Shiva Keihaninejad

STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation

Anatomical segmentation of structures of interest is critical to quantitative analysis in medical imaging. Several automated multi-atlas based segmentation propagation methods that utilise manual delineations from multiple templates appear promising. However, high levels of accuracy and reliability are needed for use in diagnosis or in clinical trials. We propose a new local ranking strategy for template selection based on the locally normalised cross correlation (LNCC) and an extension to the classical STAPLE algorithm by Warfield et al. (2004), which we refer to as STEPS for Similarity and Truth Estimation for Propagated Segmentations. It addresses the well-known problems of local vs. global image matching and the bias introduced in the performance estimation due to structure size. We assessed the method on hippocampal segmentation using a leave-one-out cross validation with optimised model parameters; STEPS achieved a mean Dice score of 0.925 when compared with manual segmentation. This was significantly better in terms of segmentation accuracy when compared to other state-of-the-art fusion techniques. Furthermore, due to the finer anatomical scale, STEPS also obtains more accurate segmentations even when using only a third of the templates, reducing the dependence on large template databases. Using a subset of Alzheimers Disease Neuroimaging Initiative (ADNI) scans from different MRI imaging systems and protocols, STEPS yielded similarly accurate segmentations (Dice=0.903). A cross-sectional and longitudinal hippocampal volumetric study was performed on the ADNI database. Mean±SD hippocampal volume (mm(3)) was 5195 ± 656 for controls; 4786 ± 781 for MCI; and 4427 ± 903 for Alzheimers disease patients and hippocampal atrophy rates (%/year) of 1.09 ± 3.0, 2.74 ± 3.5 and 4.04 ± 3.6 respectively. Statistically significant (p<10(-3)) differences were found between disease groups for both hippocampal volume and volume change rates. Finally, STEPS was also applied in a multi-label segmentation propagation scenario using a leave-one-out cross validation, in order to parcellate 83 separate structures of the brain. Comparisons of STEPS with state-of-the-art multi-label fusion algorithms showed statistically significant segmentation accuracy improvements (p<10(-4)) in several key structures.

Magnetic resonance imaging evidence for presymptomatic change in thalamus and caudate in familial Alzheimer's disease.

Amyloid imaging studies of presymptomatic familial Alzheimer’s disease have revealed the striatum and thalamus to be the earliest sites of amyloid deposition. This study aimed to investigate whether there are associated volume and diffusivity changes in these subcortical structures during the presymptomatic and symptomatic stages of familial Alzheimer’s disease. As the thalamus and striatum are involved in neural networks subserving complex cognitive and behavioural functions, we also examined the diffusion characteristics in connecting white matter tracts. A cohort of 20 presenilin 1 mutation carriers underwent volumetric and diffusion tensor magnetic resonance imaging, neuropsychological and clinical assessments; 10 were symptomatic, 10 were presymptomatic and on average 5.6 years younger than their expected age at onset; 20 healthy control subjects were also studied. We conducted region of interest analyses of volume and diffusivity changes in the thalamus, caudate, putamen and hippocampus and examined diffusion behaviour in the white matter tracts of interest (fornix, cingulum and corpus callosum). Voxel-based morphometry and tract-based spatial statistics were also used to provide unbiased whole-brain analyses of group differences in volume and diffusion indices, respectively. We found that reduced volumes of the left thalamus and bilateral caudate were evident at a presymptomatic stage, together with increased fractional anisotropy of bilateral thalamus and left caudate. Although no significant hippocampal volume loss was evident presymptomatically, reduced mean diffusivity was observed in the right hippocampus and reduced mean and axial diffusivity in the right cingulum. In contrast, symptomatic mutation carriers showed increased mean, axial and in particular radial diffusivity, with reduced fractional anisotropy, in all of the white matter tracts of interest. The symptomatic group also showed atrophy and increased mean diffusivity in all of the subcortical grey matter regions of interest, with increased fractional anisotropy in bilateral putamen. We propose that axonal injury may be an early event in presymptomatic Alzheimer’s disease, causing an initial fall in axial and mean diffusivity, which then increases with loss of axonal density. The selective degeneration of long-coursing white matter tracts, with relative preservation of short interneurons, may account for the increase in fractional anisotropy that is seen in the thalamus and caudate presymptomatically. It may be owing to their dense connectivity that imaging changes are seen first in the thalamus and striatum, which then progress to involve other regions in a vulnerable neuronal network.

