Towards Image-Guided Pediatric Atrial Septal Defect Repair

Congenital heart disease occurs in 107.6 out of 10,000 live births, with Atrial Septal Defects (ASD) accounting for 10\% of these conditions. Historically, ASDs were treated with open heart surgery using cardiopulmonary bypass, allowing a patch to be sewn over the defect. In 1976, King et al. demonstrated use of a transcatheter occlusion procedure, thus reducing the invasiveness of ASD repair. Localization during these catheter based procedures traditionally has relied on bi-plane fluoroscopy; more recently trans-esophageal echocardiography (TEE) and intra-cardiac echocardiography (ICE) have been used to navigate these procedures. Although there is a high success rate using the transcatheter occlusion procedure, fluoroscopy poses radiation dose risk to both patient and clinician. The impact of this dose to the patients is important as many of those undergoing this procedure are children, who have an increased risk associated with radiation exposure. Their longer life expectancy than adults provides a larger window of opportunity for expressing the damaging effects of ionizing radiation. In addition, epidemiologic studies of exposed populations have demonstrated that children are considerably more sensitive to the carcinogenic effects radiation. Image-guided surgery (IGS) uses pre-operative and intra-operative images to guide surgery or an interventional procedure. Central to every IGS system is a software application capable of processing and displaying patient images, registration between multiple coordinate systems, and interfacing with a tool tracking system. We have developed a novel image-guided surgery framework called Kit for Navigation by Image Focused Exploration (KNIFE). This software system serves as the core technology by which a system for reduction of radiation exposure to pediatric patients was developed. The bulk of the initial work in this research endevaour was the development of KNIFE which itself went through countless iterations before arriving at its current state as per the feature requirements established. Secondly, since this work involved the use of captured medical images and their use in an IGS software suite, a brief analysis of the physics behind the images was conducted. Through this aspect of the work, intrinsic parameters (principal point and focal point) of the fluoroscope were quantified using a 3D grid calibration phantom. A second grid phantom was traversed through the fluoroscopic imaging volume of II and flat panel based systems at 2 cm intervals building a scatter field of the volume to demonstrate pincushion and \u27S\u27 distortion in the images. Effects of projection distortion on the images was assessed by measuring the fiducial registration error (FRE) of each point used in two different registration techniques, where both methods utilized ordinary procrustes analysis but the second used a projection matrix built from the fluoroscopes calculated intrinsic parameters. A case study was performed to test whether the projection registration outperforms the rigid transform only. Using the knowledge generated were able to successfully design and complete mock clinical procedures using cardiac phantom models. These mock trials at the beginning of this work used a single point to represent catheter location but this was eventually replaced with a full shape model that offered numerous advantages. At the conclusion of this work a novel protocol for conducting IG ASD procedures was developed. Future work would involve the construction of novel EM tracked tools, phantom models for other vascular diseases and finally clinical integration and use

Quantitative Evaluation of Pulmonary Emphysema Using Magnetic Resonance Imaging and x-ray Computed Tomography

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality affecting at least 600 million people worldwide. The most widely used clinical measurements of lung function such as spirometry and plethysmography are generally accepted for diagnosis and monitoring of the disease. However, these tests provide only global measures of lung function and they are insensitive to early disease changes. Imaging tools that are currently available have the potential to provide regional information about lung structure and function but at present are mainly used for qualitative assessment of disease and disease progression. In this thesis, we focused on the application of quantitative measurements of lung structure derived from 1H magnetic resonance imaging (MRI) and high resolution computed tomography (CT) in subjects diagnosed with COPD by a physician. Our results showed that significant and moderately strong relationship exists between 1H signal intensity (SI) and 3He apparent diffusion coefficient (ADC), as well as between 1H SI and CT measurements of emphysema. This suggests that these imaging methods may be quantifying the same tissue changes in COPD, and that pulmonary 1H SI may be used effectively to monitor emphysema as a complement to CT and noble gas MRI. Additionally, our results showed that objective multi-threshold analysis of CT images for emphysema scoring that takes into account the frequency distribution of each Hounsfield unit (HU) threshold was effective in correctly classifying the patient into COPD and healthy subgroups. Finally, we found a significant correlation between whole lung average subjective and objective emphysema scores with high inter-observer agreement. It is concluded that 1H MRI and high resolution CT can be used to quantitatively evaluate lung tissue alterations in COPD subjects

