A robotic C-arm cone beam CT system for image-guided proton therapy: design and performance

Hua, Chia‐Ho; Yao, Weiguang; Kidani, T; Tomida, Kazuo; Ozawa, Saori; Nishimura, T; Fujisawa, Tatsuya; Shinagawa, R.; Merchant, Thomas E.

doi:10.1259/bjr.20170266

Cited by 35 publications

(39 citation statements)

References 25 publications

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“…Cone‐beam CT (CBCT) is widely used in photon radiotherapy clinics for patient setup and monitoring treatment response in the context of adaptive radiation therapy 1,2 . Recently, CBCT has also made its way into proton therapy centers 3–5 . The applicability of cone‐beam CT for tasks such as dose calculation or organ delineation is, however, limited by the reduced image quality when compared with regular CT. Cone‐beam CT image quality is afflicted by artifacts caused by patient 6 and detector 7 scatter, flat panel detector lag and ghosting, 8 as well as the usual beam‐hardening artifacts also found in regular CT, as summarized and corrected in Thing et al 9 .…”

Section: Introductionmentioning

confidence: 99%

“…1,2 Recently, CBCT has also made its way into proton therapy centers. [3][4][5] The applicability of cone-beam CT for tasks such as dose calculation or organ delineation is, however, limited by the reduced image quality when compared with regular CT. Cone-beam CT image quality is afflicted by artifacts caused by patient 6 and detector 7 scatter, flat panel detector lag and ghosting, 8 as well as the usual beam-hardening artifacts also found in regular CT, as summarized and corrected in Thing et al 9 These artifacts reduce contrast and cause generally incorrect CT numbers. Standard CBCT scans can therefore not be used for dose-calculation and replanning of the patient treatment, which is required for adaptive photon and proton radiotherapy.…”

Section: Introductionmentioning

confidence: 99%

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ScatterNet: A convolutional neural network for cone‐beam CT intensity correction

et al. 2018

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Using a deep convolutional neural network for CBCT intensity correction was shown to be feasible in the pelvic region for the first time. Dose calculation accuracy on CBCT was high for VMAT, but unsatisfactory for IMPT. With respect to the reference technique (CBCT ), the neural network enabled a considerable increase in speed for intensity correction and might eventually allow for on-the-fly shading correction during CBCT acquisition.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ScatterNet: A convolutional neural network for cone‐beam CT intensity correction

et al. 2018

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“…Some of the new proton therapy centers are presently using volumetric imaging for patients' alignment. [64][65][66] Successful proton therapy treatment requires accurate and reproducible patient positioning. The importance of geometrical precision and anatomical reproducibility may be even greater than for photon treatments.…”

Section: F Imaging In Proton Therapymentioning

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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“…It rotates 220° around the patient and produces a reconstructed FOV of 300 × 300 × 150 mm with 1.17 × 1.17 × 1.5 mm voxel size. The ceiling‐mounted robotic C‐arm CBCT system, designed by Hitachi and installed at St. Jude Children's Research Hospital, is capable of 360° rotation because of the addition of a rotating C‐ring coupled to the C‐arm . The FOV can be increased to 530 × 530 × 280 mm in the half‐fan mode with imager panel offset.…”

Section: Existing Image Guidance Solutions In Particle Therapymentioning

confidence: 99%

“…The ceiling-mounted robotic C-arm CBCT system, designed by Hitachi and installed at St. Jude Children's Research Hospital, is capable of 360°rotation because of the addition of a rotating C-ring coupled to the C-arm. 46 The FOV can be increased to 530 9 530 9 280 mm in the half-fan mode with imager panel offset. The introduction of ceiling rails combined with robotic arms allows this system to perform CBCT at and off the treatment isocenter.…”

Section: C Robotic C-arm Cbctmentioning

confidence: 99%

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

Landry

Hua

2018

Medical Physics

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In this review paper, we first give a short overview of radiological image guidance in photon radiotherapy, placing emphasis on the fact that linac based radiotherapy has outpaced particle therapy in the adoption of volumetric image guidance. While cone beam computed tomography (CBCT) has been an established technique in linac treatment rooms for almost two decades, the widespread adoption of volumetric image guidance in particle therapy, whether by means of CBCT or in‐room CT imaging, is recent. This lag may be attributable to the bespoke nature and lower number of particle therapy installations, as well as the differences in geometry between those installations and linac treatment rooms. In addition, for particle therapy the so called shift invariance of the dose distribution rarely applies. An overview of the different volumetric image guidance solutions found at modern particle therapy facilities is provided, covering gantry, nozzle, C‐arm, and couch‐mounted CBCT as well different in‐room CT configurations. A summary of the use of in‐room volumetric imaging data beyond anatomy‐based positioning is also presented as well as the necessary corrections to CBCT images for accurate water equivalent thickness calculation. Finally, the use of non‐ionizing imaging modalities is discussed.

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A robotic C-arm cone beam CT system for image-guided proton therapy: design and performance

Cited by 35 publications

References 25 publications

ScatterNet: A convolutional neural network for cone‐beam CT intensity correction

ScatterNet: A convolutional neural network for cone‐beam CT intensity correction

AAPM task group 224: Comprehensive proton therapy machine quality assurance

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

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