Increased PK11195 PET binding in the cortex of patients with MS correlates with disability

Objective: Activated microglia are thought to play a major role in cortical gray matter (GM) demyelination in multiple sclerosis (MS). Our objective was to evaluate microglial activation in cortical GM of patients with MS in vivo and to explore its relationship to measures of disability. Methods: Using PET and optimized modeling and segmentation procedures, we investigated cortical 11C-PK11195 (PK11195) binding in patients with relapsing-remitting MS (RRMS), patients with secondary progressive MS (SPMS), and healthy controls. Disability was assessed with the Expanded Disability Status Scale (EDSS) and Multiple Sclerosis Impact Scale (MSIS-29). Results: Patients with MS showed increased cortical GM PK11195 binding relative to controls, which was multifocal and highest in the postcentral, middle frontal, anterior orbital, fusiform, and parahippocampal gyri. Patients with SPMS also showed additional increases in precentral, superior parietal, lingual and anterior superior, medial and inferior temporal gyri. Total cortical GM PK11195 binding correlated with EDSS scores, with a stronger correlation for the subgroup of patients with SPMS. In patients with SPMS, PK11195 binding also correlated with MSIS-29 scores. No correlation with disability measures was seen for PK11195 binding in white matter. Higher EDSS scores correlated with higher levels of GM PK11195 binding in the postcentral gyrus for patients with RRMS and in precentral gyrus for those with SPMS. Conclusions: Microglial activation in cortical GM of patients with MS can be assessed in vivo. The distribution is not uniform and shows a relationship to clinical disability. We speculate that the increased PK11195 binding corresponds to enhanced microglial activation described in postmortem SPMS cortical GM.

A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T)

As population-based studies may obtain images from scanners with different field strengths, a method to normalize regional brain volumes according to intracranial volume (ICV) independent of field strength is needed. We found systematic differences in ICV estimation, tested in a cohort of healthy subjects (n = 5) that had been imaged using 1.5T and 3T scanners, and confirmed in two independent cohorts. This was related to systematic differences in the intensity of cerebrospinal fluid (CSF), with higher intensities for CSF located in the ventricles compared with CSF in the cisterns, at 3T versus 1.5T, which could not be removed with three different applied bias correction algorithms. We developed a method based on tissue probability maps in MNI (Montreal Neurological Institute) space and reverse normalization (reverse brain mask, RBM) and validated it against manual ICV measurements. We also compared it with alternative automated ICV estimation methods based on Statistical Parametric Mapping (SPM5) and Brain Extraction Tool (FSL). The proposed RBM method was equivalent to manual ICV normalization with a high intraclass correlation coefficient (ICC = 0.99) and reliable across different field strengths. RBM achieved the best combination of precision and reliability in a group of healthy subjects, a group of patients with Alzheimers disease (AD) and mild cognitive impairment (MCI) and can be used as a common normalization framework.

An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer's disease

We introduce a novel image-processing framework for tracking longitudinal changes in white matter microstructure using diffusion tensor imaging (DTI). Charting the trajectory of such temporal changes offers new insight into disease progression but to do so accurately faces a number of challenges. Recent developments have highlighted the importance of processing each subjects data at multiple time points in an unbiased way. In this paper, we aim to highlight a different challenge critical to the processing of longitudinal DTI data, namely the approach to image alignment. Standard approaches in the literature align DTI data by registering the corresponding scalar-valued fractional anisotropy (FA) maps. We propose instead a DTI registration algorithm that leverages full tensor information to drive improved alignment. This proposed pipeline is evaluated against the standard FA-based approach using a DTI dataset from an ongoing study of Alzheimers disease (AD). The dataset consists of subjects scanned at two time points and at each time point the DTI acquisition consists of two back-to-back repeats in the same scanning session. The repeated scans allow us to evaluate the specificity of each pipeline, using a test-retest design, and assess precision, using bootstrap-based method. The results show that the tensor-based pipeline achieves both higher specificity and precision than the standard FA-based approach. Tensor-based registration for longitudinal processing of DTI data in clinical studies may be of particular value in studies assessing disease progression.

The Importance of Group-Wise Registration in Tract Based Spatial Statistics Study of Neurodegeneration: A Simulation Study in Alzheimer's Disease

Tract-based spatial statistics (TBSS) is a popular method for the analysis of diffusion tensor imaging data. TBSS focuses on differences in white matter voxels with high fractional anisotropy (FA), representing the major fibre tracts, through registering all subjects to a common reference and the creation of a FA skeleton. This work considers the effect of choice of reference in the TBSS pipeline, which can be a standard template, an individual subject from the study, a study-specific template or a group-wise average. While TBSS attempts to overcome registration error by searching the neighbourhood perpendicular to the FA skeleton for the voxel with maximum FA, this projection step may not compensate for large registration errors that might occur in the presence of pathology such as atrophy in neurodegenerative diseases. This makes registration performance and choice of reference an important issue. Substantial work in the field of computational anatomy has shown the use of group-wise averages to reduce biases while avoiding the arbitrary selection of a single individual. Here, we demonstrate the impact of the choice of reference on: (a) specificity (b) sensitivity in a simulation study and (c) a real-world comparison of Alzheimers disease patients to controls. In (a) and (b), simulated deformations and decreases in FA were applied to control subjects to simulate changes of shape and WM integrity similar to what would be seen in AD patients, in order to provide a “ground truth” for evaluating the various methods of TBSS reference. Using a group-wise average atlas as the reference outperformed other references in the TBSS pipeline in all evaluations.