Study on the Method of Constructing a Statistical Shape Model and Its Application to the Segmentation of Internal Organs in Medical Images

In image processing, segmentation is one of the critical tasks for diagnostic analysis and image interpretation. In the following thesis, we describe the investigation of three problems related to the segmentation algorithms for medical images: Active shape model algorithm, 3-dimensional (3-D) statistical shape model building and organic segmentation experiments. For the development of Active shape models, the constraints of statistical model reduced this algorithm to be difficult for various biological shapes. To overcome the coupling of parameters in the original algorithm, in this thesis, the genetic algorithm is introduced to relax the shape limitation. How to construct a robust and effective 3-D point model is still a key step in statistical shape models. Generally the shape information is obtained from manually segmented voxel data. In this thesis, a two-step procedure for generating these models was designed. After transformed the voxel data to triangular polygonal data, in the first step, attitudes of these interesting objects are aligned according their surface features. We propose to reflect the surface orientations by means of their Gauss maps. As well the Gauss maps are mapped to a complex plane using stereographic projection approach. The experiment was run to align a set of left lung models. The second step is identifying the positions of landmarks on polygonal surfaces. This is solved by surface parameterization method. We proposed two simplex methods to correspond the landmarks. A semi-automatic method attempts to “copy” the phasic positions of pre-placed landmarks to all the surfaces, which have been mapped to the same parameterization domain. Another automatic corresponding method attempts to place the landmarks equidistantly. Finally, the goodness experiments were performed to measure the difference to manually corresponded results. And we also compared the affection to correspondence when using different surface mapping methods. The third part of this thesis is applying the segmentation algorithms to solve clinical problems. We did not stick to the model-based methods but choose the suitable one or their complex according to the objects. In the experiment of lung regions segmentation which includes pulmonary nodules, we propose a complementary region growing method to deal with the unpredictable variation of image densities of lesion regions. In the experiments of liver regions, instead of using region growing method in 3-D style, we turn into a slice-by-slice style in order to reduce the overflows. The image intensity of cardiac regions is distinguishable from lung regions in CT image. But as to the adjacent zone of heart and liver boundary are generally blurry. We utilized a shape model guided method to refine the segmentation results.3-D segmentation techniques have been applied widely not only in medical imaging fields, but also in machine vision, computer graphic. At the last part of this thesis, we resume some interesting topics such as 3-D visualization for medical interpretation, human face recognition and object grasping robot etc.九州工業大学博士学位論文 学位記番号:工博甲第353号　学位授与年月日:平成25年9月27日Chapter 1: Introduction|Chapter 2: Framework of Medical Image Segmentation|Chapter 3: 2-D Organic Regions Using Active Shape Model and Genetic Algorithm|Chapter 4: Alignment of 3-D Models|Chapter 5: Corespondence of 3-D Models|Chapter 6:Experiments of Organic Segmentation|Chapter 7: Visualization Technology and Its Applications|Chapter 8: Conclusions and Future Works九州工業大学平成25年

Numerical efficiency calibration of in vivo measurement systems : Monte Carlo simulations of in vivo measurement scenarios for the detection of incorporated radionuclides, including validation, analysis of efficiency-sensitive parameters and customized anthropomorphic voxel models

Monte Carlo radiation transport codes were used with virtual models of in vivo measurement equipment. Software was coded to handle memory intensive anthropomorphic voxel models for simulation of measurement scenarios. Tools, methods and models have been validated. Various parameters have been investigated for their sensitivity. Methods based on image registration techniques have been developed to transform existing human models to match with an individual test person

