Current state and future applications of radiological image guidance for particle therapy

Landry, Guillaume; Hua, Chia‐Ho

doi:10.1002/mp.12744

Cited by 87 publications

(104 citation statements)

References 117 publications

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“…The use of technologies such as IMRT (intensity modulated radiotherapy) [2] and VMAT (volumetric modulated arc therapy) [3] has increased the necessity of volumetric imaging as a way to measure and handle uncertainties. In recent years, cone beam CT (CBCT) has progressively become the standard approach to implement IGRT (image-guided radiotherapy) [4,5]. Nonetheless, CBCT exhibits intrinsic limitations, due to suboptimal image quality, poor soft tissue contrast and the additional imaging dose.…”

Section: Introductionmentioning

confidence: 99%

Medical physics challenges in clinical MR-guided radiotherapy

et al. 2020

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The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART. Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation. Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing. The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.

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Section: Introductionmentioning

confidence: 99%

Medical physics challenges in clinical MR-guided radiotherapy

et al. 2020

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“…Although currently not common practice at most proton centers, considerable efforts are being made to integrate cone beam CT, portable CT, or CT‐on‐rails into routine clinical workflow. Some of the new proton therapy centers are presently using volumetric imaging for patients' alignment …”

Section: Introductionmentioning

confidence: 99%

AAPM task group 224: Comprehensive proton therapy machine quality assurance

et al. 2019

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Purpose: Task Group (TG) 224 was established by the American Association of Physicists in Medicine's Science Council under the Radiation Therapy Committee and Work Group on Particle Beams. The group was charged with developing comprehensive quality assurance (QA) guidelines and recommendations for the three commonly employed proton therapy techniques for beam delivery: scattering, uniform scanning, and pencil beam scanning. This report supplements established QA guidelines for therapy machine performance for other widely used modalities, such as photons and electrons (TG 142, TG 40, TG 24, TG 22, TG 179, and Medical Physics Practice Guideline 2a) and shares their aims of ensuring the safe, accurate, and consistent delivery of radiation therapy dose distributions to patients. Methods: To provide a basis from which machine‐specific QA procedures can be developed, the report first describes the different delivery techniques and highlights the salient components of the related machine hardware. Depending on the particular machine hardware, certain procedures may be more or less important, and each institution should investigate its own situation. Results: In lieu of such investigations, this report identifies common beam parameters that are typically checked, along with the typical frequencies of those checks (daily, weekly, monthly, or annually). The rationale for choosing these checks and their frequencies is briefly described. Short descriptions of suggested tools and procedures for completing some of the periodic QA checks are also presented. Conclusion: Recommended tolerance limits for each of the recommended QA checks are tabulated, and are based on the literature and on consensus data from the clinical proton experience of the task group members. We hope that this and other reports will serve as a reference for clinical physicists wishing either to establish a proton therapy QA program or to evaluate an existing one.

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“…[1][2][3][4] At the same time, low-dose, frequent and accurate imaging, ideally at the treatment site, is required to ensure a safe delivery of the therapeutic doses. 5,6 Proton therapy treatment planning requires a spatial map of the relative (to water) stopping power (RSP), which in current clinical practice is acquired through a conversion from x-ray computed tomography (CT) images. [7][8][9] X-ray CT images are typically not acquired in treatment position and not prior to every treatment fraction, in order to keep treatment time short and imaging dose low enough that they do not compromise the dose benefit of proton therapy.…”

Section: Introductionmentioning

confidence: 99%

An optimization algorithm for dose reduction with fluence‐modulated proton CT

et al. 2020

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Purpose: Fluence-modulated proton computed tomography (FMpCT) using pencil beam scanning aims at achieving task-specific image noise distributions by modulating the imaging proton fluence spot-by-spot based on an object-specific noise model. In this work, we present a method for fluence field optimization and investigate its performance in dose reduction for various phantoms and image variance targets. Methods: The proposed method uses Monte Carlo simulations of a proton CT (pCT) prototype scanner to estimate expected variance levels at uniform fluence. Using an iterative approach, we calculate a stack of target variance projections that are required to achieve the prescribed image variance, assuming a reconstruction using filtered backprojection. By fitting a pencil beam model to the ratio of uniform fluence variance and target variance, relative weights for each pencil beam can be calculated. The quality of the resulting fluence modulations is evaluated by scoring imaging doses and comparing them to those at uniform fluence, as well as evaluating conformity of the achieved variance with the prescription. For three different phantoms, we prescribed constant image variance as well as two regions-of-interest (ROI) imaging tasks with inhomogeneous image variance. The shape of the ROIs followed typical beam profiles for proton therapy. Results: Prescription of constant image variance resulted in a dose reduction of 8.9% for a homogeneous water phantom compared to a uniform fluence scan at equal peak variance level. For a more heterogeneous head phantom, dose reduction increased to 16.0% for the same task. Prescribing two different ROIs resulted in dose reductions between 25.7% and 40.5% outside of the ROI at equal peak variance levels inside the ROI. Imaging doses inside the ROI were increased by 9.2% to 19.2% compared to the uniform fluence scan, but can be neglected assuming that the ROI agrees with the therapeutic dose region. Agreement of resulting variance maps with the prescriptions was satisfactory. Conclusions: We developed a method for fluence field optimization based on a noise model for a real scanner used in pCT. We demonstrated that it can achieve prescribed image variance targets. A uniform fluence field was shown not to be dose optimal and dose reductions achievable with the proposed method for FMpCT were considerable, opening an interesting perspective for image guidance and adaptive therapy.

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Current state and future applications of radiological image guidance for particle therapy

Cited by 87 publications

References 117 publications

Medical physics challenges in clinical MR-guided radiotherapy

Medical physics challenges in clinical MR-guided radiotherapy

AAPM task group 224: Comprehensive proton therapy machine quality assurance

An optimization algorithm for dose reduction with fluence‐modulated proton CT

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