The minimal preprocessing pipelines for the Human Connectome Project

The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines

Impact of susceptibility-induced distortion correction on perfusion imaging by pCASL with a segmented 3D GRASE readout

Purpose: The consensus for the clinical implementation of arterial spin labeling (ASL) perfusion imaging recommends a segmented 3D Gradient and Spin-Echo (GRASE) readout for optimal signal-to-noise-ratio (SNR). The correction of the associated susceptibility-induced geometric distortions has been shown to improve diagnostic precision, but its impact on ASL data has not been systematically assessed and it is not consistently part of pre-processing pipelines. Here, we investigate the effects of susceptibility-induced distortion correction on perfusion imaging by pseudo-continuous ASL (pCASL) with a segmented 3D GRASE readout. Methods: Data acquired from 28 women using pCASL with 3D GRASE at 3T was analyzed using three pre-processing options: without distortion correction, with distortion correction, and with spatial smoothing (without distortion correction) matched to control for blurring effects induced by distortion correction. Maps of temporal SNR (tSNR) and relative perfusion were analyzed in eight regions-of-interest (ROIs) across the brain. Results: Distortion correction significantly affected tSNR and relative perfusion across the brain. Increases in tSNR were like those produced by matched spatial smoothing in most ROIs, indicating that they were likely due to blurring effects. However, that was not the case in the frontal and temporal lobes, where we also found increased relative perfusion with distortion correction even compared with matched spatial smoothing. These effects were found in both controls and patients, with no interactions with the participant group. Conclusion: Correction of susceptibility-induced distortions significantly impacts ASL perfusion imaging using a segmented 3D GRASE readout, and this step should therefore be considered in ASL pre-processing pipelines. This is of special importance in clinical studies, reporting perfusion across ROIs defined on relatively undistorted images and when conducting group analyses requiring the alignment of images across different subjects.info:eu-repo/semantics/publishedVersio

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

Magnetic Resonance Imaging of the Brain in Moving Subjects. Application of Fetal, Neonatal and Adult Brain Studies

Imaging in the presence of subject motion has been an ongoing challenge for magnetic resonance imaging (MRI). Motion makes MRI data inconsistent, causing artifacts in conventional anatomical imaging as well as invalidating diffusion tensor imaging (DTI) reconstruction. In this thesis some of the important issues regarding the acquisition and reconstruction of anatomical and DTI imaging of moving subjects are addressed; methods to achieve high resolution and high signalto- noise ratio (SNR) volume data are proposed. An approach has been developed that uses multiple overlapped dynamic single shot slice by slice imaging combined with retrospective alignment and data fusion to produce self consistent 3D volume images under subject motion. We term this method as snapshot MRI with volume reconstruction or SVR. The SVR method has been performed successfully for brain studies on subjects that cannot stay still, and in some cases were moving substantially during scanning. For example, awake neonates, deliberately moved adults and, especially, on fetuses, for which no conventional high resolution 3D method is currently available. Fine structure of the in-utero fetal brain is clearly revealed for the first time with substantially improved SNR. The SVR method has been extended to correct motion artifacts from conventional multi-slice sequences when the subject drifts in position during data acquisition. Besides anatomical imaging, the SVR method has also been further extended to DTI reconstruction when there is subject motion. This has been validated successfully from an adult who was deliberately moving and then applied to inutero fetal brain imaging, which no conventional high resolution 3D method is currently available. Excellent fetal brain 3D apparent diffusion coefficient (ADC) maps in high resolution have been achieved for the first time as well as promising fractional Anisotropy (FA) maps. Pilot clinical studies using SVR reconstructed data to study fetal brain development in-utero have been performed. Growth curves for the normally developing fetal brain have been devised by the quantification of cerebral and cerebellar volumes as well as some one dimensional measurements. A Verhulst model is proposed to describe these growth curves, and this approach has achieved a correlation over 0.99 between the fitted model and actual data

