348 research outputs found
The number of beams in IMRT - theoretical investigations and implications for single-arc IMRT
The first purpose of this paper is to shed some new light on the old question
of selecting the number of beams in intensity-modulated radiation therapy
(IMRT). The second purpose is to illuminate the related issue of discrete
static beam angles vs. rotational techniques, which has recently re-surfaced
due to the advancement of volumetric arc therapy (VMAT). A specific objective
is to find analytical expressions that allow one to address the points raised
above. To make the problem mathematically tractable, it is assumed that the
depth dose is flat and that the lateral dose profile can be approximated by
polynomials, specifically Chebyshev polynomials of the first kind, of finite
degree. The application of methods known from image reconstruction then allows
one to answer the first question above as follows: The required number of beams
is determined by the maximum degree of the polynomials used in the
approximation of the beam profiles, which is a measure of the dose variability.
There is nothing to be gained by using more beams. In realistic cases, in which
the variability of the lateral dose profile is restricted in several ways, the
required number of beams is of the order of 10 to 20. The consequence of
delivering the beams with a `leaf sweep' technique during continuous rotation
of the gantry, as in VMAT, is also derived in analytical form. The main effect
is that the beams fan out, but the effect near the axis of rotation is small.
This result can serve as a theoretical justification of VMAT. Overall the
analytical derivations in this paper, albeit based on strong simplifications,
provide new insights into, and a deeper understanding of, the beam angle
problem in IMRT
Beam Orientation Optimization for Intensity Modulated Radiation Therapy using Adaptive l1 Minimization
Beam orientation optimization (BOO) is a key component in the process of IMRT
treatment planning. It determines to what degree one can achieve a good
treatment plan quality in the subsequent plan optimization process. In this
paper, we have developed a BOO algorithm via adaptive l_1 minimization.
Specifically, we introduce a sparsity energy function term into our model which
contains weighting factors for each beam angle adaptively adjusted during the
optimization process. Such an energy term favors small number of beam angles.
By optimizing a total energy function containing a dosimetric term and the
sparsity term, we are able to identify the unimportant beam angles and
gradually remove them without largely sacrificing the dosimetric objective. In
one typical prostate case, the convergence property of our algorithm, as well
as the how the beam angles are selected during the optimization process, is
demonstrated. Fluence map optimization (FMO) is then performed based on the
optimized beam angles. The resulted plan quality is presented and found to be
better than that obtained from unoptimized (equiangular) beam orientations. We
have further systematically validated our algorithm in the contexts of 5-9
coplanar beams for 5 prostate cases and 1 head and neck case. For each case,
the final FMO objective function value is used to compare the optimized beam
orientations and the equiangular ones. It is found that, our BOO algorithm can
lead to beam configurations which attain lower FMO objective function values
than corresponding equiangular cases, indicating the effectiveness of our BOO
algorithm.Comment: 19 pages, 2 tables, and 5 figure
Optimal partial-arcs in VMAT treatment planning
Purpose: To improve the delivery efficiency of VMAT by extending the recently
published VMAT treatment planning algorithm vmerge to automatically generate
optimal partial-arc plans.
Methods and materials: A high-quality initial plan is created by solving a
convex multicriteria optimization problem using 180 equi-spaced beams. This
initial plan is used to form a set of dose constraints, and a set of
partial-arc plans is created by searching the space of all possible partial-arc
plans that satisfy these constraints. For each partial-arc, an iterative
fluence map merging and sequencing algorithm (vmerge) is used to improve the
delivery efficiency. Merging continues as long as the dose quality is
maintained above a user-defined threshold. The final plan is selected as the
partial arc with the lowest treatment time. The complete algorithm is called
pmerge.
Results: Partial-arc plans are created using pmerge for a lung, liver and
prostate case, with final treatment times of 127, 245 and 147 seconds.
Treatment times using full arcs with vmerge are 211, 357 and 178 seconds. Dose
quality is maintained across the initial, vmerge, and pmerge plans to within 5%
of the mean doses to the critical organs-at-risk and with target coverage above
98%. Additionally, we find that the angular distribution of fluence in the
initial plans is predictive of the start and end angles of the optimal
partial-arc.
Conclusions: The pmerge algorithm is an extension to vmerge that
automatically finds the partial-arc plan that minimizes the treatment time.
VMAT delivery efficiency can be improved by employing partial-arcs without
compromising dose quality. Partial arcs are most applicable to cases with
non-centralized targets, where the time savings is greatest
Range uncertainty reductions in proton therapy may lead to the feasibility of novel beam arrangements which improve organ-at-risk sparing
Purpose In proton therapy, dose distributions are currently often conformed to organs at risk (OARs) using the less sharp dose fall-off at the lateral beam edge to reduce the effects of uncertainties in the in vivo proton range. However, range uncertainty reductions may make greater use of the sharper dose fall-off at the distal beam edge feasible, potentially improving OAR sparing. We quantified the benefits of such novel beam arrangements. Methods For each of 10 brain or skull base cases, five treatment plans robust to 2 mm setup and 0%-4% range uncertainty were created for the traditional clinical beam arrangement and a novel beam arrangement making greater use of the distal beam edge to conform the dose distribution to the brainstem. Metrics including the brainstem normal tissue complication probability (NTCP) with the endpoint of necrosis were determined for all plans and all setup and range uncertainty scenarios. Results For the traditional beam arrangement, reducing the range uncertainty from the current level of approximately 4% to a potentially achievable level of 1% reduced the brainstem NTCP by up to 0.9 percentage points in the nominal and up to 1.5 percentage points in the worst-case scenario. Switching to the novel beam arrangement at 1% range uncertainty improved these values by a factor of 2, that is, to 1.8 percentage points and 3.2 percentage points, respectively. The novel beam arrangement achieved a lower brainstem NTCP in all cases starting at a range uncertainty of 2%. Conclusion The benefits of novel beam arrangements may be of the same magnitude or even exceed the direct benefits of range uncertainty reductions. Indirect effects may therefore contribute markedly to the benefits of reducing proton range uncertainties
Algorithm and performance of a clinical IMRT beam-angle optimization system
This paper describes the algorithm and examines the performance of an IMRT
beam-angle optimization (BAO) system. In this algorithm successive sets of beam
angles are selected from a set of predefined directions using a fast simulated
annealing (FSA) algorithm. An IMRT beam-profile optimization is performed on
each generated set of beams. The IMRT optimization is accelerated by using a
fast dose calculation method that utilizes a precomputed dose kernel. A compact
kernel is constructed for each of the predefined beams prior to starting the
FSA algorithm. The IMRT optimizations during the BAO are then performed using
these kernels in a fast dose calculation engine. This technique allows the IMRT
optimization to be performed more than two orders of magnitude faster than a
similar optimization that uses a convolution dose calculation engine.Comment: Final version that appeared in Phys. Med. Biol. 48 (2003) 3191-3212.
