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

Four-dimensional Cone Beam CT Reconstruction and Enhancement using a Temporal Non-Local Means Method

Four-dimensional Cone Beam Computed Tomography (4D-CBCT) has been developed to provide respiratory phase resolved volumetric imaging in image guided radiation therapy (IGRT). Inadequate number of projections in each phase bin results in low quality 4D-CBCT images with obvious streaking artifacts. In this work, we propose two novel 4D-CBCT algorithms: an iterative reconstruction algorithm and an enhancement algorithm, utilizing a temporal nonlocal means (TNLM) method. We define a TNLM energy term for a given set of 4D-CBCT images. Minimization of this term favors those 4D-CBCT images such that any anatomical features at one spatial point at one phase can be found in a nearby spatial point at neighboring phases. 4D-CBCT reconstruction is achieved by minimizing a total energy containing a data fidelity term and the TNLM energy term. As for the image enhancement, 4D-CBCT images generated by the FDK algorithm are enhanced by minimizing the TNLM function while keeping the enhanced images close to the FDK results. A forward-backward splitting algorithm and a Gauss-Jacobi iteration method are employed to solve the problems. The algorithms are implemented on GPU to achieve a high computational efficiency. The reconstruction algorithm and the enhancement algorithm generate visually similar 4D-CBCT images, both better than the FDK results. Quantitative evaluations indicate that, compared with the FDK results, our reconstruction method improves contrast-to-noise-ratio (CNR) by a factor of 2.56~3.13 and our enhancement method increases the CNR by 2.75~3.33 times. The enhancement method also removes over 80% of the streak artifacts from the FDK results. The total computation time is ~460 sec for the reconstruction algorithm and ~610 sec for the enhancement algorithm on an NVIDIA Tesla C1060 GPU card.Comment: 20 pages, 3 figures, 2 table

A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes

On the investigation of a novel x-ray imaging techniques in radiation oncology

Radiation therapy is indicated for nearly 50% of cancer patients in Australia. Radiation therapy requires accurate delivery of ionising radiation to the neoplastic tissue and pre-treatment in situ x-ray imaging plays an important role in meeting treatment accuracy requirements. Four dimensional cone-beam computed tomography (4D CBCT) is one such pre-treatment imaging technique that can help to visualise tumour target motion due to breathing at the time of radiation treatment delivery. Measuring and characterising the target motion can help to ensure highly accurate therapeutic x-ray beam delivery. In this thesis, a novel pre-treatment x-ray imaging technique, called Respiratory Triggered 4D cone-beam Computed Tomography (RT 4D CBCT), is conceived and investigated. Specifically, the aim of this work is to progress the 4D CBCT imaging technology by investigating the use of a patient’s breathing signal to improve and optimise the use of imaging radiation in 4D CBCT to facilitate the accurate delivery of radiation therapy. These investigations are presented in three main studies: 1. Introduction to the concept of respiratory triggered four dimensional conebeam computed tomography. 2. A simulation study exploring the behaviour of RT 4D CBCT using patientmeasured respiratory data. 3. The experimental realisation of RT 4D CBCT working in a real-time acquisitions setting. The major finding from this work is that RT 4D CBCT can provide target motion information with a 50% reduction in the x-ray imaging dose applied to the patient

4D-CT Lung Registration and its Application for Lung Radiation Therapy

Radiation therapy has been successful in treating lung cancer patients, but its efficacy is limited by the inability to account for the respiratory motion during treatment planning and radiation dose delivery. Physics-based lung deformation models facilitate the motion computation of both tumor and local lung tissue during radiation therapy. In this dissertation, a novel method is discussed to accurately register 3D lungs across the respiratory phases from 4D-CT datasets, which facilitates the estimation of the volumetric lung deformation models. This method uses multi-level and multi-resolution optical flow registration coupled with thin plate splines (TPS), to address registration issue of inconsistent intensity across respiratory phases. It achieves higher accuracy as compared to multi-resolution optical flow registration and other commonly used registration methods. Results of validation show that the lung registration is computed with 3 mm Target Registration Error (TRE) and approximately 3 mm Inverse Consistency Error (ICE). This registration method is further implemented in GPU based real time dose delivery simulation to assist radiation therapy planning

Biomechanical Modeling for Lung Tumor Motion Prediction during Brachytherapy and Radiotherapy

A novel technique is proposed to develop a biomechanical model for estimating lung’s tumor position as a function of respiration cycle time. Continuous tumor motion is a major challenge in lung cancer treatment techniques where the tumor needs to be targeted; e.g. in external beam radiotherapy and brachytherapy. If not accounted for, this motion leads to areas of radiation over and/or under dosage for normal tissue and tumors. In this thesis, biomechanical models were developed for lung tumor motion predication in two distinct cases of lung brachytherapy and lung external beam radiotherapy. The lung and other relevant surrounding organs geometries, loading, boundary conditions and mechanical properties were considered and incorporated properly for each case. While using material model with constant incompressibility is sufficient to model the lung tissue in the brachytherapy case, in external beam radiation therapy the tissue incompressibility varies significantly due to normal breathing. One of the main issues tackled in this research is characterizing lung tissue incompressibility variations and measuring its corresponding parameters as a function of respiration cycle time. Results obtained from an ex-vivo porcine deflated lung indicated feasibility and reliability of using the developed biomechanical model to predict tumor motion during brachytherapy. For external beam radiotherapy, in-silico studies indicated very significant impact of considering the lung tissue incompressibility on the accuracy of predicting tumor motion. Furthermore, ex-vivo porcine lung experiments demonstrated the capability and reliability of the proposed approach for predicting tumor motion as a function of cyclic time. As such, the proposed models have a good potential to be incorporated effectively in computer assisted lung radiotherapy treatment systems

Image-guided Radiotherapy to Manage Respiratory Motion: Lung and Liver.

Organ motion as a result of respiratory and cardiac motion poses significant challenges for the accurate delivery of radiotherapy to both the thorax and the upper abdomen. Modern imaging techniques during radiotherapy simulation and delivery now permit better quantification of organ motion, which in turn reduces tumour and organ at risk position uncertainty. These imaging advances, coupled with respiratory correlated radiotherapy delivery techniques, have led to the development of a range of approaches to manage respiratory motion. This review summarises the key strategies of image-guided respiratory motion management with a focus on lung and liver radiotherapy