To develop ultrasound-guided radiotherapy, we proposed an assistant structure with embedded markers along with a novel alternative method, the Aligned Peak Response (APR) method, to alter the conventional delay-and-sum (DAS) beamformer for reconstructing ultrasound images obtained from a flexible array. We simulated imaging targets in Field-II using point target phantoms with point targets at different locations. In the experimental phantom ultrasound images, image RF data were acquired with a flexible transducer with in-house assistant structures embedded with needle targets for testing the accuracy of the APR method. The lateral full width at half maximum (FWHM) values of the objective point target (OPT) in ground truth ultrasound images, APR-delayed ultrasound images with a flat shape, and images acquired with curved transducer radii of 500 mm and 700 mm were 3.96 mm, 4.95 mm, 4.96 mm, and 4.95 mm. The corresponding axial FWHM values were 1.52 mm, 4.08 mm, 5.84 mm, and 5.92 mm, respectively. These results demonstrate that the proposed assistant structure and the APR method have the potential to construct accurate delay curves without external shape sensing, thereby enabling a flexible ultrasound array for tracking pancreatic tumor targets in real time for radiotherapy.
Image-guided interventional oncology procedures can greatly enhance the outcome of cancer treatment. As an enhancing procedure, oncology smart material delivery can increase cancer therapy's quality, effectiveness, and safety. However, the effectiveness of enhancing procedures highly depends on the accuracy of smart material placement procedures. Inaccurate placement of smart materials can lead to adverse side effects and health hazards. Image guidance can considerably improve the safety and robustness of smart material delivery. In this study, we developed a novel generative deep-learning platform that highly prioritizes clinical practicality and provides the most informative intra-operative feedback for image-guided smart material delivery. XIOSIS generates a patient-specific 3D volumetric computed tomography (CT) from three intraoperative radiographs (X-ray images) acquired by a mobile C-arm during the operation. As the first of its kind, XIOSIS (i) synthesizes the CT from small field-of-view radiographs;(ii) reconstructs the intra-operative spacer distribution; (iii) is robust; and (iv) is equipped with a novel soft-contrast cost function. To demonstrate the effectiveness of XIOSIS in providing intra-operative image guidance, we applied XIOSIS to the duodenal hydrogel spacer placement procedure. We evaluated XIOSIS performance in an image-guided virtual spacer placement and actual spacer placement in two cadaver specimens. XIOSIS showed a clinically acceptable performance, reconstructed the 3D intra-operative hydrogel spacer distribution with an average structural similarity of 0.88 and Dice coefficient of 0.63 and with less than 1 cm difference in spacer location relative to the spinal cord.
Globally, cervical cancer is the fourth leading cancer among women and is dominant in resource-poor settings in its occurrence and mortality. This study focuses on developing liquid immunogenic fiducial eluter (LIFE) Biomaterial with components that include biodegradable polymers, nanoparticles, and an immunoadjuvant. LIFE Biomaterial is designed to provide image guidance during radiotherapy similar to clinically used liquid fiducials while enhancing therapeutic efficacy for advanced cervical cancer. C57BL6 mice were used to grow subcutaneous tumors on bilateral flanks. The tumor on one flank was then treated using LIFE Biomaterial prepared with the immunoadjuvant anti-CD40, with/without radiotherapy at 6 Gy. Computed tomography (CT) and magnetic resonance (MR) imaging visibility were also evaluated in human cadavers. A pharmacodynamics study was also conducted to assess the safety of LIFE Biomaterial in healthy C57BL6 female mice. Results showed that LIFE Biomaterial could provide both CT and MR imaging contrast over time. Inhibition in tumor growth and prolonged significant survival (* p < 0.05) were consistently observed for groups treated with the combination of radiotherapy and LIFE Biomaterial, highlighting the potential for this strategy. Minimal toxicity was observed for healthy mice treated with LIFE Biomaterial with/without anti-CD40 in comparison to non-treated cohorts. The results demonstrate promise for the further development and clinical translation of this approach to enhance the survival and quality of life of patients with advanced cervical cancer.
