Technical note: Application of an optical hydrophone to ionoacoustic range detection in a tissue-mimicking agar phantom.

BACKGROUND: Ionoacoustics is a promising approach to reduce the range uncertainty in proton therapy. A miniature-sized optical hydrophone (OH) was used as a measuring device to detect weak ionoacoustic signals with a high signal-to-noise ratio in water. However, further development is necessary to prevent wave distortion because of nearby acoustic impedance discontinuities while detection is conducted on the patient's skin. PURPOSE: A prototype of the probe head attached to an OH was fabricated and the required dimensions were experimentally investigated using a 100-MeV proton beam from a fixed-field alternating gradient accelerator and k-Wave simulations. The beam range of the proton in a tissue-mimicking phantom was estimated by measuring γ-waves and spherical ionoacoustic waves with resonant frequency (SPIRE). METHODS: Four sizes of probe heads were fabricated from agar blocks for the OH. Using the prototype, the Î³-wave was detected at distal and lateral positions to the Bragg peak on the phantom surface for proton beams delivered at seven positions. For SPIRE, independent measurements were performed at distal on- and off-axis positions. The range positions were estimated by solving the linear equation using the sensitive matrix for the γ-wave and linear fitting of the correlation curve for SPIRE; they were compared with those measured using a film. RESULTS: The first peak of the γ-wave was undistorted with the 3 × 3 × 3-cm3 probe head used at the on-axis and 3-cm off-axis positions. The range positions estimated by the γ-wave agreed with the film-based range in the depth direction (the maximum deviation was 0.7 mm), although a 0.6-2.1 mm deviation was observed in the lateral direction. For SPIRE, the deviation was <1 mm for the two measurement positions. CONCLUSIONS: The attachment of a relatively small-sized probe head allowed the OH to measure the beam range on the phantom surface.

Predicting the daily gastrointestinal doses of stereotactic body radiation therapy for pancreatic cancer based on the shortest distance between the tumor and the gastrointestinal tract using daily computed tomography images.

Objectives: We aimed to investigate whether daily computed tomography (CT) images could predict the daily gastroduodenal, small intestine, and large intestine doses of stereotactic body radiation therapy (SBRT) for pancreatic cancer based on the shortest distance between the gross tumor volume (GTV) and gastrointestinal (GI) tract. Methods: Twelve patients with pancreatic cancer received SBRT of 40 Gy in five fractions. We recalculated the reference clinical SBRT plan (PLANref) using daily CT images and calculated the shortest distance from the GTV to each GI tract. The maximum dose delivered to 0.5 cc (D0.5cc) was evaluated for each planning at-risk volume of the GI tract. Spearman's correlation test was used to determine the association between the daily change in the shortest distance (Δshortest distance) and the ratio of ΔD0.5cc dose to D0.5cc dose in PLANref (ΔD0.5cc/PLANref) for quantitative analysis. Results: The median shortest distance in PLANref was 0 mm in the gastroduodenum (interquartile range, 0-2.7), 16.7 mm in the small intestine (10.0-23.7), and 16.7 mm in the large intestine (8.3-28.1 mm). The D0.5cc of PLANref in the gastroduodenum was >30 Gy in all patients, with 10 (83.3%) having the highest dose. A significant association was found between the Δshortest distance and ΔD0.5cc/ PLANref in the small or large intestine (p < 0.001) but not in the gastroduodenum (p = 0.404). Conclusions: The gastroduodenum had a higher D0.5cc and predicting the daily dose was difficult. Daily dose calculations of the GI tract are recommended for safe SBRT. Advances in knowledge: This study aimed to predict the daily doses in SBRT for pancreatic cancer from the shortest distance between the GTV and the gastrointestinal tract.Daily changes in the shortest distance can predict the daily dose to the small or large intestines, but not to the gastroduodenum.

Evaluation of short-term gastrointestinal motion and its impact on dosimetric parameters in stereotactic body radiation therapy for pancreatic cancer.

Background: The aim of this study is to quantify the short-term motion of the gastrointestinal tract (GI-tract) and its impact on dosimetric parameters in stereotactic body radiation therapy (SBRT) for pancreatic cancer. Methods: The analyzed patients were eleven pancreatic cancer patients treated with SBRT or proton beam therapy. To ensure a fair analysis, the simulation SBRT plan was generated on the planning CT in all patients with the dose prescription of 40 Gy in 5 fractions. The GI-tract motion (stomach, duodenum, small and large intestine) was evaluated using three CT images scanned at spontaneous expiration. After fiducial-based rigid image registration, the contours in each CT image were generated and transferred to the planning CT, then the organ motion was evaluated. Planning at risk volumes (PRV) of each GI-tract were generated by adding 5 mm margins, and the volume receiving at least 33 Gy (V33) < 0.5 cm3 was evaluated as the dose constraint. Results: The median interval between the first and last CT scans was 736 s (interquartile range, IQR:624-986). To compensate for the GI-tract motion based on the planning CT, the necessary median margin was 8.0 mm (IQR: 8.0-10.0) for the duodenum and 14.0 mm (12.0-16.0) for the small intestine. Compared to the planned V33 with the worst case, the median V33 in the PRV of the duodenum significantly increased from 0.20 cm3 (IQR: 0.02-0.26) to 0.33 cm3 (0.10-0.59) at Wilcoxon signed-rank test (p = 0.031). Conclusion: The short-term motions of the GI-tract lead to high dose differences.

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

PURPOSE: To quantitatively evaluate the achievable performance of volumetric imaging based on lung motion modeling by principal component analysis (PCA). METHODS: In volumetric imaging based on PCA, internal deformation was represented as a linear combination of the eigenvectors derived by PCA of the deformation vector fields evaluated from patient-specific four-dimensional-computed tomography (4DCT) datasets. The volumetric image was synthesized by warping the reference CT image with a deformation vector field which was evaluated using optimal principal component coefficients (PCs). Larger PCs were hypothesized to reproduce deformations larger than those included in the original 4DCT dataset. To evaluate the reproducibility of PCA-reconstructed volumetric images synthesized to be close to the ground truth as possible, mean absolute error (MAE), structure similarity index measure (SSIM) and discrepancy of diaphragm position were evaluated using 22 4DCT datasets of nine patients. RESULTS: Mean MAE and SSIM values for the PCA-reconstructed volumetric images were approximately 80 HU and 0.88, respectively, regardless of the respiratory phase. In most test cases including the data of which motion range was exceeding that of the modeling data, the positional error of diaphragm was less than 5 mm. The results suggested that large deformations not included in the modeling 4DCT dataset could be reproduced. Furthermore, since the first PC correlated with the displacement of the diaphragm position, the first eigenvector became the dominant factor representing the respiration-associated deformations. However, other PCs did not necessarily change with the same trend as the first PC, and no correlation was observed between the coefficients. Hence, randomly allocating or sampling these PCs in expanded ranges may be applicable to reasonably generate an augmented dataset with various deformations. CONCLUSIONS: Reasonable accuracy of image synthesis comparable to those in the previous research were shown by using clinical data. These results indicate the potential of PCA-based volumetric imaging for clinical applications.

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

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.