Siawoosh Mohammadi

Volume Estimation of the Thalamus Using Freesurfer and Stereology: Consistency between Methods

Freely available automated MR image analysis techniques are being increasingly used to investigate neuroanatomical abnormalities in patients with neurological disorders. It is important to assess the specificity and validity of automated measurements of structure volumes with respect to reliable manual methods that rely on human anatomical expertise. The thalamus is widely investigated in many neurological and neuropsychiatric disorders using MRI, but thalamic volumes are notoriously difficult to quantify given the poor between-tissue contrast at the thalamic gray-white matter interface. In the present study we investigated the reliability of automatically determined thalamic volume measurements obtained using FreeSurfer software with respect to a manual stereological technique on 3D T1-weighted MR images obtained from a 3xa0T MR system. Further to demonstrating impressive consistency between stereological and FreeSurfer volume estimates of the thalamus in healthy subjects and neurological patients, we demonstrate that the extent of agreeability between stereology and FreeSurfer is equal to the agreeability between two human anatomists estimating thalamic volume using stereological methods. Using patients with juvenile myoclonic epilepsy as a model for thalamic atrophy, we also show that both automated and manual methods provide very similar ratios of thalamic volume loss in patients. This work promotes the use of FreeSurfer for reliable estimation of global volume in healthy and diseased thalami.

Hyperelastic susceptibility artifact correction of DTI in SPM

Echo Planar Imaging (EPI) is a MRI acquisition technique that is the backbone of widely used investigation techniques in neuroscience like, e.g., Diffusion Tensor Imaging (DTI). While EPI offers considerable reduction of the acquisition time one major drawback is its high sensitivity to susceptibility artifacts. Susceptibility differences between soft tissue, bone and air cause geometrical distortions and intensity modulations of the EPI data. These susceptibility artifacts severely complicate the fusion of micro-structural information acquired with EPI and conventionally acquired structural information. In this paper, we introduce a new tool for hyperelastic susceptibility correction of DTI data (HySCO) that is integrated into the Statistical Parametric Mapping (SPM) software as a toolbox. Our new correction pipeline is based on two datasets acquired with reversed phase encoding gradients. For the correction, we integrated the variational image registration approach by Ruthotto et al. 2007 into the SPM batch mode. We briefly review the model, discuss involved parameter settings and exemplarily demonstrate the effectiveness of HySCO on a human brain DTI dataset.

The efficiency of retrospective artifact correction methods in improving the statistical power of between-group differences in spinal cord DTI

Diffusion tensor imaging (DTI) is a promising approach for investigating the white matter microstructure of the spinal cord. However, it suffers from severe susceptibility, physiological, and instrumental artifacts present in the cord. Retrospective correction techniques are popular approaches to reduce these artifacts, because they are widely applicable and do not increase scan time. In this paper, we present a novel outlier rejection approach (reliability masking) which is designed to supplement existing correction approaches by excluding irreversibly corrupted and thus unreliable data points from the DTI index maps. Then, we investigate how chains of retrospective correction techniques including (i) registration, (ii) registration and robust fitting, and (iii) registration, robust fitting, and reliability masking affect the statistical power of a previously reported finding of lower fractional anisotropy values in the posterior column and lateral corticospinal tracts in cervical spondylotic myelopathy (CSM) patients. While established post-processing steps had small effect on the statistical power of the clinical finding (slice-wise registration: −0.5%, robust fitting: +0.6%), adding reliability masking to the post-processing chain increased it by 4.7%. Interestingly, reliability masking and registration affected the t-score metric differently: while the gain in statistical power due to reliability masking was mainly driven by decreased variability in both groups, registration slightly increased variability. In conclusion, reliability masking is particularly attractive for neuroscience and clinical research studies, as it increases statistical power by reducing group variability and thus provides a cost-efficient alternative to increasing the group size.

