Evaluation of accuracy and reproducibility of a relocatable maxillary fixation system for fractionated intracranial stereotactic radiation therapy

Russo, Majella; Owen, Robert; Bernard, Anne; Moutrie, Vaughan; Foote, Matthew

doi:10.1002/jmrs.142

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…There are a variety of factors influencing the setup errors in radiation therapy for patients with thoracic tumors 5, 6, 7. In order to reduce these setup errors, many researchers tried to improve setup accuracy by improving the fixation of patients 8, 9, 10. In the study of Wang Wei etc.,11 it was shown that BMI was positively correlated with total error, indicating that BMI was an important factor in the setup errors.…”

Section: Introductionmentioning

confidence: 99%

Setup errors in radiation therapy for thoracic tumor patients of different body mass index

Zhao

Zhang

Zhai

et al. 2018

J Applied Clin Med Phys

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PurposeTo assess the setup errors in radiation therapy for thoracic tumors patients of different somatotypes, and to seek an individualized mathematical basis for defining the planning target volume (PTV).MethodsSixty patients with thoracic tumors were divided into four somatotypes according to their body mass index (BMI), and their body positions were setup by two groups of technicians independently. CT simulations were performed and the reconstructed radiography was digitally generated as reference images for location verification and error measurement. By setting positioning error ranges, the within‐range positioning correction rate was compared among groups.ResultsPosition setups for patients in the emaciated group, moderate group, and overweight group were relatively stable (with minor setup error differences between the two groups of technicians). In emaciated group, moderate group, overweight group, and obese group, setup errors in the right–left direction (R‐L) were 2.2 ± 1.3 mm, 2.2 ± 1.6 mm, 3.9 ± 3.1 mm, and 8.8 ± 3.5 mm, respectively; whereas the setup errors in the four groups in the superior–inferior(S‐I) direction were 2.4 ± 1.8 mm, 2.1 ± 1.9 mm, 3.2 ± 2.6 mm, and 5.4 ± 3.5 mm, and in the anterior–posterior (A‐P) direction were 2.2 ± 1.7 mm, 1.9 ± 1.9 mm, 3.2 ± 2.9 mm, and 6.2 ± 4.2 mm, respectively. Moreover, in the moderate group, the positioning correction rate in the three directions (R‐L, S‐I, and A‐P) was 20%, 9%, 8% within the error range of 5–10 mm, and 3%, 0%, 1% with a more than 10 mm error range. However, in overweight group and obese group, the positioning correction rate in these three directions (also with a more than 10 mm error range) was 23%, 27%, 19% and 21%, 16%, 23%, respectively.ConclusionsIn radiation therapy for patients with thoracic tumors, the definition of PTV should be individualized. Meanwhile, with the increase in BMI, positioning correction rate has a tendency to rise too.

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Section: Introductionmentioning

confidence: 99%

Setup errors in radiation therapy for thoracic tumor patients of different body mass index

Zhao

Zhang

Zhai

et al. 2018

J Applied Clin Med Phys

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“…The median translational errors were 3.0, 1.0, and 2.0 mm in the cranial‐caudal, lateral, and dorsal‐ventral directions, respectively. The population mean systematic, mean random, and International Commission on Radiation Units and Measurements overall population mean error, resultant Van Herk's error margins, as well as the mean three‐dimensional vector error (together with its standard deviation) can be found in Table . A comparison of these findings with those documented in two previously studied head positioning devices can also be found in Table .…”

Section: Resultsmentioning

confidence: 99%

“…An imprecise immobilization system would thus result in a larger setup margin. There are numerous margin “recipes” published in human literature, of which the International Commission on Radiation Units and Measurements overall population mean error and van Herk's margin recipe are two of the most widely used . Table shows that these margins calculated for the Elekta Fraxion™ system are substantially larger than those calculated for other immobilization systems …”

Section: Discussionmentioning

confidence: 99%

“…One was derived from a formula specified in the International Commission on Radiation Units and Measurements Report 62, which recommends that the overall population mean error,

\sqrt (\sum^{2} + σ^{2})

, where Σ represents the population systematic error, and σ the population random error, should be used for margin calculation. The second means of calculating the margin was determined using van Herk's margin recipe: 2.5Σ + 0.7σ . The van Herk's recipe estimates the theoretical margin by which 90% of patients in the population could be assured of receiving a minimum cumulative clinical tumor volume dose of at least 95 % of the prescribed dose.…”

Section: Methodsmentioning

confidence: 99%

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The Elekta Fraxion™ system is not suitable for maxillary fixation in canine conformal radiation therapy techniques

