Anna I. Blazejewska

Visualization of nigrosome 1 and its loss in PD Pathoanatomical correlation and in vivo 7 T MRI

Objective: This study assessed whether high-resolution 7 T MRI allowed direct in vivo visualization of nigrosomes, substructures of the substantia nigra pars compacta (SNpc) undergoing the greatest and earliest dopaminergic cell loss in Parkinson disease (PD), and whether any disease-specific changes could be detected in patients with PD. Methods: Postmortem (PM) midbrains, 2 from healthy controls (HCs) and 1 from a patient with PD, were scanned with high-resolution T2*-weighted MRI scans, sectioned, and stained for iron and neuromelanin (Perl), TH, and calbindin. To confirm the identification of nigrosomes in vivo on 7 T T2*-weighted scans, we assessed colocalization with neuromelanin-sensitive T1-weighted scans. We then assessed the ability to depict PD pathology on in vivo T2*-weighted scans by comparing data from 10 patients with PD and 8 age- and sex-matched HCs. Results: A hyperintense, ovoid area within the dorsolateral border of the otherwise hypointense SNpc was identified in the HC brains on in vivo and PM T2*-weighted MRI. Location, size, shape, and staining characteristics conform to nigrosome 1. Blinded assessment by 2 neuroradiologists showed consistent bilateral absence of this nigrosome feature in all 10 patients with PD, and bilateral presence in 7/8 HC. Conclusions: In vivo and PM MRI with histologic correlation demonstrates that high-resolution 7 T MRI can directly visualize nigrosome 1. The absence of nigrosome 1 in the SNpc on MRI scans might prove useful in developing a neuroimaging diagnostic test for PD.

Automatic Segmentation of Cerebrospinal Fluid, White and Gray Matter in Unenhanced Computed Tomography Images

RATIONALE AND OBJECTIVES Although segmentation algorithms for cerebrospinal fluid (CSF), white matter (WM), and gray matter (GM) on unenhanced computed tomographic (CT) images exist, there is no complete research in this area. To take into account poor image contrast and intensity variability on CT scans, the aim of this study was to derive and validate a novel, automatic, adaptive, and robust algorithm. MATERIALS AND METHODS Unenhanced CT scans of normal subjects from two different centers were used. The algorithm developed uses adaptive thresholding, connectivity, and domain knowledge and is based on heuristics on the shape of CT histogram. The slope of the intensity histogram corresponding to the three-dimensional largest connected region in a variable CSF intensity range is tracked to determine the critical intensity, which serves as an initial classifier of CSF-WM. Thresholds of CSF, WM, and GM are then optimally derived to minimize classification overlap. Multiple, null, and erroneous classifications are resolved by applying domain knowledge. RESULTS The ground-truth regions with the minimal partial volume effect were used to evaluate segmentation results using the statistical markers. Average sensitivity, Dice index, and specificity, respectively, for the first center were 95.7%, 97.0%, and 98.6% for CSF; 96.1%, 97.3%, and 98.8% for WM; and 95.2%, 94.3%, and 92.8% for GM. The results were consistent for the second data center. CONCLUSIONS The algorithm automatically identifies CSF, WM, and GM on unenhanced CT images with high accuracy, is robust to data from different scanners, does not require any parameter setting, and takes about 5 minutes in MATLAB to process a 512 × 512 × 30 scan. The algorithm has potential use in research and clinical applications.

Increase in the iron content of the substantia nigra and red nucleus in multiple sclerosis and clinically isolated syndrome: A 7 Tesla MRI study

To study iron deposition in the substantia nigra (SN) and red nuclei (RN), in patients with clinically isolated syndrome (CIS) and relapsing remitting MS (RRMS) and healthy controls (HC).

Detecting default mode networks in utero by integrated 4D fMRI reconstruction and analysis

Recently, there has been considerable interest, especially for in utero imaging, in the detection of functional connectivity in subjects whose motion cannot be controlled while in the MRI scanner. These cases require two advances over current studies: (1) multiecho acquisitions and (2) post processing and reconstruction that can deal with significant between slice motion during multislice protocols to allow for the ability to detect temporal correlations introduced by spatial scattering of slices into account. This article focuses on the estimation of a spatially and temporally regular time series from motion scattered slices of multiecho fMRI datasets using a full four‐dimensional (4D) iterative image reconstruction framework. The framework which includes quantitative MRI methods for artifact correction is evaluated using adult studies with and without motion to both refine parameter settings and evaluate the analysis pipeline. ICA analysis is then applied to the 4D image reconstruction of both adult and in utero fetal studies where resting state activity is perturbed by motion. Results indicate quantitative improvements in reconstruction quality when compared to the conventional 3D reconstruction approach (using simulated adult data) and demonstrate the ability to detect the default mode network in moving adults and fetuses with single‐subject and group analysis. Hum Brain Mapp 37:4158–4178, 2016.

3D in utero quantification of T2* relaxation times in human fetal brain tissues for age optimized structural and functional MRI

Maximization of the blood oxygen level–dependent (BOLD) functional MRI (fMRI) contrast requires the echo time of the MR sequence to match the T2* value of the tissue of interest, which is expected to be higher in the fetal brain compared with the brain of a child or an adult.

Reduction of across-run variability of temporal SNR in accelerated EPI time-series data through FLEET-based robust autocalibration

ABSTRACT Temporal signal‐to‐noise ratio (tSNR) is a key metric for assessing the ability to detect brain activation in fMRI data. A recent study has shown substantial variation of tSNR between multiple runs of accelerated EPI acquisitions reconstructed with the GRAPPA method using protocols commonly used for fMRI experiments. Across‐run changes in the location of high‐tSNR regions could lead to misinterpretation of the observed brain activation patterns, reduced sensitivity of the fMRI studies, and biased results. We compared conventional EPI autocalibration (ACS) methods with the recently‐introduced FLEET ACS method, measuring their tSNR variability, as well as spatial overlap and displacement of high‐tSNR clusters across runs in datasets acquired from human subjects at 7T and 3T. FLEET ACS reconstructed data had higher tSNR levels, as previously reported, as well as better temporal consistency and larger overlap of the high‐tSNR clusters across runs compared with reconstructions using conventional multi‐shot (ms) EPI ACS data. tSNR variability across two different runs of the same protocol using ms‐EPI ACS data was about two times larger than for the protocol using FLEET ACS for acceleration factors (R) 2 and 3, and one and half times larger for R=4. The level of across‐run tSNR consistency for data reconstructed with FLEET ACS was similar to within‐run tSNR consistency. The displacement of high‐tSNR clusters across two runs (inter‐cluster distance) decreased from ˜8 mm in the time‐series reconstructed using conventional ms‐EPI ACS data to ˜4 mm for images reconstructed using FLEET ACS. However, the performance gap between conventional ms‐EPI ACS and FLEET ACS narrowed with increasing parallel imaging acceleration factor. Overall, the FLEET ACS method provides a simple solution to the problem of varying tSNR across runs, and therefore helps ensure that an assumption of fMRI analysis—that tSNR is largely consistent across runs—is met for accelerated acquisitions. Graphical abstract Figure. No Caption available. HighlightsImproved across‐run tSNR consistency using FLEET ACS reconstruction vs. ms‐EPI ACS.High‐tSNR cluster displacement decreased by factor of two by using FLEET ACS.FLEET ACS reconstructed data increases sensitivity of BOLD fMRI measurements.

Parkinson's disease related signal change in the nigrosomes 1–5 and the substantia nigra using T2* weighted 7T MRI