Open-source virtual bronchoscopy for image guided navigation

This thesis describes the development of an open-source system for virtual bronchoscopy used in combination with electromagnetic instrument tracking. The end application is virtual navigation of the lung for biopsy of early stage cancer nodules. The open-source platform 3D Slicer was used for creating freely available algorithms for virtual bronchscopy. Firstly, the development of an open-source semi-automatic algorithm for prediction of solitary pulmonary nodule malignancy is presented. This approach may help the physician decide whether to proceed with biopsy of the nodule. The user-selected nodule is segmented in order to extract radiological characteristics (i.e., size, location, edge smoothness, calcification presence, cavity wall thickness) which are combined with patient information to calculate likelihood of malignancy. The overall accuracy of the algorithm is shown to be high compared to independent experts' assessment of malignancy. The algorithm is also compared with two different predictors, and our approach is shown to provide the best overall prediction accuracy. The development of an airway segmentation algorithm which extracts the airway tree from surrounding structures on chest Computed Tomography (CT) images is then described. This represents the first fundamental step toward the creation of a virtual bronchoscopy system. Clinical and ex-vivo images are used to evaluate performance of the algorithm. Different CT scan parameters are investigated and parameters for successful airway segmentation are optimized. Slice thickness is the most affecting parameter, while variation of reconstruction kernel and radiation dose is shown to be less critical. Airway segmentation is used to create a 3D rendered model of the airway tree for virtual navigation. Finally, the first open-source virtual bronchoscopy system was combined with electromagnetic tracking of the bronchoscope for the development of a GPS-like system for navigating within the lungs. Tools for pre-procedural planning and for helping with navigation are provided. Registration between the lungs of the patient and the virtually reconstructed airway tree is achieved using a landmark-based approach. In an attempt to reduce difficulties with registration errors, we also implemented a landmark-free registration method based on a balanced airway survey. In-vitro and in-vivo testing showed good accuracy for this registration approach. The centreline of the 3D airway model is extracted and used to compensate for possible registration errors. Tools are provided to select a target for biopsy on the patient CT image, and pathways from the trachea towards the selected targets are automatically created. The pathways guide the physician during navigation, while distance to target information is updated in real-time and presented to the user. During navigation, video from the bronchoscope is streamed and presented to the physician next to the 3D rendered image. The electromagnetic tracking is implemented with 5 DOF sensing that does not provide roll rotation information. An intensity-based image registration approach is implemented to rotate the virtual image according to the bronchoscope's rotations. The virtual bronchoscopy system is shown to be easy to use and accurate in replicating the clinical setting, as demonstrated in the pre-clinical environment of a breathing lung method. Animal studies were performed to evaluate the overall system performance

Functional pulmonary MRI with ultra-fast steady-state free precession

To date, computed tomography and nuclear medicine techniques are still the reference standard for lung imaging, but radiation exposure is a major concern; especially in case of longitudinal examinations and in children. Therefore, radiation-free imaging is an urgent necessity. Pulmonary magnetic resonance imaging (MRI) is radiation-free, but poses challenges since the low proton density and the presence of strong mesoscopic susceptibility variations considerably reduce the detectable MR signal. As a result, the lung typically appears as a “black hole” with conventional MRI techniques. Recently, ultra-fast balanced steady-state free precession (ufSSFP) methods were proposed for ameliorated lung morphological imaging. In this thesis, ufSSFP is employed to develop and improve several pulmonary functional imaging methods, which can be used in clinical settings using standard MR scanners and equipment. At every breath, the lung expands and contracts, and at every heartbeat, the blood is pumped through the arteries to reach the lung parenchyma. This creates signal modulations associated with pulmonary blood perfusion and ventilation that are detectable by MRI. The second chapter of this thesis focuses on the optimization of time-resolved two-dimensional (2D) ufSSFP for perfusion-weighted and ventilation-weighted imaging of the lung. Subsequently, in the third chapter, three-dimensional (3D) multi-volumetric ufSSFP breath-hold imaging is used to develop a lung model and retrieve the measure α, a novel ventilation-weighted quantitative parameter. Oxygen-enhanced MRI exploits the paramagnetic properties of oxygen dissolved in the blood, acting as a weak T1-shortening contrast agent. When breathing pure oxygen, it reaches only ventilated alveoli of the parenchyma and dissolves only in functional and perfused regions. How ufSSFP imaging in combination with a lung model can be used to calculate robust 3D oxygen enhancement maps is described in the fourth chapter. In addition, in the fifth chapter, 2D inversion recovery ufSSFP imaging is employed to map the T1 and T2 relaxation times of the lung, the change of the relaxation times after hyperoxic conditions, as well as the physiological oxygen wash-in and wash-out time (related to the time needed to shorten T1 after oxygen breathing). The objective of the last chapter of this thesis is the application of 3D ufSSFP imaging before and after intravenous gadolinium-based contrast agent administration for the investigation of signal enhancement ratio (SER) mapping: a rapid technique to visualize perfusion-related diseases of the lung parenchyma. The techniques presented in this thesis using optimized ufSSFP pulse sequences demonstrated potential to reveal new insights on pulmonary function as well as quantification, and might become part of the future standard for the evaluation and follow-up of several lung pathologies

