19 research outputs found
Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance
Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms.
In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time.
Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance.
Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed.
In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)
Engineering data compendium. Human perception and performance, volume 3
The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design of military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by system designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is Volume 3, containing sections on Human Language Processing, Operator Motion Control, Effects of Environmental Stressors, Display Interfaces, and Control Interfaces (Real/Virtual)
Reports to the President
A compilation of annual reports for the 1986-1987 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans
A combined microwave and optical sensor system with application in cancer detection
Cancer remains a significant health problem, despite great scientific advances in recent years.
Biomedical imaging procedures are commonly used to facilitate the diagnosis and treatment of
different types of cancer. However, there are still many limitations to these diagnostic techniques. To overcome some of these issues, new approaches are urgently needed.
This study aims to establish potential new techniques to improve disease staging diagnosis through more accurate detection and allow real-time monitoring of sample characteristics to help the surgeons reduce the number of biopsies for making a diagnosis.
An optical probe has been fabricated in our
laboratory with specific characteristics resulting from
modelling and experimental exploration. This probe produced encouraging results from a tissue
phantom with an ability to distinguish between different particle sizes 2, 0.8 and 0.413 μm with
various polystyrene spheres in suspension (PS) concentrations.
A Microwave cavity resonator showed the ability to distinguish between different saline dilutions
for two types of preparation and different PS concentrations with some limitations. Many
correction techniques were developed to enhance the quality of the data obtained.
A novel T- Structure and capacitive coupling technique enabled a more robust S21 measurement
to be made utilising a resonant coaxial probe at microwave frequencies between around 0.1 GHz
and 6 GHz. This structure was modelled and used in experimental scenarios leading to the ability
to distinguish between various saline dilutions and different concentrations of PS. Additional
correction techniques showed a significant improvement in PS detection limits.
Some difficulties have been overcome, relating to settling the PS particles in suspension,
corrosion of the microwave probe, and signal processing. All of this has led to a novel system
design by combined the optical and microwave sensor system to facilitate effective and efficient
tumour detection. This novelty demonstrated that this new system could distinguish between
different particles sizes by optical detection and dielectric properties by microwave
characterisation.
The concluding section of this thesis presents the simultaneous detection of PS samples of
different concentrations optically and with the microwave probe. This represents the first time
such simultaneous measurements have been carried out using a combined probe such as that
described here