Complementation‐reinforced network for integrated reconstruction and segmentation of pulmonary gas MRI with high acceleration

• Published in: Medical physics (Lancaster) Vol. 51; no. 1; pp. 378 - 393
• Main Authors: Li, Zimeng, Xiao, Sa, Wang, Cheng, Li, Haidong, Zhao, Xiuchao, Zhou, Qian, Rao, Qiuchen, Fang, Yuan, Xie, Junshuai, Shi, Lei, Ye, Chaohui, Zhou, Xin
• Format: Journal Article
• Language: English
• Published: United States 01.01.2024
• Subjects: Humans; hyperpolarized gas MRI; Image Processing, Computer-Assisted - methods; image reconstruction; Lung - diagnostic imaging; lung segmentation; Magnetic Resonance Imaging - methods; multi‐task learning; Respiration; Signal-To-Noise Ratio; ventilation defects; multi-task learning; lung segmentation; image reconstruction; ventilation defects; hyperpolarized gas MRI

Background Hyperpolarized (HP) gas MRI enables the clear visualization of lung structure and function. Clinically relevant biomarkers, such as ventilated defect percentage (VDP) derived from this modality can quantify lung ventilation function. However, long imaging time leads to image quality degra...