تقرير
Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction
| العنوان: | Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction |
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| المؤلفون: | Malkiel, Itzik, Ahn, Sangtae, Taviani, Valentina, Menini, Anne, Wolf, Lior, Hardy, Christopher J. |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning |
| الوصف: | Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning. However, these techniques sometimes struggle to reconstruct sharp images that preserve fine detail while maintaining a natural appearance. In this work, we enhance the image quality by using a Conditional Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique that stabilizes the training and minimizes the degree of artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques. |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/1905.00985Test |
| رقم الانضمام: | edsarx.1905.00985 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |