Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images

Despite its great potential in studying brain anatomy and structure, diffusion magnetic resonance imaging (dMRI) is marred by artefacts more than any other commonly used MRI technique. In this paper we present a non-parametric framework for detecting and correcting dMRI outliers (signal loss) caused by subject motion.Signal loss (dropout) affecting a whole slice, or a large connected region of a slice, is frequently observed in diffusion weighted images, leading to a set of unusable measurements. This is caused by bulk (subject or physiological) motion during the diffusion encoding part of the imaging sequence. We suggest a method to detect slices affected by signal loss and replace them by a non-parametric prediction, in order to minimise their impact on subsequent analysis. The outlier detection and replacement, as well as correction of other dMRI distortions (susceptibility-induced distortions, eddy currents (EC) and subject motion) are performed within a single framework, allowing the use of an integrated approach for distortion correction. Highly realistic simulations have been used to evaluate the method with respect to its ability to detect outliers (types 1 and 2 errors), the impact of outliers on retrospective correction of movement and distortion and the impact on estimation of commonly used diffusion tensor metrics, such as fractional anisotropy (FA) and mean diffusivity (MD). Data from a large imaging project studying older adults (the Whitehall Imaging sub-study) was used to demonstrate the utility of the method when applied to datasets with severe subject movement.The results indicate high sensitivity and specificity for detecting outliers and that their deleterious effects on FA and MD can be almost completely corrected

Deep learning‐based method for reducing residual motion effects in diffusion parameter estimation

PURPOSE: Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects. METHODS: The data-rejection approach was adopted in which motion-corrupted data are discarded before model-fitting. A deep learning-based parameter estimation algorithm, using a hierarchical convolutional neural network (H-CNN), was combined with motion assessment and corrupted volume rejection. The method was designed to overcome the limitations of existing methods of this kind that produce parameter estimations whose quality depends strongly on a proportion of the data discarded. Evaluation experiments were conducted for the estimation of diffusion kurtosis and diffusion-tensor-derived measures at both the individual and group levels. The performance was compared with the robust approach of iteratively reweighted linear least squares (IRLLS) after motion correction with and without outlier replacement. RESULTS: Compared with IRLLS, the H-CNN-based technique is minimally sensitive to motion effects. It was tested at severe motion levels when 70% to 90% of the data are rejected and when random motion is present. The technique had a stable performance independent of the numbers and schemes of data rejection. A further test on a data set from children with attention-deficit hyperactivity disorder shows the technique can potentially ameliorate spurious group-level difference caused by head motion. CONCLUSION: This method shows great potential for reducing residual motion effects in motion-corrupted diffusion-weighted-imaging data, bringing benefits that include reduced bias in derived metrics in individual scans and reduced motion-level-dependent bias in population studies employing diffusion MRI

Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction

Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross- studies harmonisation efforts

Fast and accurate Slicewise OutLIer Detection (SOLID) with informed model estimation for diffusion MRI data

The accurate characterization of the diffusion process in tissue using diffusion MRI is greatly challenged by the presence of artefacts. Subject motion causes not only spatial misalignments between diffusion weighted images, but often also slicewise signal intensity errors. Voxelwise robust model estimation is commonly used to exclude intensity errors as outliers. Slicewise outliers, however, become distributed over multiple adjacent slices after image registration and transformation. This challenges outlier detection with voxelwise procedures due to partial volume effects. Detecting the outlier slices before any transformations are applied to diffusion weighted images is therefore required. In this work, we present i) an automated tool coined SOLID for slicewise outlier detection prior to geometrical image transformation, and ii) a framework to naturally interpret data uncertainty information from SOLID and include it as such in model estimators. SOLID uses a straightforward intensity metric, is independent of the choice of the diffusion MRI model, and can handle datasets with a few or irregularly distributed gradient directions. The SOLID-informed estimation framework prevents the need to completely reject diffusion weighted images or individual voxel measurements by downweighting measurements with their degree of uncertainty, thereby supporting convergence and well-conditioning of iterative estimation algorithms. In comprehensive simulation experiments, SOLID detects outliers with a high sensitivity and specificity, and can achieve higher or at least similar sensitivity and specificity compared to other tools that are based on more complex and time-consuming procedures for the scenarios investigated. SOLID was further validated on data from 54 neonatal subjects which were visually inspected for outlier slices with the interactive tool developed as part of this study, showing its potential to quickly highlight problematic volumes and slices in large population studies. The informed model estimation framework was evaluated both in simulations and in vivo human data.Peer reviewe

Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses

A B S T R A C T The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been pro-posed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Con-vex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical stud-ies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.Peer reviewe

Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project

The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38-44 weeks post-menstrual age

