Effect of linear accelerator carbon fiber couch on radiotherapy dose.

This study aimed to explore the effect of carbon fiber couch on radiotherapy dose attenuation and gamma pass rate in intensity-modulated radiotherapy (IMRT) plans. A phantom inserted with an ionization chamber was placed at different positions of the couch, and the dose was measured by the chamber. Under the same positioning, the phantom dose was calculated using the real and virtual couch images, and the difference in the planned dose of radiotherapy was compared. Ten clinical IMRT plans were selected as dose verification data, and the gamma pass rates were compared between couch addition and non-addition conditions. When the radiation field was near 110° and 250°, the measured value attenuation coefficient of the ionization chamber at the joint of the couch was up to 34%; the attenuation coefficient of the treatment couch from the actual couch image calculated using the treatment planning system (TPS) was up to 33%; the attenuation coefficient of the virtual couch calculated using the TPS was up to 4.0%. The gamma pass rate of the dose verification near gantry angles 110° and 250° was low, and that of the joint could be lower than 85% under the condition of 3%/3 mm. The gamma pass rates of the radiation field passing through the couch were all affected. The dose was affected by the radiation field passing through the couch, with the largest effect when passing through the joint part of the treatment couch, followed by that of the main couch plate and extension plate. When the irradiation field passed through the joint and near 110° and 250° of the main couch, the dose difference was large, making it unsuitable for treatment.

Influence of beamlet width on dynamic IMRT plan quality in nasopharyngeal carcinoma.

Objective: This study aimed to identify the effects of beamlet width on dynamic intensity-modulated radiation therapy (IMRT) for nasopharyngeal carcinoma (NPC) and determine the optimal parameters for the most effective radiotherapy plan. Methods: This study evaluated 20 patients with NPC were selected for dynamic IMRT. Only the beamlet width in the optimization parameters was changed (set to 2, 4, 6, 8, and 10 mm that were named BL02, BL04, BL06, BL08, and BL10, respectively) to optimize the results of the five groups of plans. Using the plan quality scoring system, the dose results of the planning target volumes (PTVs) and organs at risks (OARs) were analyzed objectively and comprehensively. The lower the quality score, the better the quality of the plan. The efficiency and accuracy of plan execution were evaluated using monitor units (MUs) and plan delivery time (PDT). Results: The BL04 mm group had the lowest quality score for the targets and OARs (0.087), while the BL10 mm group had the highest total score (1.249). The BL04 mm group had the highest MUs (837 MUs) and longest PDT (358 s). However, the MUs range of each group plan was below 100 MUs, and the PDT range was within 30 s. In the BL02, BL04, BL06, BL08, and BL10 plans, <5 MUs segments accounted for 33%, 16%, 24%, 33%, and 40% of total segments, respectively, with which the lowest was in the BL04 mm group. Conclusion: Smaller beamlet widths have not only reduced OARs dose while maintaining high dose coverage to the PTVs, but also lead to more MUs that would produce greater PDT. Considering the quality and efficiency of dynamic IMRT, the beamlet width value of the Monaco treatment planning system set to 4 mm would be optimal for NPC.

Voxel-based automatic multi-criteria optimization for intensity modulated radiation therapy.

