Enhao Gong

Deep Learning in Neuroradiology

SUMMARY: Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method.

Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI: Deep Learning Reduces Gadolinium Dose

There are concerns over gadolinium deposition from gadolinium‐based contrast agents (GBCA) administration.

Quantitative susceptibility mapping using deep neural network: QSMnet

&NA; Deep neural networks have demonstrated promising potential for the field of medical image reconstruction, successfully generating high quality images for CT, PET and MRI. In this work, an MRI reconstruction algorithm, which is referred to as quantitative susceptibility mapping (QSM), has been developed using a deep neural network in order to perform dipole deconvolution, which restores magnetic susceptibility source from an MRI field map. Previous approaches of QSM require multiple orientation data (e.g. Calculation of Susceptibility through Multiple Orientation Sampling or COSMOS) or regularization terms (e.g. Truncated K‐space Division or TKD; Morphology Enabled Dipole Inversion or MEDI) to solve an ill‐conditioned dipole deconvolution problem. Unfortunately, they either entail challenges in data acquisition (i.e. long scan time and multiple head orientations) or suffer from image artifacts. To overcome these shortcomings, a deep neural network, which is referred to as QSMnet, is constructed to generate a high quality susceptibility source map from single orientation data. The network has a modified U‐net structure and is trained using COSMOS QSM maps, which are considered as gold standard. Five head orientation datasets from five subjects were employed for patch‐wise network training after doubling the training data using a model‐based data augmentation. Seven additional datasets of five head orientation images (i.e. total 35 images) were used for validation (one dataset) and test (six datasets). The QSMnet maps of the test dataset were compared with the maps from TKD and MEDI for their image quality and consistency with respect to multiple head orientations. Quantitative and qualitative image quality comparisons demonstrate that the QSMnet results have superior image quality to those of TKD or MEDI results and have comparable image quality to those of COSMOS. Additionally, QSMnet maps reveal substantially better consistency across the multiple head orientation data than those from TKD or MEDI. As a preliminary application, the network was further tested for three patients, one with microbleed, another with multiple sclerosis lesions, and the third with hemorrhage. The QSMnet maps showed similar lesion contrasts with those from MEDI, demonstrating potential for future applications. Graphical abstract Figure. No caption available. HighlightsNew QSM reconstruction, QSMnet, is developed using a deep neural network.QSMnet generates a highly accurate QSM map close to a gold standard (COSMOS) map.Processing time of QSMnet is only a few seconds, achieving real‐time processing.In patients, QSMnet delivers similar lesion contrasts to conventional QSM.

LOW-DOSE AMYLOID PET RECONSTRUCTION USING A PRE-TRAINED, MULTIMODAL DEEP LEARNING NETWORK

negatively associated to global cognition (lateral temporal, fusiform and occipital) but not to episodic memory, while FDG showed strong and widespread positive associations with both global cognition and episodic memory. Hippocampal FDG mediated the effect of AV1451 on global cognition in the AD group. In the Abneg-MCI group, FDG mediated the AV1451 effect on both global cognition and episodic memory in parahippocampal, lateral temporal and frontal regions. Results for other cognitive domains will be also presented. Conclusions: The direct and FDG-mediated effects of AV1451 on cognition had sharply contrasting patterns across diagnostic groups, suggesting that tau pathology may lead to cognitive deficits via different mechanisms depending on presence or absence of Ab. Multimodal PET studies may contribute to understanding pathophysiological mechanisms by which cerebral tau deposits lead to cognitive dysfunction in neurodegenerative disorders.