Fraction-variant beam orientation optimization for non-coplanar IMRT

Conventional beam orientation optimization (BOO) algorithms for IMRT assume that the same set of beam angles is used for all treatment fractions. In this paper we present a BOO formulation based on group sparsity that simultaneously optimizes non-coplanar beam angles for all fractions, yielding a fraction-variant (FV) treatment plan. Beam angles are selected by solving a multi-fraction FMO problem involving 500-700 candidate beams per fraction, with an additional group sparsity term that encourages most candidate beams to be inactive. The optimization problem is solved using the Fast Iterative Shrinkage-Thresholding Algorithm. Our FV BOO algorithm is used to create non-coplanar, five-fraction treatment plans for prostate and lung cases, as well as a non-coplanar 30-fraction plan for a head and neck case. A homogeneous PTV dose coverage is maintained in all fractions. The treatment plans are compared with fraction-invariant plans that use a fixed set of beam angles for all fractions. The FV plans reduced mean and max OAR dose on average by 3.3% and 3.7% of the prescription dose, respectively. Notably, mean OAR dose was reduced by 14.3% of prescription dose (rectum), 11.6% (penile bulb), 10.7% (seminal vesicle), 5.5% (right femur), 3.5% (bladder), 4.0% (normal left lung), 15.5% (cochleas), and 5.2% (chiasm). Max OAR dose was reduced by 14.9% of prescription dose (right femur), 8.2% (penile bulb), 12.7% (prox. bronchus), 4.1% (normal left lung), 15.2% (cochleas), 10.1% (orbits), 9.1% (chiasm), 8.7% (brainstem), and 7.1% (parotids). Meanwhile, PTV homogeneity defined as D95/D5 improved from .95 to .98 (prostate case) and from .94 to .97 (lung case), and remained constant for the head and neck case. Moreover, the FV plans are dosimetrically similar to conventional plans that use twice as many beams per fraction. Thus, FV BOO offers the potential to reduce delivery time for non-coplanar IMRT

A DVH‐guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135098/1/mp5700.pd

Spatiotemporal Fractionation in Radiotherapy

In current clinical practice, radiotherapy treatments are often fractionated, i.e. the total radiation dose is equally divided into small fractions to be delivered daily over a period of few days or weeks. It has recently been shown in silico that spatiotemporal fractionation schemes, i.e. delivering distinct dose distributions in different fractions, can potentially improve the treatment. This is possible if these dose distributions are designed such that different fractions deliver similar doses to normal tissues (i.e. exploit the fractionation effect), but each fraction delivers high single-fraction doses to alternating parts of the tumor (i.e. achieve partial hypofractionation in the tumor). Thereby, the ratio of biological dose in the tumor versus the normal tissue can be improved. In this project, we further developed this innovative and novel concept. In particular, we focused on: 1. Developing new treatment planning algorithms for spatiotemporal fractionation 2. Identifying potential clinical applications of spatiotemporal fractionation with the aim of bringing spatiotemporal fractionation towards the design and implementation of a phase I clinical trial. Spatiotemporal fractionation is associated with higher complexity in treatment planning and delivery. Different plans with distinct dose distributions for different fractions must be designed such that all fractions together deliver the prescribed biological dose to the tumor. To that end, novel mathematical optimization methods for treatment planning have been developed, which are based on the cumulative biological dose rather than the physical dose. In particular, we developed robust treatment planning methods to account for geometric uncertainty in the patient setup and biological uncertainty in the fractionation sensitivity, which may lead to a degradation of the resulting treatment if not accounted for. It was shown that spatiotemporally fractionated treatments can be obtained which are robust against setup errors and uncertainty in the fractionation sensitivity. At the same time, these robust plans maintain most of their dosimetric benefit over uniformly fractionated plans. Besides liver cancer patients and patients with large arteriovenous malformations, patients with multiple brain metastases were identified to be especially well suited for spatiotemporal fractionation, because of the high accuracy in patient positioning. For theses patients, delivering high doses to different metastases in different fractions allows for fractionation of the normal brain dose in between the metastases while increasing the biological dose within the metastases. In addition, novel extensions of spatiotemporal fractionation were investigated. Spatiotemporal fractionation has been combined with other degrees of freedom that can be exploited in fractionated radiotherapy treatments, i.e. the combination of different particle types and treatment techniques, and the use of different beam orientations in different fractions. We showed that in the context of combined proton-photon therapy, spatiotemporal fractionation can be used to determine the optimal dose contribution of the proton and photon fractions to the tumor, thereby improving on simple proportional combination of intensity modulated radiotherapy and intensity modulated proton therapy plans. Also, we demonstrated that the quality of spatiotemporally fractionated treatments can be boosted by selecting fraction-specific beam orientations that are beneficial to treat specific regions of the tumor. To that end, a treatment planning algorithm was developed that allows for simultaneous optimization of multiple non-coplanar arc treatments. Finally, the simultaneous optimization of multiple dose distributions based on the cumulative biological dose is not supported by any commercial treatment planning system. To this end, we implemented a method which allows to import treatment plans optimized using our in-house research treatment planning system into a commercial treatment planning system. Thereby, it is possible to deliver spatiotemporally fractionated treatments in the clinics

Dose-volume-based IMRT fluence optimization: A fast least-squares approach with differentiability

AbstractIn intensity-modulated radiation therapy (IMRT) for cancer treatment, the most commonly used metric for treatment prescriptions and evaluations is the so-called dose-volume constraint (DVC). These DVCs induce much needed flexibility but also non-convexity into the fluence optimization problem, which is an important step in the IMRT treatment planning. Currently, the models of choice for fluence optimization in clinical practice are weighted least-squares models. When DVCs are directly incorporated into the objective functions of least-squares models, these objective functions become not only non-convex but also non-differentiable. This non-differentiability is a problem when software packages designed for minimizing smooth functions are routinely applied to these non-smooth models in commercial IMRT planning systems. In this paper, we formulate and study a new least-squares model that allows a monotone and differentiable objective function. We devise a greedy approach for approximately solving the resulting optimization problem. We report numerical results on several clinical cases showing that, compared to a widely used existing model, the new approach is capable of generating clinically relevant plans at a much faster speed. This improvement can be more than one-order of magnitude for some large-scale problems