Zugl.: Aachen, Techn. Hochsch., Diss., 2005

The initial stage of a lung examination involves the segmentation of a CT image, a process that has been put under a lot of pressure with the high demand for chest scans and accurate segmentations. Current automatic segmentation algorithms are either non-robust for different datasets, not easily accessible, or time-consuming. Furthermore, classification of lung diseases such as IPF and NSIP is a difficult task often requiring decision-making between pathologists, radiologists and clinicians to make an accurate prognosis.  Therefore, this thesis aims to create two algorithms easily accessible through a common medical software, 3D Slicer, with simple user interfaces for more efficient lung analysis. The first one is a fully automatic segmentation algorithm with a manual adjustment option. It is robust and developed on a diverse dataset, demonstrating a high accuracy with a median Dice score of0,967. The second one is a lung vessel tree morphometry algorithm which computes various parameters correlated to the vessel tree and its structure, providing insight into morphological changes. It shows great usability but has certain limitations, making it not entirely finished for clinical research but acts as an excellent starting point for a future project. The segmentation algorithm was developed using classical image processing techniques making it comprehensible. The distinctive feature of this algorithm is the entropy map used, enabling an effective way in distinguishing between the fibrotic regions of the lungs with surrounding soft tissue and therefore increasing its applicability on lungs with various diseases. The lung vessel tree morphometry algorithm utilized a segmentation of the lung vessels to organize them into a tree-like structure. The structure was divided into branches where each branch was used to calculate different parameters such as its level within the tree hierarchy, the length of the branch and more. These parameters were displayed and color-coded for further analysis. The obtained result underscores the substantial potential and importance of these developed algorithms for clinical research by providing user-friendly, robust and reliable methods. / Det inledande skedet av en lungundersökning involverar segmenteringen av en CT-bild, en process som har satts under mycket press på grund utav den höga efterfrågan på bröstskanningar och noggrann segmentering. Aktuella automatiska segmenteringsalgoritmer är antingen icke-robusta för olika dataset, ej lättillgängliga eller tidskrävande. Dessutom är klassificering av lungsjukdomar som IPF och NSIP en svår uppgift som ofta kräver beslutsfattande mellan patologer, radiologer och kliniker för att göra en korrekt prognos. Därför syftar denna rapport till att skapa två lättillgängliga algoritmer genom en ofta användmedicinsk programvara, 3D Slicer, bestående utav enkla användargränssnitt för en effektivare analys av lungorna. Den första är en helautomatisk segmenteringsalgoritm med ett manuellt justeringsalternativ. Den är robust och utvecklad på ett mångsidigt dataset som har demonstrerat en hög noggrannhet med en median Dice-score på 0,967. Den andra är en morfometri algoritm för lungkärlsträd som beräknar olika parametrar korrelerade till kärlträdet och dess struktur, vilket ger insikt i morfologiska förändringar. Den visar stor användbarhet men innehåller begränsningar, vilket gör den ej helt färdig för klinisk forskning utan fungerar som en utmärkt utgångspunkt för framtida arbete. Segmenteringsalgoritmen utvecklades med hjälp av klassiska bildbehandlingsmetoder vilket gör den mer lättförstådd. Det utmärkande för denna algoritm är entropikartan som används, vilket möjliggör ett effektivt sätt att skilja mellan de fibrotiska regionerna i lungorna med omgivande mjukdelar, detta gör den mer användbar på lungor med olika sjukdomar. Algoritmen för lungkärlsträdets morfometri använde en segmentering av lungkärlen för att sedanorganiseras i en trädliknande struktur. Strukturen var uppdelad i grenar där varje gren användes för att beräkna olika parametrar såsom dess nivå inom trädhierarkin, grenens längd med mera. Dessutom uppvisades dessa parametrar och färgkodades för vidare analys. Det erhållna resultatet understryker den substantiella potential och betydelse som dessa utvecklade algoritmer kommer att ha i klinisk forskning genom att tillhandahålla användarvänliga, robusta och pålitliga metoder