From Diffusion to Tracts

Diffusion of water molecules within the brain tissue can be used to modulate the nuclear magnetic resonance signal that is used to form magnetic resonance images (MRI). As the signal itself can be noisy and its meaning challenging to interpret, mathematical models are generally fitted to these measurements to obtain the more accurate characterization of the brain microstructure. This, of course, requires that the mathematical model itself is sound in respect to the measurement setup. This dissertation focuses on the extensively used tensor models as they have been shown to unravel details of the physical diffusion phenomena along with various applications in the basic neuroscience, the clinical research, and even in the neurosurgery. One of the greatest challenges in the diffusion weighted MRI measurements is subject motion during the image acquisition as that can cause a complete loss of the measurement which is especially highlighted in ill or uncooperative patients studies. Due to the used acquisition technique, this loss extends to multiple measurements simultaneously resulting in an enormous gap in the sampling. Such gaps can be problematic for any model fitting, even for the currently available robust means developed to exclude outlier measurements from affecting the estimate. Hence in this dissertation, a tool coined as SOLID was developed to detect these outliers and to robustly process them during the tensor based model estimation. SOLID was implemented as a part of the widely used ExploreDTI toolbox to allow the rapid international distribution of the tool. Unfortunately, any reduction in the measurement sampling will lead to increasing error propagation during the model estimation. Mathematically this is detailed in terms of a condition number for the matrix inversion in the linear least squares fitting. Previously, the condition number has been used to optimize the diffusion weighted MRI acquisition gradient scheme but in this dissertation it was renovated into a novel quality control tool. The condition number of the matrix inversion that provides the model estimate can be calculated after the outliers are excluded to assess spatially and directionally varying error propagation to obviate any bias in subject or population studies. To motivate the importance of the robust methods and diffusion weighted MRI at large, neurocognitive studies with neonates’ visual abilities and bilinguals’ acquisition age of the second language were conducted as a part of this thesis. The findings in these studies indicated that premature birth affects the white matter structures across the brain whereas the age of acquisition of the second language affects only the speech related brain structures.Aivojen rakenteessa tapahtuvien muutosten mittaaminen on avainasemassa tutkittaessa esimerkiksi keskosena syntyneen lapsen kehitystä tai uusien taitojen, kuten kielten, oppimista. Ihmisaivojen tutkiminen on aiemmin rajoittunut aivojen toiminnan arviointiin aivosähkökäyrän ja neurokognitiivisten testien avulla. Viime vuosikymmenten kehitys magneettikuvaustekniikassa on tuonut mahdollisuuden tutkia kajoamattomasti myös aivojen rakennetta ja jopa seurata sen muutosta lapsen kasvaessa tai ihmisen oppiessa uusia taitoja. Yksi lupaavimmista aivojen tutkimusmenetelmistä on diffuusiopainotettu magneettikuvaus, jolle on löytynyt lukuisia käyttökohteita niin neurotieteessä, lääketieteellisissä tutkimuksissa kuin neurokirurgiassakin. Menetelmä perustuu vesimolekyylien lämpöliikkeen mittaamiseen aivoissa. Molekyylien liike on vapaata muun muassa valkean aineen rakenteiden myötäisesti, mutta lähes mahdotonta kohtisuoraan niiden lävitse. Jäljittämällä nämä reitit voidaan muodostaa tarkka malli aivojen rakenteesta. Mallin pohjalta on mahdollista laskea kuvaavia arvoja, jotka auttavat esimerkiksi määrittämään aivovaurion astetta. Diffuusiopainotetun magneettikuvauksen suurin haaste on menetelmän monimutkaisuus sekä mittauksen että analyysin osalta. Vain hyvin yksinkertaisissa tapauksissa asiantuntija voi arvioida suoraan diffuusiopainotetusta magneettikuvasta poikkeamia aivoissa. Yleensä käytetään matemaattisia menetelmiä kuvan tarkempaan analysointiin. Tällöin keskeistä on inversio-ongelman ratkaisu, missä potilaasta tehdyt mittaukset sovitetaan aivoja kuvaavaan matemaattiseen malliin. Sopivan mallin valinnalla on siis suuri vaikutus lopputuloksen hyödyllisyyteen. Diffuusiopainotettu magneettikuvaus on myös häiriöherkkä ja mittaukset sisältävät luonnostaan paljon kohinaa, jonka vaikutusta vähennetään tekemällä toistomittauksia. Toistomittaukset pidentävät kuvausaikaa, joka puolestaan voi olla haasteellinen potilaalle, koska potilaan pitää olla liikkumatta koko kuvauksen ajan. Potilaan pään pienikin liike voi johtaa huomattaviin mittavirheisiin, koska menetelmällä mitataan vesimolekyylien liikettä, jonka suuruus on vain kymmenien mikrometrien luokkaa. Tässä fysiikan väitöskirjassa keskityttiin diffuusiopainotetun magneettikuvauksen mallintamismenetelmien kehitystyöhön ja niiden käyttöönottoon Helsingin yliopistollisessa sairaalassa. Kehitimme kansainvälistä huomiota herättäneen SOLID-työkalun, jolla voidaan havaita sekä korjata potilaan liikkeestä aiheutuvia virheitä mittaustuloksissa. Tämän lisäksi esitimme laadunvalvonta menetelmän, jolla voidaan arvioida esimerkiksi potilaiden välisten mallinnustulosten vertailukelpoisuutta. Kehitettyjä menetelmiä testattiin ja sovellettiin kahdessa tutkimuksessa: Osoitimme, että vastasyntyneen lapsen kyky seurata katseellaan liikkuvaa kohdetta liittyy laaja-alaisiin muutoksiin aivojen valkean aineen rakenteessa. Lisäksi näytimme, että toisen kielen oppimisajankohta vaikuttaa aivojen puheentuottoon liittyvien aivorakenteiden muodostumiseen

Application of phase-based motion outlier detection to infant dMRI

Detecting and eliminating motion-corrupted slices is crucial in diffusion MRI (dMRI), and particularly essential in imaging neonates. Conventional magnitude-based outlier rejection methods are intensity-based and can usually detect and correct intra-volume movement but can miss outliers in cases of small continuous motions. Phase-based methods can be used to detect motion independently, regardless of the slice-to-volume location. The phase-based method is reasonably accurate and computationally fast, and may be better suited for real-time detection of motion in dMRI. Combining magnitude and phase methods could produce the best results. Here, we evaluate the phase-based method versus the magnitude-based method in neonatal data

The Human Connectome Project's neuroimaging approach

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease