Track-weighted imaging for neuroretina: Evaluations in healthy volunteers and ischemic optic neuropathy

Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning

The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP , to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a simple and successful streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. We perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation, and we show superior RGVP identification results using DeepRGVP. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.

Age-related changes in the healthy adult visual pathway: evidence from diffusion tensor imaging with fixel-based analysis

BACKGROUND AND PURPOSE

Fixel-based analysis (FBA) is a new method that overcomes the technical limitations of diffusion tensor imaging (DTI) by enabling the characterization of multiple fiber populations within a voxel, and provides biologically meaningful indicators. This study aimed to explore age-related changes in the visual pathway in healthy adults and to observe differences in imaging quality between data collected using different b‑values.

METHODS

In this prospective cross-sectional study, brain DTI scans which were collected with more than six uniformly distributed gradient directions and higher b‑values (up to 2000 s/mm2) than traditional DTI were performed in 72 healthy adults across the adult lifespan (20-79 years). After image preprocessing, FBA was used to process the dataset. At the same time, conventional DTI metrics were also calculated.

RESULTS

Pearson's correlation analysis showed that DTI parameters of white matter (optic nerve, optic chiasma, optic tract, and optic radiation) in the optic pathway were correlated with age. FA values were negatively correlated with age, while MD/AD/RD showed a positive correlation (P < 0.05). FBA showed that the index including FD/FC/FDC tended to decline with age (P < 0.05). Linear regression analysis showed a linear relationship between DTI metrics of the dataset collected by b‑values of 1000 and 2000 s/mm2 (P < 0.05).

CONCLUSION

FBA provides a useful method to assess age-related changes in the visual pathway, which is sensitive to diffusion. In addition, the b‑value influences DTI parameters and signal-to-noise ratio of the image.

Methods of diffusion MRI tractography for localization of the anterior optic pathway: A systematic review of validated methods

The anterior optic pathway (AOP) is a system of three structures (optic nerves, optic chiasma, and optic tracts) that convey visual stimuli from the retina to the lateral geniculate nuclei. A successful reconstruction of the AOP using tractography could be helpful in several clinical scenarios, from presurgical planning and neuronavigation of sellar and parasellar surgery to monitoring the stage of fiber degeneration both in acute (e.g., traumatic optic neuropathy) or chronic conditions that affect AOP structures (e.g., amblyopia, glaucoma, demyelinating disorders or genetic optic nerve atrophies). However, its peculiar anatomy and course, as well as its surroundings, pose a serious challenge to obtaining successful tractographic reconstructions. Several AOP tractography strategies have been adopted but no standard procedure has been agreed upon. We performed a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 guidelines in order to find the combinations of acquisition and reconstruction parameters that have been performed previously and have provided the highest rate of successful reconstruction of the AOP, in order to promote their routine implementation in clinical practice. For this purpose, we reviewed data regarding how the process of anatomical validation of the tractographies was performed. The Cochrane Handbook for Systematic Reviews of Interventions was used to assess the risk of bias and thus the study quality We identified thirty-nine studies that met our inclusion criteria, and only five were considered at low risk of bias and achieved over 80% of successful reconstructions. We found a high degree of heterogeneity in the acquisition and analysis parameters used to perform AOP tractography and different combinations of them can achieve satisfactory levels of anterior optic tractographic reconstruction both in real-life research and clinical scenarios. One thousand s/mm2 was the most frequently used b value, while both deterministic and probabilistic tractography algorithms performed morphological reconstruction of the tract satisfactorily, although probabilistic algorithms estimated a more realistic percentage of crossing fibers (45.6%) in healthy subjects. A wide heterogeneity was also found regarding the method used to assess the anatomical fidelity of the AOP reconstructions. Three main strategies can be found: direct visual direct visual assessment of the tractography superimposed to a conventional MR image, surgical evaluation, and computational methods. Because the latter is less dependent on a priori knowledge of the anatomy by the operator, computational methods of validation of the anatomy should be considered whenever possible.