Yu¹,

Morrow

Moutrie

et al. 2019

Vet Radiology Ultrasound

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In this prospective, exploratory study, we evaluated the positioning accuracy in a group of 15 dogs undergoing fractionated stereotactic radiotherapy for tumors affecting the head, using a modified human maxillary fixation device (Elekta Fraxion TM system). Positioning was assessed using on-board volumetric imaging, with a six-degrees-of-freedom image registration technique. Prior to treatment delivery, CBCT images were obtained and patient alignment was corrected, in both translational and rotational planes, using a six-degrees-of-freedom robotic patient positioning system (HexaPOD Evo RT System). The maximum angular inter-fraction motions observed were 6.1 • (yaw), 10.9 • (pitch), and 4.5 • (roll). The mean systematic translational errors were 4.7, 2.6, and 2.3 mm, mean random translational errors were 3.0, 2.2, and 2.5 mm, and mean overall translational errors were 2.4, 0.7, and 2.3 mm in the cranial-caudal, lateral, and dorsal-ventral directions, respectively. The mean systematic rotational errors were 1.17 • , 0.77 • , and 1.43 • , the mean rotational random errors were 1.65 • , 1.46 • , and 1.34 • and the mean overall rotational errors were 0.56 • , 0.22 • , and 0.29 • in the yaw, pitch, and roll directions, respectively. The mean error of the three-dimensional vector was 6.9 mm with a standard deviation of 3.8 mm. Ninety-five percent of the three-dimensional vectors were <14.8 mm. This study demonstrates that this maxillary fixation device relies on six-degrees-of-freedom registration and an ability to apply corrections using a six-degrees-of-freedom couch for accurate patient positioning and tumor targeting. Its use in conformal radiation therapy in dogs is not recommended. K E Y W O R D Sbrain tumor, nasal tumor, oncology, oral tumor, radiation therapy

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“…Lung cancer is now the most common cause of cancer death in the world. 1 Radiotherapy is one of the main treatments for malignant tumor. It is crucial to deliver exact doses to the target, so as to protect the adjacent normal tissue from harm and reduce the complication incidence.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised learning for deformable registration of thoracic CT and cone‐beam CT based on multiscale features matching with spatially adaptive weighting

Duan

Liu

et al. 2020

Medical Physics

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Purpose Cone‐beam computed tomography (CBCT) is a common on‐treatment imaging widely used in image‐guided radiotherapy. Fast and accurate registration between the on‐treatment CBCT and planning CT is significant for and precise adaptive radiotherapy treatment (ART). However, existing CT–CBCT registration methods, which are mostly affine or time‐consuming intensity‐ based deformation registration, still need further study due to the considerable CT–CBCT intensity discrepancy and the artifacts in low‐quality CBCT images. In this paper, we propose a deep learning‐based CT–CBCT registration model to promote rapid and accurate CT–CBCT registration for radiotherapy. Methods The proposed CT–CBCT registration model consists of a registration network and an innovative deep similarity metric network. The registration network is a novel fully convolution network adapted specially for patch‐wise CT–CBCT registration. The metric network, going beyond intensity, automatically evaluates the high‐dimensional attribute‐based dissimilarity between the registered CT and CBCT images. In addition, considering the artifacts in low‐quality CBCT images, we add spatial weighting (SW) block to adaptively attach more importance to those informative voxels while inhibit the interference of artifact regions. Such SW‐based metric network is expected to extract the most meaningful and discriminative deep features, and form a more reliable CT–CBCT similarity measure to train the registration network. Results We evaluate the proposed method on clinical thoracic CBCT and CT dataset, and compare the registration results with some other common image similarity metrics and some state‐of‐the‐art registration algorithms. The proposed method provides the highest Structural Similarity index (86.17 ± 5.09), minimum Target Registration Error of landmarks (2.37 ± 0.32 mm), and the best DSC coefficient (78.71 ± 10.95) of tumor volumes. Moreover, our model also obtains comparable distance error of lung surfaces (1.75 ± 0.35 mm). Conclusion The proposed model shows both efficiency and efficacy for reliable thoracic CT–CBCT registration, and can generate the matched CT and CBCT images within few seconds, which is of great significance to clinical radiotherapy.

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Evaluation of accuracy and reproducibility of a relocatable maxillary fixation system for fractionated intracranial stereotactic radiation therapy

Cited by 6 publications

References 17 publications

Setup errors in radiation therapy for thoracic tumor patients of different body mass index

Setup errors in radiation therapy for thoracic tumor patients of different body mass index

The Elekta Fraxion™ system is not suitable for maxillary fixation in canine conformal radiation therapy techniques

Unsupervised learning for deformable registration of thoracic CT and cone‐beam CT based on multiscale features matching with spatially adaptive weighting

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