Enhancing Registration for Image-Guided Neurosurgery

Pharmacologically refractive temporal lobe epilepsy and malignant glioma brain tumours are examples of pathologies that are clinically managed through neurosurgical intervention. The aims of neurosurgery are, where possible, to perform a resection of the surgical target while minimising morbidity to critical structures in the vicinity of the resected brain area. Image-guidance technology aims to assist this task by displaying a model of brain anatomy to the surgical team, which may include an overlay of surgical planning information derived from preoperative scanning such as the segmented resection target and nearby critical brain structures. Accurate neuronavigation is hindered by brain shift, the complex and non-rigid deformation of the brain that arises during surgery, which invalidates assumed rigid geometric correspondence between the neuronavigation model and the true shifted positions of relevant brain areas. Imaging using an interventional MRI (iMRI) scanner in a next-generation operating room can serve as a reference for intraoperative updates of the neuronavigation. An established clinical image processing workflow for iMRI-based guidance involves the correction of relevant imaging artefacts and the estimation of deformation due to brain shift based on non-rigid registration. The present thesis introduces two refinements aimed at enhancing the accuracy and reliability of iMRI-based guidance. A method is presented for the correction of magnetic susceptibility artefacts, which affect diffusion and functional MRI datasets, based on simulating magnetic field variation in the head from structural iMRI scans. Next, a method is presented for estimating brain shift using discrete non-rigid registration and a novel local similarity measure equipped with an edge-preserving property which is shown to improve the accuracy of the estimated deformation in the vicinity of the resected area for a number of cases of surgery performed for the management of temporal lobe epilepsy and glioma

Structured low-rank methods for robust 3D multi-shot EPI

Magnetic resonance imaging (MRI) has inherently slow acquisition speed, and Echo-Planar Imaging (EPI), as an efficient acquisition scheme, has been widely used in functional magnetic resonance imaging (fMRI) where an image series with high temporal resolution is needed to measure neuronal activity. Recently, 3D multi-shot EPI which samples data from an entire 3D volume with repeated shots has been drawing growing interest for fMRI with its high isotropic spatial resolution, particularly at ultra-high fields. However, compared to single-shot EPI, multi-shot EPI is sensitive to any inter-shot instabilities, e.g., subject movement and even physiologically induced field fluctuations. These inter-shot inconsistencies can greatly negate the theoretical benefits of 3D multi-shot EPI over conventional 2D multi-slice acquisitions. Structured low-rank image reconstruction which regularises under-sampled image reconstruction by exploiting the linear dependencies in MRI data has been successfully demonstrated in a variety of applications. In this thesis, a structured low-rank reconstruction method is optimised for 3D multi-shot EPI imaging together with a dedicated sampling pattern termed seg-CAIPI, in order to enhance the robustness to physiological fluctuations and improve the temporal stability of 3D multi-shot EPI for fMRI at 7T. Moreover, a motion compensated structured low-rank reconstruction framework is also presented for robust 3D multi-shot EPI which further takes into account inter-shot instabilities due to bulk motion. Lastly, this thesis also investigates into the improvement of structured low-rank reconstruction from an algorithmic perspective and presents the locally structured low-rank reconstruction scheme

Alignment of contrast enhanced medical images

The re-alignment of series of medical images in which there are multiple contrast variations is difficult. The reason for this is that the popularmeasures of image similarity used to drive the alignment procedure do not separate the influence of intensity variation due to image feature motion and intensity variation due to feature enhancement. In particular, the appearance of new structure poses problems when it has no representation in the original image. The acquisition of many images over time, such as in dynamic contrast enhanced MRI, requires that many images with different contrast be registered to the same coordinate system, compounding the problem. This thesis addresses these issues, beginning by presenting conditions under which conventional registration fails and proposing a solution in the form of a ’progressive principal component registration’. The algorithm uses a statistical analysis of a series of contrast varying images in order to reduce the influence of contrast-enhancement that would otherwise distort the calculation of the image similarity measures used in image registration. The algorithm is shown to be versatile in that it may be applied to series of images in which contrast variation is due to either temporal contrast enhancement changes, as in dynamic contrast-enhanced MRI or intrinsically in the image selection procedure as in diffusion weighted MRI

