Abstract #3661

Improved image quality in the Beat Mapping using a Convolutional Recurrent Neural Network (BeatMapCRNN)

Xiaofeng Qian¹, Ancong Wang¹, Yingwei Fan¹, Bowei Liu², Yongsheng Jin³, Xiangchuang Kong⁴, Peng Wu⁵, Haiyan Ding², and Rui Guo¹

¹Shool of Medical Technology, Beijing Institute of Technology, Beijing, China, ²Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, ³Department of Infectious Diseases, The Affiliated Hospital of Yan’an University, Shanxi, China, ⁴Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, ⁵Philips Healthcare, Shanghai, China

Synopsis

Keywords: Myocardium, Precision & Accuracy, SASHA

Motivation: Cardiovascular magnetic resonance parametric mappings have high diagnostic and prognostic value. However, the image quality is significantly degraded by noise, especially saturation-based T₁ mapping. A new technique for image quality enhancement is required.

Goal(s): To develop a neural network to enhance image quality in the most commonly used cardiac parametric mapping sequences by removing noise.

Approach: A convolutional recurrent neural network (BeatMapCRNN) with local and non-local mean convolutional blocks was developed and tested by mapping data of healthy volunteers and patients by MOLLI, SASHA, and T₂-prepbSSFP mapping.

Results: BeatMapCRNN could effectively remove the noise and improve map quality.

Impact: This study developed a convolutional recurrent neural network to alleviate noise artifacts in cardiac T₁ and T₂ mapping. Validation indicated that the proposed method could improve map quality for most used T₁/T₂ sequences without compromising accuracy or blurring lesions.

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