MAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generation.
Authors
Affiliations (9)
Affiliations (9)
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China.
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, 410011, China.
- Department of Electrical Engineering, City University of Hong Kong, Kowloon Tong, 999077, Hong Kong SAR, China.
- Department of Radiology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, 410008, China.
- School of Computing, The University of Georgia, Athens, 30602, USA.
- Department of Electronic Engineering, The Chinese University of Hong Kong, Ma Liu Shui, 999077, Hong Kong SAR, China. [email protected].
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China. [email protected].
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, 410011, China. [email protected].
Abstract
Liver multiphase enhanced computed tomography (MPECT) is vital in clinical practice, but its utility is limited by various factors. We aimed to develop a deep learning network capable of automatically generating MPECT images from standard non-contrast CT scans. Dataset 1 included 374 patients and was divided into three parts: a training set, a validation set and a test set. Dataset 2 included 144 patients with one specific liver disease and was used as an internal test dataset. We further collected another dataset comprising 83 patients for external validation. Then, we propose a Mask-Adaptive Normalization-based Generative Adversarial Network with Cycle-Consistency Loss (MAN-GAN) to achieve non-contrast CT to MPECT translation. To assess the efficiency of MAN-GAN, we conducted a comparative analysis with state-of-the-art methods commonly employed in diverse medical image synthesis tasks. Moreover, two subjective radiologist evaluation studies were performed to verify the clinical usefulness of the generated images. MAN-GAN outperformed the baseline network and other state-of-the-art methods in all generations of the three phases. These results were verified in internal and external datasets. According to radiological evaluation, the image quality of generated three phase images are all above average. Moreover, the similarities between real images and generated images in all three phases are satisfactory. MAN-GAN demonstrates the feasibility of liver MPECT image translation based on non-contrast images and achieves state-of-the-art performance via the subtraction strategy. It has great potential for solving the dilemma of liver CT contrast canning and aiding further liver interaction clinical scenarios.