Continuous generation of volumetric images during stereotactic body radiation therapy using periodic kV imaging and an external respiratory surrogate

Abstract

We present a technique for continuous generation of volumetric images during SBRT using periodic kV imaging and an external respiratory surrogate signal to drive a patient-specific PCA motion model. Using the on-board imager, kV radiographs are acquired every 3 seconds and used to fit the parameters of a motion model so that it matches observed changes in internal patient anatomy. A multi-dimensional correlation model is established between the motion model parameters and the external surrogate position and velocity, enabling volumetric image reconstruction between kV imaging time points. Performance of the algorithm was evaluated using 10 realistic eXtended CArdiac-Torso (XCAT) digital phantoms including 3D anatomical respiratory deformation programmed with 3D tumor positions measured with orthogonal kV imaging of implanted fiducial gold markers. The clinically measured ground truth 3D tumor positions provided a dataset with realistic breathing irregularities, and the combination of periodic on-board kV imaging with recorded external respiratory surrogate signal was used for correlation modeling to account for any changes in internalexternal correlation. The three-dimensional tumor positions are reconstructed with an average root mean square error (RMSE) of 1.47 mm, and an average 95 th percentile 3D positional error of 2.80 mm compared with the clinically measured ground truth 3D tumor positions. This technique enables continuous 3D anatomical image generation based on periodic kV imaging of internal anatomy without the additional dose of continuous kV imaging. The 3D anatomical images produced using this method can be used for treatment verification and delivered dose computation in the presence of irregular respiratory motion.

Technical note: Performance evaluation of volumetric imaging based on motion modeling by principal component analysis

“…where 𝜇 x and 𝜇 y are the average CT values in ground truth image and synthesized image respectively, 𝜎 x and 𝜎 y are variance of CT value in ground truth image and synthesized image respectively, and 𝜎 xy is a covariance of ground truth image and synthesized image. C 1 and C 2 were defined as C 1 = (0.01 × L) 2 and C 2 = (0.03 × L) 2 , respectively. L was dynamic range of the image and was defined as the maximum CT value in the ground truth image.…”

“…Some studies regarding volumetric imaging are based on motion modeling with principal component analysis (PCA). In PCA-based motion modeling, [2][3][4]7,9,12,14 internal deformation is represented as a linear combination of the eigenvectors derived by the PCA of the deformation vector field evaluated from patient-specific fourdimensional-computed tomography (4DCT) datasets.…”

“…4 Another one is based on the correlation model between PCs and external surrogate information. 2 Recently, a method using convolutional neural networks (CNNs) has also been proposed. 7 In this approach, PCA was also applied for data augmentation to generate the training dataset, including various deformations.…”

“…Volumetric imaging, [1][2][3][4][5][6][7][8][9][10][11][12][13] which can visualize threedimensional (3D) anatomical structures during treatment, is expected to be clinically practical as a motion management technique in radiation therapy. Some studies regarding volumetric imaging are based on motion modeling with principal component analysis (PCA).…”

Automatic liver tumor localization using deep learning‐based liver boundary motion estimation and biomechanical modeling (DL‐Bio)

“…16 Moreover, marker migration is possible and leads to tumor localization errors. Noninvasive surrogates such as breathing signals from surface imaging can also be used for real-time tumor tracking, [17][18][19][20] but the motion correlation between the external markers and internal target motions may not be perfect 21 and is usually patient-specific. Markerless tracking using online magnetic resonance imaging has been used to track tumors.…”