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Commissioning, Benchmarking and Clinical Application of a Novel Fiber Optic CT Scanner for Precise Three-Dimensional Radiation Dosimetry
Radiotherapy is a prominent cancer treatment modality in medicine, aiming to deliver adequate doses to the target while minimizing harm to healthy tissue. Recent advancements in computer technology, machine engineering, and imaging have facilitated intricate treatment planning and accurate radiation administration. These advancements have allowed for more precise dose distributions to be delivered to cancer patients. However, even small discrepancies in setup or delivery can result in significant dose variations. While treatment planning systems provide 3D dose calculations, there is currently a lack of 3D measurement tools in the clinic to verify the accuracy of dose calculation and delivery. Presently, medical physicists rely on 2D dose plane comparisons with treatment planning calculations using gamma index analyses. However, these results do not directly correlate with clinical dose-volume constraints, and detecting delivery errors using 1D or 2D dosimetry is challenging. The implementation of 3D dosimetry not only ensures the safety of radiation treatment but also facilitates the development of new emerging radiation treatment techniques. This study aims to commission and validate a clinically viable optical scanner for 3D dosimetry and apply the developed system to address current clinical and pre-clinical challenges, thereby advancing our understanding of treatment uncertainties in modern radiotherapy.
The optical CT scanner that was developed comprises four key components: an LED illuminator, an aquarium with matching fluid, a fiber optic taper, and a CCD camera. The LED illuminator emits uniform and parallel red light at a peak wavelength of 625 nm and a full width at half maximum (FWHM) of 20 nm in continuous mode. The aquarium is constructed with transparent acrylic walls and is designed to accommodate the 3D dosimeter PRESAGE, which can be fixed on a rotation stage inside the tank. Clear acrylic has excellent optical clarity and light transmission, with a refractive index of 1.49 that is close to the average refractive index (1.54) of PRESAGE. To match the refractive index of the 3D dosimeters, a matching liquid composed of 90% Octyl Salicylate and 10% Octyl-P-Methoxy Cinnamate is filled in the tank. The fiber optic taper serves two functions: first, it demagnifies the projection images while preserving their shape, and second, it effectively reduces the acceptance angle of the light reaching the CCD camera. The CCD camera used in the system is an Allied Vision model with a resolution of 0.016 mm, capable of acquiring 2D projection images from various angles. The principle of the optical CT scanner follows that of CT imaging, where 2D projection images from different angles are used to reconstruct volumetric 3D dose images using the filtered back projection technique. To validate the dosimetric measurements and assess the uncertainties of the 3D dosimetry system, 21 benchmark experiments, including mechanical, imaging, and dosimetry tests were conducted. Furthermore, the developed system was employed for various applications, including patient-specific IMRT QA, small field dosimetry using kilovoltage and megavoltage beams, as well as end-to-end testing of stereotactic radiosurgery.
A comprehensive analysis assessed uncertainties in each scanner component. Mechanical tests showed maximum uncertainties below 1%. By employing background subtraction and calibration techniques, measurement uncertainty was reduced to <1% in the optimal dose range. Background subtraction resulted in a remarkable 77% reduction in uncertainty by mitigating artifacts, ambient light, and refractive light. Reproducibility was excellent, with mean and standard deviation of dose differences below 0.4% and 1.1%, respectively, in three repeat scans. Dose distribution measurements exhibited strong agreement (passing rates: 98%-100%) between 3D measurements, treatment planning calculations, and EBT3 film dosimetry. Results confirm the optical CT scanner's robustness and accuracy for clinical 3D radiation dosimetry. The study also demonstrates that the developed 3D dosimetry system surpasses the limitations of traditional 2D gamma tests by providing clinicians with more clinically relevant information. This includes measured dose-volume histograms (DVHs) and the evaluation of gamma failing points in 3D space, enabling a comprehensive assessment of individual treatment plans. Furthermore, the study showcased the feasibility of utilizing this system to characterize a radiosurgery platform. It successfully assessed mechanical and dosimetric errors in off-axis delivery and evaluated the accuracy of treatment planning dose calculations, including modeling small fields, out-of-field dose, and multi-leaf collimator (MLC) characteristics. In addition, compelling evidence was presented that the high-resolution 3D dosimeter used in this study is capable of accurate dosimetry for both megavoltage and kilovoltage small fields. Importantly, the dosimeter exhibits no energy or dose rate dependence, further supporting its reliability and suitability for precise dosimetry measurements.
