Diffusion MRI harmonization via personalized template mapping.

One fundamental challenge in diffusion magnetic resonance imaging (dMRI) harmonization is to disentangle the contributions of scanner-related effects from the variable brain anatomy for the observed imaging signals. Conventional harmonization methods rely on establishing an atlas space to resolve anatomical variability and generate a unified inter-site mapping function. However, this approach is limited in accounting for the misalignment of neuroanatomy that still widely persists even after registration, especially in regions close to cortical boundaries. To overcome this challenge, we propose a personalized framework in this paper to more effectively address the confounding from the misalignment of neuroanatomy in dMRI harmonization. Instead of using a common template representing site-effects for all subjects, the main novelty of our method is the adaptive computation of personalized templates for both source and target scanning sites to estimate the inter-site mapping function. We integrate our method with the rotation invariant spherical harmonics (RISH) features to achieve the harmonization of dMRI signals between sites. In our experiments, the proposed approach is applied to harmonize the dMRI data acquired from two scanning platforms: Siemens Prisma and GE MR750 from the Adolescent Brain Cognitive Development dataset and compared with a state-of-the-art method based on RISH features. Our results indicate that the proposed harmonization framework achieves superior performance not only in reducing inter-site variations due to scanner differences but also in preserving sex-related biological variability in original cohorts. Moreover, we assess the impact of harmonization on the estimation of fiber orientation distributions and show the robustness of the personalized harmonization procedure in preserving the fiber orientation of original dMRI signals.

Choroidal vascularity index in different types of central serous chorioretinopathy: A meta-analysis.

PURPOSE: To evaluate the choroidal vascularity index (CVI) in different types of central serous chorioretinopathy (CSC), healthy control eyes, and fellow eyes. METHODS: Relevant studies published up to January 2023 were identified by searching multiple databases, including PubMed, Embase, Web of Science, Cochrane Library, and China National Knowledge Infrastructure (CNKI). Studies investigating the difference in CVI between CSC and control eyes were included. Data from these studies were analyzed using Stata (version 17) software. Weighted mean difference (WMD) and 95% confidence interval (95%CI) were calculated for the CVI in CSC eyes, control eyes, and fellow eyes. RESULTS: The meta-analysis included 15 studies, with 213 acute CSC eyes, 153 chronic CSC eyes, 92 uncategorized CSC eyes, 40 resolved CSC eyes, 409 eyes of normal healthy controls, and 318 fellow eyes. The result revealed that CVI was higher in acute CSC eyes (WMD = 5.40, 95%CI = 2.36-8.44, P = 0.001) compared to control eyes. Also, CVI in chronic CSC eyes was higher than in control eyes (WMD = 1.26, 95%CI = 0.03-2.49, p = 0.046). The fellow eyes of acute CSC had a higher CVI when compared to control eyes (WMD = 2.53, 95%CI = 0.78-4.28, p = 0.005). There was no significant difference in CVI between acute and chronic CSC eyes (WMD = 0.75, 95%CI = -0.31-1.82, P = 0.167). In the sub-analysis based on the area selected for CVI calculation, the WMDs in the whole image subgroups were lower than the main analysis for the comparisons of fellow eyes of acute CSC and control eyes, acute CSC eyes and control eyes, and acute CSC eyes and fellow eyes. In the macular area subgroups, the WMDs were higher than in the whole image subgroups, suggesting a potential regional variation of CVI in CSC eyes. CONCLUSIONS: The results demonstrated that CVI is increased in CSC eyes and fellow eyes of acute CSC. There is no significant difference in CVI between acute and chronic CSC eyes. The area selected for CVI calculation can influence the outcome, which requires further clinical research to clarify.

Case report: A case of Acute Macular Neuroretinopathy secondary to Influenza A virus during Long COVID.

Ocular abnormalities have been reported in association with viral infections, including Long COVID, a debilitating illness caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This report presents a case of a female patient diagnosed with Acute Macular Neuroretinopathy (AMN) following an Influenza A virus infection during Long COVID who experienced severe inflammation symptoms and ocular complications. We hypothesize that the rare occurrence of AMN in this patient could be associated with the immune storm secondary to the viral infection during Long COVID.