A lung cancer detection approach based on shape index and curvedness superpixel candidate selection

Orientador : Lucas Ferrari de OliveiraDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 29/08/2016Inclui referências : f. 72-76Área de concentração: Sistemas eletrônicosResumo: Câncer é uma das causas com mais mortalidade mundialmente. Câncer de pulmão é o tipo de câncer mais comum (excluíndo câncer de pele não-melanoma). Seus sintomas aparecem em estágios mais avançados, o que dificulta o seu tratamento. Para diagnosticar o paciente, a tomografia computadorizada é utilizada. Ela é composta de diversos cortes, que mapeiam uma região 3D de interesse. Apesar de fornecer muitos detalhes, por serem gerados vários cortes, a análise de exames de tomografia computadorizada se torna exaustiva, o que pode influenciar negativamente no diagnóstico feito pelo especialista. O objetivo deste trabalho é o desenvolvimento de métodos para a segmentação do pulmão e a detecção de nódulos em imagens de tomografia computadorizada do tórax. As imagens são segmentadas para separar o pulmão das outras estruturas e após, detecção de nódulos utilizando a técnicas de superpixeis são aplicadas. A técnica de Rótulamento dos Eixos teve uma média de preservação de nódulos de 93,53% e a técnica Monotone Chain Convex Hull apresentou melhores resultados com uma taxa de 97,78%. Para a detecção dos nódulos, as técnicas Felzenszwalb e SLIC são empregadas para o agrupamento de regiões de nódulos em superpixeis. Uma seleção de candidatos à nódulos baseada em shape index e curvedness é aplicada para redução do número de superpixeis. Para a classificação desses candidatos, foi utilizada a técnica de Florestas Aleatórias. A base de imagens utilizada foi a LIDC, que foi dividida em duas sub-bases: uma de desenvolvimento, composta pelos pacientes 0001 a 0600, e uma de validação, composta pelos pacientes 0601 a 1012. Na base de validação, a técnica Felzenszwalb obteve uma sensibilidade de 60,61% e 7,2 FP/exame. Palavras-chaves: Câncer de pulmão. Detecção de nódulos. Superpixel. Shape index.Abstract: Cancer is one of the causes with more mortality worldwide. Lung cancer is the most common type (excluding non-melanoma skin cancer). Its symptoms appear mostly in advanced stages, which difficult its treatment. For patient diagnostic, computer tomography (CT) is used. CT is composed of many slices, which maps a 3D region of interest. Although it provides many details, its analysis is very exhaustive, which may has negatively influence in the specialist's diagnostic. The objective of this work is the development of lung segmentation and nodule detection methods in chest CT images. These images are segmented to separate the lung region from other parts and, after that, nodule detection using superpixel methods is applied. The Axes' Labeling had a mean of nodule preservation of 93.53% and the Monotone Chain Convex Hull method presented better results, with a mean of 97.78%. For nodule detection, the Felzenszwalb and SLIC methods are employed to group nodule regions. A nodule candidate selection based on shape index and curvedness is applied for superpixel reduction. Then, classification of these candidates is realized by the Random Forest. The LIDC database was divided into two data sets: a development data set composed of the CT scans of patients 0001 to 0600, and a untouched, validation data set, composed of patients 0601 to 1012. For the validation data set, the Felzenszwalb method had a sensitivity of 60.61% and 7.2 FP/scan. Key-words: Lung cancer. Nodule detection. Superpixel. Shape index

Pattern recognition methods applied to medical imaging: lung nodule detection in computed tomography images