The intricate and three-dimensional nature of dose distributions in modern radiotherapy necessitated the development of 3D dosimetry measurements, particularly for treatments with precise margins, such as SRS and SBRT. The newly developed 3D dosimetry system offers significant enhancements to current QA practices, delivering more clinically relevant comparison results and bolstering patient safety. Furthermore, it can be utilized for independent inspections across multiple institutions or remote dosimetry verification. Beyond its applications in clinical settings, the presented 3D dosimetry system holds the potential to expedite the development and utilization of novel radiation platforms
Understanding the experience of âbrain fogâ in coeliac disease: an interpretative phenomenological analysis
This thesis is submitted by Emily May Ahmed in partial fulfilment of the degree of Doctor of Clinical Psychology at the University of Birmingham. The thesis is comprised of three chapters. The first chapter is a meta-analysis which aims to provide a current prevalence estimate of depression in adults with coeliac disease, including evaluation of risk of bias factors. Additionally, it includes a brief secondary analysis, within the appendix, describing prevalence and relative risk estimates for other mental health disorders associated with coeliac disease. The second chapter is a qualitative empirical study which uses interpretative phenomenological analysis (IPA) methodology to explore the complex lived experiences of one of the lesser-known symptoms associated with coeliac disease â âbrain fogâ, in seven participants. Both the meta-analysis and empirical studies have clear clinical implications for the cognitive and psychological support that individuals with coeliac disease should be offered during and after diagnosis. Finally, the third chapter is comprised of two press release documents, which provides an accessible summary of the main findings of both the meta-analysis and the empirical research study
Improving diagnostic procedures for epilepsy through automated recording and analysis of patientsâ history
Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach
Study of soft materials, flexible electronics, and machine learning for fully portable and wireless brain-machine interfaces
Over 300,000 individuals in the United States are afflicted with some form of limited motor function from brainstem or spinal-cord related injury resulting in quadriplegia or some form of locked-in syndrome. Conventional brain-machine interfaces used to allow for communication or movement require heavy, rigid components, uncomfortable headgear, excessive numbers of electrodes, and bulky electronics with long wires that result in greater data artifacts and generally inadequate performance. Wireless, wearable electroencephalograms, along with dry non-invasive electrodes can be utilized to allow recording of brain activity on a mobile subject to allow for unrestricted movement. Additionally, multilayer microfabricated flexible circuits, when combined with a soft materials platform allows for imperceptible wearable data acquisition electronics for long term recording. This dissertation aims to introduce new electronics and training paradigms for brain-machine interfaces to provide remedies in the form of communication and movement for these individuals. Here, training is optimized by generating a virtual environment from which a subject can achieve immersion using a VR headset in order to train and familiarize with the system. Advances in hardware and implementation of convolutional neural networks allow for rapid classification and low-latency target control. Integration of materials, mechanics, circuit and electrode design results in an optimized brain-machine interface allowing for rehabilitation and overall improved quality of life.Ph.D
An investigation into mild traumatic brain injury identification, management, and mitigation
Concussion is classified as a mild traumatic brain injury which can be induced by biomechanical forces such as a physical impact to the head or body, which results in a transient neurological disturbance without obvious structural brain damage. Immediate access to tools that can identify, diagnosis and manage concussion are wide ranging and can lack consistency in application. It is well documented that there are frequent incidences of concussion across amateur and professional sport such as popular contact sports like rugby union.
