Lebenswissenschaften und Ethik
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Quantification of growth in experimental F98 and C6 rat brain tumours was performed on 51 rat brains, 17 of which have been further assessed by 3D tumour reconstruction. Brains were cryosliced and radio-labelled with a ligand of the peripheral type benzodiazepine-receptor (pBR), 3H-Pk11195 [(1-(2-chlorophenyl)-N-methyl-N-(1-methyl-propylene)-3-isoquinoline-carboxamide)] by receptor autoradiography. Manually segmented and automatically registered tumours have been 3D-reconstructed for volumetric comparison on the basis of 3H-Pk11195-based tumour recognition. Furthermore automatically computed areas of −300 μm inner (marginal) zone as well as 300 μm and 600 μm outer tumour space were quantified. These three different regions were transferred onto other adjacent slices that had been labelled by receptor autoradiography with the A1 Adenosine receptor (A1AR)-ligand 3H-CPFPX (3H-8-cyclopentyl-3-(3-fluorpropyl)-1-propylxanthine) for quantitative assessment of A1AR in the three different tumour zones. Hence, a method is described for quantifying various receptor protein systems in the tumour as well as in the marginal invasive zones around experimentally implanted rat brain tumours and their representation in the tumour microenvironment as well as in 3D space. Furthermore, a tool for automatically reading out radio-labelled rat brain slices from auto radiographic films was developed, reconstructed into a consistent 3D-tumour model and the zones around the tumour were visualized. A1AR expression was found to depend upon the tumour volume in C6 animals, but is independent on the time of tumour development. In F98 animals, a significant increase in A1AR receptor protein was found in the Peritumoural zone as a function of time of tumour development and tumour volume.
Reduktion von Rissartefakten durch nicht-lineare Registrierung in histologischen Schnittbildern
(2009)
In dieser Arbeit wird ein Verfahren vorgestellt, das Rissartefakte, die in histologischen Rattenhirnschnitten vorkommen können, durch nicht-lineare Registrierung reduziert. Um die Optimierung in der Rissregion zu leiten, wird der Curvature Registrierungsansatz um eine Metrik basierend auf der Segmentierung der Bilder erweitert. Dabei erzielten Registrierungen mit der ausschließlichen Segmentierung des Risses bessere Ergebnisse als Registrierungen mit einer Segmentierung des gesamten Hirnschnitts. Insgesamt zeigt sich eine deutliche Verbesserung in der Rissregion, wobei der verbleibende reduzierte Riss auf die Glattheitsbedingungen des Regularisierers zurückzuführen ist.
In dieser Arbeit wird die Segmentierung von Gehirngewebe aus Kopfaufnahmen von Ratten mittels Level-Set-Methoden vorgeschlagen. Dazu wird ein zweidimensionaler, kontrastbasierter Ansatz zu einem dreidimensionalen, lokal an die Bildintensität adaptierten Segmentierer erweitert. Es wird gezeigt, dass mit diesem echten 3D-Ansatz die lokalen Bildstrukturen besser berücksichtigt werden können. Insbesondere Magnet-Resonanz-Tomographien (MRTs) mit globalen Helligkeitsgradienten, beispielsweise bedingt durch Oberflächenspulen, können auf diese Weise zuverlässiger und ohne weitere Vorverarbeitungsschritte segmentiert werden. Die Leistungsfähigkeit des Algorithmus wird experimentell an Hand dreier Rattenhirn-MRTs demonstriert.
In neuroscience, small-animal studies frequently involve dealing with series of images from multiple modalities such as histology and autoradiography. The consistent and bias-free restacking of multi-modality image series is obligatory as a starting point for subsequent non-rigid registration procedures and for quantitative comparisons with positron emission tomography (PET) and other in vivo data. Up to now, consistency between 2D slices without cross validation using an inherent 3D modality is frequently presumed to be close to the true morphology due to the smooth appearance of the contours of anatomical structures. However, in multi-modality stacks consistency is difficult to assess. In this work, consistency is defined in terms of smoothness of neighboring slices within a single modality and between different modalities. Registration bias denotes the distortion of the registered stack in comparison to the true 3D morphology and shape. Based on these metrics, different restacking strategies of multi-modality rat brain slices are experimentally evaluated. Experiments based on MRI-simulated and real dual-tracer autoradiograms reveal a clear bias of the restacked volume despite quantitatively high consistency and qualitatively smooth brain structures. However, different registration strategies yield different inter-consistency metrics. If no genuine 3D modality is available, the use of the so-called SOP (slice-order preferred) or MOSOP (modality-and-slice-order preferred) strategy is recommended.
