297 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
Online adaptive planning methods for intensity-modulated radiotherapy
Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current anatomy to account for inter-fraction variations before daily treatment delivery. As this process needs to be accomplished while the patient is immobilized on the treatment couch, it requires time-efficient adaptive planning methods to generate a quality daily treatment plan rapidly. The conventional planning methods do not meet the time requirement of online adaptive radiation therapy because they often involve excessive human intervention, significantly prolonging the planning phase. This article reviews the planning strategies employed by current commercial online adaptive radiation therapy systems, research on online adaptive planning, and artificial intelligence's potential application to online adaptive planning.<br/
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
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
Labels direct infants’ attention to commonalities during novel category learning
Recent studies have provided evidence that labeling can influence the outcome of infants’ visual categorization. However, what exactly happens during learning remains unclear. Using eye-tracking, we examined infants’ attention to object parts during learning. Our analysis of looking behaviors during learning provide insights going beyond merely observing the learning outcome. Both labeling and non-labeling phrases facilitated category formation in 12-month-olds but not 8-month-olds (Experiment 1). Non-linguistic sounds did not produce this effect (Experiment 2). Detailed analyses of infants’ looking patterns during learning revealed that only infants who heard labels exhibited a rapid focus on the object part successive exemplars had in common. Although other linguistic stimuli may also be beneficial for learning, it is therefore concluded that labels have a unique impact on categorization
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