A primary aim of this thesis was to establish the current modalities of âpitch sideâ concussion management, identification, and diagnosis across amateur and professional sporting populations. Furthermore, the research sought to understand existing concussion management and concussion experiences by means of recording the playerâs experiences and perceptions (retired professional rugby union players). These qualitative studies sought to gain insights into concussion experiences, the language used to discuss concussion and the duty of care which medical staff, coaching personnel, and club owners have towards professional rugby players in their employment.
In addition, possible interventions to reduce the incidence of concussion in amateur and professional sports were investigated. These included a âproof of conceptâ using inertial measurement units and a smartphone application, a tackle technique coaching app for amateur sports. Other research data investigating the use of neurological function data and neuromuscular fatigue in current professional rugby players as a novel means of monitoring injury risk were included in this research theme.
The findings of these studies suggest that there is an established head injury assessment process for professional sports. However, in amateur sport settings, this is not the existing practice and may expose amateur players to an increased risk of post-concussion syndrome or early retirement. Many past professional rugby union players stated that they did not know the effects of cumulative repetitive head impacts. They discussed how they minimised and ignored repeated concussions due to peer pressure or pressure from coaches or their own internal pressures of maintaining a livelihood. These data suggest that players believed that strong willed medical staff, immutable to pressures from coaching staff or even athletes themselves, were essential for player welfare and that club owners have a long-term duty of care to retired professional rugby union players. However, there are anecdotal methods suggested to reduce concussion incidence. For example, neck strengthening techniques to mitigate against collision impacts. There is, no longitudinal evidence to suggest that neck strength can reduce the impacts of concussion in adult populations . Additionally, other factors such as lowering the tackle height in the professional and amateur game is currently being investigated as a mitigating factor to reduce head injury risk.
The final theme of the thesis investigated possible methods to reduce injury incidence in amateur and professional athletes. The novel tackle technique platform could assist inexperienced amateur coaches on how to coach effective tackle technique to youth players. The findings from the neurological function data suggests that this may be an alternative way for coaches to assess and gather fatigue data on professional rugby union players alongside additional subjective measures and neuromuscular function data. Recently, the awareness of concussion as an injury and the recognition of concussion in many sports settings has improved. These incremental improvements have led to increased discussion regarding possible measures to mitigate the effects of concussion. There are many additional procedures to be implemented before a comprehensive concussion management is universally available, particularly in amateur and community sports. These necessary processes could be technological advances (e.g., using smart phone technology) for parents and amateur coaches to assist in the early identification of concussion or evidence-based concussion reduction strategies
Ergonomics in laparoscopic surgery: a work system analysis to reduce work-related musculoskeletal disorders across surgeons in Peruvian hospitals
Laparoscopic surgery, also called minimally invasive surgery, is a type of surgery in which the surgeon operates by viewing the surgery on a screen that projects images from a camera inserted into the patient's abdomen. Laparoscopic tools are long (usually up to 35 cm) and require fine motor skills and visual perception for manipulation, restricting the degrees of freedom to move within the patient. This restriction causes surgeons to operate with limited vision and restricted movement and force them to work with assistants who assist in conducting the cameras, acting as "the surgeons' eyes".
Because of its minimally invasive nature, laparoscopic surgery is well accepted by patients but is challenging and complex for the surgeon. This is due to the restriction of movement and perception that forces surgeons to adopt awkward postures with high exposition, which increases the likelihood of work-related musculoskeletal disorders (WRMSD). WRMSDs are detrimental to surgeons' health and potentially may impact patient safety. Studies often highlight the problems of surgeons in high-income countries, whose solutions and clinical guides often cannot be applied to countries like Peru, which have severe deficiencies in its healthcare system.
For this reason, the thesis proposes a contextualised investigation of the Peruvian surgical work system to investigate the main factors contributing to the development of WRMSD in laparoscopic surgeons, which may affect patient safety. The analysis aimed to propose possible recommendations to support redesigning the laparoscopic surgery work system in Peruvian hospitals. Five studies were developed to achieve the aims based on the Systems Engineering Initiative for patient safety model, an ergonomics model for healthcare systems analysis. The first three studies were developed parallel with a mixed convergent design approach concluding in an integrating study. The last two studies (study four and five) had a quantitative approach.
