Purpose We describe a treatment plan optimization method for intensity-modulated proton therapy (IMPT) that avoids high values of linear energy transfer (LET) in critical structures located within or near the target volume, while limiting degradation of the best possible physical dose distribution. Methods To allow fast optimization based on dose and LET, a GPU-based Monte-Carlo code was extended to provide dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of LET and physical dose (LETxD). To first approximation, LETxD represents a measure of the additional biological dose that is caused by high LET. Results The method is effective for treatments where serial critical structures with maximum dose constraints are located within or near the target. We report on 5 patients with intra-cranial tumors (high-grade meningiomas, base-of-skull chordomas, ependymomas) where the target volume overlaps with the brainstem and optic structures. In all cases, high LETxD in critical structures could be avoided while minimally compromising physical dose planning objectives. Conclusion LET-based reoptimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based and RBE-based planning. The method makes IMPT treatments safer by mitigating a potentially increased risk of side effects due to elevated relative biological effectiveness (RBE) of proton beams near the end of range.
Methods to overcome metal artifacts in computed tomography (CT) images have been researched and developed for nearly 40 years. When X-rays pass through a metal object, depending on its size and density, different physical effects will negatively affect the measurements, most notably beam hardening, scatter, noise, and the non-linear partial volume effect. These phenomena severely degrade image quality and hinder the diagnostic power and treatment outcomes in many clinical applications. In this paper, we first review the fundamental causes of metal artifacts, categorize metal object types, and present recent trends in the CT metal artifact reduction (MAR) literature. To improve image quality and recover information about underlying structures, many methods and correction algorithms have been proposed and tested. We comprehensively review and categorize these methods into six different classes of MAR: metal implant optimization, improvements to the data acquisition process, data correction based on physics models, modifications to the reconstruction algorithm (projection completion and iterative reconstruction), and image-based post-processing. The primary goals of this paper are to identify the strengths and limitations of individual MAR methods and overall classes, and establish a relationship between types of metal objects and the classes that most effectively overcome their artifacts. The main challenges for the field of MAR continue to be cases with large, dense metal implants, as well as cases with multiple metal objects in the field of view. Severe photon starvation is difficult to compensate for with only software corrections. Hence, the future of MAR seems to be headed toward a combined approach of improving the acquisition process with dual-energy CT, higher energy X-rays, or photon-counting detectors, along with advanced reconstruction approaches. Additional outlooks are addressed, including the need for a standardized evaluation system to compare MAR methods.
Purpose To investigate the feasibility and potential clinical benefit of linear energy transfer (LET) guided plan optimization in intensity-modulated proton therapy (IMPT). Methods and Materials A multi-criteria optimization (MCO) module was utilized to generate series of Pareto-optimal IMPT base plans (BPs), corresponding to defined objectives, for 5 headand- neck and 2 pancreatic cancer cases. A Monte Carlo platform was used to calculate dose and LET distributions for each BP. A custom-designed MCO navigation module allowed the user to interpolate between BPs to produce deliverable Pareto-optimal solutions. Differences among the BPs, were evaluated for each patient, based on dose- and LET-volume histograms and 3D distributions. An LET-based RBE (relative biological effectiveness) model was employed to evaluate the potential clinical benefit when navigating the space of Pareto-optimal BPs. Results Mean LET values for the target varied up to 30% among the BPs for the head-and-neck cases, and up to 14% for the pancreatic cancer cases. Variations were more prominent in organs-atrisk (OARs), where mean LET values differed by up to a factor of 2 among the BPs for the same patient. An inverse relation between dose and LET distributions for the OARs was typically observed. Accounting for LET-dependent variable RBE values, a potential improvement on RBE weighted dose of up to 40%, averaged over several structures under study, was noticed during MCO navigation. Conclusions We present a novel strategy for optimizing proton therapy to maximize doseaveraged LET in tumor targets while simultaneously minimizing dose-averaged LET in normal tissue structures. MCO BPs show substantial LET variations, leading to potentially significant differences in RBE-weighted doses. Pareto-surface navigation, utilizing both dose and LET distributions for guidance, provides the means for evaluating a large variety of deliverable plans, and aids in identifying the clinically optimum solution.
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