Dr. Yan Wu Profile

Dr. Yan Wu

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Proceedings Article | 16 March 2020 Presentation + Paper

Deciphering tissue relaxation parameters from a single MR image using deep learning

Yan Wu, Yajun Ma, Jiang Du, Lei Xing

Proceedings Volume 11314, 113140Q (2020) https://doi.org/10.1117/12.2546025

KEYWORDS: Magnetic resonance imaging, Tissues, Convolutional neural networks, Feature extraction, Data acquisition, Neural networks

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Despite its superior soft tissue contrast, conventional MRI is qualitative in nature and this presents a bottleneck problem in quantitative image analysis and data-driven medicine. Various investigations have been devoted to overcoming this limitation, but practical solutions remain elusive. Leveraging from the unique ability of emerging deep learning in feature extraction, we investigate a data-driven strategy of separating contributions of various contributing factors intertwined in a single T₁ weighted image to derive quantitative T₁ and ρ maps without any additional image acquisition. Furthermore, in the proposed deep learning framework, compensation for radiofrequency field inhomogeneities is automatically achieved without extra measurement of B₁ map. The tasks are accomplished using self-attention deep convolutional neural networks, which make efficient use of both local and non-local information. The premise of the approach is that qualitative and quantitative MRI, named Q²MRI, can be attained simultaneously without changing the existing imaging protocol. Q²MRI lays foundation for next generation of digital medicine and provides a promising quantitative imaging tool for a wide spectrum of biomedical applications, ranging from disease diagnosis, treatment planning, prognosis to assessment of therapeutic response.

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