DVH-Based Inverse Planning Using Monte Carlo Dosimetry for LDR Prostate Brachytherapy

Mountris, Konstantinos A.; Visvikis, Dimitris; Bert, Julien

doi:10.1016/j.ijrobp.2018.09.041

Cited by 14 publications

(15 citation statements)

References 45 publications

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“…Inspired by the work of [15], we performed an inverse planning based on dose-volume-histograms (DVHs) by using the Fast Simulated Annealing (FSA) algorithm [2]. The cost function to minimize, given by the equation 1, includes the different DVH dosimetric criteria given by the AAPM TG-137 recommendations [1]:…”

Section: Dvh-based Fsa For Inverse Planningmentioning

confidence: 99%

“…Even though, the inverse planning algorithms usually perform a thousand of iterations before obtaining a satisfactory solution, leading to a very long-time calculation to get the final optimized plan. A DVH-based optimization algorithm using MCS dosimetry for LDR prostate brachytherapy has previously proposed [15]. In this work, MCS is used to compute dose kernels for each single seed position, followed by the use of FSA.…”

Section: Introductionmentioning

confidence: 99%

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Fast Monte Carlo-Based Inverse Planning for Prostate Brachytherapy by Using Deep Learning

Villa

Bert

Valéri

et al. 2022

IEEE Trans. Radiat. Plasma Med. Sci.

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Inverse planning is an essential tool for optimizing the delivered radiation dose on low-dose-rate (LDR) prostate brachytherapy. Clinical inverse planning systems use the TG-43 dose computation formalism in order to perform a fast optimization. However, this method is an approximation that often leads to a dose overestimation, resulting on sub-optimal plans. Alternatively, Monte Carlo simulation (MCS) can be used to obtain an accurate dose distribution, but considerably increasing the estimation time. We propose a fast inverse planning method for LDR prostate brachytherapy that uses a deep convolutional neural network (DCNN) trained on a GPU-based MCS generated database to estimate the dose distribution on the prostate and organs at risk. Segmentations of the organs and seeds' positions are given as the DCNN input. The mean percent error on the test set was -1.19±0.94% within the prostate. The DCNN was used to estimate the dosimetric parameters in each organ for every configuration of the optimization loop. The dosimetric parameters of the final DCNN-based brachytherapy plans were in good agreement compared to the same plans recalculated with a full MCS. The proposed inverse planning based on DCNN was capable to reach an equivalent level of accuracy with Monte Carlo with a runtime in less than 1 min using conventional GPU card.

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Section: Dvh-based Fsa For Inverse Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Monte Carlo-Based Inverse Planning for Prostate Brachytherapy by Using Deep Learning

Villa

Bert

Valéri

et al. 2022

IEEE Trans. Radiat. Plasma Med. Sci.

Self Cite

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“…After the completion of a conventional trial-and-error inverse planning procedure, the dose interval is identified to locate the voxels that receive the dose in the selected range. Mountris et al proposed an inverse planning approach that can improve planning quality by combining DVH without compromising the overall execution times [22]. However, most DVH-based optimization algorithms are mostly used in external radiotherapy, and only a small number of specialists consider the evaluation of DVH with the automatic optimization of seeds distribution.…”

Section: Purposementioning

confidence: 99%

Hybrid optimization based on non-coplanar needles for brachytherapy dose planning

