No abstract

Arc-modulated radiation therapy (AMRT) is a novel rotational intensity-modulated radiation therapy (IMRT) technique developed for a clinical linear accelerator that aims to deliver highly conformal radiation treatment using just one arc of gantry rotation. Compared to fixed-gantry IMRT and the multiple-arc intensity-modulated arc therapy (IMAT) techniques, AMRT promises the same treatment quality with a single-arc delivery. In this paper, we present a treatment planning scheme for AMRT, which addresses the challenges in inverse planning, leaf sequencing and dose calculation. The feasibility and performance of this AMRT treatment planning scheme have been verified with multiple clinical cases of various sites on Varian linear accelerators.

Purpose-A dosimetric comparison between multiple static-field intensity-modulated radiation therapy (IMRT), multi-arc intensity-modulated arc therapy (IMAT) and single-arc arc-modulated radiation therapy (AMRT) is performed to evaluate their clinical advantages and shortcomings.Methods and Materials-Twelve cases were selected for this study, including 3 head-and-neck (HN), 3 brain (CNS), 3 lung, and 3 prostate cases. An IMRT, IMAT, and AMRT plan was generated for each of the patient cases with clinically-relevant planning constraints. For fair comparison, the same parameters were used for the IMRT, IMAT, and AMRT planning for each patient.Results-Multi-arc IMAT provided the best plan quality while single-arc AMRT achieved comparable dose distributions to IMRT, especially in the complicated HN and brain cases. Both AMRT and IMAT showed effective normal tissue sparing without compromising target coverage and delivered a lower total dose to the surrounding normal tissues in some cases.Conclusions-IMAT provides the most uniform and conformal dose distributions especially for the cases with large and complex targets with a similar delivery time to IMRT. AMRT achieves results comparable to IMRT with significantly faster treatment delivery.

Intensity-modulated arc therapy (IMAT) is a rotational IMRT technique. It uses a set of overlapping or nonoverlapping arcs to create a prescribed dose distribution. Despite its numerous advantages, IMAT has not gained widespread clinical applications. This is mainly due to the lack of an effective IMAT leaf-sequencing algorithm that can convert the optimized intensity patterns for all beam directions into IMAT treatment arcs. To address this problem, we have developed an IMAT leaf-sequencing algorithm and software using graph algorithms in computer science. The input to our leaf-sequencing software includes (1) a set of (continuous) intensity patterns optimized by a treatment planning system at a sequence of equally spaced beam angles (typically 10 degrees apart), (2) a maximum leaf motion constraint, and (3) the number of desired arcs, k. The output is a set of treatment arcs that best approximates the set of optimized intensity patterns at all beam angles with guaranteed smooth delivery without violating the maximum leaf motion constraint. The new algorithm consists of the following key steps. First, the optimized intensity patterns are segmented into intensity profiles that are aligned with individual MLC leaf pairs. Then each intensity profile is segmented into k MLC leaf openings using a k-link shortest path algorithm. The leaf openings for all beam angles are subsequently connected together to form 1D IMAT arcs under the maximum leaf motion constraint using a shortest path algorithm. Finally, the 1D IMAT arcs are combined to form IMAT treatment arcs of MLC apertures. The performance of the implemented leaf-sequencing software has been tested for four treatment sites (prostate, breast, head and neck, and lung). In all cases, our leaf-sequencing algorithm produces efficient and highly conformal IMAT plans that rival their counterpart, the tomotherapy plans, and significantly improve the IMRT plans. Algorithm execution times ranging from a few seconds to 2 min are observed on a laptop computer equipped with a 2.0 GHz Pentium M processor.

The 3-D static leaf sequencing (SLS) problem arises in radiation therapy for cancer treatments, aiming to deliver a prescribed radiation dose to a target tumor accurately and efficiently. The treatment time and machine delivery error are two crucial factors to the solution (i.e., a treatment plan) for the SLS problem. In this paper, we prove that the 3-D SLS problem is NP-hard, and present the first ever algorithm for the 3-D SLS problem that can determine a tradeoff between the treatment time and machine delivery error (also called the "tongue-and-groove" error in medical literature). Our new 3-D SLS algorithm with error control gives the users (e.g., physicians) the option of specifying a machine delivery error bound, and subject to the given error bound, the algorithm computes a treatment plan with the minimum treatment time. We formulate the SLS problem with error control as computing a k-weight shortest path in a directed graph and build the graph by computing g-matchings and minimum cost flows. Further, we extend our 3-D SLS algorithm to all the popular radiotherapy machine models with different constraints. In our extensions, we model the SLS problems for some of the radiotherapy systems as computing a minimum g-path cover of a directed acyclic graph. We implemented our new 3-D SLS algorithm suite and conducted an extensive comparison study with commercial planning systems and well-known algorithms in medical literature. Some of our experimental results based on real medical data are presented.