For accurate registration of histological sections blockface images are frequently used as three dimensional reference. However, due to the use of endocentric lenses the images suffer from perspective errors such as scaling and seemingly relative movement of planes which are located in different distances parallel to the imaging sensor. The suggested correction of those errors is based on the estimation of scaling factors derived from image registration of regions characterized by differing distances to the point of view in neighboring sections. The correction allows the generation of a consistent three dimensional blockface volume.
Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been established as a powerful technique for the determination of metal and nonmetal distributions within biological systems with high sensitivity. An imaging LA-ICP-MS technique for Fe, Cu, Zn, and Mn was developed to produce large series of quantitative element maps in native brain sections of mice subchronically intoxicated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridin (MPTP) as a model of Parkinson’s disease. Images were calibrated using matrix-matched laboratory standards. A software solution allowing a precise delineation of anatomical structures was implemented. Coronal brain sections were analyzed crossing the striatum and the substantia nigra, respectively. Animals sacrificed 2 h, 7 d, or 28 d after the last MPTP injection and controls were investigated.
We observed significant decreases of Cu concentrations in the periventricular zone and the fascia dentata at 2 h and 7d and a recovery or overcompensation at 28 d, most pronounced in the rostral periventricular zone (+40%). In the cortex Cu decreased slightly to −10%. Fe increased in the interpeduncular nucleus (+40%) but not in the substantia nigra. This pattern is in line with a differential regulation of periventricular and parenchymal Cu, and with the histochemical localization of Fe, and congruent to regions of preferential MPTP binding described in the rodent brain.
The LA-ICP-MS technique yielded valid and statistically robust results in the present study on 39 slices from 19 animals. Our findings underline the value of routine micro-local analytical techniques in the life sciences and affirm a role of Cu availability in Parkinson’s disease.
Bioimaging of Metals by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS)
(2010)
The distribution analysis of (essential, beneficial, or toxic) metals (e.g., Cu, Fe, Zn, Pb, and others), metalloids, and non‐metals in biological tissues is of key interest in life science. Over the past few years, the development and application of several imaging mass spectrometric techniques has been rapidly growing in biology and medicine. Especially, in brain research metalloproteins are in the focus of targeted therapy approaches of neurodegenerative diseases such as Alzheimer's and Parkinson's disease, or stroke, or tumor growth. Laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS) using double‐focusing sector field (LA‐ICP‐SFMS) or quadrupole‐based mass spectrometers (LA‐ICP‐QMS) has been successfully applied as a powerful imaging (mapping) technique to produce quantitative images of detailed regionally specific element distributions in thin tissue sections of human or rodent brain. Imaging LA‐ICP‐QMS was also applied to investigate metal distributions in plant and animal sections to study, for example, the uptake and transport of nutrient and toxic elements or environmental contamination. The combination of imaging LA‐ICP‐MS of metals with proteomic studies using biomolecular mass spectrometry identifies metal‐containing proteins and also phosphoproteins. Metal‐containing proteins were imaged in a two‐dimensional gel after electrophoretic separation of proteins (SDS or Blue Native PAGE). Recent progress in LA‐ICP‐MS imaging as a stand‐alone technique and in combination with MALDI/ESI‐MS for selected life science applications is summarized.
Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been developed and established as an emerging technique in the generation of quantitative images of metal distributions in thin tissue sections of brain samples (such as human, rat and mouse brain), with applications in research related to neurodegenerative disorders. A new analytical protocol is described which includes sample preparation by cryo-cutting of thin tissue sections and matrix-matched laboratory standards, mass spectrometric measurements, data acquisition, and quantitative analysis. Specific examples of the bioimaging of metal distributions in normal rodent brains are provided. Differences to the normal were assessed in a Parkinson’s disease and a stroke brain model. Furthermore, changes during normal aging were studied. Powerful analytical techniques are also required for the determination and characterization of metal-containing proteins within a large pool of proteins, e.g., after denaturing or non-denaturing electrophoretic separation of proteins in one-dimensional and two-dimensional gels. LA-ICP-MS can be employed to detect metalloproteins in protein bands or spots separated after gel electrophoresis. MALDI-MS can then be used to identify specific metal-containing proteins in these bands or spots. The combination of these techniques is described in the second section.