A unified global tractography framework for automatic visual pathway reconstruction

The human visual pathway starts from the retina, passes through the retinogeniculate visual pathway, the optic radiation, and finally connects to the primary visual cortex. Diffusion MRI tractography is the only technology that can noninvasively reconstruct the visual pathway. However, complete and accurate visual pathway reconstruction is challenging because of the skull base environment and complex fiber geometries. Specifically, the optic nerve within the complex skull base environment can cause abnormal diffusion signals. The crossing and fanning fibers at the optic chiasm, and a sharp turn of Meyer's loop at the optic radiation, contribute to complex fiber geometries of the visual pathway. A fiber trajectory distribution (FTD) function-based tractography method of our previous work and several high sensitivity tractography methods can reveal these complex fiber geometries, but are accompanied by false-positive fibers. Thus, the related studies of the visual pathway mostly applied the expert region of interest selection strategy. However, interobserver variability is an issue in reconstructing an accurate visual pathway. In this paper, we propose a unified global tractography framework to automatically reconstruct the visual pathway. We first extend the FTD function to a high-order streamline differential equation for global trajectory estimation. At the global level, the tractography process is simplified as the estimation of global trajectory distribution coefficients by minimizing the cost between trajectory distribution and the selected directions under the prior guidance by introducing the tractography template as anatomic priors. Furthermore, we use a deep learning-based method and tractography template prior information to automatically generate the mask for tractography. The experimental results demonstrate that our proposed method can successfully reconstruct the visual pathway with high accuracy.

Current insights of applying MRI in Graves' ophthalmopathy

Graves' ophthalmopathy (GO) is an autoimmune disease related to Grave's disease (GD). The therapeutic strategies for GO patients are based on precise assessment of the activity and severity of the disease. However, the current assessment systems require development to accommodate updates in treatment protocols. As an important adjunct examination, magnetic resonance imaging (MRI) can help physicians evaluate GO more accurately. With the continuous updating of MRI technology and the deepening understanding of GO, the assessment of this disease by MRI has gone through a stage from qualitative to precise quantification, making it possible for clinicians to monitor the microstructural changes behind the eyeball and better integrate clinical manifestations with pathology. In this review, we use orbital structures as a classification to combine pathological changes with MRI features. We also review some MRI techniques applied to GO clinical practice, such as disease classification and regions of interest selection.

Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI

The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for RGVP reconstruction. In this study, four tractography methods are compared, including constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD‐Stream) methods, and multi‐fiber (UKF‐2T) and single‐fiber (UKF‐1T) unscented Kalman filter (UKF) methods. Experiments use diffusion MRI data from 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Quantitative anatomical measurements and expert anatomical judgment are used to assess the advantages and limitations of the four tractography methods. Overall, we conclude that UKF‐2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF‐2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy and have the highest spatial overlap across subjects. Overall, we find that it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. We suggest that future algorithm development for RGVP tractography should take consideration of both of these two points.

Track density imaging in progressive supranuclear palsy: A pilot study

Progressive supranuclear palsy (PSP) is a neurodegenerative disorder characterized by white matter (WM) changes in different supra- and infratentorial brain structures. We used track density imaging (TDI) to characterize WM microstructural alterations in patients with PSP-Richardson's Syndrome (PSP-RS). Moreover, we investigated the diagnostic utility of TDI in distinguishing patients with PSP-RS from those with Parkinson's disease and healthy controls (HC). Twenty PSP-RS patients, 21 PD patients, and 23 HC underwent a 3 T MRI diffusion-weighted (DW) imaging. Then, we combined constrained spherical deconvolution and WM probabilistic tractography to reconstruct track density maps by calculating the number of WM streamlines traversing each voxel. Voxel-wise analysis was performed to assess group differences in track density maps. A support vector machine (SVM) approach was also used to evaluate the performance of TDI for discriminating between groups. Relative to PD patients, decreases in track density in PSP-RS patients were found in brainstem, cerebellum, thalamus, corpus callosum, and corticospinal tract. Similar findings were obtained between PSP-RS patients and HC. No differences in TDI were observed between PD and HC. SVM approach based on whole-brain analysis differentiated PD patients from PSP-RS with an area under the curve (AUC) of 0.82. The AUC reached a value of 0.98 considering only the voxels belonging to the superior cerebellar peduncle. This study shows that TDI may represent a useful approach for characterizing WM alterations in PSP-RS patients. Moreover, track density decrease in PSP could be considered a new feature for the differentiation of patients with PSP-RS from those with PD.