Improved markers for the progression of Parkinsons disease (PD) are required. Previous work has proven that iron dependent MRI scans can detect the largest Nigrosome (N1) within the substantia nigra (SN) pars compacta and changes in PD. Histopathological studies have shown that N1 is particularly affected in early PD whereas the other nigrosomes (N2–N5) and the surrounding iron-rich SN are affected later. In this study we aimed to determine whether MRI can detect the smaller nigrosomes (N2–N5) and whether graded signal alterations can be detected on T2*-weighted MRI at different disease stages consistent with histopathological changes. An observational prospective study was performed within the research imaging centre at the University of Nottingham, UK. Altogether 26 individuals with confirmed PD (median Hoehn&Yahr stage = 1, Unified PD Rating Scale [UPDRS] = 12.5) and 15 healthy controls participated. High resolution T2*weighted 7T MRI of the brain was performed and visibility of N1-N5 within the SN was qualitatively rated. Normalised T2*weighted signal intensities in manually segmented N1–N5 regions and iron-rich SN were calculated. We performed group comparisons and correlations with severity based on UPDRS. Qualitative measures were a nigrosome visibility score and a confidence score for identification. Quantitative measures were T2*weighted contrast of N1–5 and iron-rich SN relative to white matter. We found that visual assessment of the SN for N1–N5 revealed normal range visibility scores in 14 of 15 controls. N1 was identified with the highest confidence and visibility was in abnormal range in all 26 PD patients. The other nigrosomes were less well visible and less confidently identified. There was a larger PD induced signal reduction in all nigrosomes than in the iron-rich SN (median signal difference N1–5 PD compared to controls: 19.4% [IQR = 24%], iron-rich SN 11% [IQR = 24%, p = 0.017]). The largest PD induced signal reduction was in N1: 37.2% [IQR = 19%] which inversely correlated with UPDRS in PD (R2 = 0.19). All nigrosomes can be detected using 7T MRI, and PD induced T2*weighted signal reduction was greatest in the nigrosomes (especially N1). The graded T2*weighted signal alterations in the nigrosomes match previously described differential histopathological effects of PD. N1 was identified with the highest confidence and T2*weighted signal in N1 correlated with UPDRS confirming N1 as the most promising SN marker of PD pathology.

Robust R ∗ 2 map estimation from motion scattered slices for fetal fMRI

A major challenge for fMRI analysis of the developing brain is subject motion, which can corrupt T2*-weighted (T2*-w) signal intensity with spin history effects. Multi echo multislice EPI acqusitions can be used to create parameteric R2* mapping for fMRI that can provide independence from such signal variation. However, motion between slice acquisitions scatters the measurements over the anatomy of interest at each time frame. Such motion scattering cannot be optimally accounted for using direct interpolation, as data at each voxel can be missing or duplicated due to motion. Conventional BOLD contrast techniques have recently employed iterative time series reconstruction schemes to make best use of acquired data. Here we propose an extension to such techniques to deal with the case of multi-echo parametric fitting from scattered slices. Specifically, we formulate a robust R2* map estimation scheme for scattered multi-slice slice data, making use of robust fitting of R2* values to the spatially scattered T2*-w slices. We present results on simulated data and real in-utero imaging that indicate the approach outperforms the conventional interpolation based recovery of R2* timeseries.

Comparing consistency of R2* and T2*-weighted BOLD analysis of resting state fetal fMRI

Understanding when and how resting state brain functional activity begins in the human brain is an increasing area of interest in both basic neuroscience and in the clinical evaluation of the brain during pregnancy and after premature birth. Although fMRI studies have been carried out on pregnant women since the 1990s, reliable mapping of brain function in utero is an extremely challenging problem due to the unconstrained fetal head motion. Recent studies have employed scrubbing to exclude parts of the time series and whole subjects from studies in order to control the confounds of motion. Fundamentally, even after correction of the location of signals due to motion, signal intensity variations are a fundamental limitation, due to coil sensitivity and spin history effects. An alternative technique is to use a more parametric MRI signal derived from multiple echoes that provides a level of independence from basic MRI signal variation. Here we examine the use of R2* mapping combined with slice based multi echo geometric distortion correction for in-utero studies. The challenges for R2* mapping arise from the relatively low signal strength of in-utero data. In this paper we focus on comparing activation detection in-utero using T2W and R2* approaches. We make use a subset of studies with relatively limited motion to compare the activation patterns without the additional confound of significant motion. Results at different gestational ages indicate comparable agreement in many activation patterns when limited motion is present, and the detection of some additional networks in the R2* data, not seen in the T2W results.