Polarized light imaging (PLI) enables the evaluation of fiber orientations in histological sections of human postmortem brains, with ultra-high spatial resolution. PLI is based on the birefringent properties of the myelin sheath of nerve fibers. As a result, the polarization state of light propagating through a rotating polarimeter is changed in such a way that the detected signal at each measurement unit of a charged-coupled device (CCD) camera describes a sinusoidal signal. Vectors of the fiber orientation defined by inclination and direction angles can then directly be derived from the optical signals employing PLI analysis. However, noise, light scatter and filter inhomogeneities interfere with the original sinusoidal PLI signals. We here introduce a novel method using independent component analysis (ICA) to decompose the PLI images into statistically independent component maps. After decomposition, gray and white matter structures can clearly be distinguished from noise and other artifacts. The signal enhancement after artifact rejection is quantitatively evaluated in 134 histological whole brain sections. Thus, the primary sinusoidal signals from polarized light imaging can be effectively restored after noise and artifact rejection utilizing ICA. Our method therefore contributes to the analysis of nerve fiber orientation in the human brain within a micrometer scale.
Polarised light imaging (PLI) utilises the birefringence of the myelin sheaths in order to visualise the orientation of nerve fibres in microtome sections of adult human post-mortem brains at ultra-high spatial resolution. The preparation of post-mortem brains for PLI involves fixation, freezing and cutting into 100-μm-thick sections. Hence, geometrical distortions of histological sections are inevitable and have to be removed for 3D reconstruction and subsequent fibre tracking. We here present a processing pipeline for 3D reconstruction of these sections using PLI derived multimodal images of post-mortem brains. Blockface images of the brains were obtained during cutting; they serve as reference data for alignment and elimination of distortion artefacts. In addition to the spatial image transformation, fibre orientation vectors were reoriented using the transformation fields, which consider both affine and subsequent non-linear registration. The application of this registration and reorientation approach results in a smooth fibre vector field, which reflects brain morphology. PLI combined with 3D reconstruction and fibre tracking is a powerful tool for human brain mapping. It can also serve as an independent method for evaluating in vivo fibre tractography.
Signal transmission between different brain regions requires connecting fiber tracts, the structural basis of the human connectome. In contrast to animal brains, where a multitude of tract tracing methods can be used, magnetic resonance (MR)-based diffusion imaging is presently the only promising approach to study fiber tracts between specific human brain regions. However, this procedure has various inherent restrictions caused by its relatively low spatial resolution. Here, we introduce 3D-polarized light imaging (3D-PLI) to map the three-dimensional course of fiber tracts in the human brain with a resolution at a submillimeter scale based on a voxel size of 100 μm isotropic or less. 3D-PLI demonstrates nerve fibers by utilizing their intrinsic birefringence of myelin sheaths surrounding axons. This optical method enables the demonstration of 3D fiber orientations in serial microtome sections of entire human brains. Examples for the feasibility of this novel approach are given here. 3D-PLI enables the study of brain regions of intense fiber crossing in unprecedented detail, and provides an independent evaluation of fiber tracts derived from diffusion imaging data.
Mass spectrometric imaging (MSI) is a young innovative analytical technique and combines different fields of advanced mass spectrometry and biomedical research with the aim to provide maps of elements and molecules, complexes or fragments. Especially essential metals such as zinc, copper, iron and manganese play a functional role in signaling, metabolism and homeostasis of the cell. Due to the high degree of spatial organization of metals in biological systems their distribution analysis is of key interest in life sciences. We have developed analytical techniques termed BrainMet using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging to measure the distribution of trace metals in biological tissues for biomedical research and feasibility studies—including bioaccumulation and bioavailability studies, ecological risk assessment and toxicity studies in humans and other organisms. The analytical BrainMet techniques provide quantitative images of metal distributions in brain tissue slices which can be combined with other imaging modalities such as photomicrography of native or processed tissue (histochemistry, immunostaining) and autoradiography or with in vivo techniques such as positron emission tomography or magnetic resonance tomography.
Prospective and instrumental developments will be discussed concerning the development of the metalloprotein microscopy using a laser microdissection (LMD) apparatus for specific sample introduction into an inductively coupled plasma mass spectrometer (LMD-ICP-MS) or an application of the near field effect in LA-ICP-MS (NF-LA-ICP-MS). These nano-scale mass spectrometric techniques provide improved spatial resolution down to the single cell level.
