Diffusion tractography is an MRI technique that shows how fibers are organized inside tissues by tracking the movement of water molecules. It has been used mostly to study the white matter pathways in the brain, but researchers are now applying it to other parts of the body. This review looks at how tractography is being used in organs and tissues outside the brain, including the heart, spinal cord, peripheral nerves, kidneys, muscles, and prostate. Each of these areas requires adjustments in how the images are collected and analyzed because they move, have different tissue properties, or contain more complex structures than brain tissue. Although there are still challenges, such as dealing with motion during scanning and interpreting fiber patterns in tissues with less organized structure, tractography outside the brain offers a valuable, noninvasive way to study how tissues are organized at a microscopic level. These advances open new possibilities for both biomedical research and clinical care.

Fig. 1

Tractography of ex vivo mouse heart in 4-chamber view acquired at 35ĚýÎĽm isotropic resolution illustrates the transmural transition in orientation of cardiomyocytes in the left ventricular septal wall from subendocardium to subepicardium (highlighted white); underlying data fromĚý(Teh et al.Ěý). Tracks are coloured according to directions: along the heart long-axis (blue), anterior-posterior (green) and septal-lateral directions (red)

The first annual International Society of Tractography (IST) debate, held in Corsica in 2024, focused on key challenges and controversies in tractography, a technique that maps brain connections using diffusion MRI. The debate centered on the provocative statement: “Tractography cannot give us anything we can’t get from an atlas template.” On one side, white matter atlas templates were highlighted as standardized, population-based maps of the brain that are especially useful for studying brain structure and making group comparisons. On the other side, subject-specific tractography was presented as a powerful approach that reconstructs the unique brain connections of each individual, allowing an in vivo “virtual dissection” of white matter pathways. This article introduces these concepts, summarizes the arguments for and against the statement, and, while acknowledging the strengths of both methods, underscores the unique value of tractography in advancing our understanding of individual brain connectivity.

Fig. 1

Schematic illustration of distinctions between atlas vs. subject-specific tractography. Atlas: volumetric or streamline-based atlases can be warped to an individual subject in a process called label/streamline propagation, enabling the identification and labeling of white matter pathways or regions. Subject-specific Tractography: bundle segmentation can be performed using a targeted approach (often using inclusion/exclusion regions of interest), or by generating a whole brain set of streamlines and subsequent processing to dissect and segment the streamlines of interest. We note that bundle dissection can be performed using manual regions of interest, or even making use of atlases to compare shape/location similarity when extracting and labelling subject-specific streamlines

Multiple sclerosis (MS) is a chronic disease of the nervous system that causes inflammation, damage to the protective covering of nerve fibers (demyelination), and degeneration of axons. These changes can be studied using diffusion tensor imaging (DTI), which measures microstructural damage in the brain and spinal cord. In the brain, researchers often use white matter (WM) tractography to examine changes along specific pathways. In the spinal cord (SC), however, anatomy is naturally divided into cervical levels, which provides a different way to study regional changes.

In this study, we used an along-level approach to measure both microstructural features (such as fractional anisotropy, a DTI measure of tissue integrity) and macrostructural features (such as cross-sectional area) of the SC in people with relapsing-remitting MS (pwRRMS) compared to healthy controls (HCs).

The results showed that analyzing the SC level by level was more sensitive to detecting group differences than averaging across the whole cord. Segmenting the cord into WM tracts and gray matter (GM) subregions revealed specific, localized changes along the cord and within its cross-sections. Importantly, GM atrophy was linked with greater clinical disability, whereas microstructural changes did not show significant associations with disability measures.

These findings highlight the value of level-specific analysis for identifying localized spinal cord pathology and suggest a more refined framework for studying SC changes in MS.

Figure 1. Depiction of healthy and MS cord processing, including delineation of the masks relative to healthy or lesioned tissue. The contrasts are included in the right column. CSA and diffusion-derived indices were calculated for HCs, and CSA, diffusion-derived indices, and lesion load were calculated for pwRRMS.

