Quantitative evaluation of image recognition performance of fiducial markers in real-time tumor-tracking radiation therapy

Miyamoto, Naoki; Maeda, Kenichiro; Abo, Daisuke; Morita, Ryo; Sugiyama, Takao; Matsuura, Tetsuya; Katoh, Norio; Umegaki, Kikuo; Shimizu, Satoru; Shirato, Hiroki

doi:10.1016/j.ejmp.2019.08.004

Cited by 15 publications

(10 citation statements)

References 28 publications

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“…For the location error, depending on the type of tracking chosen, we can expect different levels of noise amplitudes. In the case of fiducial-based tumor tracking, we can expect a submillimeter accuracy on 2D images and an error within 1 mm for 3D calculation error, as reported in a recent study on fiducial markers, 18 hence a noise amplitude < 2 mm. For markerless tracking, a recent paper achieved a tracking accuracy of 1.64 ± 0.73 mm 19 ; hence, a noise amplitude of 2 mm seems to be a correct expected error for today's tumor tracking accuracy.…”

Section: Robustness To Noise: Analysis Of One Patientsupporting

confidence: 72%

Real‐time image‐guided treatment of mobile tumors in proton therapy by a library of treatment plans: a simulation study

Hamaide

Souris²,

Dasnoy

et al. 2022

Medical Physics

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To improve target coverage and reduce the dose in the surrounding organs-at-risks (OARs), we developed an image-guided treatment method based on a precomputed library of treatment plans controlled and delivered in real-time. Methods: A library of treatment plans is constructed by optimizing a plan for each breathing phase of a four dimensional computed tomography (4DCT).Treatments are delivered by simulation on a continuous sequence of synthetic computed tomographies (CTs) generated from real magnetic resonance imaging (MRI) sequences. During treatment, the plans for which the tumor are at a close distance to the current tumor position are selected to deliver their spots. The study is conducted on five liver cases. Results: We tested our approach under imperfect knowledge of the tumor positions with a 2 mm distance error. On average, compared to a 4D robustly optimized treatment plan, our approach led to a dose homogeneity increase of 5% (defined as 1 − D 5 −D 95 prescription ) in the target and a mean liver dose decrease of 23%. The treatment time was roughly increased by a factor of 2 but remained below 4 min on average. Conclusions: Our image-guided treatment framework outperforms state-ofthe-art 4D-robust plans for all patients in this study on both target coverage and OARs sparing, with an acceptable increase in treatment time under the current accuracy of the tumor tracking technology.

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Section: Robustness To Noise: Analysis Of One Patientsupporting

confidence: 72%

Real‐time image‐guided treatment of mobile tumors in proton therapy by a library of treatment plans: a simulation study

Hamaide

Souris²,

Dasnoy

et al. 2022

Medical Physics

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“…The fiducial markers such as gold sphere of which diameter is 1.5 or 2.0 mm can be recognized in x‐ray images by means of pattern matching technique in most cases in lung or liver. Note that there could be a risk of miss detection of the markers in one or both x‐ray images if the fiducial markers such as a thin coiled shape are used 31 . In the current RTRT, the fiducial markers should be recognized in both x‐ray images for three‐dimensional calculation.…”

Section: Discussionmentioning

confidence: 99%

“…Note that there could be a risk of miss detection of the markers in one or both x-ray images if the fiducial markers such as a thin coiled shape are used. 31 In the current RTRT, the fiducial markers should be recognized in both x-ray images for three-dimensional calculation. On the other hand, the treatment systems that have dual imaging functions during treatment are not widely used yet.…”

Section: Discussionmentioning

confidence: 99%

Real‐time CT image generation based on voxel‐by‐voxel modeling of internal deformation by utilizing the displacement of fiducial markers