Increased PK11195-PET binding in normal-appearing white matter in clinically isolated syndrome

The most accurate predictor of the subsequent development of multiple sclerosis in clinically isolated syndrome is the presence of lesions at magnetic resonance imaging. We used in vivo positron emission tomography with (11)C-(R)-PK11195, a biomarker of activated microglia, to investigate the normal-appearing white matter and grey matter of subjects with clinically isolated syndrome to explore its role in the development of multiple sclerosis. Eighteen clinically isolated syndrome and eight healthy control subjects were recruited. Baseline assessment included: history, neurological examination, expanded disability status scale, magnetic resonance imaging and PK11195-positron emission tomography scans. All assessments except the PK11195-positron emission tomography scan were repeated over 2 years. SUPERPK methodology was used to measure the binding potential relative to the non-specific volume, BPND. We show a global increase of normal-appearing white matter PK11195 BPND in clinically isolated syndrome subjects compared with healthy controls (P = 0.014). Clinically isolated syndrome subjects with T2 magnetic resonance imaging lesions had higher PK11195 BPND in normal-appearing white matter (P = 0.009) and their normal-appearing white matter PK11195 BPND correlated with the Expanded Disability Status Scale (P = 0.007; r = 0.672). At 2 years those who developed dissemination in space or multiple sclerosis, had higher PK11195 BPND in normal-appearing white matter at baseline (P = 0.007 and P = 0.048, respectively). Central grey matter PK11195 BPND was increased in subjects with clinically isolated syndrome compared to healthy controls but no difference was found in cortical grey matter PK11195 BPND. Microglial activation in clinically isolated syndrome normal-appearing white matter is diffusely increased compared with healthy control subjects and is further increased in those who have magnetic resonance imaging lesions. Furthermore microglial activation in clinically isolated syndrome normal-appearing white matter is also higher in those subjects who developed multiple sclerosis at 2 years. Our finding, if replicated in a larger study, could be of prognostic value and aid early treatment decisions in clinically isolated syndrome.

Tailored excitation in 3D with spiral nonselective (SPINS) RF pulses

Brain images acquired at 3T often display central brightening with spatially varying tissue contrast, caused by inhomogeneity in the transmit radiofrequency fields used for excitation. Tailored radiofrequency pulses can provide mitigation of radiofrequency field inhomogeneity, but previous designs have been unsuitable for 3D imaging in rapid pulse sequences. This article presents a nonselective pulse design based on a short (1 ms) 3D spiral k‐space trajectory that covers low spatial frequencies. The resulting excitations are optimized to produce a uniform excitation within a specified volume of interest covering the whole brain. B1 mapping and pulse calculation times were reduced by optimizing in only five slices within the brain. The method has been tested with both single and parallel transmission: in phantom experiments, normalized root‐mean‐square error in excitation was 0.022 for single and 0.020 for parallel transmission. The corresponding results in vivo were 0.066 and 0.055 respectively. A pilot brain imaging study using the proposed pulses for excitation within the Alzheimers disease neuroimaging initiative magnetization prepared rapid gradient echo (MP‐RAGE) protocol, yielded excellent image quality with improved signal to noise ratio in peripheral brain regions and enhanced uniformity of contrast compared with standard excitation. Greatest performance enhancement was achieved using parallel transmission, but single channel transmission offers significant improvement over standard excitation pulses. Magn Reson Med, 2012.

Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation.

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study.

Multi-scale hierarchical generation of PET parametric maps: Application and testing on [11C]DPN study

We propose a general approach to generate parametric maps. It consists in a multi-stage hierarchical scheme where, starting from the kinetic analysis of the whole brain, we then cascade the kinetic information to anatomical systems that are akin in terms of receptor densities, and then down to the voxel level. A-priori classes of voxels are generated either by anatomical atlas segmentation or by functional segmentation using unsupervised clustering. Kinetic properties are transmitted to the voxels in each class using maximum a posteriori (MAP) estimation method. We validate the novel method on a [11C]diprenorphine (DPN) test-retest data-set that represents a challenge to estimation given [11C]DPNs slow equilibration in tissue. The estimated parametric maps of volume of distribution (VT) reflect the opioid receptor distributions known from previous [11C]DPN studies. When priors are derived from the anatomical atlas, there is an excellent agreement and strong correlation among voxel MAP and ROI results and excellent test-retest reliability for all subjects but one. Voxel level results did not change when priors were defined through unsupervised clustering. This new method is fast (i.e. 15 min per subject) and applied to [11C]DPN data achieves accurate quantification of VT as well as high quality VT images. Moreover, the way the priors are defined (i.e. using an anatomical atlas or unsupervised clustering) does not affect the estimates.