Entwicklung von Fluor-19 und Protonen-Magnetresonanztomographie und ihre Anwendung bei Neuroentzündung

The experimental autoimmune encephalomyelitis (EAE) is used to study multiple sclerosis (MS) pathology and develop novel technologies to quantify inflammation over time. Magnetic resonance imaging (MRI) with gadolinium-based contrast agents (GBCAs) is the state-of-the-art method to assess inflammation in MS patients and its animal model. Fluorine (19F)-MRI is one novel technology to quantify inflammatory immune cells in vivo using 19F-nanoparticles. T1 mapping of contrast-enhancing images is another method that could be implemented to quantify inflammatory lesions. Transient macroscopic changes in the EAE brain confound quantification and necessitate registration methods to spatially align images in longitudinal studies. For 19F-MRI, an additional challenge is the low signal-to-noise ratio (SNR) due to low number of 19F-labeled immune cells in vivo. Transceive surface radiofrequency (RF) probes and SNR-efficient imaging techniques such as RARE (Rapid Acquisition with Relaxation Enhancement) are combined to increase sensitivity in 19F-MRI. However, the strong spatially-varying RF field (B1 inhomogeneity) of transceive surface RF probes further hampers quantification. Retrospective B1 correction methods typically use signal intensity equations, unavailable for complex acquisition methods like RARE. The main goal of this work is to investigate novel B1 correction and registration methods to enable the study of inflammatory diseases using 1H- and 19F-MRI following GBCA and 19F-nanoparticle administration, respectively. For correcting B1 inhomogeneities in 1H- and 19F-MR transceive surface RF probes, a model-based method was developed using empirical measurements and simulations, and then validated and compared with a sensitivity method and a hybrid of both. For 19F-MRI, a workflow to measure anatomical images in vivo and a method to compute 19F-concentration uncertainty after correction using Monte Carlo simulations were developed. To overcome the challenges of EAE brain macroscopic changes, a pipeline for registering images throughout longitudinal studies was developed. The proposed B1 correction methods demonstrated dramatic improvements in signal quantification and T1 contrast on images of test phantoms and mouse brains, allowing quantitative measurement with transceive surface RF probes. For low-SNR scenarios, the model-based method yielded reliable 19F-quantifications when compared to volume resonators. Uncertainty after correction depended linearly on the SNR (≤10% with SNR≥10.1, ≤25% when SNR≥4.25). The implemented registration approach provided successful image alignment despite substantial morphological changes in the EAE brain over time. Consequently, T1 mapping was shown to objectively quantify gadolinium lesion burden as a measure of inflammatory activity in EAE. The 1H- and 19F-MRI methods proposed here are highly relevant for quantitative MR of neuroinflammatory diseases, enabling future (pre)clinical investigations.Die experimentelle Autoimmun-Enzephalomyelitis (EAE) wird zur Untersuchung Multipler Sklerose (MS) und zur Entwicklung neuer Technologien zur Entzündungsquantifizierung eingesetzt. Magnetresonanztomographie (MRT) mit Gadolinium-haltigen Kontrastmitteln (GBCAs) ist die modernste Methode zur Beurteilung von Entzündungen bei MS-Patienten und im Tiermodell. Fluor (19F)-MRT unter Verwendung von 19F-Nanopartikeln ist eine neue Technologie zur Quantifizierung entzündlicher Immunzellen in vivo. T1-Kartierung ist eine MRT-Methode, die zur Quantifizierung entzündlicher Läsionen eingesetzt werden könnte. Temporäremorphologische Veränderungen im EAE-Gehirn erschweren die Quantifizierung und erfordern Registrierungsmethoden, um MRT-Bilder in Längsschnittstudien räumlichabzugleichen. Das niedrige Signal-Rausch-Verhältnis (SNR) ist aufgrund der geringen Anzahl 19F-markierter Immunzellen in vivo eine zusätzliche Herausforderung der 19F-MRT. Um deren Empfindlichkeit zu erhöhen, werden Sende-/Empfangsoberflächen-Hochfrequenzspulen (TX/RX-HF-Spule) und SNR-effiziente MRT-Techniken wie RARE (Rapid Acquisition with Relaxation Enhancement) kombiniert. Jedoch verhindert die starke räumliche Variation des HF-Feldes (B1-Inhomogenität) dieser Spulen die Signalquantifizierung. Retrospektive B1-Korrekturmethoden verwenden in der Regel Signalintensitätsgleichungen, die für komplexe MRT-Techniken wie RARE nicht existieren. Das Hauptziel dieser Arbeit ist die Untersuchung neuartiger B1-Korrektur- und Bildregistrierungsmethoden, um in vivo 1H- und 19F-MRT Studien von Entzündungsprozessen zu ermöglichen. Zur Korrektur von B1-Inhomogenitäten wurde eine modellbasierte Methode entwickelt. Diese verwendet empirische Messungen und Simulationen, wurde in Phantomexperimenten validiert und mit Referenzmethoden verglichen. Für 19F-MRT wurden ein Protokoll zur Messung anatomischer Bilder in vivo und eine Methode zur Berechnung der 19F-Konzentrationsunsicherheit nach Korrektur mittels Monte-Carlo-Simulationen entwickelt. Um morphologische Veränderungen im EAE-Gehirn in longitudinalen Studien zu kompensieren, wurde zur Bildregistrierung eine Software-Bibliothek entwickelt. Die B1-Korrekturmethoden zeigten in Testobjekten und Mäusehirnen drastische Verbesserungen der Signal- und T1 Quantifizierung und ermöglichten so quantitative Messungen mit TX/RX-HF-Spulen. Die modellbasierte Methode lieferte für geringe SNRs zuverlässige 19F-Quantifizierungen, deren Genauigkeit mit dem SNR korrelierte. Die implementierte Registrierungsmethode ermöglichte einen erfolgreichen Abgleich von Bildserientrotz erheblicher morphologischer Veränderungen im EAE-Hirn. Folglich wurde gezeigt, dass MRT basierte T1-Kartierung die Gadolinium-Läsionslast als Maß entzündlicher Aktivität bei EAE objektiv quantifizieren kann. Die hier unterscuhten Methoden sind für quantitative 1H- und 19F-MRT neuroinflammatorischer Erkrankungen sehr relevant und ermöglichen künftige (prä)klinische Untersuchungen

