Adaptive radiation therapy

Yan, Di; Vicini, Frank A.; Wong, John; Martínez, Álvaro

doi:10.1088/0031-9155/42/1/008

Cited by 556 publications

(296 citation statements)

References 20 publications

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“…The clinical relevance of these innovative strategies has to be validated within sound and comprehensive settings. Although the rationale for biologically adaptive radiotherapy is evident (Yan et al, 1997;Tanderup et al, 2010), there is still a long way to go before personalized adaptive radiotherapy may become a standard approach in radiation oncology.…”

Section: Radiation Physics and Technologymentioning

confidence: 99%

Grand challenges in biomedical physics

Townsend¹,

Cheng²,

Georg³

et al. 2013

Front. Physics

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Section: Radiation Physics and Technologymentioning

confidence: 99%

Grand challenges in biomedical physics

Townsend¹,

Cheng²,

Georg³

et al. 2013

Front. Physics

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“…These variations in patient anatomy can be accounted for in reasonable time by considering auto‐segmentation techniques that can use voxel‐by‐voxel deformation of the patient CT images used in treatment planning to current CBCT images obtained from daily patient setup 8. Patient motion leads to variations in patient anatomy inter‐ and intrafractions where ART will provide tools to manage the geometric and dosimetric discrepancies between patient CT simulation and treatment planning and dose delivery 2. The dose variations due to changes in patient anatomy can be evaluated from the deformed initial dose maps on a voxel‐by‐voxel basis using DIR algorithms.…”

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of the performance of different deformable image registration algorithms in helical, axial, and cone‐beam CT images using a mobile phantom

Ali

Alsbou

Jaskowiak

et al. 2018

J Applied Clin Med Phys

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The goal of this project is to investigate quantitatively the performance of different deformable image registration algorithms (DIR) with helical (HCT), axial (ACT), and cone‐beam CT (CBCT). The variations in the CT‐number values and lengths of well‐known targets moving with controlled motion were evaluated. Four DIR algorithms: Demons, Fast‐Demons, Horn‐Schunck and Lucas‐Kanade were used to register intramodality CT images of a mobile phantom scanned with different imaging techniques. The phantom had three water‐equivalent targets inserted in a low‐density foam with different lengths (10–40 mm) and moved with adjustable motion amplitudes (0–20 mm) and frequencies (0–0.5 Hz). The variations in the CT‐number level, volumes and shapes of these targets were measured from the spread‐out of the CT‐number distributions. In CBCT, most of the DIR algorithms were able to produce the actual lengths of the mobile targets; however, the CT‐number values obtained from the DIR algorithms deviated from the actual CT‐number of the targets. In HCT, the DIR algorithms were successful in deforming the images of the mobile targets to the images of the stationary targets producing the CT‐number values and lengths of the targets for motion amplitudes <20 mm. Similarly in ACT, all DIR algorithms produced the actual CT‐number values and lengths of the stationary targets for low‐motion amplitudes <15 mm. The optical flow‐based DIR algorithms such as the Horn‐Schunck and Lucas‐Kanade performed better than the Demons and Fast‐Demons that are based on attraction forces particularly at large motion amplitudes. In conclusion, most of the DIR algorithms did not reproduce well the CT‐number values and lengths of the targets in images that have artifacts induced by large motion amplitudes. The deviations in the CT‐number values and variations in the volume of the mobile targets in the deformed CT images produced by the different DIR algorithms need to be considered carefully in the treatment planning for accurate dose calculation dose coverage of the tumor, and sparing of critical structures.

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“…It combines real‐time magnetic resonance (MR) image guidance 3 , 4 and intensity‐modulated radiation therapy (IMRT (5) ) technologies to allow soft tissue visualization, accurate tumor targeting, and simultaneous radiation delivery. In addition, the MRIdian has the capability of online treatment plan adaptation 6 , 7 based on daily volumetric MR imaging; therefore, it could optimize the patient radiation treatment plan by adapting to the patient anatomy status of the day. Such important treatment plan adaptation function could potentially maximize the treatment accuracy while minimizing the toxicities to organs‐at‐risk surrounding the treatment target.…”

Section: Introductionmentioning

confidence: 99%

A software tool to automatically assure and report daily treatment deliveries by a cobalt‐60 radiation therapy device

Yang

Wooten

Green

et al. 2016

J Applied Clin Med Phys

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The aims of this study were to develop a method for automatic and immediate verification of treatment delivery after each treatment fraction in order to detect and correct errors, and to develop a comprehensive daily report which includes delivery verification results, daily image‐guided radiation therapy (IGRT) review, and information for weekly physics reviews. After systematically analyzing the requirements for treatment delivery verification and understanding the available information from a commercial MRI‐guided radiotherapy treatment machine, we designed a procedure to use 1) treatment plan files, 2) delivery log files, and 3) beam output information to verify the accuracy and completeness of each daily treatment delivery. The procedure verifies the correctness of delivered treatment plan parameters including beams, beam segments and, for each segment, the beam‐on time and MLC leaf positions. For each beam, composite primary fluence maps are calculated from the MLC leaf positions and segment beam‐on time. Error statistics are calculated on the fluence difference maps between the plan and the delivery. A daily treatment delivery report is designed to include all required information for IGRT and weekly physics reviews including the plan and treatment fraction information, daily beam output information, and the treatment delivery verification results. A computer program was developed to implement the proposed procedure of the automatic delivery verification and daily report generation for an MRI guided radiation therapy system. The program was clinically commissioned. Sensitivity was measured with simulated errors. The final version has been integrated into the commercial version of the treatment delivery system. The method automatically verifies the EBRT treatment deliveries and generates the daily treatment reports. Already in clinical use for over one year, it is useful to facilitate delivery error detection, and to expedite physician daily IGRT review and physicist weekly chart review.PACS number(s): 87.55.km

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Adaptive radiation therapy

Cited by 556 publications

References 20 publications

Grand challenges in biomedical physics

Grand challenges in biomedical physics

Quantitative evaluation of the performance of different deformable image registration algorithms in helical, axial, and cone‐beam CT images using a mobile phantom

A software tool to automatically assure and report daily treatment deliveries by a cobalt‐60 radiation therapy device

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