Lung cancer is one of the main public health issues in developed countries. The overall 5-year survival rate is only 10−16%, although the mortality rate among men in the United States has started to decrease by about 1.5% per year since 1991 and a similar trend for the male population has been observed in most European countries. By contrast, in the case of the female population, the survival rate is still decreasing, despite a decline in the mortality of young women has been ob- served over the last decade. Approximately 70% of lung cancers are diagnosed at too advanced stages for the treatments to be effective. The five-year survival rate for early-stage lung cancers (stage I), which can reach 70%, is sensibly higher than for cancers diagnosed at more advanced stages. Lung cancer most commonly manifests itself as non-calcified pulmonary nodules. The CT has been shown as the most sensitive imaging modality for the detection of small pulmonary nodules, particularly since the introduction of the multi-detector-row and helical CT technologies. Screening programs based on Low Dose Computed Tomography (LDCT) may be regarded as a promising technique for detecting small, early-stage lung cancers. The efficacy of screening programs based on CT in reducing the mortality rate for lung cancer has not been fully demonstrated yet, and different and opposing opinions are being pointed out on this topic by many experts. However, the recent results obtained by the National Lung Screening Trial (NLST), involving 53454 high risk patients, show a 20% reduction of mortality when the screening program was carried out with the helical CT, rather than with a conventional chest X-ray. LDCT settings are currently recommended by the screening trial protocols. However, it is not trivial in this case to identify small pulmonary nodules,due to the noisier appearance of the images in low-dose CT with respect to the standard-dose CT. Moreover, thin slices are generally used in screening programs, thus originating datasets of about 300 − 400 slices per study. De- pending on the screening trial protocol they joined, radiologists can be asked to identify even very small lung nodules, which is a very difficult and time- consuming task. Lung nodules are rather spherical objects, characterized by very low CT values and/or low contrast. Nodules may have CT values in the same range of those of blood vessels, airway walls, pleura and may be strongly connected to them. It has been demonstrated, that a large percent- age of nodules (20 − 35%) is actually missed in screening diagnoses. To support radiologists in the identification of early-stage pathological objects, about one decade ago, researchers started to develop CAD methods to be applied to CT examinations. Within this framework, two CAD sub-systems are proposed: CAD for internal nodules (CADI), devoted to the identification of small nodules embedded in the lung parenchyma, i.e. Internal Nodules (INs) and CADJP, devoted the identification of nodules originating on the pleura surface, i.e. Juxta-Pleural Nodules (JPNs) respectively. As the training and validation sets may drastically influence the performance of a CAD system, the presented approaches have been trained, developed and tested on different datasets of CT scans (Lung Image Database Consortium (LIDC), ITALUNG − CT) and finally blindly validated on the ANODE09 dataset. The two CAD sub-systems are implemented in the ITK framework, an open source C++ framework for segmentation and registration of medical im- ages, and the rendering of the obtained results are achieved using VTK, a freely available software system for 3D computer graphics, image processing and visualization. The Support Vector Machines (SVMs) are implemented in SVMLight. The two proposed approaches have been developed to detect solid nodules, since the number of Ground Glass Opacity (GGO) contained in the available datasets has been considered too low. This thesis is structured as follows: in the first chapter the basic concepts about CT and lung anatomy are explained. The second chapter deals with CAD systems and their evaluation methods. In the third chapter the datasets used for this work are described. In chapter 4 the lung segmentation algorithm is explained in details, and in chapter 5 and 6 the algorithms to detect internal and juxta-pleural candidates are discussed. In chapter 7 the reduction of false positives findings is explained. In chapter 8 results of the train and validation sessions are shown. Finally in the last chapter the conclusions are drawn

A multi-technique hierarchical X-ray phase-based approach for the characterization and quantification of the effects of novel radiotherapies