Our study aims to quantify the impact of spectral separation on achieved theoretical prediction accuracy of proton-stopping power when the volume discrepancy between calibration phantom and scanned object is observed. Such discrepancy can be commonly seen in our CSI pediatric patients. One of the representative image-domain DECT models is employed on a virtual phantom to derive electron density and effective atomic number for a total of 34 ICRU standard human tissues. The spectral pairs used in this study are 90 kVp/140 kVp, without and with 0.1 mm to 0.5 mm additional tin filter. The two DECT images are reconstructed via a conventional filtered back projection algorithm (FBP) on simulated noiseless projection data. The best-predicted accuracy occurs at a spectral pair of 90 kVp/140 kVp with a 0.3 mm tin filter, and the root-mean-squared average error is 0.12% for tissue substitutes. The results reveal that the selected image-domain model is sensitive to spectral pair deviation when there is a discrepancy between calibration and scanning conditions. This study suggests that an optimization process may be needed for clinically available DECT scanners to yield the best proton-stopping power estimation.
Flexible array transducers can adapt to patient-specific geometries during real-time ultrasound (US) image-guided therapy monitoring. This makes the system radiation-free and less user-dependency. Precise estimation of the flexible transducer's geometry is crucial for the delay-and-sum (DAS) beamforming algorithm to reconstruct B-mode US images. The primary innovation of this research is to build a system named FLexible transducer with EXternal tracking (FLEX) to estimate the position of each element of the flexible transducer and reconstruct precise US images. FLEX utilizes customized optical markers and a tracker to monitor the probe's geometry, employing a polygon fitting algorithm to estimate the position and azimuth angle of each transducer element. Subsequently, the traditional DAS algorithm processes the delay estimation from the tracked element position, reconstructing US images from radio-frequency (RF) channel data. The proposed method underwent evaluation on phantoms and cadaveric specimens, demonstrating its clinical feasibility. Deviations in tracked probe geometry compared to ground truth were minimal, measuring 0.50 ± 0.29 mm for the CIRS phantom, 0.54 ± 0.35 mm for the deformable phantom, and 0.36 ± 0.24 mm on the cadaveric specimen. Reconstructing the US image using tracked probe geometry significantly outperformed the untracked geometry, as indicated by a Dice score of 95.1 ± 3.3% versus 62.3 ± 9.2% for the CIRS phantom. The proposed method achieved high accuracy (<0.5 mm error) in tracking the element position for various random curvatures applicable for clinical deployment. The evaluation results show that the radiation-free proposed method can effectively reconstruct US images and assist in monitoring image-guided therapy with minimal user dependency.
Algorithms , Transducers , Humans , Ultrasonography , Phantoms, Imaging , Cadaver
Pancreatic cancer is the fourth leading cause of cancer-related death, with nearly 60,000 cases each year and less than a 10% 5-year overall survival rate. Radiation therapy (RT) is highly beneficial as a local-regional anticancer treatment. As anatomical variation is of great concern, motion management techniques, such as DIBH, are commonly used to minimize OARs toxicities; however, the variability between DIBHs has not been well studied. Here, we present an unprecedented systematic analysis of patients' anatomical reproducibility over multiple DIBH motion-management technique uses for pancreatic cancer RT. We used data from 20 patients; four DIBH scans were available for each patient to design 80 SBRT plans. Our results demonstrated that (i) there is considerable variation in OAR geometry and dose between same-subject DIBH scans; (ii) the RT plan designed for one scan may not be directly applicable to another scan; (iii) the RT treatment designed using a DIBH simulation CT results in different dosimetry in the DIBH treatment delivery; and (iv) this confirms the importance of adaptive radiation therapy (ART), such as MR-Linacs, for pancreatic RT delivery. The ART treatment delivery technique can account for anatomical variation between referenced and scheduled plans, and thus avoid toxicities of OARs because of anatomical variations between DIBH patient setups.
Pancreatic cancer with less than 10% 3-year survival rate is one of deadliest cancer types and greatly benefits from enhanced radiotherapy. Organ motion monitoring helps spare the normal tissue from high radiation and, in turn, enables the dose escalation to the target that has been shown to improve the effectiveness of RT by doubling and tripling post-RT survival rate. The flexible array transducer is a novel and promising solution to address the limitation of conventional US probes. We proposed a novel shape estimation for flexible array transducer using two sequential algorithms: (i) an optical tracking-based system that uses the optical markers coordinates attached to the probe at specific positions to estimate the array shape in real-time and (ii) a fully automatic shape optimization algorithm that automatically searches for the optimal array shape that results in the highest quality reconstructed image. We conducted phantom and in vivo experiments to evaluate the estimated array shapes and the accuracy of reconstructed US images. The proposed method reconstructed US images with low full-width-at-half-maximum (FWHM) of the point scatters, correct aspect ratio of the cyst, and high-matching score with the ground truth. Our results demonstrated that the proposed methods reconstruct high-quality ultrasound images with significantly less defocusing and distortion compared with those without any correction. Specifically, the automatic optimization method reduced the array shape estimation error to less than half-wavelength of transmitted wave, resulting in a high-quality reconstructed image.