Original EPS figures have been converted to PNG files due to size limi
A Dynamic Programming Approach to Adaptive Fractionation
We conduct a theoretical study of various solution methods for the adaptive
fractionation problem. The two messages of this paper are: (i) dynamic
programming (DP) is a useful framework for adaptive radiation therapy,
particularly adaptive fractionation, because it allows us to assess how close
to optimal different methods are, and (ii) heuristic methods proposed in this
paper are near-optimal, and therefore, can be used to evaluate the best
possible benefit of using an adaptive fraction size.
The essence of adaptive fractionation is to increase the fraction size when
the tumor and organ-at-risk (OAR) are far apart (a "favorable" anatomy) and to
decrease the fraction size when they are close together. Given that a fixed
prescribed dose must be delivered to the tumor over the course of the
treatment, such an approach results in a lower cumulative dose to the OAR when
compared to that resulting from standard fractionation. We first establish a
benchmark by using the DP algorithm to solve the problem exactly. In this case,
we characterize the structure of an optimal policy, which provides guidance for
our choice of heuristics. We develop two intuitive, numerically near-optimal
heuristic policies, which could be used for more complex, high-dimensional
problems. Furthermore, one of the heuristics requires only a statistic of the
motion probability distribution, making it a reasonable method for use in a
realistic setting. Numerically, we find that the amount of decrease in dose to
the OAR can vary significantly (5 - 85%) depending on the amount of motion in
the anatomy, the number of fractions, and the range of fraction sizes allowed.
In general, the decrease in dose to the OAR is more pronounced when: (i) we
have a high probability of large tumor-OAR distances, (ii) we use many
fractions (as in a hyper-fractionated setting), and (iii) we allow large daily
fraction size deviations.Comment: 17 pages, 4 figures, 1 tabl
Optimal margin and edge-enhanced intensity maps in the presence of motion and uncertainty
In radiation therapy, intensity maps involving margins have long been used to counteract the effects of dose blurring arising from motion. More recently, intensity maps with increased intensity near the edge of the tumour (edge enhancements) have been studied to evaluate their ability to offset similar effects that affect tumour coverage. In this paper, we present a mathematical methodology to derive margin and edge-enhanced intensity maps that aim to provide tumour coverage while delivering minimum total dose. We show that if the tumour is at most about twice as large as the standard deviation of the blurring distribution, the optimal intensity map is a pure scaling increase of the static intensity map without any margins or edge enhancements. Otherwise, if the tumour size is roughly twice (or more) the standard deviation of motion, then margins and edge enhancements are preferred, and we present formulae to calculate the exact dimensions of these intensity maps. Furthermore, we extend our analysis to include scenarios where the parameters of the motion distribution are not known with certainty, but rather can take any value in some range. In these cases, we derive a similar threshold to determine the structure of an optimal margin intensity map.National Cancer Institute (U.S.) (grant R01-CA103904)National Cancer Institute (U.S.) (grant R01-CA118200)Natural Sciences and Engineering Research Council of Canada (NSERC)Siemens AktiengesellschaftMassachusetts Institute of Technology. Hugh Hampton Young Memorial Fund fellowshi
Reconstruction from Radon projections and orthogonal expansion on a ball
The relation between Radon transform and orthogonal expansions of a function
on the unit ball in \RR^d is exploited. A compact formula for the partial
sums of the expansion is given in terms of the Radon transform, which leads to
algorithms for image reconstruction from Radon data. The relation between
orthogonal expansion and the singular value decomposition of the Radon
transform is also exploited.Comment: 15 page
Disfluency in dialogue:an intentional signal from the speaker?
Disfluency is a characteristic feature of spontaneous human speech, commonly seen as a consequence of problems with production. However, the question remains open as to why speakers are disfluent: Is it a mechanical by-product of planning difficulty, or do speakers use disfluency in dialogue to manage listeners' expectations? To address this question, we present two experiments investigating the production of disfluency in monologue and dialogue situations. Dialogue affected the linguistic choices made by participants, who aligned on referring expressions by choosing less frequent names for ambiguous images where those names had previously been mentioned. However, participants were no more disfluent in dialogue than in monologue situations, and the distribution of types of disfluency used remained constant. Our evidence rules out at least a straightforward interpretation of the view that disfluencies are an intentional signal in dialogue. © 2012 Psychonomic Society, Inc
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