BACKGROUND: Automatic multi-criteria optimization is necessary for intensity modulated radiation therapy (IMRT) because of low planning efficiency and large plan quality uncertainty in current clinical practice. Most studies focused on imitating dosimetrists' planning procedures to automate this process and ignored the fact that organ-based objective functions typically used in commercial treatment planning systems (such as dose-volume function) usually lead to sub-optimal plans. To guarantee the optimum results and to automate this process, we incorporate an improved automation strategy and a voxel-based optimization algorithm to generate a novel automatic multi-criteria optimization framework. We then evaluate it in clinical cases. METHODS: This novel automatic multi-criteria optimization framework incorporates a ranked priority-list based automatic constraints adjustment strategy and an in-house developed voxel-based optimization algorithm. Constraints are sequentially adjusted following a pre-defined priority list. Afterward, a voxel-based fluence map optimization (FMO) with an orientation to the newly updated constraints is launched to find a Pareto optimal solution. Loops of constraints adjustment are repeated until each of them could not be relaxed or tightened. The feasibility of the framework is evaluated with 10 automatic generated gynecology (GYN) cancer IMRT cases by comparing the dosimetric performance with the original. RESULTS: Plan quality improvement is observed for our automatic multi-criteria optimization method. Comparable DVHs are found for the planning target volume (PTV), but with better organs-at-risk (OAR) dose sparing. Among 13 evaluated dosimetric endpoints, 5 of them show significant improvements in automatically generated plans compared with the original plans. Investigation of improvement tendency during optimization exhibits gradual change as the optimization stage proceeds. An initial voxel-based optimization stage and in-low-priority dosimetric criteria tighten can significantly contribute to the optimization procedure. CONCLUSIONS: We have successfully developed an automatic multi-criteria optimization framework that can dramatically reduce the current trial-and-error patterned planning workload while affording an efficient method to assure high plan quality consistency. This optimization framework is expected to greatly facilitate precise radiation therapy because of its advantages of planning efficiency and plan quality improvement.

[Constraint priority list-based multi-objective optimization for intensity-modulated radiation therapy].

OBJECTIVE: In intensity-modulated radiation therapy (IMRT), it is time-consuming to repeatedly adjust the objectives manually to obtain the best tradeoff between the prescribed dose of the planning target volume and sparing the organs-at-risk. Here we propose a new method to realize automatic multi-objective IMRT optimization, which quantifies the clinical preferences into the constraint priority list and adjusts the dose constraints based on the list to obtain the optimal solutions under the dose constraints. This method contains automatic adjustment mechanism of the dose constraint and automatic voxel weighting factor-based FMO model. Every time the dose constraint is adjusted, the voxel weighting factor-based FMO model is launched to find a global optimal solution that satisfied the current constraints. We tested the feasibility and effectiveness of this method in 6 cases of cervical cancer with IMRT by comparing the original plan and the automatic optimization plan generated by this method. The results showed that with the same PTV coverage and uniformity, the automatic optimization plan had a better a dose sparing of the organs-at-risk and a better plan quality than the original plan, and resulted in obvious reductions of the average V45 of the rectum from (41.99∓13.31)% to (32.55∓22.27)% and of the bladder from (44.37∓4.08)% to (28.99∓15.25)%.

[Prediction of three-dimensional dose distribution in intensity-modulated radiation therapy based on neural network learning].

OBJECTIVE: To establish the association between the geometric anatomical characteristics of the patients and the corresponding three-dimensional (3D) dose distribution of radiotherapy plan via feed-forward back-propagation neural network for clinical prediction of the plan dosimetric features. METHODS: A total of 25 fixed 13-field clinical prostate cancer intensity-modulated radiation therapy (IMRT)/stereotactic body radiation therapy (SBRT) plans were collected with a prescribed dose of 50 Gy. With the distance from each voxel to the planned target volume (PTV) boundary, the distance from each voxel to each organ-at-risk (OAR), and the volume of PTV as the geometric anatomical characteristics of the patients, the voxel deposition dose was used as the plan dosimetric feature. A neural network was used to construct the correlation model between the selected input features and output dose distribution, and the model was trained with 20 randomly selected cases and verified in 5 cases. RESULTS: The constructed model showed a small model training error, small dose differences among the verification samples, and produced accurate prediction results. In the model training, the point-to-point mean dose difference (hereinafter dose difference) of the 3D dose distribution was no greater than 0.0919∓3.6726 Gy, and the average of the relative volume values corresponding to the fixed dose sequence in the DVH (hereinafter DVH difference) did not exceed 1.7%. The dose differences among the 5 samples for validation was 0.1634∓10.5246 Gy with percent dose differences within 2.5% and DVH differences within 3%. The 3D dose distribution showed that the dose difference was small with reasonable predicted dose distribution. This model showed better performances for dose distribution prediction for bladder and rectum than for the femoral heads. CONCLUSION: We established the relationships between the geometric anatomical characteristics of the patients and the corresponding planning 3D dose distribution via feed-forward back-propagation neural network in patients receiving IMRT/SBRT for the same tumor site. The proposed model provides individualized quality standards for automatic plan quality control.