Cochlea-sparing acoustic neuroma treatment with 4π radiation therapy

Woods, Kaley; Lee, Percy; Kaprealian, Tania; Yang, Isaac; Sheng, Ke

doi:10.1016/j.adro.2018.01.004

Cited by 12 publications

(10 citation statements)

References 39 publications

(56 reference statements)

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“…, left) modeled from mouse CT data was used to demonstrate a realistic application for the SOC, as described in Part I. Mouse liver and kidney structures were transferred from a contrast‐enhanced mouse CT, and the liver was targeted with a dose of 10 Gy using five beam angles optimized with the 4π algorithm . Film was inserted between the two phantom halves for dose measurement.…”

Section: Methodsmentioning

confidence: 99%

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A sparse orthogonal collimator for small animal intensity‐modulated radiation therapy. Part II: hardware development and commissioning

et al. 2019

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Purpose A dose‐modulation device for small animal radiotherapy is required to use clinically analogous treatment techniques, which will likely increase the translatability of preclinical research results. Because the clinically used multileaf collimator (MLC) is impractical for miniaturization, we have developed a simpler, better‐suited sparse orthogonal collimator (SOC) for delivering small animal intensity‐modulated radiation therapy (IMRT) using a rectangular aperture optimization (RAO) treatment planning system. Methods The SOC system was modeled in computer‐aided design software and fabricated with machined tungsten leaves and three‐dimensional (3D) printed leaf housing. A graphical user interface was developed for controlling and calibrating the SOC leaves, which are driven by Arduino‐controlled stepper motors. A Winston‐Lutz test was performed to assess mechanical alignment, and abutting field and grid dose patterns were created to analyze intra‐ and intercalibration leaf positioning error. Leaf transmission and penumbra were measured over the full range of gantry angles and leaf positions, respectively. Three SOC test plans were delivered, and film measurements were compared to the intended dose distributions. The differences in maximum, mean, and minimum, as well as pixelwise absolute dose differences, were compared for each structure, and a gamma analysis was performed for the target structures using criteria of 4% dose difference and 0.3 mm distance to agreement. Results The Winston‐Lutz test revealed maximum directional offsets between the SOC and primary collimator axes of 0.53 mm at 0° and 0.68 mm over the full 360°. Upper and lower abutting field patterns had maximum dose deviations of 18.8 ± 3.1% and 15.5 ± 2.9%, respectively, and grid patterns showed intra‐ and intercalibration repeatability of 93% and 91%, respectively. Extremely low midleaf (0.15 ± 0.05%) and interleaf (0.27 ± 0.22%) transmission was measured, with no significant rotational variation. The average penumbra was ~0.8 mm for all leaves at field center, with a range of 0.17 mm for all leaf positions. A highly concave test plan was delivered with a ~ 95% gamma analysis pass rate, and a realistic mouse phantom liver irradiation plan achieved a pass rate of ~98%. A highly complex dose distribution was also created with 551 SOC apertures averaging 2.4 mm in size. Conclusions A sparse orthogonal collimator was developed and commissioned, with promising preliminary dosimetry results. The SOC design, with its limited moving components and high dose‐modulation resolution, is ideal for delivering high‐quality small animal IMRT with our RAO‐based treatment planning system.

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Section: Methodsmentioning

confidence: 99%

“…Mouse liver and kidney structures were transferred from a contrast-enhanced mouse CT, and the liver was targeted with a dose of 10 Gy using five beam angles optimized with the 4p algorithm. [48][49][50][51][52][53][54][55][56][57][58] Film was inserted between the two phantom halves for dose measurement.…”

Section: F Dosimetric Testingmentioning

confidence: 99%

A sparse orthogonal collimator for small animal intensity‐modulated radiation therapy. Part II: hardware development and commissioning

et al. 2019

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“…CG has been shown to be effective for BOO in radiation therapy problems. 15,21,61 While CG does not guarantee an optimal solution, the CG algorithm has been shown to have superior performance when compared to clinical plans, 10,15,21,30,50,52,53,[62][63][64][65][66][67][68][69][70] and the algorithm is summarized in the sections that follow. A list of all indices, parameters, variables, and functions used in this paper and their definitions are provided in Table I.…”

Section: C Full and Limited Boo Problemmentioning

confidence: 99%

A fast deep learning approach for beam orientation optimization for prostate cancer treated with intensity‐modulated radiation therapy

