17 Papers
18 Citations
Gao Ma is an academic researcher from Nanjing Medical University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 5, co-authored 7 publications.
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Papers
Preliminary study of using diffusion kurtosis imaging for characterizing parotid gland tumors
TL;DR: DKI may be a promising imaging technique for characterizing parotid gland tumors and showed excellent inter-observer agreements during quantitative measurements.
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Histogram analysis of diffusion kurtosis imaging of nasopharyngeal carcinoma: Correlation between quantitative parameters and clinical stage.
TL;DR: Wu et al. as discussed by the authors evaluated the correlation between histogram parameters derived from diffusion-kurtosis (DK) imaging and the clinical stage of nasopharyngeal carcinoma (NPC).
Utility of Readout-Segmented Echo-Planar Imaging-Based Diffusion Kurtosis Imaging for Differentiating Malignant from Benign Masses in Head and Neck Region
TL;DR: Compared to DWI, DKI could provide additional data related to tumor heterogeneity with significantly better differentiating performance and its derived quantitative metrics could serve as a promising imaging biomarker for differentiating malignant from benign masses in head and neck region.
ISP-Net: Fusing features to predict ischemic stroke infarct core on CT perfusion maps
Haichen Zhu,Yang Chen,Tianyu Tang,Gao Ma,Jia-Ying Zhou,Jiulou Zhang,Shan-Shan Lu,Feiyun Wu,Limin Luo,Sheng Liu,Shenghong Ju,Hai-Bin Shi +11 more
TL;DR: Wang et al. as discussed by the authors developed an encoder-decoder based semantic model to predict infarct core after thrombolysis treatment on CT perfusion (CTP) maps.
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Prognostic value of post-treatment fluid-attenuated inversion recovery vascular hyperintensity in ischemic stroke after endovascular thrombectomy
TL;DR: Post-treatment FVH may be an effective prognostic marker associated with clinical outcome in patients with AIS after EVT and combined models integrating all three independent predictors significantly outperformed the combined model without post- treatment FVh (recanalization+NIHSSpre+post-treatmentFVH) in predicting clinical outcome.
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