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Acute Infarct Segmentation on Diffusion-Weighted Imaging Using Deep Learning Algorithm and RAPID MRI

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참고문헌 (10)

  1. Nael K, "Six-minute magnetic resonance imaging protocol for evaluation of acute ischemic stroke : pushing the boundaries", Stroke, 45, pp.1985~1991 (2014)
  2. Chung H, "MR image denoising and super-resolution using regularized reverse diffusion", IEEE Trans Med Imaging, 42, pp.922~934 (2023)
  3. Deutschmann H, "Automated MRI perfusion-diffusion mismatch estimation may be significantly different in individual patients when using different software packages", Eur Radiol, 31, pp.658~665 (2021)
  4. Kim YC, "Evaluation of diffusion lesion volume measurements in acute ischemic stroke using encoder-decoder convolutional network", Stroke, 50, pp.1444~1451 (2019)
  5. Nazari-Farsani S, "Predicting final ischemic stroke lesions from initial diffusion-weighted images using a deep neural network", Neuroimage Clin, 37, pp.103278~ (2023)
  6. Menon BK, "Efficacy of endovascular thrombectomy in patients with M2 segment middle cerebral artery occlusions : meta-analysis of data from the HERMES collaboration", J Neurointerv Surg, 11, pp.1065~1069 (2019)
  7. Tao C, "Trial of endovascular treatment of acute basilar-artery occlusion", N Engl J Med, 387, pp.1361~1372 (2022)
  8. Petersen NH, "Decreases in blood pressure during thrombectomy are associated with larger infarct volumes and worse functional outcome", Stroke, 50, pp.1797~1804 (2019)
  9. Terasawa Y, "Neurological deterioration in small vessel disease may be associated with increase of infarct volume", J Neurol Sci, 269, pp.35~40 (2008)
  10. Prabhakaran S, "Predictors of early infarct recurrence in patients with symptomatic intracranial atherosclerotic disease", Stroke, 52, pp.1961~1966 (2021)

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