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

Carrozzi, Alessandro; Gramegna, Laura Ludovica; Sighinolfi, Giovanni; Zoli, Matteo; Mazzatenta, Diego; Testa, Claudia; Lodi, Raffaele; Tonon, Caterina; Manners, David Neil

doi:10.1016/j.nicl.2023.103494

Cited by 2 publications

(1 citation statement)

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“…Xu et al, 2019), e.g., RAS (Right, Anterior, Superior) coordinates. While this spatial information is effective in differentiating white matter fiber tracts that have large shape and position dissimilarities, there can be very small geometric differences between RGVP streamlines and nearby non-RGVP streamlines that may belong to other structures (e.g., orbital muscles) or result from false positive streamline tracking due to partial voluming and low SNR issues (Carrozzi et al, 2023;J. He et al, 2021;Hofer et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Zhang,

Yao

et al. 2024

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%