Deep learning based imaging data completion for improved brain disease diagnosis. Academic Article uri icon


  • Combining multi-modality brain data for disease diagnosis commonly leads to improved performance. A challenge in using multimodality data is that the data are commonly incomplete; namely, some modality might be missing for some subjects. In this work, we proposed a deep learning based framework for estimating multi-modality imaging data. Our method takes the form of convolutional neural networks, where the input and output are two volumetric modalities. The network contains a large number of trainable parameters that capture the relationship between input and output modalities. When trained on subjects with all modalities, the network can estimate the output modality given the input modality. We evaluated our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, where the input and output modalities are MRI and PET images, respectively. Results showed that our method significantly outperformed prior methods.

published proceedings

  • Med Image Comput Comput Assist Interv

altmetric score

  • 3.25

author list (cited authors)

  • Li, R., Zhang, W., Suk, H., Wang, L. i., Li, J., Shen, D., & Ji, S.

citation count

  • 319

complete list of authors

  • Li, Rongjian||Zhang, Wenlu||Suk, Heung-Il||Wang, Li||Li, Jiang||Shen, Dinggang||Ji, Shuiwang

publication date

  • January 2014