Ma¹,

Yang²,

Jiang³

et al. 2019

jcb

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Purpose: An ideal dose distribution in a target is the ultimate goal of preoperative dose planning. Furthermore, avoiding vital organs or tissues such as blood vessels or bones during the puncture procedure is significant in lowdose-rate brachytherapy. The aim of this work is to develop a hybrid inverse optimization method based on non-coplanar needles to assist the physician during conformal dose planning, which cannot be properly achieved with a traditional coplanar template. Material and methods: The hybrid inverse optimization technique include two novel technologies: an inverse optimization algorithm and a dose volume histogram evaluation method. Brachytherapy treatment planning system was designed as an experimental platform. Left lung adenocarcinoma case was used to test the performance of the method in non-coplanar and coplanar needles, and malignant tumor of spine case was involved to test the practical application of this technique. In addition, the optimization time of every test was also recorded. Results: The proposed method can achieve an ideal dose distribution, avoiding vital organs (bones). In the first experiment, 13 non-coplanar needles and 24 seeds were used to get an ideal dose distribution to cover the target, whereas 11 coplanar needles and 23 seeds were used to cover the same target. In the second experiment, the new method used 22 non-coplanar needles and 65 seeds to cover the target, while 63 seeds and 22 needles were used in the actual operation. In addition, the computation time of the hybrid inverse optimization method was 20.5 seconds in the tumor of 94.67 cm 3 by using 22 needles, which was fast enough for clinical application. Conclusions: The hybrid inverse optimization method achieved high conformity in the clinical practice. The non-coplanar needle can help to achieve a better dose distribution than the coplanar needle.

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“…In this paper we propose a novel method for automatic seeds localization in 3D US images. Connected to a fast replanning, 3 this allows to envision a fully automatic adaptive brachytherapy, that is an intraoperative update of the seeds placement planning, based on the localization and actual dosimetry map of the previously implanted seeds.…”

Section: Introductionmentioning

confidence: 99%

“…showed on patient date that orientations errors may have in impact on the dose delivered to organs at risks. Moreover adaptive brachytherapy involves more complex dose models 3 requiring a full localization of the seed. Finally, published methods differ in their level of evaluation.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and registration for automatic seed localization in 3D US images for prostate brachytherapy

2021

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Purpose New radiation therapy protocols, in particular adaptive, focal or boost brachytherapy treatments, require determining precisely the position and orientation of the implanted radioactive seeds from real‐time ultrasound (US) images. This is necessary to compare them to the planned one and to adjust automatically the dosimetric plan accordingly for next seeds implantations. The image modality, the small size of the seeds, and the artifacts they produce make it a very challenging problem. The objective of the presented work is to setup and to evaluate a robust and automatic method for seed localization in three‐dimensional (3D) US images. Methods The presented method is based on a prelocalization of the needles through which the seeds are injected in the prostate. This prelocalization allows focusing the search on a region of interest (ROI) around the needle tip. Seeds localization starts by binarizing the ROI and removing false positives using, respectively, a Bayesian classifier and a support vector machine (SVM). This is followed by a registration stage using first an iterative closest point (ICP) for localizing the connected set of seeds (named strand) inserted through a needle, and secondly refining each seed position using sum of squared differences (SSD) as a similarity criterion. ICP registers a geometric model of the strand to the candidate voxels while SSD compares an appearance model of a single seed to a subset of the image. The method was evaluated both for 3D images of an Agar‐agar phantom and a dataset of clinical 3D images. It was tested on stranded and on loose seeds. Results Results on phantom and clinical images were compared with a manual localization giving mean errors of 1.09 ± 0.61 mm on phantom image and 1.44 ± 0.45 mm on clinical images. On clinical images, the mean errors of individual seeds orientation was 4.33±8.51∘. Conclusions The proposed algorithm for radioactive seed localization is robust, tested on different US images, accurate, giving small mean error values, and returns the five cylindrical seeds degrees of freedom.

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DVH-Based Inverse Planning Using Monte Carlo Dosimetry for LDR Prostate Brachytherapy

Cited by 14 publications

References 45 publications

Fast Monte Carlo-Based Inverse Planning for Prostate Brachytherapy by Using Deep Learning

Fast Monte Carlo-Based Inverse Planning for Prostate Brachytherapy by Using Deep Learning

Hybrid optimization based on non-coplanar needles for brachytherapy dose planning

Machine learning and registration for automatic seed localization in 3D US images for prostate brachytherapy

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