between the neural network and expert ( p = .30, .90, respectively), whereas the end point was significantly different between the two groups ( p < .05). The start point was not significantly different between the general operator and expert ( p = .09), whereas the rest duration and end point were significantly different between the two groups ( p < .05). The normalized RMSEs of the rest duration, start point, and end point of the neural network were 0.88, 0.64, and 0.33 ms, respectively, which were lower than those of the general operator (normalized RMSE values were 0.98, 0.68, and 0.51 ms, respectively). Conclusions The neural network can determine the resting phase instantly with better accuracy than the manual detection of general operators.