atTRACTive: Semi-automatic white matter tract segmentation using active learning

Accurately identifying white matter tracts in medical images is essential for various applications, including surgery planning and tract-specific analysis. Supervised machine learning models have reached state-of-the-art solving this task automatically. However, these models are primarily trained on healthy subjects and struggle with strong anatomical aberrations, e.g. caused by brain tumors. This limitation makes them unsuitable for tasks such as preoperative planning, wherefore time-consuming and challenging manual delineation of the target tract is typically employed. We propose semi-automatic entropy-based active learning for quick and intuitive segmentation of white matter tracts from whole-brain tractography consisting of millions of streamlines. The method is evaluated on 21 openly available healthy subjects from the Human Connectome Project and an internal dataset of ten neurosurgical cases. With only a few annotations, the proposed approach enables segmenting tracts on tumor cases comparable to healthy subjects (dice=0.71), while the performance of automatic methods, like TractSeg dropped substantially (dice=0.34) in comparison to healthy subjects. The method is implemented as a prototype named atTRACTive in the freely available software MITK Diffusion. Manual experiments on tumor data showed higher efficiency due to lower segmentation times compared to traditional ROI-based segmentation

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. Diffusion MRI tractography is the only method that enables the study of the anatomy and variability of the CST pathway in human health. In this work, we explored the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. We perform experiments using diffusion MRI data from the Human Connectome Project. Four quantitative measurements including reconstruction rate, the WM-GM interface coverage, anatomical distribution of streamlines, and correlation with cortical volumes to assess the advantages and limitations of each method. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face area) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.Comment: 41 pages, 19 figure

Better Generalization of White Matter Tract Segmentation to Arbitrary Datasets with Scaled Residual Bootstrap

White matter (WM) tract segmentation is a crucial step for brain connectivity studies. It is performed on diffusion magnetic resonance imaging (dMRI), and deep neural networks (DNNs) have achieved promising segmentation accuracy. Existing DNN-based methods use an annotated dataset for model training. However, the performance of the trained model on a different test dataset may not be optimal due to distribution shift, and it is desirable to design WM tract segmentation approaches that allow better generalization of the segmentation model to arbitrary test datasets. In this work, we propose a WM tract segmentation approach that improves the generalization with scaled residual bootstrap. The difference between dMRI scans in training and test datasets is most noticeably caused by the different numbers of diffusion gradients and noise levels. Since both of them lead to different signal-to-noise ratios (SNRs) between the training and test data, we propose to augment the training scans by adjusting the noise magnitude and develop an adapted residual bootstrap strategy for the augmentation. To validate the proposed approach, two dMRI datasets were used, and the experimental results show that our method consistently improved the generalization of WM tract segmentation under various settings

New neurophysiological and imaging methods for detection of microstructural changes in mild traumatic brain injury

Mild traumatic brain injury is a very common health problem. Although outcome is generally good, a significant proportion of patients have persistent symptoms or an incomplete functional recovery. The mechanisms of this are incompletely understood, but believed to include microstructural injuries that may be undetectable by presently used diagnostic tests. This thesis aims at exploring new diagnostic methods that could be utilised in examining mild traumatic brain injury. I study tested transcranial magnetic stimulation defined motor thresholds in a sample of chronic phase mild traumatic brain injury patients. Elevated motor thresholds were found compared to healthy controls, associated with altered excitability of the corticospinal tract. II study used transcranial magnetic stimulation combined with electroencephalography to probe responses of frontal brain regions. The employed method is reported to be sensitive to changes in excitability and connectivity of the brain. Differences were found between samples of fully recovered and persistently symptomatic patients with mild traumatic brain injury and healthy controls. On basis of this, transcranial magnetic stimulation and electroencephalography could be used to detect functional changes that are not paralleled by lesions on routine magnetic resonance imaging. III study compared diffusion tensor imaging based deterministic tractography and a newer method, based on constrained spherical deconvolution, automatic, deep learning based segmentation and probabilistic tractography. Participants were patients with symptomatic mild traumatic brain injury and healthy controls. The newer approach was able to find differences between the groups, while diffusion tensor method was not. This suggests the new approach may be more sensitive in detecting microstructural changes related to mild traumatic brain injury. These results show that mild traumatic brain injury can be associated with functional and structural changes in the absence of trauma-related findings on routine MRI. The methods evaluated may provide new ways to detect these changes.Uusia neurofysiologisia ja kuvantamismenetelmiä lievään aivovammaan liittyvien mikrorakenteellisten muutosten toteamisessa Lievä aivovamma on erittäin tavallinen. Toipuminen on yleensä hyvää, mutta osalle potilaista jää pitkäkestoisia oireita tai toimintakyvyn vajavuutta. Näiden syntymekanismia ei täysin ymmärretä, mutta ajatellaan sen voivan liittyä aivojen mikrorakenteellisiin muutoksiin, joiden toteamiseen nykyiset diagnostiset testit voivat olla riittämättömiä. Tämä väitöstutkimus selvittää uusia keinoja, joita voitaisiin hyödyntää lievän aivovamman arvioinnissa. I osatyössä tutkittiin transkraniaalisen magneettistimulaation avulla motorisia kynnyksiä. Tutkimusjoukkona oli lievän aivovamman saaneita, kroonisen vaiheen potilaita. Potilasjoukolla todettiin terveisiin verrokkeihin nähden korkeampia motorisia kynnyksiä, joka liittyy muutoksiin kortikospinaaliradan ärtyvyydessä. II osatyö hyödynsi transkraniaalista magneettistimulaatiota ja elektroenkefalografiaa frontaalisten aivoalueiden vasteiden tutkimisessa. Aiempien julkaisujen perusteella menetelmä on herkkä aivojen ärtyvyyden ja aivoalueiden välisten yhteyksien muutosten toteamisessa. Menetelmällä löydettiin eroja lievästä aivovammasta oireettomiksi toipuneista, pitkäkestoisesti oireilevista ja terveistä verrokeista koostuneiden osallistujajoukkojen välillä. Transkraniaalisen magneettistimulaation ja elektroenkefalografian yhdistelmällä saatetaan siten havaita toimin-nallisia muutoksia, joille ei ole vastinetta tavallisissa magneettikuvissa. III osatyössä verrattiin diffuusiotensorikuvantamista ja determinististä traktografiaa uudempaan menetelmään, joka perustui constrained spherical deconvolution -laskentaan, automaattiseen, syväoppimiseen perustuvaan segmentaatioon ja probabilistiseen traktografiaan. Tutkimusjoukkona oli lievän aivovamman saaneita, oireisia potilaita ja terveitä verrokkeja. Uudella menetelmällä löydettiin eroja ryhmien välillä, mutta vertailumenetelmällä eroja ei havaittu. Tällä perusteella uusi menetelmä vaikuttaa herkemmältä aivovammaan liittyvien mikrorakenteellisten muutosten toteamisessa. Tulokset osoittavat, että lievään aivovammaan voi liittyä toiminnallisia ja rakenteellisia muutoksia, vaikka tavanomaisen magneettikuvauksen löydös olisi normaali. Näiden muutosten toteaminen voi olla mahdollista arvioiduilla menetelmillä

