Unsupervised Multi Class Segmentation of 3D Images with Intensity Inhomogeneities

Intensity inhomogeneities in images constitute a considerable challenge in image segmentation. In this paper we propose a novel biconvex variational model to tackle this task. We combine a total variation approach for multi class segmentation with a multiplicative model to handle the inhomogeneities. Our method assumes that the image intensity is the product of a smoothly varying part and a component which resembles important image structures such as edges. Therefore, we penalize in addition to the total variation of the label assignment matrix a quadratic difference term to cope with the smoothly varying factor. A critical point of our biconvex functional is computed by a modified proximal alternating linearized minimization method (PALM). We show that the assumptions for the convergence of the algorithm are fulfilled by our model. Various numerical examples demonstrate the very good performance of our method. Particular attention is paid to the segmentation of 3D FIB tomographical images which was indeed the motivation of our work

Intensity Segmentation of the Human Brain with Tissue dependent Homogenization

High-precision segmentation of the human cerebral cortex based on T1-weighted MRI is still a challenging task. When opting to use an intensity based approach, careful data processing is mandatory to overcome inaccuracies. They are caused by noise, partial volume effects and systematic signal intensity variations imposed by limited homogeneity of the acquisition hardware. We propose an intensity segmentation which is free from any shape prior. It uses for the first time alternatively grey (GM) or white matter (WM) based homogenization. This new tissue dependency was introduced as the analysis of 60 high resolution MRI datasets revealed appreciable differences in the axial bias field corrections, depending if they are based on GM or WM. Homogenization starts with axial bias correction, a spatially irregular distortion correction follows and finally a noise reduction is applied. The construction of the axial bias correction is based on partitions of a depth histogram. The irregular bias is modelled by Moody Darken radial basis functions. Noise is eliminated by nonlinear edge preserving and homogenizing filters. A critical point is the estimation of the training set for the irregular bias correction in the GM approach. Because of intensity edges between CSF (cerebro spinal fluid surrounding the brain and within the ventricles), GM and WM this estimate shows an acceptable stability. By this supervised approach a high flexibility and precision for the segmentation of normal and pathologic brains is gained. The precision of this approach is shown using the Montreal brain phantom. Real data applications exemplify the advantage of the GM based approach, compared to the usual WM homogenization, allowing improved cortex segmentation

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

A Review on MR Image Intensity Inhomogeneity Correction

Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Low-frequency models are widely used, but they tend to corrupt the low-frequency components of the tissue. Hypersurface models and statistical models can be adaptive to the image and generally more stable, but they are also generally more complex and consume more computer memory and CPU time. They are often formulated together with image segmentation within one framework and the overall performance is highly dependent on the segmentation process. Beside these three popular models, this paper also summarizes other techniques based on different principles. In addition, the issue of quantitative evaluation and comparative study are discussed

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

B(1) inhomogeneity correction of RARE MRI with transceive surface radiofrequency probes

PURPOSE: The use of surface radiofrequency (RF) coils is common practice to boost sensitivity in (pre)clinical MRI. The number of transceive surface RF coils is rapidly growing due to the surge in cryogenically cooled RF technology and ultrahigh‐field MRI. Consequently, there is an increasing need for effective correction of the excitation field (B(1)(+)) inhomogeneity inherent in these coils. Retrospective B(1) correction permits quantitative MRI, but this usually requires a pulse sequence‐specific analytical signal intensity (SI) equation. Such an equation is not available for fast spin‐echo (Rapid Acquisition with Relaxation Enhancement, RARE) MRI. Here we present, test, and validate retrospective B(1) correction methods for RARE. METHODS: We implemented the commonly used sensitivity correction and developed an empirical model‐based method and a hybrid combination of both. Tests and validations were performed with a cryogenically cooled RF probe and a single‐loop RF coil. Accuracy of SI quantification and T(1) contrast were evaluated after correction. RESULTS: The three described correction methods achieved dramatic improvements in B(1) homogeneity and significantly improved SI quantification and T(1) contrast, with mean SI errors reduced from >40% to >10% following correction in all cases. Upon correction, images of phantoms and mouse heads demonstrated homogeneity comparable to that of images acquired with a volume resonator. This was quantified by SI profile, SI ratio (error 80% in vivo and ex vivo compared to PIU > 87% with the reference RF coil). CONCLUSIONS: This work demonstrates the efficacy of three B(1) correction methods tailored for transceive surface RF probes and RARE MRI. The corrected images are suitable for quantification and show comparable results between the three methods, opening the way for T(1) measurements and X‐nuclei quantification using surface transceiver RF coils. This approach is applicable to other MR techniques for which no analytical SI exists