et al. 2021

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Purpose To show the feasibility of real‐time CT image generation technique utilizing internal fiducial markers that facilitate the evaluation of internal deformation. Methods In the proposed method, a linear regression model that can derive internal deformation from the displacement of fiducial markers is built for each voxel in the training process before the treatment session. Marker displacement and internal deformation are derived from the four‐dimensional computed tomography (4DCT) dataset. In the treatment session, the three‐dimensional deformation vector field is derived according to the marker displacement, which is monitored by the real‐time imaging system. The whole CT image can be synthesized by deforming the reference CT image with a deformation vector field in real‐time. To show the feasibility of the technique, image synthesis accuracy and tumor localization accuracy were evaluated using the dataset generated by extended NURBS‐Based Cardiac‐Torso (XCAT) phantom and clinical 4DCT datasets from six patients, containing 10 CT datasets each. In the validation with XCAT phantom, motion range of the tumor in training data and validation data were about 10 and 15 mm, respectively, so as to simulate motion variation between 4DCT acquisition and treatment session. In the validation with patient 4DCT dataset, eight CT datasets from the 4DCT dataset were used in the training process. Two excluded inhale CT datasets can be regarded as the datasets with large deformations more than training dataset. CT images were generated for each respiratory phase using the corresponding marker displacement. Root mean squared error (RMSE), normalized RMSE (NRMSE), and structural similarity index measure (SSIM) between the original CT images and the synthesized CT images were evaluated as the quantitative indices of the accuracy of image synthesis. The accuracy of tumor localization was also evaluated. Results In the validation with XCAT phantom, the mean NRMSE, SSIM, and three‐dimensional tumor localization error were 7.5 ± 1.1%, 0.95 ± 0.02, and 0.4 ± 0.3 mm, respectively. In the validation with patient 4DCT dataset, the mean RMSE, NRMSE, SSIM, and three‐dimensional tumor localization error in six patients were 73.7 ± 19.6 HU, 9.2 ± 2.6%, 0.88 ± 0.04, and 0.8 ± 0.6 mm, respectively. These results suggest that the accuracy of the proposed technique is adequate when the respiratory motion is within the range of the training dataset. In the evaluation with a marker displacement larger than that of the training dataset, the mean RMSE, NRMSE, and tumor localization error were about 100 HU, 13%, and <2.0 mm, respectively, except for one case having large motion variation. The performance of the proposed method was similar to those of previous studies. Processing time to generate the volumetric image was <100 ms. Conclusion We have shown the feasibility of the real‐time CT image generation technique for volumetric imaging.

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“…Unstable respiratory motion will introduce artifacts in CT images. In addition, metal artifacts may also induce uncertainty for the determination of the marker since it depends on the shape, size and mass of the marker [ 26 ]. The other is the confirmation of the geometrical relationship of the markers.…”

Section: Discussionmentioning

confidence: 99%

Prediction of target position from multiple fiducial markers by partial least squares regression in real-time tumor-tracking radiation therapy

Ukon

Arai

Sugiyama

et al. 2021

Journal of Radiation Research

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The purpose of this work is to show the usefulness of a prediction method of tumor location based on partial least squares regression (PLSR) using multiple fiducial markers. The trajectory data of respiratory motion of four internal fiducial markers inserted in lungs were used for the analysis. The position of one of the four markers was assumed to be the tumor position and was predicted by other three fiducial markers. Regression coefficients for prediction of the position of the tumor-assumed marker from the fiducial markers’ positions is derived by PLSR. The tracking error and the gating error were evaluated assuming two possible variations. First, the variation of the position definition of the tumor and the markers on treatment planning computed tomograhy (CT) images. Second, the intra-fractional anatomical variation which leads the distance change between the tumor and markers during the course of treatment. For comparison, rigid predictions and ordinally multiple linear regression (MLR) predictions were also evaluated. The tracking and gating errors of PLSR prediction were smaller than those of other prediction methods. Ninety-fifth percentile of tracking/gating error in all trials were 3.7/4.1 mm, respectively in PLSR prediction for superior–inferior direction. The results suggested that PLSR prediction was robust to variations, and clinically applicable accuracy could be achievable for targeting tumors.

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Quantitative evaluation of image recognition performance of fiducial markers in real-time tumor-tracking radiation therapy

Cited by 15 publications

References 28 publications

Real‐time image‐guided treatment of mobile tumors in proton therapy by a library of treatment plans: a simulation study

Real‐time image‐guided treatment of mobile tumors in proton therapy by a library of treatment plans: a simulation study

Real‐time CT image generation based on voxel‐by‐voxel modeling of internal deformation by utilizing the displacement of fiducial markers

Prediction of target position from multiple fiducial markers by partial least squares regression in real-time tumor-tracking radiation therapy

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