The first study used a qualitative approach by collecting information through interviews with laparoscopic surgeons and observing their work in real surgeries. The second study adopted a quantitative approach through a questionnaire-based survey applied to 140 surgeons in Peru. The third study analysed the extent to which the postures adopted by surgeons in real surgeries increase the risk of WRMSD and their association with factors in the work system using the RULA method.
The results of the three studies were integrated into an integrative study, concluding that the raised height of the operating table and other system factors related to tasks, person and technology raises the risk of WRMSD. Based on these results, the fourth study analysed the relationship between surgeons and operating tables to understand how many surgeons could reach suitable working heights. The study concluded that no operating table available in Peruvian hospitals nor in the market would be suitable for 90% of Peruvian surgeons. The tables were too high to accommodate surgeons with optimal working surface height to perform laparoscopic surgery. Then, a fifth study was conducted to determine an acceptable working height based on surgeon preferences and system factors and concluded that surgeons would accept a working height between 49 cm to 70 cm in height, which is lower than current operating tables. The lowest height was reached when surgeons had to operate on obese patients and perform intracorporeal suturing tasks.
Finally, the thesis concludes with recommendations for redesigning working heights for 90% of the Peruvian medical population, considering work system elements of the Peruvian context
A Cognitive Intervention for Everyday Executive Function in Female Survivors of Intimate Partner Violence Related Traumatic Brain Injury, A Single-Case Experimental Design (SCED)
An estimated 31,500,000 females have experienced at least one intimate partner violence (IPV) related traumatic brain injury (TBI), or IPV-TBI in their lifetime in the United States of America (USA) alone. Survivors often experience executive function (EF) impairments, resulting in numerous functional and psychological challenges. Despite this, there are currently no studies into EF interventions for IPV-TBI survivors available. Compensatory cognitive rehabilitation and EF coaching have shown positive outcomes for EF in TBI. The current study aimed to investigate the effects of an intervention, combining cognitive rehabilitation and EF coaching for female survivors of IPV-TBI with EF impairments. A multiple baseline single case experimental design (MB-SCED) was used. Two female participants (age M=51.5, range=44-59) completed the study. The independent variable was a four-week cognitive intervention, the dependent variables were everyday executive function, goal attainment, and health-related quality of life (HRQoL). Analysis revealed that the intervention may have benefits for EF goal attainment, self-reported EF and HRQoL. However, these should be interpreted with caution due to the study limitations. The study highlights the need for further clinical interventions and research for IPV-TBI survivors
Ovarian hormones shape brain structure, function, and chemistry: A neuropsychiatric framework for female brain health
There are robust sex differences in brain anatomy, function, as well as neuropsychiatric and neurodegenerative disease risk (1-6), with women approximately twice as likely to suffer from a depressive illness as well as Alzheimerâs Disease. Disruptions in ovarian hormones likely play a role in such disproportionate disease prevalence, given that ovarian hormones serve as key regulators of brain functional and structural plasticity and undergo major fluctuations across the female lifespan (7-9). From a clinical perspective, there is a wellreported increase in depression susceptibility and initial evidence for cognitive impairment or decline during hormonal transition states, such as the postpartum period and perimenopause (9-14). What remains unknown, however, is the underlying mechanism of how fluctuations in ovarian hormones interact with other biological factors to influence brain structure, function, and chemistry. While this line of research has translational relevance for over half the population, neuroscience is notably guilty of female participant exclusion in research studies, with the male brain implicitly treated as the default model and only a minority of basic and clinical neuroscience studies including a female sample (15-18). Female underrepresentation in neuroscience directly limits opportunities for basic scientific discovery; and without basic knowledge of the biological underpinnings of sex differences, we cannot address critical sexdriven differences in pathology. Thus, my doctoral thesis aims to deliberately investigate the influence of sex and ovarian hormones on brain states in health as well as in vulnerability to depression and cognitive impairment:Table of Contents
List of Abbreviations ..................................................................................................................... i