Purpose: The study aimed to implement a novel, deeply accelerated adaptive radiation therapy (DAART) approach for lung cancer radiotherapy (RT). Lung cancer is the most common cause of cancer-related death, and RT is the preferred medically inoperable treatment for early stage non-small cell lung cancer (NSCLC). In the current lengthy workflow, it takes a median of four weeks from diagnosis to RT treatment, which can result in complete restaging and loss of local control with delay. We implemented the DAART approach, featuring a novel deepPERFECT system, to address unwanted delays between diagnosis and treatment initiation. Materials and methods: We developed a deepPERFECT to adapt the initial diagnostic imaging to the treatment setup to allow initial RT planning and verification. We used data from 15 patients with NSCLC treated with RT to train the model and test its performance. We conducted a virtual clinical trial to evaluate the treatment quality of the proposed DAART for lung cancer radiotherapy. Results: We found that deepPERFECT predicts planning CT with a mean high-intensity fidelity of 83 and 14 HU for the body and lungs, respectively. The shape of the body and lungs on the synthesized CT was highly conformal, with a dice similarity coefficient (DSC) of 0.91, 0.97, and Hausdorff distance (HD) of 7.9 mm, and 4.9 mm, respectively, compared with the planning CT scan. The tumor showed less conformality, which warrants acquisition of treatment Day1 CT and online adaptive RT. An initial plan was designed on synthesized CT and then adapted to treatment Day1 CT using the adapt to position (ATP) and adapt to shape (ATS) method. Non-inferior plan quality was achieved by the ATP scenario, while all ATS-adapted plans showed good plan quality. Conclusion: DAART reduces the common online ART (ART) treatment course by at least two weeks, resulting in a 50% shorter time to treatment to lower the chance of restaging and loss of local control.
Major sources of delay in the standard of care RT workflow are the need for multiple appointments and separate image acquisition. In this work, we addressed the question of how we can expedite the workflow by synthesizing planning CT from diagnostic CT. This idea is based on the theory that diagnostic CT can be used for RT planning, but in practice, due to the differences in patient setup and acquisition techniques, separate planning CT is required. We developed a generative deep learning model, deepPERFECT, that is trained to capture these differences and generate deformation vector fields to transform diagnostic CT into preliminary planning CT. We performed detailed analysis both from an image quality and a dosimetric point of view, and showed that deepPERFECT enabled the preliminary RT planning to be used for preliminary and early plan dosimetric assessment and evaluation.
Pancreatic cancer with more than 60,000 new cases each year has less than 10 percent 5-year overall survival. Radiation therapy (RT) is an effective treatment for Locally advanced pancreatic cancer (LAPC). The current clinical RT workflow is lengthy and involves separate image acquisition for diagnostic CT (dCT) and planning CT (pCT). Studies have shown a reduction in mortality rate from expeditious radiotherapy treatment. dCT and pCT are acquired separately because of the differences in the image acquisition setup and patient body. We are presenting deepPERFECT: deep learning-based model to adapt the shape of the patient body on dCT to the treatment delivery setup. Our method expedites the treatment course by allowing the design of the initial RT planning before the pCT acquisition. Thus, the physicians can evaluate the potential RT prognosis ahead of time, verify the plan on the treatment day-one CT and apply any online adaptation if needed. We used the data from 25 pancreatic cancer patients. The model was trained on 15 cases and tested on the remaining ten cases. We evaluated the performance of four different deep-learning architectures for this task. The synthesized CT (sCT) and regions of interest (ROIs) were compared with ground truth (pCT) using Dice similarity coefficient (DSC) and Hausdorff distance (HD). We found that the three-dimensional Generative Adversarial Network (GAN) model trained on large patches has the best performance. The average DSC and HD for body contours were 0.93, and 4.6 mm. We found no statistically significant difference between the synthesized CT plans and the ground truth. We showed that employing deepPERFECT shortens the current lengthy clinical workflow by at least one week and improves the effectiveness of treatment and the quality of life of pancreatic cancer patients.
