J Stroke.  2023 Sep;25(3):425-429. 10.5853/jos.2023.02145.

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

• Ryu WS
• Kang YR
• Noh YG
• Park JH
• Kim D
• Kim BC
• Park MS
• Kim BJ
• Kim JT

• KMID: 2546446
• DOI: http://doi.org/10.5853/jos.2023.02145

Figure

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  Figure 1. Comparison of software-estimated and manually segmented infarct volumes. (A) Ground truth infarct volume by manual segmentation vs. estimated infarct volume by RAPID MRI (red) and JBS-01K (blue). The right lower figure enlarged the gray shaded (<10 mL) area. Black dotted lines indicate lesion volume cutoffs of 21, 31, and 51 mL. Green dotted line and shared areas indicate lesion volume cutoff of 70 mL. The threshold of 21, 31, or 51 mL was used in the DAWN trial (DWI or CTP Assessment With Clinical Mismatch in the Triage of Wake-Up and Late Presenting Strokes Undergoing Neurointervention With Trevo) for inclusion of patients, whereas the 70 mL threshold was used in the DEFUSE-3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke) and EXTEND-IA (Extending the Time for Thrombolysis in Emergency Neurological Deficits With Intra-Arterial Therapy) trials. (B) Bland-Altman plot between ground truth infarct volume and estimated infarct volume by JBS-01K. (C) Bland-Altman plot between ground truth infarct volume and estimated infarct volume by RAPID MRI. Green line and black dotted lines indicate mean percentage difference and its 95% limit. Note that if the software did not find infarct, the percent difference was calculated as 200%. DWI, diffusion-weighted imaging; CTP, computed tomography perfusion.

Reference

References

1. Nael K, Khan R, Choudhary G, Meshksar A, Villablanca P, Tay J, et al. Six-minute magnetic resonance imaging protocol for evaluation of acute ischemic stroke: pushing the boundaries. Stroke. 2014; 45:1985–1991.

2. Chung H, Lee ES, Ye JC. MR image denoising and super-resolution using regularized reverse diffusion. IEEE Trans Med Imaging. 2023; 42:922–934.

3. Deutschmann H, Hinteregger N, Wießpeiner U, Kneihsl M, Fandler-Höfler S, Michenthaler M, et al. Automated MRI perfusion-diffusion mismatch estimation may be significantly different in individual patients when using different software packages. Eur Radiol. 2021; 31:658–665.

4. Kim YC, Lee JE, Yu I, Song HN, Baek IY, Seong JK, et al. Evaluation of diffusion lesion volume measurements in acute ischemic stroke using encoder-decoder convolutional network. Stroke. 2019; 50:1444–1451.

5. Nazari-Farsani S, Yu Y, Duarte Armindo R, Lansberg M, Liebeskind DS, Albers G, et al. Predicting final ischemic stroke lesions from initial diffusion-weighted images using a deep neural network. Neuroimage Clin. 2023; 37:103278.

6. Menon BK, Hill MD, Davalos A, Roos YBWEM, Campbell BCV, Dippel DWJ, et al. Efficacy of endovascular thrombectomy in patients with M2 segment middle cerebral artery occlusions: meta-analysis of data from the HERMES collaboration. J Neurointerv Surg. 2019; 11:1065–1069.

7. Tao C, Nogueira RG, Zhu Y, Sun J, Han H, Yuan G, et al. Trial of endovascular treatment of acute basilar-artery occlusion. N Engl J Med. 2022; 387:1361–1372.

8. Petersen NH, Ortega-Gutierrez S, Wang A, Lopez GV, Strander S, Kodali S, et al. Decreases in blood pressure during thrombectomy are associated with larger infarct volumes and worse functional outcome. Stroke. 2019; 50:1797–1804.

9. Terasawa Y, Iguchi Y, Kimura K, Kobayashi K, Aoki J, Matsumoto N, et al. Neurological deterioration in small vessel disease may be associated with increase of infarct volume. J Neurol Sci. 2008; 269:35–40.

10. Prabhakaran S, Liebeskind DS, Cotsonis G, Nizam A, Feldmann E, Sangha RS, et al. Predictors of early infarct recurrence in patients with symptomatic intracranial atherosclerotic disease. Stroke. 2021; 52:1961–1966.