Shuang Luan

Arc-modulated radiation therapy (AMRT) : a single-arc form of intensity-modulated arc therapy

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.

Leaf-sequencing for intensity-modulated arc therapy using graph algorithms

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.

COMPARING RADIATION TREATMENTS USING INTENSITY-MODULATED BEAMS, MULTIPLE ARCS, AND SINGLE ARCS

PURPOSE A dosimetric comparison of multiple static-field intensity-modulated radiation therapy (IMRT), multiarc intensity-modulated arc therapy (IMAT), and single-arc arc-modulated radiation therapy (AMRT) was performed to evaluate their clinical advantages and shortcomings. METHODS AND MATERIALS Twelve cases were selected for this study, including three head-and-neck, three brain, three lung, and three prostate cases. An IMRT, IMAT, and AMRT plan was generated for each of the cases, with clinically relevant planning constraints. For a fair comparison, the same parameters were used for the IMRT, IMAT, and AMRT planning for each patient. RESULTS Multiarc IMAT provided the best plan quality, while single-arc AMRT achieved dose distributions comparable to those of IMRT, especially in the complicated head-and-neck 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, but with a delivery time similar to that of IMRT; whereas AMRT achieves results comparable to IMRT with significantly faster treatment delivery.

Optimal Terrain Construction Problems and Applications in Intensity-Modulated Radiation Therapy

AbstractIn this paper we study several rectilinear terrain construction problems, which model the leaf sequencing problems in intensity-modulated radiation therapy (IMRT). We present a novel unified approach based on geometric techniques for solving these terrain construction problems. Our ideas include formulating the terrain construction problems as computing shortest paths in a weighted directed graph and building the graph by computing optimal bipartite matchings on various geometric objects subject to specific constraints of each of the problems. Further, since we need to compute optimal bipartite matchings on many sets of geometric objects, we use techniques for computing such matchings in a batch fashion to speed up these matching computations. Our approach leads to the first algorithms for several leaf sequencing problems in IMRT that are practically fast and guarantee an output which is optimal for a large sub-class of solutions. The previously known leaf sequencing algorithms which are currently used in radiation therapy practice are all heuristics that do not guarantee any good quality of the output solutions and may run in a long time. Our implementation results show that our terrain construction algorithms run very fast on real medical data (all under few seconds).

GEOMETRIC ALGORITHMS FOR STATIC LEAF SEQUENCING PROBLEMS IN RADIATION THERAPY

The static leaf sequencing (SLS) problem arises in radiation therapy for cancer treatments, aiming to accomplish the delivery of a radiation prescription to a target tumor in the minimum amount of delivery time. Geometrically, the SLS problem can be formulated as a 3-D partition problem for which the 2-D problem of partitioning a polygonal domain (possibly with holes) into a minimum set of monotone polygons is a special case. In this paper, we present new geometric algorithms for a basic case of the 3-D SLS problem (which is also of clinical value) and for the general 3-D SLS problem. Our basic 3-D SLS algorithm, based on new geometric observations, produces guaranteed optimal quality solutions using O(1) Steiner points in polynomial time; the previously best known basic 3-D SLS algorithm gives optimal outputs only for the case without considering any Steiner points, and its time bound involves a multiplicative factor of a factorial function of the input. Our general 3-D SLS algorithm is based on our basic 3-D SLS algorithm and a polynomial time algorithm for partitioning a polygonal domain (possibly with holes) into a minimum set of x-monotone polygons, and has a fast running time. Experiments of our SLS algorithms and software in clinical settings have shown substantial improvements over the current most popular commercial treatment planning system and the most well-known SLS algorithm in medical literature. The radiotherapy plans produced by our software not only take significantly shorter delivery times, but also have a much better treatment quality. This proves the feasibility of our software and has led to its clinical applications at the Department of Radiation Oncology at the University of Maryland Medical Center. Some of our techniques and geometric procedures (e.g., for partitioning a polygonal domain into a minimum set of x-monotone polygons) are interesting in their own right.

