Using temporal GAN to translate the current CTP scan to follow-up MRI, for predicting final acute ischemic stroke lesions

Mohsen Soltanpour; Muhammad Yousefnezhad; Russ Greiner; Pierre Boulange; Brian Buck

doi:10.1117/12.2654069

7 April 2023 Using temporal GAN to translate the current CTP scan to follow-up MRI, for predicting final acute ischemic stroke lesions

Mohsen Soltanpour, Muhammad Yousefnezhad, Russ Greiner, Pierre Boulange, Brian Buck

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124653D (2023) https://doi.org/10.1117/12.2654069
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Over 795,000 Americans suffer a stroke every year, leading to a death every 3.5 minutes. Approximately 87% of all strokes are Acute Ischemic Strokes (AIS), i.e., an abrupt interruption in cerebral circulation due to a blocked artery. Early prediction of AIS final outcomes (AIS lesions) is crucial to effective treatment planning for AIS patients. Due to its speed, availability, and lack of contraindications, Computed Tomography Perfusion (CTP) is preferred over other imaging modalities with higher resolution (e.g., MRI), for AIS lesion prediction. However, the low contrast of baseline CTP images makes it difficult to determine AIS lesions precisely, while follow-up MRI images do. Therefore, this paper proposes a method of synthesizing follow-up MRI images from baseline CTP scans by a Temporal Generative Adversarial Network (TGAN) — which encodes baseline CTP frames with a series of encoders, followed by a decoder that forecasts the high-resolution follow-up MRIs. It also uses a discriminator that competes with the generator to identify whether its input MRI is real or fake. Furthermore, our TGAN includes a segmentor that can identify AIS lesions in those synthesized MRI images. The generator, discriminator, and segmentor in TGAN each use MultiRes U-Nets, an extension of the original U-Net architecture, which can robustly segment objects of various scales and shapes. Our experiments with Leave-One-person-Out Cross-Validation (LOOCV) obtained an average dice coefficient of 56.73%, with a significant p<0.05. In comparison to traditional methods using CTP perfusion parameters, we found that our novel method was more accurate in predicting AIS lesions.

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Mohsen Soltanpour, Muhammad Yousefnezhad, Russ Greiner, Pierre Boulange, and Brian Buck "Using temporal GAN to translate the current CTP scan to follow-up MRI, for predicting final acute ischemic stroke lesions", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124653D (7 April 2023); https://doi.org/10.1117/12.2654069

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KEYWORDS

Artificial intelligence

Magnetic resonance imaging

Image segmentation

Ischemic stroke

Gallium nitride

Brain

Arteries

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