PURPOSE: Iodination of rectal hydrogel spacer increases the computed tomography (CT) visibility. The effect of iodinated hydrogel spacer material on the accuracy of proton dosimetry has not been fully studied yet. We presented a systematic study to determine the effect of iodination on proton dosimetry accuracy during proton therapy (PT). METHODS: PT plans were designed for 20 prostate cancer patients with rectal hydrogel spacer. Three variations of hydrogel density were considered. First, as the ground truth, the true elemental composition of hydrogel true material (TM), verified by our measurement of spacer stopping power ratio, was used for plan optimization and Monte Carlo dose calculation. The dose distribution was recalculated with (1) no material (NM) override based on the CT intensity of the iodinated spacer, and (2) the water material (WM) override, where spacer material was replaced by water. The plans were compared with the ground truth using the metrics of gamma index (GI) and dosimetric indices. RESULTS: The iodination of hydrogel spacer affected the proton dose distribution with the NM scenario showing the most deviation from the ground truth. The iodination of spacer resulted in a notable increase in CT intensity and led to the treatment planning systems mistreating the iodinated spacer as a high-density material. Among the structures adjacent to the target, neurovascular bundles showed the largest dose difference, up to 350 cGy or about 5% of the prescribed dose with NM. Compared to the WM scenario, dose distribution similarity and GI passing ratios were lower in the NM scenario. CONCLUSION: The inaccurate CT intensity-based material for iodinated spacer resulted in errors in PT dose calculation. We found that the error was negligible if the iodinated spacer was replaced with water. Water density can be used as a clinically accessible and convenient alternative material override to true spacer material.
Prostatic Neoplasms , Proton Therapy , Male , Humans , Proton Therapy/methods , Protons , Hydrogels , Radiometry , Rectum , Prostatic Neoplasms/radiotherapy , Water , Radiotherapy Dosage , Radiotherapy Planning, Computer-Assisted/methods
Sensory systems must continuously adapt to optimally encode stimuli encountered within the natural environment. The prevailing view is that such optimal coding comes at the cost of increased ambiguity, yet to date, prior studies have focused on artificial stimuli. Accordingly, here we investigated whether such a trade-off between optimality and ambiguity exists in the encoding of natural stimuli in the vestibular system. We recorded vestibular nuclei and their target vestibular thalamocortical neurons during naturalistic and artificial self-motion stimulation. Surprisingly, we found no trade-off between optimality and ambiguity. Using computational methods, we demonstrate that thalamocortical neural adaptation in the form of contrast gain control actually reduces coding ambiguity without compromising the optimality of coding under naturalistic but not artificial stimulation. Thus, taken together, our results challenge the common wisdom that adaptation leads to ambiguity and instead suggest an essential role in underlying unambiguous optimized encoding of natural stimuli.
Motion Perception , Vestibule, Labyrinth , Brain , Motion , Motion Perception/physiology , Neurons/physiology , Vestibule, Labyrinth/physiology
PURPOSE: Pancreatic cancer is the fourth leading cause of cancer-related death with a 10% 5-year overall survival rate (OS). Radiation therapy (RT) in addition to dose escalation improves the outcome by significantly increasing the OS at 2 and 3 years but is hindered by the toxicity of the duodenum. Our group showed that the insertion of hydrogel spacer reduces duodenal toxicity, but the complex anatomy and the demanding procedure make the benefits highly uncertain. Here, we investigated the feasibility of augmenting the workflow with intraoperative feedback to reduce the adverse effects of the uncertainties. MATERIALS AND METHODS: We simulated three scenarios of the virtual spacer for four cadavers with two types of gross tumor volume (GTV) (small and large); first, the ideal injection; second, the nonideal injection that incorporates common spacer placement uncertainties; and third, the corrective injection that uses the simulation result from nonideal injection and is designed to compensate for the effect of uncertainties. We considered two common uncertainties: (1) "Narrowing" is defined as the injection of smaller spacer volume than planned. (2) "Missing part" is defined as failure to inject spacer in the ascending section of the duodenum. A total of 32 stereotactic body radiation therapy (SBRT) plans (33 Gy in 5 fractions) were designed, for four cadavers, two GTV sizes, and two types of uncertainties. The preinjection scenario for each case was compared with three scenarios of virtual spacer placement from the dosimetric and geometric points of view. RESULTS: We found that the overlapping PTV space with the duodenum is an informative quantity for determining the effective location of the spacer. The ideal spacer distribution reduced the duodenal V33Gy for small and large GTV to less than 0.3 and 0.1cc, from an average of 3.3cc, and 1.2cc for the preinjection scenario. However, spacer placement uncertainties reduced the efficacy of the spacer in sparing the duodenum (duodenal V33Gy: 1.3 and 0.4cc). The separation between duodenum and GTV decreased by an average of 5.3 and 4.6 mm. The corrective feedback can effectively bring back the expected benefits from the ideal location of the spacer (averaged V33Gy of 0.4 and 0.1cc). CONCLUSIONS: An informative feedback metric was introduced and used to mitigate the effect of spacer placement uncertainties and maximize the benefits of the EUS-guided procedure.
