Key Points Question Is near-infrared low-level light therapy (LLLT) feasible and safe after moderate traumatic brain injury, and does LLLT affect the brain and exhibit neuroreactivity? Findings In this randomized clinical trial including 68 patients with moderate traumatic brain injury who were randomized to receive LLLT or sham therapy, 28 patients completed at least 1 LLLT session without any reported adverse events. In the late subacute stage, there were statistically significant differences in the magnetic resonance imaging–derived diffusion parameters of the white matter tracts between the sham- and light-treated groups, demonstrating neuroreactivity of LLLT. Meaning The results of this clinical trial show that transcranial LLLT is feasible, safe, and affects the brain in a measurable manner.

INTRODUCTION: Segmenting brain structures around a tumor on brain images is important for radiotherapy and surgical planning. Current auto-segmentation methods often fail to segment brain anatomy when it is distorted by tumors. OBJECTIVE: To develop and validate 3D capsule networks (CapsNets) that can segment brain structures with novel spatial features that were not represented in the training data. Methods: We developed, trained, and tested 3D CapsNets using 3430 brain MRIs acquired in a multi-institutional study. We compared our CapsNets with U-Nets using multiple performance measures, including accuracy in segmenting various brain structures, segmenting brain structures with spatial features not represented in the training data, performance when the models are trained using limited data, memory requirements, and computation times. RESULTS: 3D CapsNets can segment third ventricle, thalamus, and hippocampus with Dice scores of 94%, 94%, and 91%, respectively. 3D CapsNets outperform 3D U-Nets in segmenting brain structures that were not represented in the training data, with Dice scores more than 30% higher. 3D CapsNets are also remarkably smaller models compared to 3D U-Nets, with 93% fewer trainable parameters. This led to faster convergence of 3D CapsNets during training, making them faster to train compared to U-Nets. The two models were equally fast during testing. CONCLUSION: 3D CapsNets can segment brain structures with high accuracy, outperform U-Nets in segmenting brain structures with features that were not represented during training, and are remarkably more efficient compared to U-Nets, achieving similar results while their size is one order of magnitude smaller.

Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance with limited training data, required computational memory, and computational speed during training and deployment. The 3D, 2.5D, and 2D approaches respectively gave the highest to lowest Dice scores across all models. 3D models maintained higher Dice scores when the training set size was decreased from 3199 MRIs down to 60 MRIs. 3D models converged 20% to 40% faster during training and were 30% to 50% faster during deployment. However, 3D models require 20 times more computational memory compared to 2.5D or 2D models. This study showed that 3D models are more accurate, maintain better performance with limited training data, and are faster to train and deploy. However, 3D models require more computational memory compared to 2.5D or 2D models.

Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance with limited training data, required computational memory, and computational speed during training and deployment. 3D, 2.5D, and 2D approaches respectively gave the highest to lowest Dice scores across all models. 3D models maintained higher Dice scores when the training set size was decreased from 3199 MRIs down to 60 MRIs. 3D models converged 20% to 40% faster during training and were 30% to 50% faster during deployment. However, 3D models require 20 times more computational memory compared to 2.5D or 2D models. This study showed that 3D models are more accurate, maintain better performance with limited training data, and are faster to train and deploy compared. However, 3D models require more computational memory compared to 2.5D or 2D models.

Recent studies demonstrate that low-level light therapy (LLLT) modulates recovery in patients with traumatic brain injury (TBI). However, the impact of LLLT on brain activity following TBI has not been well described. Here we use a randomized, double-blind, placebo-controlled design to investigate the effect of LLLT on resting-state connectivity at acute (within 1-week), subacute (2–3 weeks), and late-subacute (3-month) time-points following moderate TBI. A characteristic connectivity profile was observed during TBI recovery in both sham- (n = 21) and LLLT-treated patients (n = 17) compared to healthy controls, with increased resting-state connectivity between frontal and parietal cortices. Temporal comparisons between LLLT- and sham-treated patients showed that the acute-to-subacute changes in resting-state connectivity were significantly greater in LLLT-treated patients. These results demonstrate that LLLT increased resting-state connectivity in the presence of a regional hyperconnectivity response to moderate TBI, suggesting that LLLT can modulate activity in the injured brain and encouraging its further exploration as a therapy for TBI.