In needle-based surgical interventions, accurate alignment and insertion of the tool is paramount for providing proper treatment at a target site while minimizing healthy tissue damage. While manually-aligned interventions are well-established, robotics platforms promise to reduce procedure time, increase precision, and improve patient comfort and survival rates. Conducting interventions in an MRI scanner can provide real-time, closed-loop feedback for a robotics platform, improving its accuracy, yet the tight environment potentially impairs motion, and perceiving this limitation when planning a procedure can be challenging. This project developed a surgical workflow and software system for evaluating the workspace and planning the motions of a robotics platform within the confines of an MRI scanner. 3D Slicer, a medical imaging visualization and processing platform, provided a familiar and intuitive interface for operators to quickly plan procedures with the robotics platform over OpenIGTLink. Robotics tools such as ROS and MoveIt! were utilized to analyze the workspace of the robot within the patient and formulate the motion planning solution for positioning of the robot during surgical procedures. For this study, a 7 DOF robot arm designed for ultrasonic ablation of brain tumors was the targeted platform. The realized system successfully yielded prototype capabilities on the neurobot for conducting workspace analysis and motion planning, integrated systems using OpenIGTLink, provided an opportunity to evaluate current software packages, and informed future work towards production-grade medical software for MRI-guided, needle-based robotic interventions.

MRT und CT liefern komplementäre Informationen über die Strukturen der Cochlea. Um die genaue Lage der Elektrode nach Implantation eines CIs beurteilen zu können, wurden in der vorliegenden Arbeit präoperative MRT-Datensätze und postoperative CT-Datensätze mit dem frei erhältlichen Programm "3D-Slicer" fusioniert. Nach 1350 erfolgten Implantationen am Universitätsklinikum Würzburg konnte bei 16 Ohren die Qualität der Fusion beurteilt und bei 15 Ohren die intracochleäre Lage der CI-Elektroden evaluiert werden. Die manuelle Fusion der Datensätze wurde in einer reproduzierbaren Vorgehensweise umgesetzt und war der automatischen Registrierung überlegen. Bildfusion und -analyse ließen sich umso präziser und sicherer durchführen, je besser die Bildqualität und je kürzer der zeitliche Abstand zwischen der Akquisition von MRT und CT waren. Da die Cochlea bei Geburt bereits ausgewachsen ist, war die Fusion selbst bei den Kindern möglich, deren Schädel in der Zwischenzeit gewachsen war. Aufgrund der seltenen Indikation eines postoperativen CTs und mangelnder Standardisierung der Bildgebung konnte eine Analyse lediglich bei 15 der insgesamt 1350 Ohren mit CI durchgeführt werden. In diesen Fällen ließ sich die Fusion jedoch sehr gut durchführen. Die Sicherheit bei der Beurteilung der Elektrodenlage nimmt in den einzelnen Abschnitten der Cochlea von basal nach apikal ab. Unabhängig davon waren die Entscheidungen für die Elektrodenlage in der Scala tympani mit einer größeren Sicherheit gefällt worden als die für die Lage in der Scala vestibuli. Die genaue Elektrodenlage konnte im Rahmen dieser Studie zwar nicht anhand histologischer Schnitte bewiesen werden, jedoch stimmen die in den fusionierten Bildern analysierten Insertionsstellen mit den in den OP-Berichten dokumentierten Angaben überein. / MRI and CI offer complementary information on the temporal bone's structures. Preoperative MRIs and postoperative CTs were registered with the free programm "3D-Slicer" in order to evaluate the intracochlear position of the electrode of cochlear implants. Manual registration was performed in a reproducible procedure

This thesis contains basic theoretical information about SVM-based image segmentation and data classification. Basic information about 3D Slicer software are presented. Aspects of medical images segmentation are described. Workplan and implemetation of SVM method for MRI segmentation in 3D Slicer sofware as extension module is created. SVM method is compared with simple segmentation algorithms included in 3D Slicer. Quality of segmentation, based on SVM, tested on real subjects is experimentaly demonstrated.

This semestral thesis deals with visualization of intracranial EEG. In the first part, theoretical basics of EEG is mentioned. After that, image registration, as a needed tool for visualization is described followed by research of methods of visualization of high frequency oscilations from intracranial EEG. Finally, method for visualization of high frequency oscilations from EEG in real MRI patient scans is designed and implemented.

