Toward Reliable MR-Based Brain Structure Morphometry

Dementing neurodegenerative disorders have more and more prominent public health implications with the most frequent cause being the currently non reversible Alzheimer's Disease (AD). However, over the past 15 years, an abundant literature has demonstrated the particular value of structural MRI from which brain morphological changes can be detected using automated volumetry and morphometry. These techniques offer promise to diagnose AD in early potentially-reversible stages as regional atrophy is currently "the" structural hallmark. Nevertheless, an essential question arises: to what extent are these quantitative measures dependent upon image quality? MRI quality can be mainly affected by machine-specific or patient-related artifacts, with motion being the most significant source of degradations. As we are currently not aware of any technique that reliably captures and corrects patient motion particularly for long high-resolution anatomical scans, rigorous quality assessment is hence of great importance to reliably derive qualitative or quantitative diagnostic information and consists in the first challenge addressed in this thesis. We developed a fully-automated method to classify the image quality based on a single MR image. Quality measures are derived by analyzing the air background of magnitude image and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring and ghosting. The method has been validated on 1670 multi-slice 2D, 3D T1- T2- PD- weighted 1.5T and 3T head scans acquired on Siemens and GE scanners operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the Alzheimer's Disease Neuroimaging Initiative (ADNI) quality control center (taken as the reference standard). The derived quality indices are independent of the MR system used and agree with the reference standard with sensitivity and specificity higher than 90 %. With acceptable computational performance (less than 10 seconds), we incorporated our algorithm in the Siemens scanner software for routine clinical use. As for many other domains, quality criterion does not exhibit all-or-none properties (i.e., a unique quality cutoff value cannot discriminate high- versus low-quality scans) but is modulated by two main aspects addressed in this thesis: acquisition parameters (what kind of image?) and application (what for?). On the one hand, depending on acquisition parameters, more or less strong signal in the object is generated inducing more or less visible artifacts. Therefore, our quality criterion has to be adjusted accordingly. On the other hand, it appears quite natural that different quality criteria are required for algorithms segmenting global gray matter contrary to small brain structure such as hippocampus. Hence comes the second challenge addressed in this thesis: developing models that allow customizing quality criterion according to the acquisition parameters and the required performance of a target application. Taking up the first part of such challenge, we investigated certain acquisition parameters effects (i.e., echo time, bandwidth, signal suppression, field strength) and proposed an exploratory model with reasonable predictive performance (86.6 % sensitivity 92 % specificity). In order to customize quality cutoff levels according to the required performance of a target application, we developed a framework based on motion simulations. The idea is to progressively and realistically corrupt an image with motion and analyze the influence of such degradations (i.e., measured by means of our quality assessment technique) on the accuracy of a brain morphometry algorithm. To be faithful to the challenging research context of early diagnosis of AD, we targeted the automated morphometry of gray matter (GM) tissue (i.e., important imaging-based marker of pathological atrophy). Our framework reveals that 2 % error on the global volume of GM can be induced by a 1 mm patient motion halfway through the acquisition (critical low-frequency sampling of rectilinear k-space). However, this small movement is reliably caught by our quality index whose variation can be further characterized as an exponential function of the error. This model thus allows determining whether an image is of sufficient quality to warrant further quantitative analysis. In conclusion, this thesis underscores the importance of a rigorous quality assessment of MR images to support high-quality outputs of quantitative morphometry tools, ultimately increasing probability for higher diagnostic accuracy. Overall, we envision that such quality assessment could greatly improve clinical workflow through its ability to rule-out the need for a repeat scan while the patient is still in the magnet bore. Just as importantly, it would be an important step forward for incorporating morphometry analysis into regular brain-imaging protocols and thus, further exploit the clinical potential of MRI.