Personalized dMRI Harmonization on Cortical Surface.

The inter-site variability of diffusion magnetic resonance imaging (dMRI) hinders the aggregation of dMRI data from multiple centers. This necessitates dMRI harmonization for removing non-biological site-effects. Recently, the emergence of high-resolution dMRI data across various connectome imaging studies allows the large-scale analysis of cortical micro-structure. Existing harmonization methods, however, perform poorly in the harmonization of dMRI data in cortical areas because they rely on image registration methods to factor out anatomical variations, which have known difficulty in aligning cortical folding patterns. To overcome this fundamental challenge in dMRI harmonization, we propose a framework of personalized dMRI harmonization on the cortical surface to improve the dMRI harmonization of gray matter by adaptively estimating the inter-site harmonization mappings. In our experiments, we demonstrate the effectiveness of the proposed method by applying it to harmonize dMRI across the Human Connectome Project (HCP) and the Lifespan Human Connectome Projects in Development (HCPD) studies and achieved much better performance in comparison with conventional methods based on image registration.

Groupwise track filtering via iterative message passing and pruning.

Tractography is an important tool for the in vivo analysis of brain connectivity based on diffusion MRI data, but it also has well-known limitations in false positives and negatives for the faithful reconstruction of neuroanatomy. These problems persist even in the presence of strong anatomical priors in the form of multiple region of interests (ROIs) to constrain the trajectories of fiber tractography. In this work, we propose a novel track filtering method by leveraging the groupwise consistency of fiber bundles that naturally exists across subjects. We first formalize our groupwise concept with a flexible definition that characterizes the consistency of a track with respect to other group members based on three important aspects: degree, affinity, and proximity. An iterative algorithm is then developed to dynamically update the localized consistency measure of all streamlines via message passing from a reference set, which then informs the pruning of outlier points from each streamline. In our experiments, we successfully applied our method to diffusion imaging data of varying resolutions from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Human Connectome Project (HCP) for the consistent reconstruction of three important fiber bundles in human brain: the fornix, locus coeruleus pathways, and corticospinal tract. Both qualitative evaluations and quantitative comparisons showed that our method achieved significant improvement in enhancing the anatomical fidelity of fiber bundles.

Multi-Parameter Ensemble Learning for Automated Vertebral Body Segmentation in Heterogeneously Acquired Clinical MR Images.

The development of quantitative imaging biomarkers in medicine requires automatic delineation of relevant anatomical structures using available imaging data. However, this task is complicated in clinical medicine due to the variation in scanning parameters and protocols, even within a single medical center. Existing literature on automatic image segmentation using MR data is based on the analysis of highly homogenous images obtained using a fixed set of pulse sequence parameters (TR/TE). Unfortunately, algorithms that operate on fixed scanning parameters do not avail themselves to real-world daily clinical use due to the existing variation in scanning parameters and protocols. Thus, it is necessary to develop algorithmic techniques that can address the challenge of MR image segmentation using real clinical data. Toward this goal, we developed a multi-parametric ensemble learning technique to automatically detect and segment lumbar vertebral bodies using MR images of the spine. We use spine imaging data to illustrate our techniques since low back pain is an extremely common condition and a typical spine clinic evaluates patients that have been referred with a wide range of scanning parameters. This method was designed with special emphasis on robustness so that it can perform well despite the inherent variation in scanning protocols. Specifically, we show how a single multi-parameter ensemble model trained with manually labeled T2 scans can autonomously segment vertebral bodies on scans with echo times varying between 24 and 147 ms and relaxation times varying between 1500 and 7810 ms. Furthermore, even though the model was trained using T2-MR imaging data, it can accurately segment vertebral bodies on T1-MR and CT, further demonstrating the robustness and versatility of our methodology. We believe that robust segmentation techniques, such as the one presented here, are necessary for translating computer assisted diagnosis into everyday clinical practice.