Assessment and Reduction of the Clinical Range Prediction Uncertainty in Proton Therapy

Unsicherheiten in der Reichweitevorhersage limitieren wesentlich das Ausnutzen der Vorteile von Protonentherapie gegenüber konventioneller Strahlentherapie. Die Verwendung von Zwei-Spektren-Computertomographie (DECT) zur direkten Vorhersage des Bremsvermögen (DirectSPR) ermöglicht eine relevante Verbesserung der Reichweitevorhersage gegenüber der üblicherweise verwendeten Ein-Spektren-Computertomographie (SECT). Im Rahmen dieser Dissertation wurde die Variation in der Reichweitevorhersage zwischen 17 europäischen Partikeltherapiezentren experimentell verglichen. Die Genauigkeit der Reichweitevorhersage bei Verwendung einer DirectSPR-Implementierung wurde umfassend quantifiziert und die Implementierung in die klinische Routine integriert. Dies führte zu einer Reduzierung des klinischen Sicherheitssaum um ca. 35% für die Behandlung von quasistatischen Tumoren in Kopf und Becken und damit einer Schonung des Normalgewebes sowie der das Zielgebiet umgebenden Risikoorgane. Darüber hinaus wurde die DirectSPR-Implementierung zur Bestimmung von Gewebeparametern sowie deren Variabilität für zehn Organe im Kopf und Becken in einer Patienkohorte genutzt. Die vorgestellten Ergebnisse etablieren DECT weiter als zukünftiges Standard-Bildgebungsverfahren in der Partikeltherapie.:1. Introduction 2. Proton therapy 2.1. Physical principles of proton therapy 2.2. Treatment with protons 2.3. Accuracy in proton therapy 3. CT Imaging for proton therapy 3.1. Principles of CT imaging 3.2. CT-based range prediction 3.3. Investigated phantoms and materials 3.4. DECT scan acquisition 3.5. Determination of proton stopping power for reference materials 4. Accuracy of stopping-power prediction in European proton centres 4.1. Study design 4.2. Experimental setup and analysis 4.3. Results 4.4. Discussion of determined deviations 4.5. Conclusion and outlook 4.6. Establishment of guidelines for HLUT calibration 5. Range uncertainties in DirectSPR-based treatment planning 5.1. Clinical implementation of DirectSPR 5.2. Uncertainty quantification 5.3. Resulting uncertainties in SPR prediction 5.4. Experimental validation 5.5. Dosimetric effect of range uncertainty reduction 5.6. Discussion 6. In-vivo tissue characterisation using DirectSPR 6.1. Tissue parameter determination by Woodard and White 6.2. Data preparation and analysis 6.3. Determined tissue parameters and variations 6.4. Discussion 7. The future of image-based range prediction 7.1. Particle imaging 7.2. Creation of synthetic CT images 7.3. Photon-counting computed tomography 8. Summary 9. Zusammenfassung A. Supplement A.1. Investigated materials A.2. EPTN study: Individual results A.3. DirectSPR validation resultsImaging-related range uncertainties effectively limit the full exploitation of the benefits proton therapy offers with respect to conventional photon radiotherapy. The use of dual-energy computed tomography (DECT) for direct stopping-power prediction (DirectSPR) was determined to provide relevant improvements in range prediction over commonly used singleenergy CT (SECT). Within this thesis, the variation in range prediction accuracy between 17 European particle treatment centres were experimentally quantified to determine the current status quo in the community. The overall range uncertainty when using a DirectSPR implementation in treatment planning was comprehensively quantified and the implementation integrated into the clinical workflow. This led to a reduction of clinical safety margins by about 35% for the treatment of quasi-static tumours in the head and pelvis, effectively reducing the dose to surrounding healthy tissue and organs at risk. The DirectSPR implementation was furthermore utilised to assess tissue parameters and their inter- and intra-patient variability for ten organs in the head and pelvis from a cohort of patients. The presented results further establish DirectSPR as the future standard imaging modality in particle therapy.:1. Introduction 2. Proton therapy 2.1. Physical principles of proton therapy 2.2. Treatment with protons 2.3. Accuracy in proton therapy 3. CT Imaging for proton therapy 3.1. Principles of CT imaging 3.2. CT-based range prediction 3.3. Investigated phantoms and materials 3.4. DECT scan acquisition 3.5. Determination of proton stopping power for reference materials 4. Accuracy of stopping-power prediction in European proton centres 4.1. Study design 4.2. Experimental setup and analysis 4.3. Results 4.4. Discussion of determined deviations 4.5. Conclusion and outlook 4.6. Establishment of guidelines for HLUT calibration 5. Range uncertainties in DirectSPR-based treatment planning 5.1. Clinical implementation of DirectSPR 5.2. Uncertainty quantification 5.3. Resulting uncertainties in SPR prediction 5.4. Experimental validation 5.5. Dosimetric effect of range uncertainty reduction 5.6. Discussion 6. In-vivo tissue characterisation using DirectSPR 6.1. Tissue parameter determination by Woodard and White 6.2. Data preparation and analysis 6.3. Determined tissue parameters and variations 6.4. Discussion 7. The future of image-based range prediction 7.1. Particle imaging 7.2. Creation of synthetic CT images 7.3. Photon-counting computed tomography 8. Summary 9. Zusammenfassung A. Supplement A.1. Investigated materials A.2. EPTN study: Individual results A.3. DirectSPR validation result