Towards efficient neurosurgery: Image analysis for interventional MRI

Interventional magnetic resonance imaging (iMRI) is being increasingly used for performing imageguided neurosurgical procedures. Intermittent imaging through iMRI can help a neurosurgeon visualise the target and eloquent brain areas during neurosurgery and lead to better patient outcome. MRI plays an important role in planning and performing neurosurgical procedures because it can provide highresolution anatomical images that can be used to discriminate between healthy and diseased tissue, as well as identify location and extent of functional areas. This is of significant clinical utility as it helps the surgeons maximise target resection and avoid damage to functionally important brain areas. There is clinical interest in propagating the pre-operative surgical information to the intra-operative image space as this allows the surgeons to utilise the pre-operatively generated surgical plans during surgery. The current state of the art neuronavigation systems achieve this by performing rigid registration of pre-operative and intra-operative images. As the brain undergoes non-linear deformations after craniotomy (brain shift), the rigidly registered pre-operative images do not accurately align anymore with the intra-operative images acquired during surgery. This limits the accuracy of these neuronavigation systems and hampers the surgeon’s ability to perform more aggressive interventions. In addition, intra-operative images are typically of lower quality with susceptibility artefacts inducing severe geometric and intensity distortions around areas of resection in echo planar MRI images, significantly reducing their utility in the intraoperative setting. This thesis focuses on development of novel methods for an image processing workflow that aims to maximise the utility of iMRI in neurosurgery. I present a fast, non-rigid registration algorithm that can leverage information from both structural and diffusion weighted MRI images to localise target lesions and a critical white matter tract, the optic radiation, during surgical management of temporal lobe epilepsy. A novel method for correcting susceptibility artefacts in echo planar MRI images is also developed, which combines fieldmap and image registration based correction techniques. The work developed in this thesis has been validated and successfully integrated into the surgical workflow at the National Hospital for Neurology and Neurosurgery in London and is being clinically used to inform surgical decisions

Double volumetric navigators for real-time simultaneous shim and motion measurement and correction in Glycogen Chemical Exchange Saturation Transfer (GlycoCEST) MRI

Glycogen is the primary glucose storage mechanism in in living systems and plays a central role in systemic glucose homeostasis. The study of muscle glycogen concentrations in vivo still largely relies on tissue sampling methods via needle biopsy. However, muscle biopsies are invasive and limit the frequency of measurements and the number of sites that can be assessed. Non-invasive methods for quantifying glycogen in vivo are therefore desirable in order to understand the pathophysiology of common diseases with dysregulated glycogen metabolism such as obesity, insulin resistance, and diabetes, as well as glycogen metabolism in sports physiology. Chemical Exchange Saturation Transfer (CEST) MRI has emerged as a non-invasive contrast enhancement technique that enables detection of molecules, like glycogen, whose concentrations are too low to impact the contrast of standard MR imaging. CEST imaging is performed by selectively saturating hydrogen nuclei of the metabolites that are in chemical exchange with those of water molecules and detecting a reduction in MRI signal in the water pool resulting from continuous chemical exchange. However, CEST signal can easily be compromised by artifacts. Since CEST is based on chemical shift, it is very sensitive to field inhomogeneity which may arise from poor initial shimming, subject respiration, heating of shim iron, mechanical vibrations or subject motion. This is a particular problem for molecules that resonate close to water, such as - OH protons in glycogen, where small variations in chemical shift cause misinterpretation of CEST data. The purpose of this thesis was to optimize the CEST MRI sequence for glycogen detection and implement a real-time simultaneous motion and shim correction and measurement method. First, analytical solution of the Bloch-McConnell equations was used to find optimal continuous wave RF pulse parameters for glycogen detection, and results were validated on a phantom with varying glycogen concentrations and in vivo on human calf muscle. Next, the CEST sequence was modified with double volumetric navigators (DvNavs) to measure pose changes and update field of view and zero- and first-order shim parameters. Finally, the impact of B0 field fluctuations on the scan-rescan reproducibility of CEST was evaluated in vivo in 9 volunteers across 10 different scans. Simulation results showed an optimal RF saturation power of 1.5µT and duration of 1s for glycoCEST. These parameters were validated experimentally in vivo and the ability to detect varying glycogen concentrations was demonstrated in a phantom. Phantom data showed that the DvNav-CEST sequence accurately estimates system frequency and linear shim gradient changes due to motion and corrects resulting image distortions. In addition, DvNav-CEST was shown to yield improved CEST quantification in vivo in the presence of motion and motion-induced field inhomogeneity. B0 field fluctuations were found to lower the reproducibility of CEST measures: the mean coefficient of variation (CoV) for repeated scans was 83.70 ± 70.79 % without shim correction. However, the DvNav-CEST sequence was able to measure and correct B0 variations, reducing the CoV to 2.6 ± 1.37 %. The study confirms the possibility of detecting glycogen using CEST MRI at 3 T and shows the potential of the real-time shim and motion navigated CEST sequence for producing repeatable results in vivo by reducing the effect of B0 field fluctuations