TBI Contusion Segmentation from MRI using Convolutional Neural Networks

Traumatic brain injury (TBI) is caused by a sudden trauma to the head that may result in hematomas and contusions and can lead to stroke or chronic disability. An accurate quantification of the lesion volumes and their locations is essential to understand the pathophysiology of TBI and its progression. In this paper, we propose a fully convolutional neural network (CNN) model to segment contusions and lesions from brain magnetic resonance (MR) images of patients with TBI. The CNN architecture proposed here was based on a state of the art CNN architecture from Google, called Inception. Using a 3-layer Inception network, lesions are segmented from multi-contrast MR images. When compared with two recent TBI lesion segmentation methods, one based on CNN (called DeepMedic) and another based on random forests, the proposed algorithm showed improved segmentation accuracy on images of 18 patients with mild to severe TBI. Using a leave-one-out cross validation, the proposed model achieved a median Dice of 0.75, which was significantly better (p<0.01) than the two competing methods.Comment: https://ieeexplore.ieee.org/abstract/document/8363545/, IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018

Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation

Deep learning algorithms utilizing magnetic resonance (MR) images have demonstrated cutting-edge proficiency in autonomously segmenting multiple sclerosis (MS) lesions. Despite their achievements, these algorithms may struggle to extend their performance across various sites or scanners, leading to domain generalization errors. While few-shot or one-shot domain adaptation emerges as a potential solution to mitigate generalization errors, its efficacy might be hindered by the scarcity of labeled data in the target domain. This paper seeks to tackle this challenge by integrating one-shot adaptation data with harmonized training data that incorporates labels. Our approach involves synthesizing new training data with a contrast akin to that of the test domain, a process we refer to as "contrast harmonization" in MRI. Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation. Notably, domain adaptation using exclusively harmonized training data achieved comparable or even superior performance compared to one-shot adaptation. Moreover, all adaptations required only minimal fine-tuning, ranging from 2 to 5 epochs for convergence

Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?

Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-processing. The first contribution of this study is to model tag fading by considering the interplay between $T_1$ relaxation and the repeated application of radio frequency (RF) pulses during serial imaging sequences. This is a factor that has been overlooked in prior research on tMRI post-processing. Further, we have observed an emerging trend of utilizing raw tagged MRI within a deep learning-based (DL) registration framework for motion estimation. In this work, we evaluate and analyze the impact of commonly used image similarity objectives in training DL registrations on raw tMRI. This is then compared with the Harmonic Phase-based approach, a traditional approach which is claimed to be robust to tag fading. Our findings, derived from both simulated images and an actual phantom scan, reveal the limitations of various similarity losses in raw tMRI and emphasize caution in registration tasks where image intensity changes over time.Comment: Accepted to SPIE Medical Imaging 2024 (oral

Lasting deficit in inhibitory control with mild traumatic brain injury

Abstract Being able to focus on a complex task and inhibit unwanted actions or interfering information (i.e., inhibitory control) are essential human cognitive abilities. However, it remains unknown the extent to which mild traumatic brain injury (mTBI) may impact these critical functions. In this study, seventeen patients and age-matched healthy controls (HC) performed a variant of the Stroop task and attention-demanding 4-choice response tasks (4CRT) with identical stimuli but two contexts: one required only routine responses and the other with occasional response conflicts. The results showed that mTBI patients performed equally well as the HC when the 4CRT required only routine responses. However, when the task conditions included occasional response conflicts, mTBI patients with even a single concussion showed a significant slow-down in all responses and higher error rates relative to the HC. Results from event-related functional magnetic resonance imaging (efMRI) revealed altered neural activity in the mTBI patients in the cerebellum-thalamo-cortical and the fronto-basal-ganglia networks regulating inhibitory control. These results suggest that even without apparent difficulties in performing complex attention-demanding but routine tasks, patients with mTBI may experience long-lasting deficits in regulating inhibitory control when situations call for rapid conflict resolutions