Improving Radiotherapy Targeting for Cancer Treatment Through Space and Time

Radiotherapy is a common medical treatment in which lethal doses of ionizing radiation are preferentially delivered to cancerous tumors. In external beam radiotherapy, radiation is delivered by a remote source which sits several feet from the patient\u27s surface. Although great effort is taken in properly aligning the target to the path of the radiation beam, positional uncertainties and other errors can compromise targeting accuracy. Such errors can lead to a failure in treating the target, and inflict significant toxicity to healthy tissues which are inadvertently exposed high radiation doses. Tracking the movement of targeted anatomy between and during treatment fractions provides valuable localization information that allows for the reduction of these positional uncertainties. Inter- and intra-fraction anatomical localization data not only allows for more accurate treatment setup, but also potentially allows for 1) retrospective treatment evaluation, 2) margin reduction and modification of the dose distribution to accommodate daily anatomical changes (called `adaptive radiotherapy\u27), and 3) targeting interventions during treatment (for example, suspending radiation delivery while the target it outside the path of the beam). The research presented here investigates the use of inter- and intra-fraction localization technologies to improve radiotherapy to targets through enhanced spatial and temporal accuracy. These technologies provide significant advancements in cancer treatment compared to standard clinical technologies. Furthermore, work is presented for the use of localization data acquired from these technologies in adaptive treatment planning, an investigational technique in which the distribution of planned dose is modified during the course of treatment based on biological and/or geometrical changes of the patient\u27s anatomy. The focus of this research is directed at abdominal sites, which has historically been central to the problem of motion management in radiation therapy

Medical Robotics for use in MRI Guided Endoscopy

Interventional Magnetic Resonance Imaging (MRI) is a developing field that aims to provide intra-operative MRI to a clinician to guide diagnostic or therapeutic medical procedures. MRI provides excellent soft tissue contrast at sub-millimetre resolution in both 2D and 3D without the need for ionizing radiation. Images can be acquired in near real-time for guidance purposes. Operating in the MR environment brings challenges due to the high static magnetic field, switching magnetic field gradients and RF excitation pulses. In addition high field closed bore scanners have spatial constraints that severely limit access to the patient. This thesis presents a system for MRI-guided Endoscopic Retrograde Cholangio-pancreatography (ERCP). This includes a remote actuation system that enables an MRI-compatible endoscope to be controlled whilst the patient is inside the MRI scanner, overcoming the spatial and procedural constraints imposed by the closed scanner bore. The modular system utilises non-magnetic ultrasonic motors and is designed for image-guided user-in-the-loop control. A novel miniature MRI compatible clutch has been incorporated into the design to reduce the need for multiple parallel motors. The actuation system is MRI compatible does not degrade the MR images below acceptable levels. User testing showed that the actuation system requires some degree of training but enables completion of a simulated ERCP procedure with no loss of performance. This was demonstrated using a tailored ERCP simulator and kinematic assessment tool, which was validated with users from a range of skill levels to ensure that it provides an objective measurement of endoscopic skill. Methods of tracking the endoscope in real-time using the MRI scanner are explored and presented here. Use of the MRI-guided ERCP system was shown to improve the operator’s ability to position the endoscope in an experimental environment compared with a standard fluoroscopic-guided system.Open Acces

Using needle orientation sensing as surrogate signal for respiratory motion estimation in percutaneous interventions

Purpose To develop and evaluate an approach to estimate the respiratory-induced motion of lesions in the chest and abdomen. Materials and methods The proposed approach uses the motion of an initial reference needle inserted into a moving organ to estimate the lesion (target) displacement that is caused by respiration. The needles position is measured using an inertial measurement unit (IMU) sensor externally attached to the hub of an initially placed reference needle. Data obtained from the IMU sensor and the target motion are used to train a learning-based approach to estimate the position of the moving target. An experimental platform was designed to mimic respiratory motion of the liver. Liver motion profiles of human subjects provided inputs to the experimental platform. Variables including the insertion angle, target depth, target motion velocity and target proximity to the reference needle were evaluated by measuring the error of the estimated target position and processing time. Results: The mean error of estimation of the target position ranged between 0.86 and 1.29 mm. The processing maximum training and testing time was 5 ms which is suitable for real-time target motion estimation using the needle position sensor. Conclusion: The external motion of an initially placed reference needle inserted into a moving organ can be used as a surrogate, measurable and accessible signal to estimate in real-time the position of a moving target caused by respiration; this technique could then be used to guide the placement of subsequently inserted needles directly into the target

