q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans

Abstract

Numerous scientific fields rely on elaborate but partly suboptimal data processing pipelines. An example is diffusion magnetic resonance imaging (diffusion MRI), a non-invasive microstructure assessment method with a prominent application in neuroimaging. Advanced diffusion models providing accurate microstructural characterization so far have required long acquisition times and thus have been inapplicable for children and adults who are uncooperative, uncomfortable, or unwell. We show that the long scan time requirements are mainly due to disadvantages of classical data processing. We demonstrate how deep learning, a group of algorithms based on recent advances in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step. This modification allows obtaining scalar measures from advanced models at twelve-fold reduced scan time and detecting abnormalities without using diffusion models. We set a new state of the art by estimating diffusion kurtosis measures from only 12 data points and neurite orientation dispersion and density measures from only 8 data points. This allows unprecedentedly fast and robust protocols facilitating clinical routine and demonstrates how classical data processing can be streamlined by means of deep learning.

Learning a variational network for reconstruction of accelerated MRI data

“…Taking encouragement from early successes in image classification tasks (3), recent advances also address semantic labeling (4), optical flow (5) and image restoration (6). In medical imaging, deep learning has also been applied to areas like segmentation (7, 8), q-space image processing (9), and skull stripping (10). However, in these applications, deep learning was seen as a tool for image processing and interpretation.…”

Comparing Two Processing Pipelines to Measure Subcortical and Cortical Volumes in Patients with and without Mild Traumatic Brain Injury

“…In particular, magnetic resonance imaging (MRI) has seen technological improvements in resolution, speed of acquisition, the addition of new sequences, and 3-dimensional visualization of complex anatomy. [1][2][3][4][5] Combined, these improvements have changed the way images are used in clinical settings and there are many efforts to establish more standardized evidence-based uses that will inform clinical understanding of the extent, location, type, progression, and prognosis of an injury or disease process. Current clinical use of medical imaging requires visual inspection and clinical judgment of competently trained radiologists.…”

Data‐driven synthetic MRI FLAIR artifact correction via deep neural network

“…Recently, deep learning (DL) using a neural network has shown remarkable potential for similar problems in which model-based analytical approaches are difficult to apply. [12][13][14][15][16][17] The method can learn a nonlinear mapping from an input space to an output space when enough dataset pairs are given. This property has led to many applications in medical image reconstructions such as improving image quality, 12,13 solving inverse problems, [14][15][16][17] and synthetic image generation.…”

“…[12][13][14][15][16][17] The method can learn a nonlinear mapping from an input space to an output space when enough dataset pairs are given. This property has led to many applications in medical image reconstructions such as improving image quality, 12,13 solving inverse problems, [14][15][16][17] and synthetic image generation. [18][19][20][21] Considering the current outcomes of deep neural networks on these applications, the approach may be applicable for correcting the artifacts of synthetic FLAIR images.…”