Individualised profiling of white matter organisation in moderate-to-severe traumatic brain injury patients

Background and purpose Approximately 65% of moderate-to-severe traumatic brain injury (m-sTBI) patients present with poor long-term behavioural outcomes, which can significantly impair activities of daily living. Numerous diffusion-weighted MRI studies have linked these poor outcomes to decreased white matter integrity of several commissural tracts, association fibres and projection fibres in the brain. However, most studies have focused on group-based analyses, which are unable to deal with the substantial between-patient heterogeneity in m-sTBI. As a result, there is increasing interest and need in conducting individualised neuroimaging analyses. Materials and methods Here, we generated a detailed subject-specific characterisation of microstructural organisation of white matter tracts in 5 chronic patients with m-sTBI (29 – 49y, 2 females), presented as a proof-of-concept. We developed an imaging analysis framework using fixel-based analysis and TractLearn to determine whether the values of fibre density of white matter tracts at the individual patient level deviate from the healthy control group (n = 12, 8F, Mage = 35.7y, age range 25 – 64y). Results Our individualised analysis revealed unique white matter profiles, confirming the heterogenous nature of m-sTBI and the need of individualised profiles to properly characterise the extent of injury. Future studies incorporating clinical data, as well as utilising larger reference samples and examining the test–retest reliability of the fixel-wise metrics are warranted. Conclusions Individualised profiles may assist clinicians in tracking recovery and planning personalised training programs for chronic m-sTBI patients, which is necessary to achieve optimal behavioural outcomes and improved quality of life

Methods for Data Management in Multi-Centre MRI Studies and Applications to Traumatic Brain Injury

Neuroimaging studies are becoming increasingly bigger, and multi-centre collaborations to collect data under similar protocols, but different scanning sites, are now commonplace.However, with increasing sample size the complexity of databases and the entailed data management as well as computational burden are growing. This thesis aims to highlight and address challenges faced by large multi-centre magnetic resonance imaging(MRI) studies. The methods implemented are then applied to traumatic brain injury (TBI) data.Firstly, a pre-processing pipeline for both anatomical and diffusion MRI was proposed, that allows for a high throughput of MRI scans. After describing the choices for processing tools,the performance of the integrated quality assurance was assessed based on the results from a large multi-centre dataset for TBI. Secondly, the applicability of the pipelines for processing mild TBI (mTBI) data from three sites was shown in a case study. For this, volumetric and diffusion metrics in the acute phase are analysed for their prognostic potential. Further-more, the cohort was examined for longitudinal changes. Thirdly, independent scan-rescan datasets are examined to gain a better understanding of the degree of reproducibility which can be achieved in imaging studies. This involves analysing the robustness of brain parcellations based on structural or diffusion imaging. The effect of using different MRI scanners or imaging protocols was also assessed and discussed. Fourthly, sources of diffusion MRI variability and different approaches to cope with these are reviewed. Using this foundation,state-of-the art methods for diffusion MRI harmonisation were compared against each other using both a benchmark dataset and mTBI cohort. Lastly, a solution to localise brain lesions was proposed. Its implications for lesion analysis, are assessed in the light of an application to a more severe TBI patient cohort, imaged on two different scanners. Furthermore, a lesion matching algorithm was introduced to automatically examine lesion evolution with time post-injury. In summary, this thesis explored different options for MRI data analysis in the context of large multi-centre studies. Different approaches are studied and compared using a number of different MRI datasets, including scan-rescan data across different MRI scanners and imaging protocols. The potential of the optimised solutions was illustrated through applications to TBI data.CENTER-TB