List of Figures .............................................................................................................................. ii
Acknowledgements .....................................................................................................................iii
1 INTRODUCTION .....................................................................................................................1
1.1 Lifespan approach: Sex, hormones, and metabolic risk factors for cognitive health .......3
1.2 Reproductive years: Healthy models of ovarian hormones, serotonin, and the brain ......4
1.2.1 Ovarian hormones and brain structure across the menstrual cycle ........................4
1.2.2 Serotonergic modulation and brain function in oral contraceptive users .................6
1.3 Neuropsychiatric risk models: Reproductive subtypes of depression ...............................8
1.3.1 Hormonal transition states and brain chemistry measured by PET imaging ...........8
1.3.2 Serotonin transporter binding across the menstrual cycle in PMDD patients .......10
2 PUBLICATIONS ....................................................................................................................12
2.1 Publication 1: Association of estradiol and visceral fat with structural brain networks
and memory performance in adults .................................................................................13
2.2 Publication 2: Longitudinal 7T MRI reveals volumetric changes in subregions of
human medial temporal lobe to sex hormone fluctuations ..............................................28
2.3 Publication 3: One-week escitalopram intake alters the excitation-inhibition balance
in the healthy female brain ...............................................................................................51
2.4 Publication 4: Using positron emission tomography to investigate hormone-mediated
neurochemical changes across the female lifespan: implications for depression ..........65
2.5 Publication 5: Increase in serotonin transporter binding across the menstrual cycle in
patients with premenstrual dysphoric disorder: a case-control longitudinal neuro-
receptor ligand PET imaging study ..................................................................................82
3 SUMMARY ...........................................................................................................................100
References ..............................................................................................................................107
Supplementary Publications ...................................................................................................114
Author Contributions to Publication 1 .....................................................................................184
Author Contributions to Publication 2 .....................................................................................186
Author Contributions to Publication 3 .....................................................................................188
Author Contributions to Publication 4 .....................................................................................190
Author Contributions to Publication 5 .....................................................................................191
Declaration of Authenticity ......................................................................................................193
Curriculum Vitae ......................................................................................................................194
List of Publications ................................................................................................................195
List of Talks and Posters ......................................................................................................19
Deep Multimodality Image-Guided System for Assisting Neurosurgery
Intrakranielle Hirntumoren gehören zu den zehn hĂ€ufigsten bösartigen Krebsarten und sind fĂŒr eine erhebliche MorbiditĂ€t und MortalitĂ€t verantwortlich. Die gröĂte histologische Kategorie der primĂ€ren Hirntumoren sind die Gliome, die ein Ă€uĂerst heterogenes Erschei-nungsbild aufweisen und radiologisch schwer von anderen HirnlĂ€sionen zu unterscheiden sind. Die Neurochirurgie ist meist die Standardbehandlung fĂŒr neu diagnostizierte Gliom-Patienten und kann von einer Strahlentherapie und einer adjuvanten Temozolomid-Chemotherapie gefolgt werden.
Die Hirntumorchirurgie steht jedoch vor groĂen Herausforderungen, wenn es darum geht, eine maximale Tumorentfernung zu erreichen und gleichzeitig postoperative neurologische Defizite zu vermeiden. Zwei dieser neurochirurgischen Herausforderungen werden im Folgenden vorgestellt. Erstens ist die manuelle Abgrenzung des Glioms einschlieĂlich seiner Unterregionen aufgrund seines infiltrativen Charakters und des Vorhandenseins einer heterogenen KontrastverstĂ€rkung schwierig. Zweitens verformt das Gehirn seine Form ̶ die so genannte "Hirnverschiebung" ̶ als Reaktion auf chirurgische Manipulationen, Schwellungen durch osmotische Medikamente und AnĂ€sthesie, was den Nutzen prĂ€opera-tiver Bilddaten fĂŒr die Steuerung des Eingriffs einschrĂ€nkt.
