X-ray or gamma ray systems or devices – Specific application – Absorption
Reexamination Certificate
2001-05-07
2004-09-14
Bruce, David V (Department: 2882)
X-ray or gamma ray systems or devices
Specific application
Absorption
C378S064000
Reexamination Certificate
active
06792073
ABSTRACT:
FIELD OF THE INVENTION
This invention relates generally to accelerating Monte Carlo calculations, and more particularly to determining radiation dose distributions in a patient for radiotherapy treatment planning by accelerating Monte Carlo dose distribution calculations through denoising of raw Monte Carlo dose distributions.
BACKGROUND OF THE INVENTION
Medical equipment for radiation therapy treats tumorous (malignant) tissue with high energy radiation. The amount of radiation and its placement must be accurately controlled to ensure that the tumor receives sufficient radiation to be destroyed and that damage to the surrounding and adjacent non-tumorous tissue is minimized.
Internal source radiation therapy (referred to as brachytherapy) places capsules of radioactive material inside the patient in proximity to the tumorous tissue. Dose and placement are accurately controlled by the physical positioning of the isotope. However, internal source radiation therapy has disadvantages similar to those present with surgically invasive procedures, which include patient discomfort and risk of infection.
External source radiation therapy uses high energy radiation, collimated to direct a beam into the patient to the tumor site. Although the size and strength of the radiation beam from the external source may be accurately controlled outside of the patient, the dose received by a given volume within the patient may vary because of radiation scattering and absorption by intervening tissue. For these reasons, a determination of the proper dose and placement of the dose requires an estimation of the effects of treated tissue and the tissue surrounding the treated area in scattering or attenuation of the radiation beam.
The Monte Carlo method is the most accurate method for predicting dose distributions. The path of the particles (electrons, photons, protons, or neutrons) through the patient are simulated by Monte Carlo software. Particle interactions (scattering, attenuation, and energy deposition) are simulated one at a time. The Monte Carlo method traces paths of several million particles through a patient model, the patient model accurately reflecting the three dimensional variations of electron density within the volume of the patient under study. For large numbers of source radiation particles (typically above 10
7
), the Monte Carlo method produces an accurate representation of the dose distribution. For these reasons, the Monte Carlo method is clinically preferred for the calculation of radiation dose in electron beam radiotherapy.
Unfortunately, the Monte Carlo method is extremely time consuming, taking upwards of an hour to compute a single dose distribution using current computers. One crucial and unique feature to Monte Carlo results is that the Monte Carlo method has no well-defined preset “finish” time. Instead, dose distributions can be produced at any time, even after simulating only one source particle. However, the resulting dose distributions are “noisy” (distorted), in inverse proportion to the number of source particles simulated. The Monte Carlo calculations are terminated when the noise level falls below a level deemed acceptable by the user.
Often, radiotherapy treatment planners wish to compare many dose distributions before selecting a final distribution for treatment. Hence, the need exists for a method of dose modeling which is as accurate as the Monte Carlo method but which has greater computational efficiency than the Monte Carlo method alone. This need especially exists as Monte Carlo programs are being developed for most commercial clinical systems.
More generally, the problem of noise is inherent in all Monte Carlo calculations that produce values on a grid. The problem of noise exists whether the grid elements are regularly spaced or irregularly spaced. For these reasons, there also exists a need to reduce noise and provide an accurate and computationally efficient estimate for Monte Carlo calculations in general. The present invention also satisfies this need, providing a method hereafter referred to as Monte Carlo denoising.
SUMMARY OF THE INVENTION
The present invention provides an estimate of an actual radiation dose distribution by reducing noise (denoising) from raw results of Monte Carlo generated dose distributions. The resulting denoised dose distribution more quickly and efficiently converges to the required level of accuracy for radiotherapy treatment planning than Monte Carlo calculations alone, and at a reduced computational cost. Optimal denoising reduces Monte Carlo run times by a factor of at least 3 to 6.
The present invention provides a method of accounting for details of the radiation source geometry and materials, and local changes in density of the patient at different points in the irradiated volume. This is done by denoising Monte Carlo dose distributions without significantly increasing the running time or introducing distortions to the underlying dose distributions.
The insight underlying the present invention is that a true (noiseless) radiation dose distribution is smoother (more spatially coherent) than a raw Monte Carlo result. In the present invention, it is the dose distribution itself that is accurately smoothed to reduce noise while not distorting the true underlying (signal) dose distribution.
It is our novel insight into the details of radiation transport physics which indicates that denoising is feasible and desirable (Deasy, June 2000, Denoising of electron beam Monte Carlo dose distributions using digital filtering techniques,
Physics in Medicine and Biology,
45:1765-1779), which publication is incorporated in its entirety herein by reference. In simple terms, diffusive radiation transport implies that the actual dose distribution is smooth, whereas the Monte Carlo result without denoising contains statistical fluctuations which are more rough than the expected underlying dose distribution. Therefore, appropriate denoising techniques can be used to reduce the higher frequency noise of Monte Carlo results while not distorting the true underlying (signal) dose distribution.
In one aspect of the present invention, a method and an apparatus for accomplishing the method is provided where a raw Monte Carlo data distribution D
0
(D
0
everywhere refers to the raw Monte Carlo result) is first obtained and then denoised to produce a denoised distribution D
1
(D
1
everywhere refers to the denoised Monte Carlo result). This aspect of the present invention could be used to simply accelerate the computation of any Monte Carlo result or the present invention could be more specifically used to determine a dose distribution in a patient for radiation therapy treatment planning.
In another aspect of the present invention, denoising of the raw Monte Carlo dose distribution D
0
could employ digital filtering, wavelet denoising, kernel smoothing or non-parametric regression smoothing. The digital filtering techniques could employ Binomial/Gaussian filters or local-least squares filters.
In another aspect of the present invention, denoising the raw Monte Carlo dose distribution D
0
could first involve transforming the Monte Carlo dose distribution D
0
by computing the square root of each data element of the Monte Carlo dose distribution D
0
. Digital filtering techniques could then be applied to the transformed data elements to obtain a filtered result. Then, the elements of the filtered result are squared to obtain a final best estimate of the dose distribution. The digital filtering techniques could employ Binomial/Gaussian filters or local-least squares filters. If employed, the Binomial/Gaussian filters could use a higher degree polynomial for less aggressive denoising. If employed, the local-least squares filters could use a smaller neighborhood for less aggressive denoising.
In a further aspect of the present invention, denoising the raw Monte Carlo dose distribution D
0
employs either a spatially adaptive iterative filtering (SAIF) algorithm, a wavelet shrinkage threshold denoising algorithm, a spatially adaptive wavelet d
Deasy Joseph O.
Wickerhauser Mladen Victor
Bruce David V
Washington University
Woodcock & Washburn LLP
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