Development and validation of real-time simulation of X-ray imaging with respiratory motion

International audienceWe present a framework that combines evolutionary optimisation, soft tissue modelling and ray tracing on GPU to simultaneously compute the respiratory motion and X-ray imaging in real-time. Our aim is to provide validated building blocks with high fidelity to closely match both the human physiology and the physics of X-rays. A CPU-based set of algorithms is presented to model organ behaviours during respiration. Soft tissue deformation is computed with an extension of the Chain Mail method. Rigid elements move according to kinematic laws. A GPU-based surface rendering method is proposed to compute the X-ray image using the Beer-Lambert law. It is provided as an open-source library. A quantitative validation study is provided to objectively assess the accuracy of both components: i) the respiration against anatomical data, and ii) the X-ray against the Beer-Lambert law and the results of Monte Carlo simulations. Our implementation can be used in various applications, such as interactive medical virtual environment to train percutaneous transhepatic cholangiography in interventional radiology, 2D/3D registration, computation of digitally reconstructed radiograph, simulation of 4D sinograms to test tomography reconstruction tools

SUBJECT-SPECIFIC 4D ULTRASOUND LIVER MODEL AND DYNAMIC PHANTOM FOR ROBOTIC-ASSISTED LIVER BIOPSY STUDY

Ph.DDOCTOR OF PHILOSOPH

Translational Research of Audiovisual Biofeedback: An investigation of respiratory-guidance in lung and liver cancer patient radiation therapy

Through the act of breathing, thoracic and abdominal anatomy is in constant motion and is typically irregular. This irregular motion can exacerbate errors in radiation therapy, breathing guidance interventions operate to minimise these errors. However, much of the breathing guidance investigations have not directly quantified the impact of regular breathing on radiation therapy accuracy. The first aim of this thesis was to critically appraise the literature in terms of the use of breathing guidance interventions via systematic review. This review found that 21 of the 27 identified studies yielded significant improvements from the use of breathing guidance. None of the studies were randomised and no studies quantified the impact on 4DCT image quality. The second aim of this thesis was to quantify the impact of audiovisual biofeedback breathing guidance on 4DCT. This study utilised data from an MRI study to program the motion of a digital phantom prior to then simulating 4DCT imaging. Audiovisual biofeedback demonstrated to significantly improved 4DCT image quality over free breathing. The third aim of this thesis was to assess the impact of audiovisual biofeedback on liver cancer patient breathing over a course of stereotactic body radiation therapy (SBRT). The findings of this study demonstrated the effectiveness of audiovisual biofeedback in producing consistent interfraction respiratory motion over a course of SBRT. The fourth aim of this thesis was to design and implement a phase II clinical trial investigating the use and impact of audiovisual biofeedback in lung cancer radiation therapy. The findings of a retrospective analysis were utilised to design and determine the statistics of the most comprehensive breathing guidance study to date: a randomised, stratified, multi-site, phase II clinical trial.. The fifth aim of this thesis was to explore the next stages of audiovisual biofeedback in terms of translating evidence into broader clinical use through commercialisation. This aim was achieved by investigating the the product-market fit of the audiovisual biofeedback technology. The culmination of these findings demonstrates the clinical benefit of the audiovisual biofeedback respiratory guidance system and the possibility to make breathing guidance systems more widely available to patients

Focal Spot, Winter 2006/2007

https://digitalcommons.wustl.edu/focal_spot_archives/1104/thumbnail.jp