Dielectric shimming : exploiting dielectric interactions in High Field MRI

This thesis reports on the utility of high permittivity dielectric materials for adjusting the radiofrequency (RF) field in high field MR. The performance-driven trend towards higher static magnetic field strengths drives MR operation into the regime where the dimensions of the body section being imaged are comparable to the RF wavelength. This results in areas of RF interference within the body, and associated variations in signal intensity and tissue contrast, which can severely reduce the diagnostic image quality. However, the underlying electromagnetic interactions raise the question of whether these mechanisms may also be exploited to establish a remediation. This approach is termed "dielectric shimming," and is the subject of this thesis. The main conclusions from this thesis are that dielectric shimming presents a very simple and effective method for improving MR operation at high field strength. The high permittivity materials allow for tailoring the B1 field without increasing SAR. The technique improves body applications at 3T as well as neuro applications at 7T, and theoretical foundations are presented to harness and exploit this approach. The obtained solutions are low-cost, vendor-independent, do not require any major hardware or software modifications and can therefore be very easily implemented in clinical protocols.UBL - phd migration 201

Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning

Recent development of quantitative myocardial blood flow (MBF) mapping allows direct evaluation of absolute myocardial perfusion, by computing pixel-wise flow maps. Clinical studies suggest quantitative evaluation would be more desirable for objectivity and efficiency. Objective assessment can be further facilitated by segmenting the myocardium and automatically generating reports following the AHA model. This will free user interaction for analysis and lead to a 'one-click' solution to improve workflow. This paper proposes a deep neural network based computational workflow for inline myocardial perfusion analysis. Adenosine stress and rest perfusion scans were acquired from three hospitals. Training set included N=1,825 perfusion series from 1,034 patients. Independent test set included 200 scans from 105 patients. Data were consecutively acquired at each site. A convolution neural net (CNN) model was trained to provide segmentation for LV cavity, myocardium and right ventricular by processing incoming 2D+T perfusion Gd series. Model outputs were compared to manual ground-truth for accuracy of segmentation and flow measures derived on global and per-sector basis. The trained models were integrated onto MR scanners for effective inference. Segmentation accuracy and myocardial flow measures were compared between CNN models and manual ground-truth. The mean Dice ratio of CNN derived myocardium was 0.93 +/- 0.04. Both global flow and per-sector values showed no significant difference, compared to manual results. The AHA 16 segment model was automatically generated and reported on the MR scanner. As a result, the fully automated analysis of perfusion flow mapping was achieved. This solution was integrated on the MR scanner, enabling 'one-click' analysis and reporting of myocardial blood flow.Comment: This work has been submitted to Radiology: Artificial Intelligence for possible publicatio

Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15,000 patients

Purpose: Quantification of myocardial perfusion has the potential to improve the detection of regional and global flow reduction. Significant effort has been made to automate the workflow, where one essential step is the arterial input function (AIF) extraction. Failure to accurately identify the left ventricle (LV) prevents AIF estimation required for quantification, therefore high detection accuracy is required. This study presents a robust LV detection method using the convolutional neural network (CNN). Methods: CNN models were trained by assembling 25,027 scans (N = 12,984 patients) from three hospitals, seven scanners. Performance was evaluated using a hold‐out test set of 5721 scans (N = 2805 patients). Model inputs were a time series of AIF images (2D+T). Two variations were investigated: (1) two classes (2CS) for background and foreground (LV mask), and (2) three classes (3CS) for background, LV, and RV. The final model was deployed on MRI scanners using the Gadgetron reconstruction software framework. Results: Model loading on the MRI scanner took ~340 ms and applying the model took ~180 ms. The 3CS model successfully detected the LV in 99.98% of all test cases (1 failure out of 5721). The mean Dice ratio for 3CS was 0.87 ± 0.08 with 92.0% of all cases having Dice >0.75. The 2CS model gave a lower Dice ratio of 0.82 ± 0.22 (P .2) comparing automatically extracted AIF signals with signals from manually drawn contours. Conclusions: A CNN‐based solution to detect the LV blood pool from the arterial input function image series was developed, validated, and deployed. A high LV detection accuracy of 99.98% was achieved

Quantification of myocardial perfusion by cardiovascular magnetic resonance

The potential of contrast-enhanced cardiovascular magnetic resonance (CMR) for a quantitative assessment of myocardial perfusion has been explored for more than a decade now, with encouraging results from comparisons with accepted "gold standards", such as microspheres used in the physiology laboratory. This has generated an increasing interest in the requirements and methodological approaches for the non-invasive quantification of myocardial blood flow by CMR. This review provides a synopsis of the current status of the field, and introduces the reader to the technical aspects of perfusion quantification by CMR. The field has reached a stage, where quantification of myocardial perfusion is no longer a claim exclusive to nuclear imaging techniques. CMR may in fact offer important advantages like the absence of ionizing radiation, high spatial resolution, and an unmatched versatility to combine the interrogation of the perfusion status with a comprehensive tissue characterization. Further progress will depend on successful dissemination of the techniques for perfusion quantification among the CMR community

Advanced methods for mapping the radiofrequency magnetic fields in MRI

As MRI systems have increased in static magnetic field strength, the radiofrequency (RF) fields that are used for magnetisation excitation and signal reception have become significantly less uniform. This can lead to image artifacts and errors when performing quantitative MRI. A further complication arises if the RF fields vary substantially in time. In the first part of this investigation temporal variations caused by respiration were explored on a 3T scanner. It was found that fractional changes in transmit field amplitude between inhalation and expiration ranged from 1% to 14% in the region of the liver in a small group of normal subjects. This observation motivated the development of a pulse sequence and reconstruction method to allow dynamic observation of the transmit field throughout the respiratory cycle. However, the proposed method was unsuccessful due to the inherently time-consuming nature of transmit field mapping sequences. This prompted the development of a novel data reconstruction method to allow the acceleration of transmit field mapping sequences. The proposed technique posed the RF field reconstruction as a nonlinear least-squares optimisation problem, exploiting the fact that the fields vary smoothly. It was shown that this approach was superior to standard reconstruction approaches. The final component of this thesis presents a unified approach to RF field calibration. The proposed method uses all measured data to estimate both transmit and receive sensitivities, whilst simultaneously insisting that they are smooth functions of space. The resulting maps are robust to both noise and imperfections in regions of low signal

