Evaluating the impact of MR image harmonization on thalamus deep network segmentation

Deep learning for the harmonization of structural MRI scans: a survey

Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements.

Trans-Synaptic Degeneration Following Acute Optic Neuritis in Multiple Sclerosis

OBJECTIVE

To explore longitudinal changes in brain volumetric measures and retinal layer thicknesses following acute optic neuritis (AON) in people with multiple sclerosis (PwMS), to investigate the process of trans-synaptic degeneration, and determine its clinical relevance.

METHODS

PwMS were recruited within 40 days of AON onset (n = 49), and underwent baseline retinal optical coherence tomography and brain magnetic resonance imaging followed by longitudinal tracking for up to 5 years. A comparator cohort of PwMS without a recent episode of AON were similarly tracked (n = 73). Mixed-effects linear regression models were used.

RESULTS

Accelerated atrophy of the occipital gray matter (GM), calcarine GM, and thalamus was seen in the AON cohort, as compared with the non-AON cohort (-0.76% vs -0.22% per year [p = 0.01] for occipital GM, -1.83% vs -0.32% per year [p = 0.008] for calcarine GM, -1.17% vs -0.67% per year [p = 0.02] for thalamus), whereas rates of whole-brain, cortical GM, non-occipital cortical GM atrophy, and T2 lesion accumulation did not differ significantly between the cohorts. In the AON cohort, greater AON-induced reduction in ganglion cell+inner plexiform layer thickness over the first year was associated with faster rates of whole-brain (r = 0.32, p = 0.04), white matter (r = 0.32, p = 0.04), and thalamic (r = 0.36, p = 0.02) atrophy over the study period. Significant relationships were identified between faster atrophy of the subcortical GM and thalamus, with worse visual function outcomes after AON.

INTERPRETATION

These results provide in-vivo evidence for anterograde trans-synaptic degeneration following AON in PwMS, and suggest that trans-synaptic degeneration may be related to clinically-relevant visual outcomes. ANN NEUROL 2023;93:76-87.