Cancer is the first or second leading cause of premature deaths worldwide with an overall rapidly growing burden. Standard cancer therapies include surgery, chemotherapy and radiotherapy (RT) and often a combination of the three is applied to improve the probability of tumour control. Standard therapy protocols have been established for many types of cancers and new approaches are under study especially for treating radio-resistant tumours associated to an overall poor prognosis, as for brain and lung cancers. Follow up techniques able to monitor and investigate the effects of therapies are important for surveying the efficacy of conventionally applied treatments and are key for accessing the curing capabilities and the onset of acute and late adverse effects of new therapies. In this framework, this doctoral Thesis proposes the X-ray Phase Contrast Im-aging - Computed Tomography (XPCI-CT) technique as an imaging-based tool to study and quantify the effects of novel RTs, namely Microbeam and Minibeam Radiation therapy (MRT and MB), and to compare them to the standard Broad Beam (BB) induced effects on brain and lungs. MRT and MB are novel radiotherapies that deliver an array of spatially fractionated X-ray beamlets issued from a synchrotron radiation source, with widths of tens or hundreds of micrometres, respectively. MRT and MB exploit the so-called dose-volume effect: hundreds of Grays are well tolerated by healthy tissues and show a preferential effect on tumour cells and vasculature when delivered in a micrometric sized micro-plane, while induce lethal effects if applied over larger uniform irradiation fields. Such highly collimated X-ray beams need a high-resolution and a full-organ approach that can visualize, with high sensitivity, the effects of the treatment along and outside the beamlets path. XPCI-CT is here suggested and proven as a powerful imaging technique able to determine and quantify the effects of the radiation on normal and tumour-bearing tissues. Moreover, it is shown as an effective technique to complement, with 3D information, the histology findings in the follow-up of the RT treatments. Using a multi-scale and multi-technique X-ray-based approach, I have visualized and analysed the effects of RT delivery on healthy and glioblastoma multiforme (GBM)-bearing rat brains as well as on healthy rat lungs. Ex-vivo XPCI-CT datasets acquired with isotropic voxel sizes in the range 3.253 – 0.653 μm3 could distinguish, with high sensitivity, the idiopathic effects of MRT, MB and BB therapies. Histology, immunohistochemistry, Small- and Wide-Angle X-ray Scattering and X-ray Fluorescence experiments were also carried out to accurately interpret and complement the XPCI-CT findings as well as to obtain a detailed structural and chemical characterization of the detected pathological features. Overall, this multi-technique approach could detect: i) a different radio-sensitivity for the MRT-treated brain areas; ii) Ca and Fe deposits, hydroxyapatite crystals formation; iii) extended and isolated fibrotic contents. Full-organ XPCI-CT datasets allowed for the quantification of tumour and mi-crocalcifications’ volumes in treated brains and the amount of scarring tissue in irradiated lungs. Herein, the role of XPCI-CT as a 3D virtual histology technique for the follow-up of ex-vivo RT effects has been assessed as a complementary method for an accurate volumetric investigation of normal and pathological states in brains and lungs, in a small animal model. Moreover, the technique is proposed as a guidance and auxiliary tool for conventional histology, which is the gold standard for pathological evaluations, owing to its 3D capabilities and the possibility of virtually navigating within samples. This puts a landmark for XPCI-CT inclusion in the pre-clinical studies pipeline and for advancing towards in-vivo XPCI-CT imaging of treated organs.Weltweit gilt Krebs als häufigste bzw. zweithäufigste Ursache eines zu früh erfolgenden Todes, wobei die Zahlen rasch ansteigen. Standardmäßige Krebstherapien umfassen chirurgische Eingriffe, Chemotherapie und Strahlentherapie (radiotherapy, RT); oft kommt eine Kombination daraus zur Anwendung, um die Wahrscheinlichkeit der