Metals are involved in many processes of life. They are needed for enzymatic reactions, are involved in healthy processes but also yield diseases if the metal homeostasis is disordered. Therefore, the interest to assess the spatial distribution of metals is rising in biomedical science. Imaging metal (and non-metal) isotopes by laser ablation mass spectrometry with inductively coupled plasma (LA-ICP-MS) requires a special software solution to process raw data obtained by scanning a sample line-by-line. As no software ready to use was available we developed an interactive software tool for Image Generation and Analysis (IMAGENA). Unless optimised for LA-ICP-MS, IMAGENA can handle other raw data as well. The general purpose was to reconstruct images from a continuous list of raw data points, to visualise these images, and to convert them into a commonly readable image file format that can be further analysed by standard image analysis software. The generation of the image starts with loading a text file that holds a data column of every measured isotope. Specifying general spatial domain settings like the data offset and the image dimensions is done by the user getting a direct feedback by means of a preview image. IMAGENA provides tools for calibration and to correct for a signal drift in the y-direction. Images are visualised in greyscale as well a pseudo-colours with possibilities for contrast enhancement. Image analysis is performed in terms of smoothed line plots in row and column direction.
Biological applications like vesicle membrane analysis involve the precise segmentation of 3D structures in noisy volumetric data, obtained by techniques like magnetic resonance imaging (MRI) or laser scanning microscopy (LSM). Dealing with such data is a challenging task and requires robust and accurate segmentation methods. In this article, we propose a novel energy model for 3D segmentation fusing various cues like regional intensity subdivision, edge alignment and orientation information. The uniqueness of the approach consists in the definition of a new anisotropic regularizer, which accounts for the unbalanced slicing of the measured volume data, and the generalization of an efficient numerical scheme for solving the arising minimization problem, based on linearization and fixed-point iteration. We show how the proposed energy model can be optimized globally by making use of recent continuous convex relaxation techniques. The accuracy and robustness of the presented approach are demonstrated by evaluating it on multiple real data sets and comparing it to alternative segmentation methods based on level sets. Although the proposed model is designed with focus on the particular application at hand, it is general enough to be applied to a variety of different segmentation tasks.
GraphMIC is a cross-platform image processing application utilizing the libraries ITK and OpenCV. The abstract structure of image processing pipelines is visually represented by user interface components based on modern QtQuick technology and allows users to focus on arrangement and parameterization of operations rather than implementing the equivalent functionality natively in C++. The application's central goal is to improve and simplify the typical workflow by providing various high level features and functions like multi threading, image sequence processing and advanced error handling. A built-in python interpreter allows the creation of custom nodes, where user defined algorithms can be integrated to extend basic functionality. An embedded 2d/3d visual-izer gives feedback of the resulting image of an operation or the whole pipeline. User inputs like seed points, contours or regions are forwarded to the processing pipeline as parameters to offer semi-automatic image computing. We report the main concept of the application and introduce several features and their implementation. Finally, the current state of development as well as future perspectives of GraphMIC are discussed
Despite multimodal treatment, the prognosis of high-grade gliomas is grim. As tumor growth is critically dependent on new blood vessel formation, antiangiogenic treatment approaches offer an innovative treatment strategy. Bevacizumab, a humanized monoclonal antibody, has been in the spotlight of antiangiogenic approaches for several years. Currently, MRI including contrast-enhanced T1-weighted and T2/fluid-attenuated inversion recovery (FLAIR) images is routinely used to evaluate antiangiogenic treatment response (Response Assessment in Neuro-Oncology criteria). However, by restoring the blood–brain barrier, bevacizumab may reduce T1 contrast enhancement and T2/FLAIR hyperintensity, thereby obscuring the imaging-based detection of progression. The aim of this review is to highlight the recent role of imaging biomarkers from MR and PET imaging on measurement of disease progression and treatment effectiveness in antiangiogenic therapies. Based on the reviewed studies, multimodal imaging combining standard MRI with new physiological MRI techniques and metabolic PET imaging, in particular amino acid tracers, may have the ability to detect antiangiogenic drug susceptibility or resistance prior to morphological changes. As advances occur in the development of therapies that target specific biochemical or molecular pathways and alter tumor physiology in potentially predictable ways, the validation of physiological and metabolic imaging biomarkers will become increasingly important in the near future.
Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment.