Fast diffusion MRI based on sparse acquisition and reconstruction for long-term population imaging

Diffusion weighted magnetic resonance imaging (dMRI) is a unique MRI modality to probe the diffusive molecular transport in biological tissue. Due to its noninvasiveness and its ability to investigate the living human brain at submillimeter scale, dMRI is frequently performed in clinical and biomedical research to study the brain’s complex microstructural architecture. Over the last decades large prospective cohort studies have been set up with the aim to gain new insights into the development and progression of brain diseases across the life span and to discover biomarkers for disease prediction and potentially prevention. To allow for diverse brain imaging using different MRI modalities, stringent scan time limits are typically imposed in population imaging. Nevertheless, population studies aim to apply advanced and thereby time consuming dMRI protocols that deliver high quality data with great potential for future analysis. To allow for time-efficient but also versatile diffusion imaging, this thesis contributes to the investigation of accelerating diffusion spectrum imaging (DSI), an advanced dMRI technique that acquires imaging data with high intra-voxel resolution of tissue microstructure. Combining state-of-the-art parallel imaging and the theory of compressed sensing (CS) enables the acceleration of spatial encoding and diffusion encoding in dMRI. In this way, the otherwise long acquisition times in DSI can be reduced significantly. In this thesis, first, suitable q-space sampling strategies and basis functions are explored that fulfill the requirements of CS theory for accurate sparse DSI reconstruction. Novel 3D q-space sample distributions are investigated for CS-DSI. Moreover, conventional CS-DSI based on the discrete Fourier transform is compared for the first time to CS-DSI based on the continuous SHORE (simple harmonic oscillator based reconstruction and estimation) basis functions. Based on these findings, a CS-DSI protocol is proposed for application in a prospective cohort study, the Rhineland Study. A pilot study was designed and conducted to evaluate the CS-DSI protocol in comparison with state-of-the-art 3-shell dMRI and dedicated protocols for diffusion tensor imaging (DTI) and for the combined hindered and restricted model of diffusion (CHARMED). Population imaging requires processing techniques preferably with low computational cost to process and analyze the acquired big data within a reasonable time frame. Therefore, a pipeline for automated processing of CS-DSI acquisitions was implemented including both in-house developed and existing state-of-the-art processing tools. The last contribution of this thesis is a novel method for automatic detection and imputation of signal dropout due to fast bulk motion during the diffusion encoding in dMRI. Subject motion is a common source of artifacts, especially when conducting clinical or population studies with children, the elderly or patients. Related artifacts degrade image quality and adversely affect data analysis. It is, thus, highly desired to detect and then exclude or potentially impute defective measurements prior to dMRI analysis. Our proposed method applies dMRI signal modeling in the SHORE basis and determines outliers based on the weighted model residuals. Signal imputation reconstructs corrupted and therefore discarded measurements from the sparse set of inliers. This approach allows for fast and robust correction of imaging artifacts in dMRI which is essential to estimate accurate and precise model parameters that reflect the diffusive transport of water molecules and the underlying microstructural environment in brain tissue.Die diffusionsgewichtete Magnetresonanztomographie (dMRT) ist ein einzigartiges MRTBildgebungsverfahren, um die Diffusionsbewegung von Wassermolekülen in biologischem Gewebe zu messen. Aufgrund der Möglichkeit Schichtbilder nicht invasiv aufzunehmen und das lebende menschliche Gehirn im Submillimeter-Bereich zu untersuchen, ist die dMRT ein häufig verwendetes Bildgebungsverfahren in klinischen und biomedizinischen Studien zur Erforschung der komplexen mikrostrukturellen Architektur des Gehirns. In den letzten Jahrzehnten wurden große prospektive Kohortenstudien angelegt, um neue Einblicke in die Entwicklung und den Verlauf von Gehirnkrankheiten über die Lebenspanne zu erhalten und um Biomarker zur Krankheitserkennung und -vorbeugung zu bestimmen. Um durch die Verwendung unterschiedlicher MRT-Verfahren verschiedenartige Schichtbildaufnahmen des Gehirns zu ermöglich, müssen Scanzeiten typischerweise stark begrenzt werden. Dennoch streben Populationsstudien die Anwendung von fortschrittlichen und daher zeitintensiven dMRT-Protokollen an, um Bilddaten in hoher Qualität und mit großem Potential für zukünftige Analysen zu akquirieren. Um eine zeiteffizente und gleichzeitig vielseitige Diffusionsbildgebung zu ermöglichen, leistet diese Dissertation Beiträge zur Untersuchung von Beschleunigungsverfahren für die Bildgebung mittels diffusion spectrum imaging (DSI). DSI ist ein fortschrittliches dMRT-Verfahren, das Bilddaten mit hoher intra-voxel Auflösung der Gewebestruktur erhebt. Werden modernste Verfahren zur parallelen MRT-Bildgebung mit der compressed sensing (CS) Theorie kombiniert, ermöglicht dies eine Beschleunigung der räumliche Kodierung und der Diffusionskodierung in der dMRT. Dadurch können die ansonsten langen Aufnahmezeiten für DSI erheblich reduziert werden. In dieser Arbeit werden zuerst geeigenete Strategien zur Abtastung des q-space sowie Basisfunktionen untersucht, welche die Anforderungen der CS-Theorie für eine korrekte Signalrekonstruktion der dünnbesetzten DSI-Daten erfüllen. Neue 3D-Verteilungen von Messpunkten im q-space werden für die Verwendung in CS-DSI untersucht. Außerdem wird konventionell auf der diskreten Fourier-Transformation basierendes CS-DSI zum ersten Mal mit einem CS-DSI Verfahren verglichen, welches kontinuierliche SHORE (simple harmonic oscillator based reconstruction and estimation) Basisfunktionen verwendet. Aufbauend auf diesen Ergebnissen wird ein CS-DSI-Protokoll zur Anwendung in einer prospektiven Kohortenstudie, der Rheinland Studie, vorgestellt. Eine Pilotstudie wurde entworfen und durchgeführt, um das CS-DSI-Protokoll im Vergleich mit modernster 3-shell-dMRT und mit dedizierten Protokollen für diffusion tensor imaging (DTI) und für das combined hindered and restricted model of diffusion (CHARMED) zu evaluieren. Populationsbildgebung erfordert Prozessierungsverfahren mit möglichst geringem Rechenaufwand, um große akquirierte Datenmengen in einem angemessenen Zeitrahmen zu verarbeiten und zu analysieren. Dafür wurde eine Pipeline zur automatisierten Verarbeitung von CS-DSI-Daten implementiert, welche sowohl eigenentwickelte als auch bereits existierende moderene Verarbeitungsprogramme enthält. Der letzte Beitrag dieser Arbeit ist eine neue Methode zur automatischen Detektion und Imputation von Signalabfall, welcher durch schnelle Bewegungen während der Diffusionskodierung in der dMRT entsteht. Bewegungen der Probanden während der dMRT-Aufnahme sind eine häufige Ursache für Bildfehler, vor allem in klinischen oder Populationsstudien mit Kindern, alten Menschen oder Patienten. Diese Artefakte vermindern die Datenqualität und haben einen negativen Einfluss auf die Datenanalyse. Daher ist es das Ziel, fehlerhafte Messungen vor der dMRI-Analyse zu erkennen und dann auszuschließen oder wenn möglich zu ersetzen. Die vorgestellte Methode verwendet die SHORE-Basis zur dMRT-Signalmodellierung und bestimmt Ausreißer mit Hilfe von gewichteten Modellresidualen. Die Datenimputation rekonstruiert die unbrauchbaren und daher verworfenen Messungen mit Hilfe der verbleibenden, dünnbesetzten Menge an Messungen. Dieser Ansatz ermöglicht eine schnelle und robuste Korrektur von Bildartefakten in der dMRT, welche erforderlich ist, um korrekte und präzise Modellparameter zu schätzen, die die Diffusionsbewegung von Wassermolekülen und die zugrundeliegende Mikrostruktur des Gehirngewebes reflektieren