Bildgesteuerte Systeme bieten Ărzten einen unschĂ€tzbaren Einblick in anatomische oder pathologische Ziele auf der Grundlage moderner BildgebungsmodalitĂ€ten wie Magnetreso-nanztomographie (MRT) und Ultraschall (US). Bei den bildgesteuerten Instrumenten handelt es sich hauptsĂ€chlich um computergestĂŒtzte Systeme, die mit Hilfe von Computer-Vision-Methoden die DurchfĂŒhrung perioperativer chirurgischer Eingriffe erleichtern. Die Chirurgen mĂŒssen jedoch immer noch den Operationsplan aus prĂ€operativen Bildern gedanklich mit Echtzeitinformationen zusammenfĂŒhren, wĂ€hrend sie die chirurgischen Instrumente im Körper manipulieren und die Zielerreichung ĂŒberwachen. Daher war die Notwendigkeit einer BildfĂŒhrung wĂ€hrend neurochirurgischer Eingriffe schon immer ein wichtiges Anliegen der Ărzte.
Ziel dieser Forschungsarbeit ist die Entwicklung eines neuartigen Systems fĂŒr die peri-operative bildgefĂŒhrte Neurochirurgie (IGN), nĂ€mlich DeepIGN, mit dem die erwarteten Ergebnisse der Hirntumorchirurgie erzielt werden können, wodurch die GesamtĂŒberle-bensrate maximiert und die postoperative neurologische MorbiditĂ€t minimiert wird. Im Rahmen dieser Arbeit werden zunĂ€chst neuartige Methoden fĂŒr die Kernbestandteile des DeepIGN-Systems der Hirntumor-Segmentierung im MRT und der multimodalen prĂ€ope-rativen MRT zur intraoperativen US-Bildregistrierung (iUS) unter Verwendung der jĂŒngs-ten Entwicklungen im Deep Learning vorgeschlagen. AnschlieĂend wird die Ergebnisvor-hersage der verwendeten Deep-Learning-Netze weiter interpretiert und untersucht, indem fĂŒr den Menschen verstĂ€ndliche, erklĂ€rbare Karten erstellt werden. SchlieĂlich wurden Open-Source-Pakete entwickelt und in weithin anerkannte Software integriert, die fĂŒr die Integration von Informationen aus Tracking-Systemen, die Bildvisualisierung und -fusion sowie die Anzeige von Echtzeit-Updates der Instrumente in Bezug auf den Patientenbe-reich zustĂ€ndig ist.
Die Komponenten von DeepIGN wurden im Labor validiert und in einem simulierten Operationssaal evaluiert. FĂŒr das Segmentierungsmodul erreichte DeepSeg, ein generisches entkoppeltes Deep-Learning-Framework fĂŒr die automatische Abgrenzung von Gliomen in der MRT des Gehirns, eine Genauigkeit von 0,84 in Bezug auf den WĂŒrfelkoeffizienten fĂŒr das Bruttotumorvolumen. Leistungsverbesserungen wurden bei der Anwendung fort-schrittlicher Deep-Learning-AnsĂ€tze wie 3D-Faltungen ĂŒber alle Schichten, regionenbasier-tes Training, fliegende Datenerweiterungstechniken und Ensemble-Methoden beobachtet.
Um Hirnverschiebungen zu kompensieren, wird ein automatisierter, schneller und genauer deformierbarer Ansatz, iRegNet, fĂŒr die Registrierung prĂ€operativer MRT zu iUS-Volumen als Teil des multimodalen Registrierungsmoduls vorgeschlagen. Es wurden umfangreiche Experimente mit zwei Multi-Location-Datenbanken durchgefĂŒhrt: BITE und RESECT. Zwei erfahrene Neurochirurgen fĂŒhrten eine zusĂ€tzliche qualitative Validierung dieser Studie durch, indem sie MRT-iUS-Paare vor und nach der deformierbaren Registrierung ĂŒberlagerten. Die experimentellen Ergebnisse zeigen, dass das vorgeschlagene iRegNet schnell ist und die besten Genauigkeiten erreicht. DarĂŒber hinaus kann das vorgeschlagene iRegNet selbst bei nicht trainierten Bildern konkurrenzfĂ€hige Ergebnisse liefern, was seine AllgemeingĂŒltigkeit unter Beweis stellt und daher fĂŒr die intraoperative neurochirurgische FĂŒhrung von Nutzen sein kann.