et al. 2020

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Purpose Beam orientation selection, whether manual or protocol‐based, is the current clinical standard in radiation therapy treatment planning, but it is tedious and can yield suboptimal results. Many algorithms have been designed to optimize beam orientation selection because of its impact on treatment plan quality, but these algorithms suffer from slow calculation of the dose influence matrices of all candidate beams. We propose a fast beam orientation selection method, based on deep learning neural networks (DNN), capable of developing a plan comparable to those developed by the state‐of‐the‐art column generation (CG) method. Our model's novelty lies in its supervised learning structure (using CG to teach the network), DNN architecture, and ability to learn from anatomical features to predict dosimetrically suitable beam orientations without using dosimetric information from the candidate beams. This may save hours of computation. Methods A supervised DNN is trained to mimic the CG algorithm, which iteratively chooses beam orientations one‐by‐one by calculating beam fitness values based on Karush‐Kush‐Tucker optimality conditions at each iteration. The DNN learns to predict these values. The dataset contains 70 prostate cancer patients — 50 training, 7 validation, and 13 test patients — to develop and test the model. Each patient’s data contains 6 contours: PTV, body, bladder, rectum, and left and right femoral heads. Column generation was implemented with a GPU‐based Chambolle‐Pock algorithm, a first‐order primal‐dual proximal‐class algorithm, to create 6270 plans. The DNN trained over 400 epochs, each with 2500 steps and a batch size of 1, using the Adam optimizer at a learning rate of 1 × 10−5 and a sixfold cross‐validation technique. Results The average and standard deviation of training, validation, and testing loss functions among the six folds were 0.62 ± 0.09%, 1.04 ± 0.06%, and 1.44 ± 0.11%, respectively. Using CG and supervised DNN, we generated two sets of plans for each scenario in the test set. The proposed method took at most 1.5 s to select a set of five beam orientations and 300 s to calculate the dose influence matrices for 5 beams and finally 20 s to solve the fluence map optimization (FMO). However, CG needed around 15 h to calculate the dose influence matrices of all beams and at least 400 s to solve both the beam orientation selection and FMO problems. The differences in the dose coverage of PTV between plans generated by CG and by DNN were 0.2%. The average dose differences received by organs at risk were between 1 and 6 percent: Bladder had the smallest average difference in dose received (0.956 ± 1.184%), then Rectum (2.44 ± 2.11%), Left Femoral Head (6.03 ± 5.86%), and Right Femoral Head (5.885 ± 5.515%). The dose received by Body had an average difference of 0.10 ± 0.1% between the generated treatment plans. Conclusions We developed a fast beam orientation selection method based on a DNN that selects beam orientations in seconds and is therefore suitable for clinical routines. In the ...

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“…The voxel resolution was 0.5 mm 3 for the Mouse U and UC plans and 0.1 mm 3 for the Mouse Liver and Audrey plans. The 4p beam orientation optimization method, which differs from the RAO method described and has been previously published extensively, [34][35][36][37][38][39][40][41][42][43][44] was then used to find optimal beam angles for each plan. This optimization is based on a greedy column generation approach which iteratively selects beams from a candidate pool.…”

Section: B Rao Evaluationmentioning

confidence: 99%

A sparse orthogonal collimator for small animal intensity‐modulated radiation therapy part I: Planning system development and commissioning

et al. 2019

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Purpose To achieve more translatable preclinical research results, small animal irradiation needs to more closely simulate human radiotherapy. Although the clinical gold standard is intensity‐modulated radiation therapy (IMRT), the direct translation of this method for small animals is impractical. In this study we describe the treatment planning system for a novel dose modulation device to address this challenge. Methods Using delineated target and avoidance structures, a rectangular aperture optimization (RAO) problem was formulated to penalize deviations from a desired dose distribution and limit the number of selected rectangular apertures. RAO was used to create IMRT plans with highly concave targets in the mouse brain, and the plan quality was compared to that using a hypothetical miniaturized multileaf collimator (MLC). RAO plans were also created for a realistic application of mouse whole liver irradiation and for a highly complex two‐dimensional (2D) dose distribution as a proof‐of‐principle. Beam commissioning data, including output and off‐axis factors and percent depth dose (PDD) curves, were acquired for our small animal irradiation system and incorporated into the treatment planning system. A plan post‐processing step was implemented for aperture size‐specific dose recalculation and aperture weighting reoptimization. Results The first RAO test case achieved highly conformal doses to concave targets in the brain, with substantially better dose gradient, conformity, and target dose homogeneity than the hypothetical miniaturized MLC plans. In the second test case, a highly conformal dose to the liver was achieved with significant sparing of the kidneys. RAO also successfully replicated a complex 2D dose distribution with three prescription dose levels. Energy spectra for field sizes 1 to 20 mm were calculated to match the measured PDD curves, with maximum and mean dose deviations of 4.47 ± 0.30% and 1.71 ± 0.18%. The final reoptimization of aperture weightings for the complex RAO test plan was able to reduce the maximum and mean dose deviations between the optimized and recalculated dose distributions from 10.3% to 6.6% and 4.0% to 2.8%, respectively. Conclusions Using the advanced optimization techniques, complex IMRT plans were achieved using a simple dose modulation device. Beam commissioning data were incorporated into the treatment planning process to more accurately predict the resulting dose distribution. This platform substantially reduces the gap in treatment plan quality between clinical and preclinical radiotherapy, potentially increasing the value and flexibility of small animal studies.

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Cochlea-sparing acoustic neuroma treatment with 4π radiation therapy

Cited by 12 publications

References 39 publications

A sparse orthogonal collimator for small animal intensity‐modulated radiation therapy. Part II: hardware development and commissioning

A sparse orthogonal collimator for small animal intensity‐modulated radiation therapy. Part II: hardware development and commissioning

A fast deep learning approach for beam orientation optimization for prostate cancer treated with intensity‐modulated radiation therapy

A sparse orthogonal collimator for small animal intensity‐modulated radiation therapy part I: Planning system development and commissioning

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