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.

3D printing a CAD modelnormally requires conversion into a polygon mesh, usually an STL-file, in orderto be able to load the model in the slicer. This conversion destroys roundsurfaces and replaces them with flat surfaces. Slicing a polygon mesh resultsin one or more polygons, consisting of a number of straight lines. This canaffect both dimensional accuracy and surface smoothness. Modern 3D-printerscan, in addition to straight lines, handle arcs. However, today’s commonslicers can not generate arcs as the input does not contain any curvedfeatures. This project aims at finding an alternative solution. By directslicing of CAD models the slices can contain arcs, and the slicer can producearc commands for the 3D-printer. During this project a prototype slicer isconstructed as a proof of concept. The prototype handles STEP-files as inputand creates both linear and circular movement for the 3D-printer. The resultsshow that both the intermediate files (STEP/STL) and the resulting G-code filescan get smaller, yet preserving the original shape, by using this method. Theproposed solution has a positive effect on the 3D-printing workflow as well, asthe intermediate files can be imported back into the CAD system. The projectconcludes that there is possibly a bright future for direct slicing, but thereare more problems to solve before it can become reality.

Rapid development of domain specialized medical imaging tools is essential for deploying medical imaging technologies to advance clinical research and clinical practice. This work describes the development process, deployment method, and evaluation of modules constructed within the 3D Slicer environment. These tools address critical problems encountered in four different clinical domains: quality control review of large repositories of medical images, rule-based automated label map cleaning, quantification of calcification in the heart using low-dose radiation scanning, and waist circumference measurement from abdominal scans. Each of these modules enables and accelerates clinical research by incorporating medical imaging technologies that minimize manual human effort. They are distributed within the multi-platform 3D Slicer Extension Manager environment for use in the computational environment most convenient to the clinician scientist.

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

Spectral domain optical coherence tomography (SD-OCT) is a dispersed interferometric technology used to obtain tomographic images, typically of tissue for medical applications. OCT is a competing technology with confocal microscopy (CM) and confocal fluorescent microscopy (CFM), which are both used for biopsy imaging for pathology as the gold standard. OCT offers several advantages over CM/CFM: it is able to acquire a full 3D image in a single pass, it requires little or no sample preparation time, and the axial (depth) and lateral (transverse) resolution are not dependent on one another. SD-OCT is limited in imaging depth to a few millimetres due to the quality performance of the spectrograph section of the instrument--that which determines the sensitivity of the SD-OCT system. In this thesis a design for an SD-OCT system is presented that is suitable for biopsy imaging for pathological studies, i.e. an OCT microscope. The purpose of this system is to provide a fast diagnosis to be made in a surgical environment to reduce the amount of tissue removed from a patient and lower the chance of a returned visit at a later date due to insufficient tissue removal. The secondary purpose of the SD-OCT microscope is to serve as a research testbed system for implementing novel hardware advancements. One such technology, called an optical pupil slicer (OPS), will be implemented in the instrument to improve the depth imaging performance of the SD-OCT system over conventional SD-OCT systems. The OPS is a device that generally improves the performance of a dispersive-type spectrograph by increasing the spectral resolution without a loss in throughput, thereby increasing the sensitivity of the SD-OCT system.

Spectral domain optical coherence tomography (SD-OCT) is a dispersed interferometric technology used to obtain tomographic images, typically of tissue for medical applications. OCT is a competing technology with confocal microscopy (CM) and confocal fluorescent microscopy (CFM), which are both used for biopsy imaging for pathology as the gold standard. OCT offers several advantages over CM/CFM: it is able to acquire a full 3D image in a single pass, it requires little or no sample preparation time, and the axial (depth) and lateral (transverse) resolution are not dependent on one another. SD-OCT is limited in imaging depth to a few millimetres due to the quality performance of the spectrograph section of the instrument--that which determines the sensitivity of the SD-OCT system. In this thesis a design for an SD-OCT system is presented that is suitable for biopsy imaging for pathological studies, i.e. an OCT microscope. The purpose of this system is to provide a fast diagnosis to be made in a surgical environment to reduce the amount of tissue removed from a patient and lower the chance of a returned visit at a later date due to insufficient tissue removal. The secondary purpose of the SD-OCT microscope is to serve as a research testbed system for implementing novel hardware advancements. One such technology, called an optical pupil slicer (OPS), will be implemented in the instrument to improve the depth imaging performance of the SD-OCT system over conventional SD-OCT systems. The OPS is a device that generally improves the performance of a dispersive-type spectrograph by increasing the spectral resolution without a loss in throughput, thereby increasing the sensitivity of the SD-OCT system.

Thesis (D. Min.)--Harding University Graduate School of Religion, 2005. / "May 2005." Includes bibliographical references ( leaves 209-221).

Thesis (D. Min.)--Harding University Graduate School of Religion, 2005. / "May 2005." Includes bibliographical references ( leaves 209-221).