A new method for joint susceptibility artefact correction and super-resolution for dMRI

Diffusion magnetic resonance imaging (dMRI) has become increasingly relevant in clinical research and neuroscience. It is commonly carried out using the ultra-fast MRI acquisition technique Echo-Planar Imaging (EPI). While offering crucial reduction of acquisition times, two limitations of EPI are distortions due to varying magnetic susceptibilities of the object being imaged and its limited spatial resolution. In the recent years progress has been made both for susceptibility artefact correction and increasing of spatial resolution using image processing and reconstruction methods. However, so far, the interplay between both problems has not been studied and super-resolution techniques could only be applied along one axis, the slice-select direction, limiting the potential gain in spatial resolution. In this work we describe a new method for joint susceptibility artefact correction and super-resolution in EPI-MRI that can be used to increase resolution in all three spatial dimensions and in particular increase in-plane resolutions. The key idea is to reconstruct a distortion-free, high-resolution image from a number of low-resolution EPI data that are deformed in different directions. Numerical results on dMRI data of a human brain indicate that this technique has the potential to provide for the first time in-vivo dMRI at mesoscopic spatial resolution (i.e. 500μm); a spatial resolution that could bridge the gap between white-matter information from ex-vivo histology (≈1μm) and in-vivo dMRI (≈2000μm).

NODDI-DTI: Estimating Neurite Orientation and Dispersion Parameters from a Diffusion Tensor in Healthy White Matter

The NODDI-DTI signal model is a modification of the NODDI signal model that formally allows interpretation of standard single-shell DTI data in terms of biophysical parameters in healthy human white matter (WM). The NODDI-DTI signal model contains no CSF compartment, restricting application to voxels without CSF partial-volume contamination. This modification allowed derivation of analytical relations between parameters representing axon density and dispersion, and DTI invariants (MD and FA) from the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. NODDI-DTI parameters were estimated by applying the proposed analytical relations to DTI parameters estimated from the first shell of data, and compared to parameters estimated by fitting the NODDI-DTI model to both shells of data (reference dataset) in the WM of 14 in vivo diffusion datasets recorded with two different protocols, and in simulated data. The first two datasets were also fit to the NODDI-DTI model using only the first shell (as for DTI) of data. NODDI-DTI parameters estimated from DTI, and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The simulations showed the NODDI-DTI method to be more noise-robust than the two-shell fitting procedure. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model, it was found that diffusional kurtosis strongly biased DTI-based MD values, and so, making assumptions based on healthy WM, a novel heuristic correction requiring only DTI data was derived and used to mitigate this bias. Since validations were only performed on healthy WM, application to grey matter or pathological WM would require further validation. Our results demonstrate NODDI-DTI to be a promising model and technique to interpret restricted datasets acquired for DTI analysis in healthy white matter with greater biophysical specificity, though its limitations must be borne in mind.

Microstructural imaging of human neocortex in vivo

Abstract The neocortex of the human brain is the seat of higher brain function. Modern imaging techniques, chief among them magnetic resonance imaging (MRI), allow non‐invasive imaging of this important structure. Knowledge of the microstructure of the neocortex has classically come from post‐mortem histological studies of human tissue, and extrapolations from invasive animal studies. From these studies, we know that the scale of important neocortical structure spans six orders of magnitude, ranging from the size of axonal diameters (microns), to the size of cortical areas responsible for integrating sensory information (centimetres). MRI presents an opportunity to move beyond classical methods, because MRI is non‐invasive and MRI contrast is sensitive to neocortical microstructure over all these length scales. MRI thus allows inferences to be made about neocortical microstructure in vivo, i.e. MRI‐based in vivo histology. We review recent literature that has applied and developed MRI‐based in vivo histology to probe the microstructure of the human neocortex, focusing specifically on myelin, iron, and neuronal fibre mapping. We find that applications such as cortical parcellation (using Symbol maps as proxies for myelin content) and investigation of cortical iron deposition with age (using Symbol maps) are already contributing to the frontiers of knowledge in neuroscience. Neuronal fibre mapping in the cortex remains challenging in vivo, but recent improvements in diffusion MRI hold promise for exciting applications in the near future. The literature also suggests that utilising multiple complementary quantitative MRI maps could increase the specificity of inferences about neocortical microstructure relative to contemporary techniques, but that further investment in modelling is required to appropriately combine the maps. Symbol. No caption available. Symbol. No caption available. In vivo histology of human neocortical microstructure is undergoing rapid development. Future developments will improve its specificity, sensitivity, and clinical applicability, granting an ever greater ability to investigate neuroscientific and clinical questions about the human neocortex. HighlightsMRI can probe neocortical microstructure in vivo.In vivo cortical parcellation using myelin markers is possible.Can detect ageing‐related iron accumulation.Future developments will increase specificity and sensitivity.

