A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy

Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

Quality assurance of positron emission tomography/computed tomography for radiation therapy

Recent advances in radiation delivery techniques, such as intensity-modulated radiation therapy, provide unprecedented ability to exquisitely control three-dimensional dose distribution. Development of on-board imaging and other image-guidance methods significantly improved our ability to better target a radiation beam to the tumor volume. However, in reality, accurate definition of the location and boundary of the tumor target is still problematic. Biologic and physiologic imaging promises to solve the problem in a fundamental way and has a more and more important role in patient staging, treatment planning, and therapeutic assessment in radiation therapy clinics. The last decade witnessed a dramatic increase in the use of positron emission tomography and computed tomography in radiotherapy practice. To ensure safe and effective use of nuclide imaging, a rigorous quality assurance (QA) protocol of the imaging tools and integration of the imaging data must be in place. The application of nuclide imaging in radiation oncology occurs at different levels of sophistication. Quantitative use of the imaging data in treatment planning through image registration and standardized uptake value calculation is often involved. Thus, QA should not be limited to the performance of the scanner, but should also include the process of implementing image data in treatment planning, such as data transfer, image registration, and quantitation of data for delineation of tumors and sensitive structures. This presentation discusses various aspects of nuclide imaging as applied to radiotherapy and describes the QA procedures necessary for the success of biologic image-guided radiation therapy.

Computational Challenges for Image-Guided Radiation Therapy: Framework and Current Research

It is arguable that the imaging and delivery hardware necessary for delivering real-time adaptive image-guided radiotherapy is available on high-end linear accelerators. Robust and computationally efficient software is the limiting factor in achieving highly accurate and precise radiotherapy to the constantly changing anatomy of a cancer patient. The limitations are not caused by the availability of algorithms but rather issues of reliability, integration, and calculation time. However, each of the software components is an active area of research and development at academic and commercial centers. This article describes the software solutions in 4 broad areas: deformable image registration, adaptive replanning, real-time image guidance, and dose calculation and accumulation. Given the pace of technological advancement, the integration of these software solutions to develop real-time adaptive image-guided radiotherapy and the associated challenges they bring will be implemented to varying degrees by all major manufacturers over the coming years.

Simulation study of respiratory-induced errors in cardiac positron emission tomography/computed tomography

Heart disease is a leading killer in Canada and positron emission tomography (PET) provides clinicians with in vivo metabolic information for diagnosing heart disease. Transmission data are usually acquired with 68Ge, although the advent of PET/CT scanners has made computed tomography (CT) an alternative option. The fast data acquisition of CT compared to PET may cause potential misregistration problems, leading to inaccurate attenuation correction (AC). Using Monte Carlo simulations and an anthropomorphic dynamic computer phantom, this study determines the magnitude and location of respiratory-induced errors in radioactivity uptake measured in cardiac PET/CT. A homogeneous tracer distribution in the heart was considered. The AC was based on (1) a time-averaged attenuation map, (2) CT maps from a single phase of the respiratory cycle, and (3) CT maps phase matched to the emission data. Circumferential profiles of the heart uptake were compared and differences of up to 24% were found between the single-phase CT-AC method and the true phantom values. Simulation results were supported by a PET/CT canine study which showed differences of up to 10% in the heart uptake in the lung-heart boundary region when comparing 68Ge- to CT-based AC with the CT map acquired at end inhalation.

Model-based image reconstruction for four-dimensional PET

Positron emission tonography (PET) is useful in diagnosis and radiation treatment planning for a variety of cancers. For patients with cancers in thoracic or upper abdominal region, the respiratory motion produces large distortions in the tumor shape and size, affecting the accuracy in both diagnosis and treatment. Four-dimensional (4D) (gated) PET aims to reduce the motion artifacts and to provide accurate measurement of the tumor volume and the tracer concentration. A major issue in 4D PET is the lack of statistics. Since the collected photons are divided into several frames in the 4D PET scan, the quality of each reconstructed frame degrades as the number of frames increases. The increased noise in each frame heavily degrades the quantitative accuracy of the PET imaging. In this work, we propose a method to enhance the performance of 4D PET by developing a new technique of 4D PET reconstruction with incorporation of an organ motion model derived from 4D-CT images. The method is based on the well-known maximum-likelihood expectation-maximization (ML-EM) algorithm. During the processes of forward- and backward-projection in the ML-EM iterations, all projection data acquired at different phases are combined together to update the emission map with the aid of deformable model, the statistics is therefore greatly improved. The proposed algorithm was first evaluated with computer simulations using a mathematical dynamic phantom. Experiment with a moving physical phantom was then carried out to demonstrate the accuracy of the proposed method and the increase of signal-to-noise ratio over three-dimensional PET. Finally, the 4D PET reconstruction was applied to a patient case.