Organs at Risk , Radiosurgery , Cadaver , Duodenum/radiation effects , Feedback , Humans , Hydrogels , Organs at Risk/radiation effects , Radiosurgery/methods , Radiotherapy Dosage , Radiotherapy Planning, Computer-Assisted/methods
Purpose: Pancreatic cancer is the fourth leading cause of cancer-related death, with a very low 5-year overall survival rate (OS). Radiation therapy (RT) together with dose escalation significantly increases the OS at 2 and 3 years. However, dose escalation is very limited due to the proximity of the duodenum. Hydrogel spacers are an effective way to reduce duodenal toxicity, but the complexity of the anatomy and the procedure makes the success and effectiveness of the spacer procedure highly uncertain. To provide a preoperative simulation of hydrogel spacers, we presented a patient-specific spacer simulator algorithm and used it to create a decision support system (DSS) to provide a preoperative optimal spacer location to maximize the spacer benefits. Materials and Methods: Our study was divided into three phases. In the validation phase, we evaluated the patient-specific spacer simulator algorithm (FEMOSSA) for the duodenal spacer using the dice similarity coefficient (DSC), overlap volume histogram (OVH), and radial nearest neighbor distance (RNND). For the simulation phase, we simulated four virtual spacer scenarios based on the location of the spacer in para-duodenal space. Next, stereotactic body radiation therapy (SBRT) plans were designed and dosimetrically analyzed. Finally, in the prediction phase, using the result of the simulation phase, we created a Bayesian DSS to predict the optimal spacer location and biological effective dose (BED). Results: A realistic simulation of the spacer was achieved, reflected in a statistically significant increase in average target and duodenal DSC for the simulated spacer. Moreover, the small difference in average mean and 5th-percentile RNNDs (0.5 and 2.1 mm) and OVH thresholds (average of less than 0.75 mm) showed that the simulation attained similar separation as the real spacer. We found a spacer-location-independent decrease in duodenal V20Gy, a highly spacer-location-dependent change in V33Gy, and a strong correlation between L1cc and V33Gy. Finally, the Bayesian DSS predicted the change in BED with a root mean squared error of 3.6 Gys. Conclusions: A duodenal spacer simulator platform was developed and used to systematically study the dosimetric effect of spacer location. Further, L1cc is an informative anatomical feedback to guide the DSS to indicate the spacer efficacy, optimum location, and expected improvement.
PURPOSE: We proposed a Haar feature-based method for tracking endoscopic ultrasound (EUS) probe in diagnostic computed tomography (CT) and Magnetic Resonance Imaging (MRI) scans for guiding hydrogel injection without external tracking hardware. This study aimed to assess the feasibility of implementing our method with phantom and patient images. MATERIALS AND METHODS: Our methods included the pre-simulation section and Haar features extraction steps. Firstly, the simulated EUS set was generated based on anatomic information of interpolated CT/MRI images. Secondly, the efficient Haar features were extracted from simulated EUS images to create a Haar feature dictionary. The relative EUS probe position was estimated by searching the best matched Haar feature vector of the dictionary with Haar feature vector of target EUS images. The utilization of this method was validated using EUS phantom and patient CT/MRI images. RESULTS: In the phantom experiment, we showed that our Haar feature-based EUS probe tracking method can find the best matched simulated EUS image from a simulated EUS dictionary which includes 123 simulated images. The errors of all four target points between the real EUS image and the best matched EUS images were within 1 mm. In the patient CT/MRI scans, the best matched simulated EUS image was selected by our method accurately, thereby confirming the probe location. However, when applying our method in MRI images, our method is not always robust due to the low image resolution. CONCLUSIONS: Our Haar feature-based method is capable to find the best matched simulated EUS image from the dictionary. We demonstrated the feasibility of our method for tracking EUS probe without external tracking hardware, thereby guiding the hydrogel injection between the head of the pancreas and duodenum.
