Expanding the use of real-time electromagnetic tracking in radiation oncology.

In the past 10 years, techniques to improve radiotherapy delivery, such as intensity-modulated radiation therapy (IMRT), image-guided radiation therapy (IGRT) for both inter- and intrafraction tumor localization, and hypofractionated delivery techniques such as stereotactic body radiation therapy (SBRT), have evolved tremendously. This review article focuses on only one part of that evolution, electromagnetic tracking in radiation therapy. Electromagnetic tracking is still a growing technology in radiation oncology and, as such, the clinical applications are limited, the expense is high, and the reimbursement is insufficient to cover these costs. At the same time, current experience with electromagnetic tracking applied to various clinical tumor sites indicates that the potential benefits of electromagnetic tracking could be significant for patients receiving radiation therapy. Daily use of these tracking systems is minimally invasive and delivers no additional ionizing radiation to the patient, and these systems can provide explicit tumor motion data. Although there are a number of technical and fiscal issues that need to be addressed, electromagnetic tracking systems are expected to play a continued role in improving the precision of radiation delivery

The first clinical implementation of a real-time six degree of freedom target tracking system during radiation therapy based on Kilovoltage Intrafraction Monitoring (KIM).

PURPOSE: We present the first clinical implementation of a real-time six-degree of freedom (6DoF) Kilovoltage Intrafraction Monitoring (KIM) system which tracks the cancer target translational and rotational motions during treatment. The method was applied to measure and correct for target motion during stereotactic body radiotherapy (SBRT) for prostate cancer. METHODS: Patient: A patient with prostate adenocarcinoma undergoing SBRT with 36.25Gy, delivered in 5 fractions was enrolled in the study. 6DoF KIM technology: 2D positions of three implanted gold markers in each of the kV images (125kV, 10mA at 11Hz) were acquired continuously during treatment. The 2D→3D target position estimation was based on a probability distribution function. The 3D→6DoF target rotation was calculated using an iterative closest point algorithm. The accuracy and precision of the KIM method was measured by comparing the real-time results with kV-MV triangulation. RESULTS: Of the five treatment fractions, KIM was utilised successfully in four fractions. The intrafraction prostate motion resulted in three couch shifts in two fractions when the prostate motion exceeded the pre-set action threshold of 2mm for more than 5s. KIM translational accuracy and precision were 0.3±0.6mm, -0.2±0.3mm and 0.2±0.7mm in the Left-Right (LR), Superior-Inferior (SI) and Anterior-Posterior (AP) directions, respectively. The KIM rotational accuracy and precision were 0.8°±2.0°, -0.5°±3.3° and 0.3°±1.6° in the roll, pitch and yaw directions, respectively. CONCLUSION: This treatment represents, to the best of our knowledge, the first time a cancer patient's tumour position and rotation have been monitored in real-time during treatment. The 6 DoF KIM system has sub-millimetre accuracy and precision in all three translational axes, and less than 1° accuracy and 4° precision in all three rotational axes

Encapsulated Contrast Agent Markers for MRI-based Post-implant Dosimetry

Low-dose-rate prostate brachytherapy involves the implantation of tiny radioactive seeds into the prostate to treat prostate cancer. The current standard post-implant imaging modality is computed tomography (CT). On CT images, the radioactive seeds can be distinctively localized but delineation of the prostate and surrounding soft tissue is poor. Magnetic resonance imaging (MRI) provides better prostate and soft tissue delineation, but seed localization is difficult. To aid with seed localization, MRI markers with encapsulated contrast agent that provide positive-contrast on MRI images (Sirius MRI markers; C4 Imaging, Houston, TX) have been proposed to be placed adjacent to the negative-contrast seeds. This dissertation describes the development of the Sirius MRI markers for prostate post-implant dosimetry. First, I compared the dose-volume histogram and other dosimetry parameters generated by MIM Symphony (a brachytherapy treatment planning system that allow the use of MRI images for treatment planning; MIM Software Inc., Cleveland, OH) and VariSeed (a widely used brachytherapy treatment planning system; Varian Medical Systems, Inc., Palo Alto, CA), and found the dosimetry between both brachytherapy treatment planning systems to be comparable. To gain more insight into the MRI contrast characteristics of the Sirius MRI markers, I measured the Sirius MRI marker contrast agent\u27s spin-lattice and spin-spin relaxivities, and studied the relaxation characteristics\u27 dependence on MRI field strength, temperature, and orientation. From the Sirius MRI marker\u27s contrast agent relaxation characteristics, I systematically studied the effect of varying MRI scan parameters such as flip angle, number of excitations, bandwidth, field of view, slice thickness, and encoding steps, on the Sirius MRI markers\u27 signal and contrast, as well as image noise, artifact and scan time. On patients implanted with Sirius MRI markers, I evaluated the visibility of the Sirius MRI markers and image artifacts. Lastly, I semi-automated the localization of markers and seeds to more enable the efficient incorporation of Sirius MRI markers as part of the clinical post-implant workflow. Ultimately, the Sirius MRI markers may change the paradigm from CT-based to MRI-based post-implant dosimetry, for a more accurate understanding of dose-response relationships in patients undergoing low dose rate prostate brachytherapy

Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.Comment: 36 pages, 5 Figures, 4 Table

Optimization, Delivery and Evaluation of Intensity Modulated Arc Therapy

Intensity modulated arc therapy (IMAT) is a radiation therapy technique whereby the shape of the cone beam of radiation changes as it rotates around the patient. This is in contrast to other more commonly delivered forms of advanced radiation therapy, Intensity Modulated Radiation Therapy (IMRT) or helical tomotherapy. IMRT is a radiation technique where a patient is treated with a cone beam of radiation from a number of fixed beam directions, where the shapes and weights of the radiation beams are varied and tomotherapy is treated with a fan beam of radiation that follows a helical trajectory. In this thesis two aspects of IMAT were investigated: optimization of treatment plans and delivery of plans in conjunction with and without respiratory motion management. Optimization of IMAT deliveries consisted of two studies. In the first study, an algorithm that uses dosimetric ray tracing to set multi-leaf collimator (MLC) positions then directly optimizes the MLC positions to create IMAT treatment plans with only beam shape variations was developed and tested in three phantom studies and a clinical case. The second study investigated variable angular dose rate deliveries to a concave target and assessed the optimization strategy including arc initialization strategy, angular sampling and delivery efficiency. IMAT delivery with and without respiratory gated radiation delivery was studied with dose measurement using radiographie film in a motion phantom. In addition, simulations based on delivered log files were used to confirm that motion management for IMAT is effective and within iii dosimetric tolerances. As a pilot test, plans from IMRT and tomotherapy for partial breast irradiation were first studied, comparing them to conventional treatments. An IMAT plan was generated for one patient, demonstrating feasibility and was compared with IMRT and tomotherapy. This thesis has introduced a new IMAT optimization algorithm with and without variable angular dose rate, applied to partial breast treatment, and verified its delivery under motion and gating conditions

Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis

Elasticity parameter estimation is essential for generating accurate and controlled simulation results for computer animation and medical image analysis. However, finding the optimal parameters for a particular simulation often requires iterations of simulation, assessment, and adjustment and can become a tedious process. Elasticity values are especially important in medical image analysis, since cancerous tissues tend to be stiffer. Elastography is a popular type of method for finding stiffness values by reconstructing a dense displacement field from medical images taken during the application of forces or vibrations. These methods, however, are limited by the imaging modality and the force exertion or vibration actuation mechanisms, which can be complicated for deep-seated organs. In this thesis, I present a novel method for reconstructing elasticity parameters without requiring a dense displacement field or a force exertion device. The method makes use of natural deformations within the patient and relies on surface information from segmented images taken on different days. The elasticity value of the target organ and boundary forces acting on surrounding organs are optimized with an iterative optimizer, within which the deformation is always generated by a physically-based simulator. Experimental results on real patient data are presented to show the positive correlation between recovered elasticity values and clinical prostate cancer stages. Furthermore, to resolve the performance issue arising from the high dimensionality of boundary forces, I propose to use a reduced finite element model to improve the convergence of the optimizer. To find the set of bases to represent the dimensions for forces, a statistical training based on real patient data is performed. I demonstrate the trade-off between accuracy and performance by using different numbers of bases in the optimization using synthetic data. A speedup of more than an order of magnitude is observed without sacrificing too much accuracy in recovered elasticity.Doctor of Philosoph