In the past twenty years, the combination of the advances in medical imaging technologies and therapeutic methods had a great impact in developing minimally invasive interventional procedures. Although the use of medical imaging for the surgery and therapy guidance dates back to the early days of x-ray discovery, there is an increasing evidence in using the new imaging modalities such as computed tomography (CT), magnetic reso- nance imaging (MRI) and ultrasound in the operating rooms. The focus of this thesis is on developing image-guided interventional methods and techniques to support the radiation therapy treatment of gynecologic cancers. Gynecologic cancers which involves malignan- cies of the uterus, cervix, vagina and the ovaries are one of the top causes of mortality and morbidity among the women in U.S. and worldwide. The common treatment plan for radiation therapy of gynecologic cancers is chemotherapy and external beam radiation therapy followed by brachytherapy. Gynecological brachytherapy involves placement of interstitial catheters in and around the tumor area, often with the aid of an applicator. The goal is to create an optimal brachytherapy treatment plan that leads to maximal radiation dose to the cancerous tissue and minimal destructive radiation to the organs at risk. The accuracy of the catheter placement has a leading effect in the success of the treatment. However there are several techniques are developed for navigation of catheters and needles for procedures such as prostate biopsy, brain biopsy, and cardiac ablation, it is obviously lacking for gynecologic brachytherapy procedures. This thesis proposes a technique which aims to increase the accuracy and efficiency of catheter placements in gynecologic brachytherapy by guiding the catheters with an electromagnetic tracking system. To increase the accuracy of needle placement a navigation system has been set up and the appropriate software tools were developed and released for the public use as a module in the open-source 3D Slicer software. The developed technology can be translated from benchmark to the bedside to offer the potential benefit of maximizing tumor coverage during catheter placement while avoiding damage to the adjacent organs including bladder, rectum and bowel. To test the designed system two independent experiments were designed and performed on a phantom model in order to evaluate the targeting accuracy of the tracking system and the mean targeting error over all experiments was less than 2.9 mm, which can be compared to the targeting errors in the available commercial clinical navigation systems.

This work explores machine learning as a tool for medical images' classification. A literary research is contained concerning both classical and modern approaches to image segmentation. The main purpose of this work is to design and implement an extension for the 3D Slicer platform. The extension uses machine learning to classify images using set parameters. The extension is tested on tomographic images obtained by nuclear magnetic resonance and observes the accuracy of the classification and usability in practice.

Indiana University-Purdue University Indianapolis (IUPUI) / With the recent advancement of computation and imaging technology, Image-based computational fluid dynamics (ICFD) has emerged as a great non-invasive capability to study biomedical flows. These modern technologies increase the potential of computation-aided diagnostics and therapeutics in a patient-specific environment. I studied three components of this image-based computational fluid dynamics process in this work. To ensure accurate medical assessment, realistic computational analysis is needed, for which patient-specific image segmentation of the diseased vessel is of paramount importance. In this work, image segmentation of several human arteries, veins, capillaries, and organs was conducted to use them for further hemodynamic simulations. To accomplish these, several open-source and commercial software packages were implemented. This study incorporates a new computational platform, called InVascular, to quantify the 4D velocity field in image-based pulsatile flows using the Volumetric Lattice Boltzmann Method (VLBM). We also conducted several parametric studies on an idealized case of a 3-D pipe with the dimensions of a human renal artery. We investigated the relationship between stenosis severity and Resistive index (RI). We also explored how pulsatile parameters like heart rate or pulsatile pressure gradient affect RI. As the process of ICFD analysis is based on imaging and other hemodynamic data, it is often time-consuming due to the extensive data processing time. For clinicians to make fast medical decisions regarding their patients, we need rapid and accurate ICFD results. To achieve that, we also developed surrogate models to show the potential of supervised machine learning methods in constructing efficient and precise surrogate models for Hagen-Poiseuille and Womersley flows.

<p>  </p> <p>With the recent advancement of computation and imaging technology, Image-based computational fluid dynamics (ICFD) has emerged as a great non-invasive capability to study biomedical flows. These modern technologies increase the potential of computation-aided diagnostics and therapeutics in a patient-specific environment. I studied three components of this image-based computational fluid dynamics process in this work.</p> <p>To ensure accurate medical assessment, realistic computational analysis is needed, for which patient-specific image segmentation of the diseased vessel is of paramount importance. In this work, image segmentation of several human arteries, veins, capillaries, and organs was conducted to use them for further hemodynamic simulations. To accomplish these, several open-source and commercial software packages were implemented. </p> <p>This study incorporates a new computational platform, called <em>InVascular</em>, to quantify the 4D velocity field in image-based pulsatile flows using the Volumetric Lattice Boltzmann Method (VLBM). We also conducted several parametric studies on an idealized case of a 3-D pipe with the dimensions of a human renal artery. We investigated the relationship between stenosis severity and Resistive index (RI). We also explored how pulsatile parameters like heart rate or pulsatile pressure gradient affect RI.</p> <p>As the process of ICFD analysis is based on imaging and other hemodynamic data, it is often time-consuming due to the extensive data processing time. For clinicians to make fast medical decisions regarding their patients, we need rapid and accurate ICFD results. To achieve that, we also developed surrogate models to show the potential of supervised machine learning methods in constructing efficient and precise surrogate models for Hagen-Poiseuille and Womersley flows.</p>