Indigenous Peoples’ Rights and their (new) Mobilizations in Russia. EDAP 2/2015

Issues concerning indigenous peoples (IPs) in Russia have become a “hot topic” despite the fact that they represent only 0.2 percent of the population. Constant amendments to the laws affecting the life of IPs and lawsuits filed before local Courts denouncing the violations of IPs’ rights are signs of the struggle surrounding these indigenous peoples. Moreover, between 2012 and 2013, the Russian Association of Indigenous Peoples of the North (RAIPON), the umbrella organization of IPs in the country, was ordered to shut down and subsequently given the permission to reopen by the Russian Ministry of Justice within the course of less than six months. This article aims to gain a deeper understanding of the recent developments vis-à-vis indigenous peoples’ legal protection and IPs’ increasing efforts to exercise their rights

Atrial Natriuretic Peptide, a Regulator of Nuclear Factor-κB Activation in Vivo

Natriuretic peptides (NPs) comprise a family of vasoactive hormones that play important roles in the regulation of cardiovascular and renal homeostasis. Along this line, atrial NP (ANP) (international non-proprietary name: carperitide, HANP) is an approved drug for the treatment of acute heart failure. In recent years, evidence has been given that the NP system possesses a far broader biological spectrum than the regulation of blood pressure and volume homeostasis. In fact, a substantial amount of in vitro work indicates that ANP affects important inflammatory processes and signaling pathways. Quite surprisingly, however, no information exists on the in vivo antiinflammatory potential and signaling of ANP. We show here that pretreatment of lipopolysaccharide (Salmonella abortus equi, 2.5 mg/kg)-challenged mice with ANP (5μg/kg iv, 15 min) rapidly inhibits nuclear factor-κB activation via inhibition of phosphorylation and degradation of the IκB-α protein. ANP also reduces Akt activation upon lipopolysaccharide injection. In ANP-pretreated mice, the increase of TNF-α serum concentration is markedly prevented; most importantly, the survival of these animals improved. These findings demonstrate both in vitro and in vivo an antiinflammatory profile of ANP that deserves to be further investigated in a therapeutic perspective

Identifizierung einer neuen Signalkaskade des Nervenwachstumsfaktorrezeptors TrkA : c-Abl und Mena, das Säugerhomolog von Enabled, sind potentielle Schlüsselmoleküle in der neuronalen Differenzierung

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Spatiotemporal modeling of schistosomiasis in Ghana: linking remote sensing data to infectious disease

More than 90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. The use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. The transmission of schistosomiasis, a disease acquired from contact with contaminated surface water, requires specific environmental conditions to sustain freshwater snails. If a connection between schistosomiasis and remotely sensed environmental variables can be established, then cost effective and current disease risk predictions can be made available. Schistosomiasis transmission has unknown seasonality, and the disease is difficult to study due to a long lag between infection and clinical symptoms. To overcome these challenges, we employed a comprehensive 15-year time-series built from remote sensing feeds, which is the longest environmental dataset to be used in the application of remote sensing to schistosomiasis. The following environmental variables will be used in the model: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique. This technique, improves upon the conventional Köppen-Geiger method, which has been the primary climate classification system in use the past 100 years. These predictor variables will be regressed against 8 years of national health data in Ghana, the largest health dataset of its kind to be used in this context, and acquired from freely available satellite imagery data. A benefit of remote sensing processing is that it only requires training and time in terms of resources. The results of a fixed effects model can be used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.Published versio

The direct association of the multiple PDZ domain containing proteins (MUPP-1) with the human c-Kit C-terminus is regulated by tyrosine kinase activity

AbstractWe have identified the multiple PDZ domain containing protein (MUPP-1 or MPDZ) as a novel binding partner of the human c-Kit. c-Kit binds specifically to the 10th PDZ domain of MUPP-1 via its C-terminal sequence. Furthermore, a kinase negative-mutant receptor interacted more strongly with MUPP-1 than the wild-type c-Kit. Strikingly, a constitutively activated c-Kit (D816V-Kit) did not bind to MUPP-1, although this oncogenic form retains the PDZ binding motif ‘HDDV’ at the C-terminal end. Deletion of V967 of c-Kit abolished binding to MUPP-1 and drastically reduced its tyrosine kinase activity, suggesting that the structure of the C-terminal tail of c-Kit influences its enzymatic activity

Incorporation of Medication-Assisted Treatment with Buprenorphine into Primary Care Practice

In 2015, over 3.8 million people reported misusing prescription pain medication within the last month and over 5.1 million people in the United States admitted to using heroin at some point in their lives. Despite the widespread prevalence of opioid abuse, evidence-based treatments, such as medication-assisted treatment (MAT), have yet to be made widely available and easily accessible to patients. This program plan was designed to recommend strategies to effectively incorporate MAT into the primary care setting. Interviews were compiled from key informants at primary care clinics with successful MAT programs in the Chapel Hill, North Carolina and surrounding area in order to develop a model of practice transformation that could be applied to a primary care clinic that does not currently provide MAT services. Ultimately, the key factors to consider when implementing MAT in the primary care setting include: provider buy-in, the availability of behavioral support staff, and support of key administrative and support staff.Master of Scienc

A correlative analysis of gold nanoparticles internalized by A549 cells

Fluorescently labeled nanoparticles are widely used to investigate nanoparticle cell interactions by fluorescence microscopy. Owing to limited lateral and axial resolution, nanostructures (<100 nm) cannot be resolved by conventional light micro­scopy techniques. Especially after uptake into cells, a common fate of the fluorescence label and the particle core cannot be taken for granted. In this study, a correlative approach is presented to image fluorescently labeled gold nanoparticles inside whole cells by correlative light and electron microscopy (CLEM). This approach allows for detection of the fluorescently labeled particle shell as well as for the gold core in one sample. In this setup, A549 cells are exposed to 8 nm Atto 647N-labeled gold nanoparticles (3.3 × 109 particles mL−1, 0.02 μg Au mL−1) for 5 h and are subsequently imaged by confocal laser scanning microscopy (CLSM) and transmission electron microscopy (TEM). Eight fluorescence signals located at different intracellular positions are further analyzed by TEM. Five of the eight fluorescence spots are correlated with isolated or agglomerated gold nanoparticles. Three fluorescence signals could not be related to the presence of gold, indicating a loss of the particle shell

The use of remotely sensed environmental parameters for spatial and temporal schistosomiasis prediction across climate zones in Ghana

Schistosomiasis control in sub-Saharan Africa is enacted primarily through preventive chemotherapy. Predictive models can play an important role in filling knowledge gaps in the distribution of the disease and help guide the allocation of limited resources. Previous modeling approaches have used localized cross-sectional survey data and environmental data typically collected at a discrete point in time. In this analysis, 8 years (2008-2015) of monthly schistosomiasis cases reported into Ghana's national surveillance system were used to assess temporal and spatial relationships between disease rates and three remotely sensed environmental variables: land surface temperature (LST), normalized difference vegetation index (NDVI), and accumulated precipitation (AP). Furthermore, the analysis was stratified by three major and nine minor climate zones, defined using a new climate classification method. Results showed a downward trend in reported disease rates (~ 1% per month) for all climate zones. Seasonality was present in the north with two peaks (March and September), and in the middle of the country with a single peak (July). Lowest disease rates were observed in December/January across climate zones. Seasonal patterns in the environmental variables and their associations with reported schistosomiasis infection rates varied across climate zones. Precipitation consistently demonstrated a positive association with disease outcome, with a 1-cm increase in rainfall contributing a 0.3-1.6% increase in monthly reported schistosomiasis infection rates. Generally, surveillance of neglected tropical diseases (NTDs) in low-income countries continues to suffer from data quality issues. However, with systematic improvements, our approach demonstrates a way for health departments to use routine surveillance data in combination with publicly available remote sensing data to analyze disease patterns with wide geographic coverage and varying levels of spatial and temporal aggregation.Accepted manuscrip