Computationale Neuroanatomie und Mikrostrukturbildgebung mit der Magnetresonanztomographie

BACKGROUNDnCurrent computational neuroanatomy focuses on morphological measurements of the brain using standard magnetic resonance imaging (MRI) techniques. In comparison quantitative MRI (qMRI) typically provides a better tissue contrast and also greatly improves the sensitivity and specificity with respect to the microstructural characteristics of tissue.nnnOBJECTIVEnCurrent methodological developments in qMRI are presented, which go beyond morphology because this provides standardized measurements of the microstructure of the brain. The concept of in-vivo histology is introduced, based on biophysical modelling of qMRI data (hMRI) for determination of quantitative histology-like markers of the microstructure.nnnRESULTSnThe qMRI metrics can be used as direct biomarkers of the microstructural mechanisms driving observed morphological findings. The hMRI metrics utilize biophysical models of the MRI signal in order to determine 3‑dimensional maps of histology-like measurements in the white matter.nnnCONCLUSIONnNon-invasive brain tissue characterization using qMRI or hMRI has significant implications for both scientific and clinical applications. Both approaches improve the comparability across sites and time points, facilitate multicenter and longitudinal studies as well as standardized diagnostics. The hMRI is expected to shed new light on the relationship between brain microstructure, function and behavior both in health and disease. In the future hMRI will play an indispensable role in the field of computational neuroanatomy.ZusammenfassungHintergrundAktuell beschränkt sich die computationale Neuroanatomie auf morphologische Maße des Gehirns und basiert auf herkömmlichen Magnetresonanztomographie (MRT)-Aufnahmetechniken. Im Vergleich dazu besitzt die quantitative MRT (qMRT) in der Regel nicht nur einen besseren Gewebekontrast, sondern hat auch eine deutlich verbesserte Sensitivität und Spezifität gegenüber mikrostrukturellen Eigenschaften des Gewebes.FragestellungAktuelle methodische Entwicklungen in der qMRT werden dargestellt, die über die Morphologie hinausgehen, indem sie standardisierte Informationen über die Mikrostruktur des Gehirns liefern. Das Konzept der In-vivo-Histologie wird eingeführt, basierend auf biophysikalischer Modellierung von qMRT-Daten (hMRT) zur Bestimmung von quantitativen Histologie-ähnlichen Markern der Mikrostruktur.ErgebnisseqMRT-Maße können als direkte Biomarker für mikrostrukturelle Mechanismen verwendet werden, die mit morphologischen Veränderungen einhergehen oder diese verursachen. hMRT-Metriken verwenden biophysikalische Modelle des MRT-Signals, um 3‑dimensionale quantitative Karten von Histologie-ähnlichen Maßen in weißer Substanz zu bestimmen.SchlussfolgerungNichtinvasive Hirngewebecharakterisierung mit qMRT oder hMRT hat ein erhebliches Potenzial für wissenschaftlichen und klinischen Einsatz. Beide Ansätze verbessern die Vergleichbarkeit über Standorte und Zeitpunkte, erleichtern multizentrische sowie Längsschnittstudien und eine standardisierte Diagnostik. hMRT kann einen wesentlichen Beitrag liefern, das Verhältnis zwischen Gehirnmikrostruktur, Funktion und Verhalten besser zu verstehen und außerdem die Mechanismen, die Gesundheit und Krankheit zugrunde liegen. In Zukunft wird hMRT eine unverzichtbare Rolle im Feld der computationalen Neuroanatomie spielen.AbstractBackgroundCurrent computational neuroanatomy focuses on morphological measurements of the brain using standard magnetic resonance imaging (MRI) techniques. In comparison quantitative MRI (qMRI) typically provides a better tissue contrast and also greatly improves the sensitivity and specificity with respect to the microstructural characteristics of tissue.ObjectiveCurrent methodological developments in qMRI are presented, which go beyond morphology because this provides standardized measurements of the microstructure of the brain. The concept of in-vivo histology is introduced, based on biophysical modelling of qMRI data (hMRI) for determination of quantitative histology-like markers of the microstructure.ResultsThe qMRI metrics can be used as direct biomarkers of the microstructural mechanisms driving observed morphological findings. The hMRI metrics utilize biophysical models of the MRI signal in order to determine 3‑dimensional maps of histology-like measurements in the white matter.ConclusionNon-invasive brain tissue characterization using qMRI or hMRI has significant implications for both scientific and clinical applications. Both approaches improve the comparability across sites and time points, facilitate multicenter and longitudinal studies as well as standardized diagnostics. The hMRI is expected to shed new light on the relationship between brain microstructure, function and behavior both in health and disease. In the future hMRI will play an indispensable role in the field of computational neuroanatomy.