Biomedical subjects

Matthew A Earl

Publications and source records attributed to Matthew A Earl.

5 recordsLinked to original sources

PubMed2006

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 Pinnacle's convolution/superposition based dose calculation. On average, the CIMO algorithm provided a 54% reduction in the number of beam segments as compared with Pinnacle's 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.

Algorithms↗

PubMed2005

Using a photon phase-space source for convolution/superposition dose calculations in radiation therapy.

For a given linac design, the dosimetric characteristics of a photon beam are determined uniquely by the energy and radial distributions of the electron beam striking the x-ray target. However, in the usual commissioning of a beam from measured data, a large number of variables can be independently tuned, making it difficult to derive a unique and self-consistent beam model. For example, the measured dosimetric penumbra in water may be attributed in various proportions to the lateral secondary electron range, the focal spot size and the transmission through the tips of a non-divergent collimator; the head-scatter component in the tails of the transverse profiles may not be easy to resolve from phantom scatter and head leakage; and the head-scatter tails corresponding to a certain extra-focal source model may not agree self-consistently with in-air output factors measured on the central axis. To reduce the number of adjustable variables in beam modelling, we replace the focal and extra-focal sources with a single phase-space plane scored just above the highest adjustable collimator in a EGS/BEAM simulation of the linac. The phase-space plane is then used as photon source in a stochastic convolution/superposition dose engine. A photon sampled from the uncollimated phase-space plane is first propagated through an arbitrary collimator arrangement and then interacted in the simulation phantom. Energy deposition kernel rays are then randomly issued from the interaction points and dose is deposited along these rays. The electrons in the phase-space file are used to account for electron contamination. 6 MV and 18 MV photon beams from an Elekta SL linac are used as representative examples. Except for small corrections for monitor backscatter and collimator forward scatter for large field sizes (<0.5% with <20 x 20 cm2 field size), we found that the use of a single phase-space photon source provides accurate and self-consistent results for both relative and absolute dose calculations.

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PubMed2004

Simultaneous integrated boost for breast cancer using IMRT: a radiobiological and treatment planning study.

PURPOSE: The purpose of this work is to explore the possibility of using intensity-modulated radiation therapy (IMRT) to deliver the boost dose to the tumor bed simultaneously with the whole-breast IMRT to reduce the radiation treatment time by 1-2 weeks. METHODS AND MATERIALS: The biologically effective dose (BED) for different treatments was calculated using the linear-quadratic (LQ) model with parameters previously derived for breast cancer from clinical data (alpha/beta = 10Gy, alpha = 0.3Gy(-1)). A potential doubling time of 15 days (from in vitro measurements) for breast cancer and a generic alpha/beta ratio of 3 Gy for normal tissues were used. A series of regimens that use IMRT as initial treatment and an IMRT simultaneous integrated boost (SIB) were derived using biologic equivalence to conventional schedules. Possible treatment plans with IMRT SIB to the tumor bed were generated for 2 selected breast patients, 1 with a shallow tumor and 1 with a deep-seated tumor. Plans with a simultaneous integrated electron boost were also generated for comparison. Dosimetric merits of these plans were evaluated based on dose volume histograms. RESULTS: A commonly used conventional treatment of 45 Gy (1.8 Gy x 25) to the whole breast and then a boost of 20 Gy (2 Gy x 10) is biologically equivalent to an alternative plan of 1.8 Gy x 25 to the whole breast with a 2.4 Gy x 25 SIB to the tumor bed. The new regime reduces treatment time from 7 to 5 weeks. For the patient with a deep-seated tumor, the IMRT plans reduce the volume of the breast that receives high doses (compared with the conventional photon boost plan) and provides good coverage of the target volumes. CONCLUSION: It is biologically and dosimetrically feasible to reduce the overall treatment time for breast radiotherapy by using an IMRT simultaneous integrated boost. For selected patient groups, IMRT plans with a new regimen can be equal to or better than conventional plans.

Breast Neoplasms↗

PubMed2003

Convolution/superposition using the Monte Carlo method.

The convolution/superposition calculations for radiotherapy dose distributions are traditionally performed by convolving polyenergetic energy deposition kernels with TERMA (total energy released per unit mass) precomputed in each voxel of the irradiated phantom. We propose an alternative method in which the TERMA calculation is replaced by random sampling of photon energy, direction and interaction point. Then, a direction is randomly sampled from the angular distribution of the monoenergetic kernel corresponding to the photon energy. The kernel ray is propagated across the phantom, and energy is deposited in each voxel traversed. An important advantage of the explicit sampling of energy is that spectral changes with depth are automatically accounted for. No spectral or kernel hardening corrections are needed. Furthermore, the continuous sampling of photon direction allows us to model sharp changes in fluence, such as those due to collimator tongue-and-groove. The use of explicit photon direction also facilitates modelling of situations where a given voxel is traversed by photons from many directions. Extra-focal radiation, for instance, can therefore be modelled accurately. Our method also allows efficient calculation of a multi-segment/multi-beam IMRT plan by sampling of beam angles and field segments according to their relative weights. For instance, an IMRT plan consisting of seven 14 x 12 cm2 beams with a total of 300 field segments can be computed in 15 min on a single CPU, with 2% statistical fluctuations at the isocentre of the patient's CT phantom divided into 4 x 4 x 4 mm3 voxels. The calculation contains all aperture-specific effects, such as tongue and groove, leaf curvature and head scatter. This contrasts with deterministic methods in which each segment is given equal importance, and the time taken scales with the number of segments. Thus, the Monte Carlo superposition provides a simple, accurate and efficient method for complex radiotherapy dose calculations.

Algorithms↗

PubMed2003

A dose delivery verification method for conventional and intensity-modulated radiation therapy using measured field fluence distributions.

Treatment verification has been a weak link in external beam radiation therapy. As new and more complicated treatment techniques, such as intensity-modulated radiation therapy (IMRT), are implemented into clinical practice, verifying the accuracy of treatment delivery becomes increasingly important. Existing methods for treatment verification are highly labor intensive. We have developed a method for verifying the delivery of external beam radiotherapy and implemented the methodology into a system consisting of both hardware and software components. The system uses grayscale images acquired on the treatment machine from the planned treatment beams. From these images, the photon fluence distribution of each beam is derived. These measured photon fluence maps are then used as input to a separate dose calculation engine to compute the delivered absolute dose and the dose distribution in the same patient, assuming that the patient is set up as required by the treatment plan. The dose distribution generated from the measured fluence maps can then be compared to that of the treatment plan. Software tools, such as overlaying isodose curves generated with this method on those imported from the plan, dose difference maps, dose difference volume histograms, and three-dimensional perspective views of the dose differences, have also been developed. The system thus provides a means to verify the dose, the dose prescription, and the monitor units applied. The potential exists with a suitable electronic portal imaging system to reduce the quality assurance efforts, especially for IMRT.

Algorithms↗