Development and implementation of ¹H magnetic resonance techniques for an improved in vivo assessment of models of cerebral metabolic disorders

Mélanie Craveiro

Nuclear magnetic resonance (NMR) is a physical phenomenon that is widely used in the biomedical field due to its non-invasive and non-destructive properties, which make it an optimal tool for the in vivo investigation of living organs such as the brain. This thesis focused on the development of 1H magnetic resonance (MR) techniques at ultra-high magnetic field strength to improve the measurement and quantification of the 1H MR signal in the rodent brain, and to accurately assess the alterations of the metabolite and water concentrations in several cerebral metabolic disorders. In rodents, 1H MR spectroscopy (MRS) allows the assessment of the concentration of up to 20 metabolites that take part in many cerebral metabolic processes such as neurotransmission, cell energy metabolism, cell growth and osmosis. 1H MRS thus provides a unique tool to non-invasively investigate the cerebral metabolism in healthy and pathological conditions in vivo. However, it is essential for reliable quantification that systematic errors and overlap of the measured metabolite signals are minimized. To that aim, the impact of a potential additional short T2 relaxation time component, which might affect the glutamine quantification at long echo times, was assessed in this thesis. The J-difference editing technique MEGA-SPECIAL was optimized to obtain the unequivocal detection of the glutamine signal at moderate echo time. As a control, the glutamine concentration obtained with this method was then compared to short echo time 1H MR spectroscopy measurements. Since the two measurements of the glutamine concentration at short and moderate echo time did not result in significant differences, this study concluded that there is a low probability of an effect of a short T2 relaxation time component on the glutamine concentration measurement. Since a reliable quantification of 1H spectra partly relies on the accurate assessment of the overlapping macromolecule contribution to the metabolites, an optimized method for the post-processing of the measured macromolecule signal was developed to ensure an accurate assessment of its contribution. This method was applied to investigate potential regional differences in the mouse brain macromolecule signals that may affect metabolite quantification when not taken into account, as well as for the assessment of macromolecule alterations in a human-glioma mouse model. No regional macro- molecule variation was found to significantly affect the metabolite quantification in the healthy mouse brain, which supports the common use of a general macromolecule spec- trum for healthy rodent brain 1H spectra quantification. However, several alterations of the macromolecule spectrum, some of which were reported for the first time, were observed in glioma tissues, and their accurate assessment was shown to be necessary for reliable metabolite quantification. Localized 1H MR spectroscopy techniques that are designed to acquire the1H MR signal from a 3D volume can also be combined with MR imaging (MRI) to acquire information on the spatial distribution of the individual metabolites, which is often of interest when heterogeneous tissues are investigated (such as in pathological conditions). This MR spectroscopic imaging (MRSI) technique is however limited in its conventional implementation by the long measurement time that is necessary to acquire the spatial information. A fast MRSI technique, spectroscopic RARE (spRARE), was therefore implemented at high magnetic field strength in this thesis in order to benefit from the advantage of high magnetic fields in addition to the rapid spatial encoding scheme offered by spRARE. Several optimizations of the metabolite signal detection were implemented in spRARE, before the efficiency of the technique was validated at 9.4 T through a comparison of the acquired in vitro metabolite signals with numerical simulations using the density matrix formalism. The in vivo quantification of the water content is also of interest as it offers perspec- tives for investigating cerebral disease progression that is associated with brain water content alterations. In addition, when the water signal is used as an internal reference for metabolite concentration quantification in MRS, such knowledge can also contribute to a more reliable quantification. A method that is based on the multi-spin-echo MRI technique was therefore developed for the absolute quantification of the water content in the rodent brain, with the aim of assessing alterations of its content in pathological conditions. It was then applied on a bile-duct-ligation rat model of chronic hepatic encephalopathy (HE) to assess the brain water content changes associated with the disease as well as its metabolic alterations. The absence of significant brain water content changes detected in the investigated HE rat model, which was supported by post-mortem brain water content measurements using gravimetry and the dry/wet weight-ratio technique, potentially opens the way for investigating cerebral metabolic processes in chronic HE in absence of edema formation. Localized 1H MRS and conventional MRSI were also applied for the investigation of rodent models of brain disorders in order to assess the metabolic profile associated with those disorders. These studies for the first time allowed the assessment of GABA as a potential early Parkinson’s disease marker, while they also provided new insights into the role of the PPAR-β brain receptor in the early neuroprotective effects following ischemia.