Continuous intensity map optimization (CIMO): A novel approach to leaf sequencing in step and shoot IMRT

A new leaf-sequencing approach has been developed that is designed to reduce the number of required beam segments for step-and-shoot intensity modulated radiation therapy (IMRT). This approach to leaf sequencing is called continuous-intensity-map-optimization (CIMO). Using a simulated annealing algorithm, CIMO seeks to minimize differences between the optimized and sequenced intensity maps. Two distinguishing features of the CIMO algorithm are (1) CIMO does not require that each optimized intensity map be clustered into discrete levels and (2) CIMO is not rule-based but rather simultaneously optimizes both the aperture shapes and weights. To test the CIMO algorithm, ten IMRT patient cases were selected (four head-and-neck, two pancreas, two prostate, one brain, and one pelvis). For each case, the optimized intensity maps were extracted from the Pinnacle3 treatment planning system. The CIMO algorithm was applied, and the optimized aperture shapes and weights were loaded back into Pinnacle. A final dose calculation was performed using Pinnacles convolution/superposition based dose calculation. On average, the CIMO algorithm provided a 54% reduction in the number of beam segments as compared with Pinnacles leaf sequencer. The plans sequenced using the CIMO algorithm also provided improved target dose uniformity and a reduced discrepancy between the optimized and sequenced intensity maps. For ten clinical intensity maps, comparisons were performed between the CIMO algorithm and the power-of-two reduction algorithm of Xia and Verhey [Med. Phys. 25(8), 1424-1434 (1998)]. When the constraints of a Varian Millennium multileaf collimator were applied, the CIMO algorithm resulted in a 26% reduction in the number of segments. For an Elekta multileaf collimator, the CIMO algorithm resulted in a 67% reduction in the number of segments. An average leaf sequencing time of less than one minute per beam was observed.

An Energy-Efficient Adaptive Sensing Framework for Gait Monitoring Using Smart Insole

Gait analysis is an important process to gauge human motion. Recently, longitudinal gait analysis received much attention from the medical and healthcare domains. The challenge in studies over extended time periods is the battery life. Due to the continuous sensing and computing, wearable gait devices cannot fulfill a full-day work schedule. In this paper, we present an energy-efficient adaptive sensing framework to address this problem. Through presampling for content understanding, a selective sensing and sparsity-based signal reconstruction method is proposed. In particular, we develop and implement the new sensing scheme in a smart insole system to reduce the number of samples, while still preserving the information integrity of gait parameters. Experimental results show the effectiveness of our method in data point reduction. Our proposed method improves the battery life to 10.47 h, while normalized mean square error is within 10%.

Coupled Path Planning, Region Optimization, and Applications in Intensity-Modulated Radiation Therapy

The couple path planning (CPP) problem seeks the motion paths of the leaves of a multileaf collimator, to optimally reproduce the prescribed dose in intensity-modulated radiation therapy (IMRT). We study two versions of the CPP problem, constrained and unconstrained CPP, based on whether the starting and ending locations of the sought paths are prespecified. The unconstrained CPP problem models the leaf sequencing problem in dynamic IMRT delivery, while the set of all constrained CPP problem instances, in which all combinations of the starting and ending locations are considered, plays a key role in an emerging IMRT technique called arc-modulated radiation therapy. We give efficient algorithms for both the constrained and unconstrained CPP problems, and for computing the set of all constrained CPP problem instances. Our results are based on several new ideas and geometric observations, and substantially improve the solutions based on standard techniques. Implementation results show that our CPP algorithms run fast and produce better IMRT treatment plans than current methods.

Approximation algorithms for minimizing segments in radiation therapy

Intensity modulated radiation therapy (IMRT) is one of the most effective modalities for modern cancer treatment. The key to successful IMRT treatment hinges on the delivery of a two-dimensional discrete radiation intensity matrix using a device called a multileaf collimator (MLC). Mathematically, the delivery of an intensity matrix using an MLC can be viewed as the problem of representing a non-negative integral matrix (i.e., the intensity matrix) by a linear combination of certain special non-negative integral matrices called segments, where each such segment corresponds to one of the allowed states of the MLC. The problem of representing the intensity matrix with the minimum number of segments is known to be NP-complete. In this paper, we present two approximation algorithms for this matrix representation problem. To the best of our knowledge, these are the first algorithms to achieve non-trivial performance guarantees for multi-row intensity matrices.

New Developments in Intensity Modulated Radiation Therapy

As intensity modulated radiation therapy (IMRT) becomes routine clinical practice, its advantages and limitations are better understood. With these new understandings, some new developments have emerged in an effort to alleviate the limitations of the current IMRT practice. This article describes a few of these efforts made at the University of Maryland, including: i) improving IMRT efficiency with direct aperture optimization; ii) broadening the scope of optimization to include the mode of delivery and beam angles; and iii) new planning methods for intensity modulated arc therapy (IMAT).