PURPOSE: We investigate two margin-based schemes for optimization target volumes (OTV), both isotropic expansion (2 mm) and beam-specific OTV, to account for uncertainties due to the setup errors and range uncertainties in pancreatic stereotactic pencil beam scanning (PBS) proton therapy. Also, as 2-mm being one of the extreme sizes of margin, we also study whether the plan quality of 2-mm uniform expansion could be comparable to other plan schemes. METHODS AND MATERIALS: We developed 2 schemes for OTV: (1) a uniform expansion of 2 mm (OTV2mm) for setup uncertainty and (2) a water equivalent thickness-based, beam-specific expansion (OTVWET) on beam direction and 2 mm expansion laterally. Six LAPC patients were planned with a prescribed dose of 33 Gy (RBE) in 5 fractions. Robustness optimization (RO) plans on gross tumor volumes, with setup uncertainties of 2 mm and range uncertainties of 3.5%, were implemented as a benchmark. RESULTS: All 3 optimization schemes achieved decent target coverage with no significant difference. The OTV2mm plans show superior organ at risk (OAR) sparing, especially for proximal duodenum. However, OTV2mm plans demonstrate severe susceptibility to range and setup uncertainties with a passing rate of 19% of the plans meeting the goal of 95% volume covered by the prescribed dose. The proposed dose spread function analysis shows no significant difference. CONCLUSIONS: The use of OTVWET mimics a union volume for all scenarios in robust optimization but saves optimization time noticeably. The beam-specific margin can be attractive to online adaptive stereotactic body proton therapy owing to the efficiency of the plan optimization.
PURPOSE: Major advances in delivery systems in recent years have turned radiotherapy (RT) into a more effective way to manage prostate cancer. Still, adjacency of organs at risk (OARs) can severely limit RT benefits. Rectal spacer implant in recto-prostatic space provides sufficient separation between prostate and rectum, and therefore, the opportunity for potential dose escalation to the target and reduction of OAR dose. Pretreatment simulation of spacer placement can potentially provide decision support to reduce the risks and increase the efficacy of the spacer placement procedure. METHODS: A novel finite element method-oriented spacer simulation algorithm, FEMOSSA, was developed in this study. We used the finite element (FE) method to model and predict the deformation of rectum and prostate wall, stemming from hydrogel injection. Ten cases of prostate cancer, which undergone hydrogel placement before the RT treatment, were included in this study. We used the pre-injection organ contours to create the FE model and post-injection spacer location to estimate the distribution of the virtual spacer. Material properties and boundary conditions specific to each patient's anatomy were assigned. The FE analysis was then performed to determine the displacement vectors of regions of interest (ROIs), and the results were validated by comparing the virtually simulated contours with the real post-injection contours. To evaluate the different aspects of our method's performance, we used three different figures of merit: dice similarity coefficient (DSC), nearest neighbor distance (NND), and overlapped volume histogram (OVH). Finally, to demonstrate a potential dosimetric application of FEMOSSA, the predicted rectal dose after virtual spacer placement was compared against the predicted post-injection rectal dose. RESULTS: Our simulation showed a realistic deformation of ROIs. The post-simulation (virtual spacer) created the same separation between prostate and rectal wall, as post-injection spacer. The average DSCs for prostate and rectum were 0.87 and 0.74, respectively. Moreover, there was a statistically significant increase in rectal contour similarity coefficient (P < 0.01). Histogram of NNDs showed the same overall shape and a noticeable shift from lower to higher values for both post-simulation and post-injection, indicative of the increase in distance between prostate and rectum. There was less than 2.2- and 2.1-mm averaged difference between the mean and fifth percentile NNDs. The difference between the OVH distances and the corresponding predicted rectal dose was, on average, less than 1 mm and 1.5 Gy, respectively. CONCLUSIONS: FEMOSSA provides a realistic simulation of the hydrogel injection process that can facilitate spacer placement planning and reduce the associated uncertainties. Consequently, it increases the robustness and success rate of spacer placement procedure that in turn improves prostate cancer RT quality.
Prostatic Neoplasms , Rectum , Finite Element Analysis , Humans , Male , Organs at Risk , Prostatic Neoplasms/radiotherapy , Radiotherapy Dosage , Radiotherapy Planning, Computer-Assisted