Inhomogeneity Correction in High Field Magnetic Resonance Images

Projecte realitzat en col.laboració amb el centre Swiss Federal Institute of Technology (EPFL)Magnetic Resonance Imaging, MRI, is one of the most powerful and harmless ways to study human inner tissues. It gives the chance of having an accurate insight into the physiological condition of the human body, and specially, the brain. Following this aim, in the last decade MRI has moved to ever higher magnetic field strength that allow us to get advantage of a better signal-to-noise ratio. This improvement of the SNR, which increases almost linearly with the field strength, has several advantages: higher spatial resolution and/or faster imaging, greater spectral dispersion, as well as an enhanced sensitivity to magnetic susceptibility. However, at high magnetic resonance imaging, the interactions between the RF pulse and the high permittivity samples, which causes the so called Intensity Inhomogeneity or B1 inhomogeneity, can no longer be negligible. This inhomogeneity causes undesired efects that afects quantitatively image analysis and avoid the application classical intensity-based segmentation and other medical functions. In this Master thesis, a new method for Intensity Inhomogeneity correction at high ¯eld is presented. At high ¯eld is not possible to achieve the estimation and the correction directly from the corrupted data. Thus, this method attempt the correction by acquiring extra information during the image process, the RF map. The method estimates the inhomogeneity by the comparison of both acquisitions. The results are compared to other methods, the PABIC and the Low-Pass Filter which try to correct the inhomogeneity directly from the corrupted data

Methodological considerations for neuroimaging in deep brain stimulation of the subthalamic nucleus in Parkinson’s disease patients

Deep brain stimulation (DBS) of the subthalamic nucleus is a neurosurgical intervention for Parkinson’s disease patients who no longer appropriately respond to drug treatments. A small fraction of patients will fail to respond to DBS, develop psychiatric and cognitive side-effects, or incur surgery-related complications such as infections and hemorrhagic events. In these cases, DBS may require recalibration, reimplantation, or removal. These negative responses to treatment can partly be attributed to suboptimal pre-operative planning procedures via direct targeting through low-field and low-resolution magnetic resonance imaging (MRI). One solution for increasing the success and efficacy of DBS is to optimize preoperative planning procedures via sophisticated neuroimaging techniques such as high-resolution MRI and higher field strengths to improve visualization of DBS targets and vasculature. We discuss targeting approaches, MRI acquisition, parameters, and post-acquisition analyses. Additionally, we highlight a number of approaches including the use of ultra-high field (UHF) MRI to overcome limitations of standard settings. There is a trade-off between spatial resolution, motion artifacts, and acquisition time, which could potentially be dissolved through the use of UHF-MRI. Image registration, correction, and post-processing techniques may require combined expertise of traditional radiologists, clinicians, and fundamental researchers. The optimization of pre-operative planning with MRI can therefore be best achieved through direct collaboration between researchers and clinicians

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Doctor of Philosophy

dissertationMagnetic resonance imaging (MRI) techniques are widely applied in various disease diagnoses and scientific research projects as noninvasive methods. However, lower signal-to-noise ratio (SNR), B1 inhomogeneity, motion-related artifact, susceptibility artifact, chemical shift artifact and Gibbs ring still play a negative role in image quality improvement. Various techniques and methods were developed to minimize and remove the degradation of image quality originating from artifacts. In the first part of this dissertation, a motion artifact reduction technique based on a novel real time self-gated pulse sequence is presented. Diffusion weighted and diffusion tensor magnetic resonance imaging techniques are generally performed with signal averaging of multiple measurements to improve the signal-to-noise ratio and the accuracy of diffusion measurement. Any discrepancy in images between different averages causes errors that reduce the accuracy of diffusion MRI measurements. The new scheme is capable of detecting a subject's motion and reacquiring motion-corrupted data in real time and helps to improve the accuracy of diffusion MRI measurements. In the second part of this dissertation, a rapid T1 mapping technique (two dimensional singleshot spin echo stimulated echo planar image--2D ss-SESTEPI), which is an EPI-based singleshot imaging technique that simultaneously acquires a spin-EPI (SEPI) and a stimulated-EPI (STEPI) after a single RF excitation, is discussed. The magnitudes of SEPI and STEPI differ by T1 decay for perfect 90o RF pulses and can be used to rapidly measure the T1 relaxation time. However, the spatial variation of B1 amplitude induces uneven splitting of the transverse magnetization for SEPI and STEPI within the imaging FOV. Therefore, correction for B1 inhomogeneity is critical for 2D ss-SESTEPI to be used for T1 measurement. In general, the EPI-based pulse sequence suffers from geometric distortion around the boundary of air-tissue or bone tissue. In the third part of this dissertation, a novel pulse sequence is discussed, which was developed based on three dimensional singleshot diffusion weighted stimulated echo planar imaging (3D ss-DWSTEPI). A parallel imaging technique was combined with 3D ss-DWSTEPI to reduce the image distortion, and the secondary spin echo formed by three RF pulses (900-1800-900) is used to improve the SNR. Image quality is improved