Biology subjects

Kikinis, R.

Publications and source records attributed to Kikinis, R..

6 recordsLinked to original sources

bioRxiv2026

Toward CT-based Tractography: Presurgical White Matter Tract Mapping in Intracerebral Hemorrhage

Presurgical mapping of key white matter (WM) fiber tracts is crucial for intracerebral hemorrhage (ICH) surgery, but it currently relies on tractography from diffusion MRI (dMRI), which has limited applicability in urgent or resource-constrained settings due to long scan times and limited MRI availability. To bridge this gap, we developed a deep learning approach designed to reconstruct critical fiber tracts directly from routinely acquired CT scans, focusing on the corticospinal tract (CST) due to its high clinical relevance. By training a novel network on a curated dataset of 150 paired CT-dMRI scans (101 ICH patients and 49 healthy subjects), we enabled the direct mapping of the CST from CT images alone, bypassing the traditional requirement for dMRI. Our results demonstrate that the CT-derived tracts achieve high anatomical plausibility, with neurosurgeon expert assessments yielding a Likert score of 3.64. Furthermore, the clinical relevance of these reconstructions was validated by a significant correlation between CT-derived tract integrity and patient motor scores (r = 0.726, p = 3.731x 10-7). These findings suggest that in complex clinical scenarios, particularly where dMRI signal quality is compromised by lesion-induced distortion, this CT-based mapping may serve as a useful anatomical reference. Overall, by enabling CT-based WM mapping, our method potentially offers a practical route to gain the key advantages of tractography in resource-limited or time-critical settings

neuroscience↗

bioRxiv2024

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.

neuroscience↗

bioRxiv2022

Superficial white matter microstructure affects processing speed in cerebral small vessel disease

White matter hyperintensities (WMH) are a typical feature of cerebral small vessel disease (CSVD). This condition contributes to about 50% of dementias worldwide, a massive health burden in aging. Microstructural alterations in the deep white matter (DWM) have been widely examined in CSVD. However, little is known about abnormalities in the superficial white matter (SWM) and their relevance for processing speed, the main cognitive deficit in CSVD. In this paper, 141 patients with CSVD were studied. Processing speed was assessed by the completion time of the Trail Making Test Part A. White matter abnormalities were assessed by WMH burden (lesion volume on T2-FLAIR) and diffusion MRI, including DTI and free-water (FW) imaging microstructure measures. The results of our study indicate that the superficial white matter may play a particularly important role in cognitive decline in CSVD. SWM imaging measures resulted in a large contribution to processing speed, despite a relatively small WMH burden in the SWM. SWM FW had the strongest association with processing speed among all imaging markers and, unlike the other diffusion MRI measures, significantly increased between two patient subgroups with the lowest WMH burdens (possibly representing early stages of disease). When comparing two patient subgroups with the highest WMH burdens, the involvement of WMH in the SWM was accompanied by significant differences in processing speed and white matter microstructure. Given significant effects of WMH volume and regional FW on processing speed, we performed a mediation analysis. SWM FW was found to fully mediate the association between WMH volume and processing speed, while no mediation effect of DWM FW was observed. Overall, our findings identify SWM abnormalities in CSVD and suggest that the SWM has an important contribution to processing speed. Results indicate that FW in the SWM is a sensitive marker of microstructural changes associated with cognition in CSVD. This study extends the current understanding of CSVD-related dysfunction and suggests that the SWM, as an understudied region, can be a potential target for monitoring pathophysiological processes in future research.

neuroscience↗

bioRxiv2020

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 non-decussating 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.

neuroscience↗

bioRxiv2020

Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification

Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.

neuroscience↗

bioRxiv2019

3D Exploration of the Brainstem in 50-micron Resolution MRI

The brainstem, a structure of vital importance in the mammals, is currently becoming a principal focus in cognitive, affective and clinical neuroscience. Midbrain, pontine and medullar structures are the epicenter of conduit, cranial nerve and such integrative functions as consciousness, emotional processing, pain and motivation. In this study, we parcellated the nuclear masses and the principal fiber pathways that were visible in a high resolution T2-weighted MRI dataset of 50-micron isotropic voxels of a postmortem human brainstem. Based on this analysis, we generated a detailed map of the human brainstem. To assess the validity of our maps, we compared our observations with histological maps of traditional human brainstem atlases. Moreover, we reconstructed the motor, sensory and integrative neural systems of the brainstem and rendered them in 3D representations. We anticipate the utilization of these maps by the neuroimaging community at large for applications in basic neuroscience as well as in neurology, psychiatry and neurosurgery, due to their versatile computational nature in 2D and 3D representations in a publicly available capacity.

neuroscience↗