FĂŒr das Modul "ErklĂ€rbarkeit" wird das NeuroXAI-Framework vorgeschlagen, um das Vertrauen medizinischer Experten in die Anwendung von KI-Techniken und tiefen neuro-nalen Netzen zu erhöhen. Die NeuroXAI umfasst sieben ErklĂ€rungsmethoden, die Visuali-sierungskarten bereitstellen, um tiefe Lernmodelle transparent zu machen. Die experimen-tellen Ergebnisse zeigen, dass der vorgeschlagene XAI-Rahmen eine gute Leistung bei der Extraktion lokaler und globaler Kontexte sowie bei der Erstellung erklĂ€rbarer Salienzkar-ten erzielt, um die Vorhersage des tiefen Netzwerks zu verstehen. DarĂŒber hinaus werden Visualisierungskarten erstellt, um den Informationsfluss in den internen Schichten des Encoder-Decoder-Netzwerks zu erkennen und den Beitrag der MRI-ModalitĂ€ten zur end-gĂŒltigen Vorhersage zu verstehen. Der ErklĂ€rungsprozess könnte medizinischen Fachleu-ten zusĂ€tzliche Informationen ĂŒber die Ergebnisse der Tumorsegmentierung liefern und somit helfen zu verstehen, wie das Deep-Learning-Modell MRT-Daten erfolgreich verar-beiten kann.
AuĂerdem wurde ein interaktives neurochirurgisches Display fĂŒr die EingriffsfĂŒhrung entwickelt, das die verfĂŒgbare kommerzielle Hardware wie iUS-NavigationsgerĂ€te und Instrumentenverfolgungssysteme unterstĂŒtzt. Das klinische Umfeld und die technischen Anforderungen des integrierten multimodalen DeepIGN-Systems wurden mit der FĂ€higkeit zur Integration von (1) prĂ€operativen MRT-Daten und zugehörigen 3D-Volumenrekonstruktionen, (2) Echtzeit-iUS-Daten und (3) positioneller Instrumentenver-folgung geschaffen. Die Genauigkeit dieses Systems wurde anhand eines benutzerdefi-nierten Agar-Phantom-Modells getestet, und sein Einsatz in einem vorklinischen Operati-onssaal wurde simuliert. Die Ergebnisse der klinischen Simulation bestĂ€tigten, dass die Montage des Systems einfach ist, in einer klinisch akzeptablen Zeit von 15 Minuten durchgefĂŒhrt werden kann und mit einer klinisch akzeptablen Genauigkeit erfolgt.
In dieser Arbeit wurde ein multimodales IGN-System entwickelt, das die jĂŒngsten Fort-schritte im Bereich des Deep Learning nutzt, um Neurochirurgen prĂ€zise zu fĂŒhren und prĂ€- und intraoperative Patientenbilddaten sowie interventionelle GerĂ€te in das chirurgi-sche Verfahren einzubeziehen. DeepIGN wurde als Open-Source-Forschungssoftware entwickelt, um die Forschung auf diesem Gebiet zu beschleunigen, die gemeinsame Nut-zung durch mehrere Forschungsgruppen zu erleichtern und eine kontinuierliche Weiter-entwicklung durch die Gemeinschaft zu ermöglichen. Die experimentellen Ergebnisse sind sehr vielversprechend fĂŒr die Anwendung von Deep-Learning-Modellen zur UnterstĂŒtzung interventioneller Verfahren - ein entscheidender Schritt zur Verbesserung der chirurgi-schen Behandlung von Hirntumoren und der entsprechenden langfristigen postoperativen Ergebnisse
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