Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs

Advancing cerebral small vessel disease diagnosis: Integrating quantitative susceptibility mapping with MRI-based radiomics

Cerebral small vessel disease (CSVD) is a neurodegenerative disease with hidden symptoms and difficult to diagnose. The diagnosis mainly depends on clinical symptoms and neuroimaging. Therefore, we explored the potential of combining clinical detection with MRI-based radiomics features for the diagnosis of CSVD in a large cohort. A total of 118 CSVD patients and 127 healthy controls underwent quantitative susceptibility mapping and 3D-T1 scans, and all completed multiple cognitive tests. Lasso regression was used to select features, and the radiomics model was constructed based on the regression coefficients of these features. Clinical cognitive and motor tests were added to the model to construct a hybrid model. All models were cross-validated to analyze the generalization ability of the models. The AUCs of the radiomics and hybrid models in the internal test set were 0.80 and 0.87, respectively. In the validation set, the AUCs were 0.77 and 0.79, respectively. The hybrid model demonstrated higher decision efficiency. The Trail Making Test, which enhances the diagnostic performance of the model, is associated with multiple brain regions, particularly the right cortical nuclei and the right fimbria. The hybrid model based on radiomics features and cognitive tests can achieve quantitative diagnosis of CSVD and improve the diagnostic efficiency. Furthermore, the reduced processing capacity due to atrophy of the right cortical nucleus and right fimbria suggests the importance of these regions in improving the diagnostic accuracy of the model.

A latent code based multi-variable modulation network for susceptibility mapping

Quantitative susceptibility mapping (QSM) is a technique for obtaining quantitative information on tissue susceptibility and has shown promising potential for clinical applications, in which the magnetic susceptibility is calculated by solving an ill-posed inverse problem. Recently, deep learning-based methods are proposed to address this issue, but the diversity of data distribution was not well considered, and thus the model generalization is limited in clinical applications. In this paper, we propose a Latent Code based Multi-Variable modulation network for QSM reconstruction (LCMnet). Particularly, a specific modulation module is exploited to incorporate three variables, i.e., field map, magnitude image, and initial susceptibility. The latent code in the modulated convolution is learned from feature maps of the field data using the encoder-decoder framework. The susceptibility map pre-estimated from simple thresholding is the constant input of the module, thereby enhancing the network stability and accelerating training convergence. As another input, multi-level features generated by a cross-fusion block integrate the information of field and magnitude data effectively. Experimental results on in vivo human brain data, challenge data, clinical data and synthetic data demonstrate that the proposed method LCMnet can achieve outstanding performance on accurate susceptibility measurement and also excellent generalization.