Book review: The asset economy by Lisa Adkins, Melinda Cooper and Martijn Konings

In The Asset Economy, Lisa Adkins, Melinda Cooper and Martijn Konings retell the story of neoliberalism through the lens of assets, showing how asset ownership and asset inflation have been driving forces behind inequality and new class divides. This book is a highly readable and timely intervention in the burgeoning debate on rentiership and will inspire future research in showing the importance of putting assets at the centre of analysis, finds Nils Peters. The Asset Economy. Lisa Adkins, Melinda Cooper and Martijn Konings. Polity. 2020

Deep Learning-based F0 Synthesis for Speaker Anonymization

Voice conversion for speaker anonymization is an emerging concept for privacy protection. In a deep learning setting, this is achieved by extracting multiple features from speech, altering the speaker identity, and waveform synthesis. However, many existing systems do not modify fundamental frequency (F0) trajectories, which convey prosody information and can reveal speaker identity. Moreover, mismatch between F0 and other features can degrade speech quality and intelligibility. In this paper, we formally introduce a method that synthesizes F0 trajectories from other speech features and evaluate its reconstructional capabilities. Then we test our approach within a speaker anonymization framework, comparing it to a baseline and a state-of-the-art F0 modification that utilizes speaker information. The results show that our method improves both speaker anonymity, measured by the equal error rate, and utility, measured by the word error rate.Comment: 5 pages, 4 figures, 6 tables, accepted to EUSIPCO 202

Automatic detection of problem-gambling signs from online texts using large language models

Problem gambling is a major public health concern and is associated with profound psychological distress and economic problems. There are numerous gambling communities on the internet where users exchange information about games, gambling tactics, as well as gambling-related problems. Individuals exhibiting higher levels of problem gambling engage more in such communities. Online gambling communities may provide insights into problem-gambling behaviour. Using data scraped from a major German gambling discussion board, we fine-tuned a large language model, specifically a Bidirectional Encoder Representations from Transformers (BERT) model, to predict signs of problem-gambling from forum posts. Training data were generated by manual annotation and by taking into account diagnostic criteria and gambling-related cognitive distortions. Using k-fold cross-validation, our models achieved a precision of 0.95 and F1 score of 0.71, demonstrating that satisfactory classification performance can be achieved by generating high-quality training material through manual annotation based on diagnostic criteria. The current study confirms that a BERT-based model can be reliably used on small data sets and to detect signatures of problem gambling in online communication data. Such computational approaches may have potential for the detection of changes in problem-gambling prevalence among online users

Comparison of position estimation methods for the rotating equatorial microphone

We present a prototype of a microphone that moves rapidly along the equator of a rigid spherical scatterer. Our prototype allows for up to 100 rotations per second. It will enable processing methods like beamforming or sound field decomposition that are conventionally performed using mi- crophone arrays. Solutions that assume one or more moving microphones have already been proposed in the literature but have not been verified in practise. Most of these methods require precise knowledge of the instantaneous microphone position, for which no convenient practical solution exists. Recent advancements in microphone array processing en- able employing simple microphone trajectories, and recent advancements in 3D printing, microcontrollers, and high- speed electric motors allow for the required control of the movement. This paper presents the design of our prototype and evaluates its performance. Of particular interest is the accuracy of the estimation of the microphone’s instantaneous position. This paper demonstrates that monitoring the pass- ing time instants of a photodiode that is integrated into the rotating sphere provides the highest precision and robustness

Voice Anonymization for All -- Bias Evaluation of the Voice Privacy Challenge Baseline System

In an age of voice-enabled technology, voice anonymization offers a solution to protect people's privacy, provided these systems work equally well across subgroups. This study investigates bias in voice anonymization systems within the context of the Voice Privacy Challenge. We curate a novel benchmark dataset to assess performance disparities among speaker subgroups based on sex and dialect. We analyze the impact of three anonymization systems and attack models on speaker subgroup bias and reveal significant performance variations. Notably, subgroup bias intensifies with advanced attacker capabilities, emphasizing the challenge of achieving equal performance across all subgroups. Our study highlights the need for inclusive benchmark datasets and comprehensive evaluation strategies that address subgroup bias in voice anonymization.Comment: Submitted to ICASSP 202

Flexible Control of Composite Parameters in Max/MSP

Fundamental to the development of musical or artistic creative work is the ability to transform raw materials. This ability implies the facility to master many facets of the material, and to shape it with plasticity. Computer music environments typically provide points of control to manipulate material by supplying parameters with controllable values. This capability to control the values of parameters is inadequate for many artistic endeavors, and does not reflect the analogous tools and methods of artists working with physical materials. Rather than viewing parameters in computer-based systems as single points of control, the authors posit that parameters must become more multifaceted and dynamic in order to serve the needs of artists. The authors propose an expanded notion of how to work with parameters in computer-centric environments for time-based art. A proposed partial solution to this problem is to give parameters additional properties that define their behavior. An example implementation of these ideas is presented in Jamoma