Literature DB >> 27782726

Computerized triplet beam orientation optimization for MRI-guided Co-60 radiotherapy.

Dan Nguyen1, David Thomas1, Minsong Cao1, Daniel O'Connor1, James Lamb1, Ke Sheng1.   

Abstract

PURPOSE: Magnetic resonance imaging (MRI)-guided Co-60 provides daily and intrafractional MRI soft tissue imaging for improved target and critical organ tracking. To increase delivery efficiency, the system uses three Co-60 sources at 120° apart, allowing up to 600 cGy combined dose rate at isocenter. Despite the potential tripling in output, creating a delivery plan that uses all three sources is considerably unintuitive. Here, the authors computerize the triplet orientation optimization using column generation, an approach that was demonstrated effective in integrated beam orientation and fluence optimization for noncoplanar therapies. To achieve a better plan quality without increasing the treatment time, the authors then solve a fluence map optimization (FMO) problem while regularizing the fluence maps to reduce the number of deliverable MLC segments.
METHODS: Three patients-one prostate, one lung, and one head and neck boost plan (H&NBoost)-were evaluated in this study. For each patient, the beamlet doses were calculated using Monte Carlo, under a 0.35 T magnetic field, for 180 equally spaced coplanar beams grouped into 60 triplets. The beamlet size is 1.05 × 0.5 cm determined by the MLC leaf thickness and step size. The triplets were selected using the column generation algorithm. The FMO problem was formulated using an L2-norm dose fidelity term and an L1-norm anisotropic total variation regularization term, which allows controlling the number of MLC segments, and hence the treatment time, with minimal degradation to the dose. The authors' Fluence Regularization and Optimized Selection of Triplets (FROST) plans were compared against the clinical treatment plans (CLNs) produced by an experienced dosimetrist. PTV homogeneity, max dose, mean dose, D95, D98, and D99 were evaluated. OAR max and mean doses, as well as R50, defined as the ratio of the 50% isodose volume over the planning target volume were investigated.
RESULTS: The mean PTV D95, D98, and D99 differ by +0.04%, +0.07%, and +0.25% of the prescription dose between planning methods. The mean PTV homogeneity was virtually same with values at 0.8788 (FROST) and 0.8812 (CLN). R50 decreased by 0.67 comparing FROST to CLN. On average, FROST reduced Dmax and Dmean of OARs by 7.30% and 6.08% of the prescription dose, respectively. The manual CLN planning processes required numerous trial and error runs. The FROST plans on the other hand required minimal human intervention.
CONCLUSIONS: Efficient delivery of MRI-guided Co-60 therapy needs the output of multiple sources yet suffers from unintuitive and laborious manual beam selection processes. Computerized triplet orientation optimization improves both planning efficiency and plan dosimetry. The novel fluence map regularization provides additional controls over the number of MLC segments and treatment time.

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Year:  2016        PMID: 27782726      PMCID: PMC5045447          DOI: 10.1118/1.4963212

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  28 in total

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Authors:  Yanle Hu; Leith Rankine; Olga L Green; Rojano Kashani; H Harold Li; Hua Li; Roger Nana; Vivian Rodriguez; Lakshmi Santanam; Shmaryu Shvartsman; James Victoria; H Omar Wooten; James F Dempsey; Sasa Mutic
Journal:  Med Phys       Date:  2015-10       Impact factor: 4.071

2.  Benchmark IMRT evaluation of a Co-60 MRI-guided radiation therapy system.

Authors:  H Omar Wooten; Vivian Rodriguez; Olga Green; Rojano Kashani; Lakshmi Santanam; Kari Tanderup; Sasa Mutic; H Harold Li
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4.  The ViewRay system: magnetic resonance-guided and controlled radiotherapy.

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Journal:  Radiother Oncol       Date:  2015-11-25       Impact factor: 6.280

7.  Patient-specific quality assurance for the delivery of (60)Co intensity modulated radiation therapy subject to a 0.35-T lateral magnetic field.