Tumorkontrolle zu erhöhen. Es wurden Standardtherapieprotokolle für zahlreiche Krebsarten eingerichtet und es wird vor allem in der Behandlung von strahlenresistenten Tumoren mit allgemein schlechter Prognose wie bei Hirn- und Lungentumoren an neuen Ansätzen geforscht. Nachverfolgungstechniken, welche die Auswirkungen von Therapien überwachen und ermitteln, sind zur Überwachung der Wirksamkeit herkömmlich angewandter Behandlungen wichtig und auch maßgeblich am Zugang zu den Fähigkeiten zur Heilung sowie zum Auftreten akuter und verzögerter Nebenwirkungen neuer Therapien beteiligt. In diesem Rahmenwerk unterbreitet diese Doktorarbeit die Technik der Röntgen-Phasenkontrast-Bildgebung über Computertomographie (X-ray Phase Contrast Imaging - Computed Tomography, XPCI‑CT) als bildverarbeitungs-basiertes Tool zur Untersuchung und Quantifizierung der Auswirkungen neuartiger Strahlentherapien, nämlich der Mikrobeam- und Minibeam-Strahlentherapie (MRT und MB), sowie zum Vergleich derselben mit den herkömmlichen durch Breitstrahlen (Broad Beam, BB) erzielten Auswirkungen auf Gehirn und Lunge. MRT und MB sind neuartige Strahlentherapien, die ein Array räumlich aufgeteilter Röntgenstrahlenbeamlets aus einer synchrotronen Strahlenquelle mit einer Breite von Zehnteln bzw. Hundersteln Mikrometern abgeben. MRT und MB nutzen den sogenannten Dosis-Volumen-Effekt: Hunderte Gray werden von gesundem Gewebe gut vertragen und wirken bei der Abgabe in einer Mikroebene im Mikrometerbereich vorrangig auf Tumorzellen und Blutgefäße, während sie bei einer Anwendung über größere gleichförmige Strahlungsfelder letale Auswirkungen aufweisen. Solche hoch kollimierten Röntgenstrahlen erfordern eine hohe Auflösung und einen Zugang zum gesamten Organ, bei dem die Auswirkungen der Behandlung entlang und außerhalb der Beamletpfade mit hoher Empfindlichkeit visualisiert werden können. Hier empfiehlt und bewährt sich die XPCI‑CT als leistungsstarke Bildverarbeitungstechnik, welche die Auswirkungen der Strahlung auf normale und tumortragende Gewebe feststellen und quantifizieren kann. Außerdem hat sich gezeigt, dass sie durch 3‑D-Informationen eine effektive Technik zur Ergänzung der histologischen Erkenntnisse in der Nachverfolgung der Strahlenbehandlung ist. Anhand eines mehrstufigen und multitechnischen röntgenbasierten Ansatzes habe ich die Auswirkungen der Strahlentherapie auf gesunde und von Glioblastomen (GBM) befallene Rattenhirne sowie auf gesunde Rattenlungen visualisiert und analysiert. Mit isotropen Voxelgrößen im Bereich von 3,53 bis 0,653 μm3 erfasste Ex-vivo-XPCI-CT-Datensätze konnten die idiopathischen Auswirkungen der MRT-, MB- und BB‑Behandlung mit hoher Empfindlichkeit unterscheiden. Es wurden auch Experimente zu Histologie, Immunhistochemie, Röntgenklein- und ‑weitwinkelstreuung und Röntgenfluoreszenz durchgeführt, um die XPCI‑CT-Erkenntnisse präzise zu interpretieren und zu ergänzen sowie eine detaillierte strukturelle und chemische Charakterisierung der nachgewiesenen pathologischen Merkmale zu erhalten. Im Allgemeinen wurde durch diesen multitechnischen Ansatz Folgendes ermittelt: i) eine un-terschiedliche Strahlenempfindlichkeit der mit MRT behandelten Gehirnbereiche; ii) Ca- und Fe-Ablagerungen und die Bildung von Hydroxylapatitkristallen; iii) ein ausgedehnter und isolierter Fibrosegehalt. XPCI‑CT-Datensätze des gesamten Organs ermöglichten die Quantifizierung der Volume von Tumoren und Mikroverkalkungen in den behandelten Gehirnen und der Menge des Narbengewebes in bestrahlten Lungen. Dabei wurde die Rolle der XPCI‑CT als virtuelle 3‑D-Histologietechnik für die Nachverfolgung von Ex-vivo-RT‑Auswirkungen als ergänzende Methode für eine präzise volumetrische Untersuchung des normalen und pathologischen Zustands von Gehirnen und Lungen im Kleintiermodell untersucht. Darüber hinaus wird die Technik aufgrund ihrer 3‑D-Fähigkeiten und der Möglichkeit zur virtuellen Navigation in den Proben als Leitfaden und Hilfstool für die herkömmliche Histologie vorgeschlagen, die der Goldstandard für die pathologische Evaluierung ist. Dies markiert einen Meilenstein für die Übernahme der XPCI‑CT in die Pipeline präklinischer Studien und für den Übergang zur In-vivo-XPCI‑CT von behandelten Organen