Over the last few decades, diffusion MRI (dMRI) streamline tractography has become the main way to estimate white matter (WM) pathways—the brain’s wiring—while a person is alive. But a big limitation is that this method usually needs a special type of scan called high angular resolution diffusion imaging (HARDI), which can be hard to get during regular medical care. This means tractography is mostly used in research settings and with certain groups of patients, limiting its use in everyday clinical practice and for rare or underfunded diseases. Because of this, having a tractography method that works with common clinical scans would be very important. Such a method would need to perform flexible tractography, use only standard clinical imaging as input, and be openly available for anyone to use. In this study, we tested a new deep learning model that uses T1-weighted (T1w) MRI scans—common clinical images—to estimate brain pathways. We compared its performance with traditional dMRI-based tractography and atlas-based methods in healthy young people, older adults, and patients with epilepsy, depression, and brain cancer. In healthy young people, our deep learning model showed slightly more error than traditional tractography, but the difference was small and less than errors seen with atlas-based methods. We also found that the model could replicate some important findings from previous dMRI studies in the clinical groups, especially for long-range brain connections that atlas methods miss, but not in all cases. These results suggest that deep learning using T1w MRI shows promise for clinical tractography, especially compared to atlas-based methods, but still needs improvement and careful testing before it can be widely used in hospitals. Additionally, our findings raise new questions about how differences between dMRI and T1w MRI scans affect tractography results, and more research on this will help us better understand what brain features influence these measurements.

Fig 1

Tractograms (left view), right arcuate fasciculi (right view), left cinguli (left view), and cortical connectomes from traditional SD_STREAM tractography and the CoRNN method in a representative in-distribution HCP participant. Arrows denote visually appreciable differences between connectomes.

Preclinical diffusion MRI (dMRI) is a valuable tool for studying tissue structure, brain connectivity, and the biological processes behind diffusion. While dMRI is commonly used for non-invasive in vivo imaging (scanning living subjects), ex vivo dMRI—where tissue samples are studied outside the body—has become increasingly useful for examining tissue at a very detailed level. Ex vivo dMRI offers advantages like high-resolution images, better signal quality, and the ability to directly compare with tissue samples using histology, a method for examining tissue structure under a microscope. However, there are many challenges to consider when conducting ex vivo dMRI experiments. The process involves several complex steps, including tissue preparation, image acquisition, data processing, and interpreting results. These steps differ significantly from in vivo imaging and can influence the kinds of questions that can be answered with the data. This paper is the third part of a series that provides recommendations for preclinical dMRI studies. It outlines best practices for preparing and processing ex vivo tissue images, with a focus on data handling and comparison with microscopic analysis. We offer guidelines where possible, discuss areas that still lack clear guidelines, and suggest future directions for research. Finally, we encourage the sharing of code and data and highlight open-source software and databases that are specifically useful for small animal and ex vivo imaging.Ěý

FIGURE 1Ěý

There are many artifacts that must be corrected for in preprocessing. These are not necessarily presented in order, and correction may not be necessary in all cases. Nevertheless, the most common order for pre-processing steps (after data important and quality check) is: (i) thermal noise reduction (referred to as denoising), (ii) Gibbs ringing correction, (iii) susceptibility distortion + motion + eddy current corrections (+ gradient non-linearity, if applicable), (iv) Rician bias correction and (v) signal drift correction. Figures kindly provided by Ileana Jelescu, Kurt Schilling, or reproduced from.,ĚýĚý

Diffusion MRI (dMRI) streamline tractography is the gold standard for estimating brain white matter (WM) pathways in vivo, traditionally reflecting macroscopic relationships with WM microstructure. However, recent advancements have shown that convolutional recurrent neural networks (CoRNN), trained with a teacher-student framework, can learn and propagate streamlines directly from T1 and anatomical contexts. Previously, training for these networks required high-resolution dMRI data.

In this study, the training mechanism is generalized to work with traditional clinical resolution data, enhancing applicability across diverse and sensitive populations. CoRNN was trained on a subset of the Baltimore Longitudinal Study of Aging (BLSA), which closely resembles typical clinical protocols. A new metric, the epsilon ball seeding method, was introduced to compare T1-based tractography with traditional diffusion tractography at the streamline level. Using this metric, it was found that T1 tractography generated by CoRNN replicates diffusion tractography with an error margin of approximately two millimeters.

These findings suggest that CoRNN can effectively generalize across different data resolutions, making T1-based tractography a viable alternative to traditional diffusion tractography in clinical settings.