EPFL2013

Comparison of accelerated T1-weighted whole-brain structural-imaging protocols

Pavel Falkovskiy, Tobias Kober, Bénédicte Marie Maréchal, Reto Meuli, Alexis Roche, Jean-Philippe Thiran

Imaging in neuroscience, clinical research and pharmaceutical trials often employs the 3D magnetisation-prepared rapid gradient-echo (MPRAGE) sequence to obtain structural T1-weighted images with high spatial resolution of the human brain. Typical research and clinical routine MPRAGE protocols with similar to 1 mm isotropic resolution require data acquisition time in the range of 5-10 min and often use only moderate two-fold acceleration factor for parallel imaging. Recent advances in MRI hardware and acquisition methodology promise improved leverage of the MR signal and more benign artefact properties in particular when employing increased acceleration factors in clinical routine and research. In this study, we examined four variants of a four-fold-accelerated MPRAGE protocol (2D-GRAPPA, CAIPIRINHA, CAIPIRINHA elliptical, and segmented MPRAGE) and compared clinical readings, basic image quality metrics (SNR, CNR), and automated brain tissue segmentation for morphological assessments of brain structures. The results were benchmarked against a widely-used two-fold-accelerated 3T ADNI MPRAGE protocol that served as reference in this study. 22 healthy subjects (age = 20-44 yrs.) were imaged with all MPRAGE variants in a single session. An experienced reader rated all images of clinically useful image quality. CAIPIRINHA MPRAGE scans were perceived on average to be of identical value for reading as the reference ADNI-2 protocol. SNR and CNR measurements exhibited the theoretically expected performance at the four-fold acceleration. The results of this study demonstrate that the four-fold accelerated protocols introduce systematic biases in the segmentation results of some brain structures compared to the reference ADNI-2 protocol. Furthermore, results suggest that the increased noise levels in the accelerated protocols play an important role in introducing these biases, at least under the present study conditions. (C) 2015 Elsevier Inc. All rights reserved.

Academic Press Inc Elsevier Science2016

Evaluating anorexia-related brain atrophy using MP2RAGE-based morphometry

Tobias Kober, François Lazeyras, Bénédicte Marie Maréchal, Alexis Roche

To evaluate brain atrophy in anorexic patients by automated cerebral segmentation with the magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) MRI sequence. Twenty patients (female; mean age, 27.9 years), presenting consecutively for brain MRI between August 2014-December 2016 with clinical suspicion of anorexia nervosa and BMI < 18.5 kg/m(2) were included. Controls were ten healthy females (mean age, 26.5 years). Automated brain morphometry was performed based on MP2RAGE. Means of morphometric results in the two groups were compared and correlation with BMI was analysed. Significantly lower volumes of total brain, grey matter (GM), white matter (WM), cerebellum and insula were found in anorexic patients. Anorexics had higher volumes of CSF, ventricles, lateral ventricles and third ventricle. When adjusted means for weight and height were compared, the volume of WM and cerebellum were not significantly different. However, volume of WM was significantly affected by weight and positively correlated with BMI. Significant positive correlations were found between BMI and volumes of total brain, GM, cortical GM and WM. BMI was negatively correlated with volumes of CSF and third ventricle. Brain atrophy was demonstrated in anorexic patients with MP2RAGE-based automated segmentation, which seems to reliably estimate brain volume. aEuro cent Automated brain segmentation based on 3-D MRI seems to reliably estimate brain volume. aEuro cent This technique detected brain atrophy in patients suffering from anorexia nervosa. aEuro cent Brain changes in anorexia nervosa can be quantitatively and qualitatively followed-up by MRI.

Springer2017