Applications of Deep Learning to Neuro-Imaging Techniques

Abstract

Many clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. There are many other innovative applications of AI in various technical aspects of medical imaging, particularly applied to the acquisition of images, ranging from removing image artifacts, normalizing/harmonizing images, improving image quality, lowering radiation and contrast dose, and shortening the duration of imaging studies. This article will address this topic and will seek to present an overview of deep learning applied to neuroimaging techniques.

Keywords: acquisition; artificial intelligence; deep learning; neuro-imaging; radiology.

Figure 1

Example of components of Biologic Neural Network (A) and Computer Neural Network (B). Reprinted with permission from Zaharchuk et al. (15). Copyright American Journal of Neuroradiology.

Figure 2

Imaging value chain. While most AI applications have focused on the downstream (or right) side of this pathway, such the use of AI to detect and classify lesions on imaging studies, it is likely that there will be earlier adoption for the tasks on the upstream (or left) side, where most of the costs of imaging are concentrated.

Figure 3

Example of low-dose contrast-enhaced MRI. Results from a deep network for predicting a 100% contrast dose image from a study obtained with 10% of the standard contrast dose. This example MRI is abtained from a patient with menigioma. Such methods may enable diagnostic quality images to be acquired more safely in a wider range of patients (Courtesy of Subtle Medical, Inc.).

Figure 4

Example of ultra-low dose 18F-florbetaben PET/MRI. Example of a positive 18F-florbetaben PET/MRI study acquired at 0.24 mCi, ~3% of a standard dose. Similar image quality is present in the 100% dose image and the synthetized image, which was created using a deep neural network along with MRI information such as T1, T2, and T2-FLAIR. As Alzheimer Disease studies are moving toward cognitively normal and younger patients, reducing dose would be helpful. Furthermore, tracer costs could be reduced if doses can be shared.

Figure 5

Deep learning for improving image quality of arterial spin labeling in a patient with right-sided Moyamoya disease. Reference scan (A) requiring 8 min to collect (nex = 6). Using a rapid scan acquired in 2 min (nex=1) (B), it is possible to create an image (F) with the SNR of a study requiring over 4.5 min (nex = 3) (E). The peak signal-to-noise (PSNR) performance is superior to existing de-noising methods such as (C) block matched 3D (BM3D) and (D) total generalized variation (TGV). Such methods could speed up MRI acquisition, enabling more functional imaging and perhaps reducing the cost of scanning.

Figure 6

Use of convolutional neural networks to perform super-resolution. High-resolution T1-weighted imaging often requires long scan times to acquire sufficient resolution to resolve the gray-white border and to estimate cortical thickness. Shorter scans may be obtained with lower resolution, and AI can be used to restore the required high resolution (Image courtesy of Subtle Medical Inc.).