Authors:  H Harold Li; Vivian L Rodriguez; Olga L Green; Yanle Hu; Rojano Kashani; H Omar Wooten; Deshan Yang; Sasa Mutic
Journal:  Int J Radiat Oncol Biol Phys       Date:  2014-10-25       Impact factor: 7.038

8.  4π noncoplanar stereotactic body radiation therapy for head-and-neck cancer: potential to improve tumor control and late toxicity.

Authors:  Jean-Claude M Rwigema; Dan Nguyen; Dwight E Heron; Allen M Chen; Percy Lee; Pin-Chieh Wang; John A Vargo; Daniel A Low; M Saiful Huq; Stephen Tenn; Michael L Steinberg; Patrick Kupelian; Ke Sheng
Journal:  Int J Radiat Oncol Biol Phys       Date:  2014-12-05       Impact factor: 7.038

9.  Longitudinal diffusion MRI for treatment response assessment: Preliminary experience using an MRI-guided tri-cobalt 60 radiotherapy system.

Authors:  Yingli Yang; Minsong Cao; Ke Sheng; Yu Gao; Allen Chen; Mitch Kamrava; Percy Lee; Nzhde Agazaryan; James Lamb; David Thomas; Daniel Low; Peng Hu
Journal:  Med Phys       Date:  2016-03       Impact factor: 4.071

10.  A dose homogeneity and conformity evaluation between ViewRay and pinnacle-based linear accelerator IMRT treatment plans.

Authors:  Daniel L Saenz; Bhudatt R Paliwal; John E Bayouth
Journal:  J Med Phys       Date:  2014-04
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  7 in total

1.  4π plan optimization for cortical-sparing brain radiotherapy.

Authors:  Vyacheslav L Murzin; Kaley Woods; Vitali Moiseenko; Roshan Karunamuni; Kathryn R Tringale; Tyler M Seibert; Michael J Connor; Daniel R Simpson; Ke Sheng; Jona A Hattangadi-Gluth
Journal:  Radiother Oncol       Date:  2018-03-05       Impact factor: 6.280

2.  Deterministic direct aperture optimization using multiphase piecewise constant segmentation.

Authors:  Dan Nguyen; Daniel O'Connor; Dan Ruan; Ke Sheng
Journal:  Med Phys       Date:  2017-09-22       Impact factor: 4.071

3.  Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy.

Authors:  Gyanendra Bohara; Azar Sadeghnejad Barkousaraie; Steve Jiang; Dan Nguyen
Journal:  Med Phys       Date:  2020-08-02       Impact factor: 4.071

4.  A reinforcement learning application of a guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapy.

Authors:  Azar Sadeghnejad-Barkousaraie; Gyanendra Bohara; Steve Jiang; Dan Nguyen
Journal:  Mach Learn Sci Technol       Date:  2021-05-13

5.  Integrated beam orientation and scanning-spot optimization in intensity-modulated proton therapy for brain and unilateral head and neck tumors.

Authors:  Wenbo Gu; Daniel O'Connor; Dan Nguyen; Victoria Y Yu; Dan Ruan; Lei Dong; Ke Sheng
Journal:  Med Phys       Date:  2018-03-01       Impact factor: 4.071

6.  Predicting liver SBRT eligibility and plan quality for VMAT and 4π plans.

Authors:  Angelia Tran; Kaley Woods; Dan Nguyen; Victoria Y Yu; Tianye Niu; Minsong Cao; Percy Lee; Ke Sheng
Journal:  Radiat Oncol       Date:  2017-04-24       Impact factor: 3.481

7.  A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning.

Authors:  Dan Nguyen; Troy Long; Xun Jia; Weiguo Lu; Xuejun Gu; Zohaib Iqbal; Steve Jiang
Journal:  Sci Rep       Date:  2019-01-31       Impact factor: 4.379

  7 in total

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