Mixed-size spot scanning with a compact large momentum acceptance superconducting (LMA-SC) gantry beamline for proton therapy.

Objective. Lowering treatment costs and improving treatment quality are two primary goals for next-generation proton therapy (PT) facilities. This work will design a compact large momentum acceptance superconducting (LMA-SC) gantry beamline to reduce the footprint and expense of the PT facilities, with a novel mixed-size spot scanning method to improve the sparing of organs at risk (OAR).Approach. For the LMA-SC gantry beamline, the movable energy slit is placed in the middle of the last achromatic bending section, and the beam momentum spread of delivered spots can be easily changed during the treatment. Simultaneously, changing the collimator size can provide spots with various lateral spot sizes. Based on the provided large-size and small-size spot models, the treatment planning with mixed spot scanning is optimized: the interior of the target is irradiated with large-size spots (to cover the uniform-dose interior efficiently), while the peripheral of the target is irradiated with small-size spots (to shape the sharp dose falloff at the peripheral accurately).Main results. The treatment plan with mixed-size spot scanning was evaluated and compared with small and large-size spot scanning for thirteen clinical prostate cases. The mixed-size spot plan had superior target dose homogeneities, better protection of OAR, and better plan robustness than the large-size spot plan. Compared to the small-size spot plan, the mixed-size spot plan had comparable plan quality, better plan robustness, and reduced plan delivery time from 65.9 to 40.0 s.Significance. The compact LMA-SC gantry beamline is proposed with mixed-size spot scanning, with demonstrated footprint reduction and improved plan quality compared to the conventional spot scanning method.

Improving delivery efficiency using spots and energy layers reduction algorithms based on a large momentum acceptance beamline.

BACKGROUND: Intensity-modulated proton therapy (IMPT) is a well-known delivery method of proton therapy. Besides higher plan quality, reducing the delivery time is also essential to IMPT plans. It can enhance patient comfort, reduce treatment costs, and improve delivery efficiency. From the perspective of treatment efficacy, it contributes to mitigating the intra-fractional motions and improving the accuracy of radiotherapy, especially for moving tumors. PURPOSE: However, there is a tradeoff problem between the plan quality and delivery time. We consider the potential of a large momentum acceptance (LMA) beamline and apply the spots and energy layers reduction method to reduce the delivery time. METHODS: The delivery time for each field consists of the energy layer switching time, spot traveling time, and dose delivery time. The larger momentum spread and higher intensity beam offered by the LMA beamline contribute to reducing the total delivery time compared to the conventional beamline. In addition to the dose fidelity term, an L1 and logarithm items were added to the objective function to increase the sparsity of the low-weighted spots and energy layers. After that, the low-weighted spots and layers were iteratively excluded in the reduced plan, which reduced the energy layer switching time and spot traveling time. We used the standard, reduced, and LMA-reduced plans to validate the proposed method and tested it on prostate and nasopharyngeal cases. Then, we compared and evaluated the plan quality, treatment time, and plan robustness against delivery uncertainty. RESULTS: Compared with the standard plans, the number of spots in the LMA-reduced plans was on average reduced by 13 400 (95.6%) for prostate cases and by 48 300 (80.7%) for nasopharyngeal cases and the number of energy layers was on average reduced by 49 (61.3%) for prostate cases and by 97 (50.5%) for nasopharyngeal cases. And, the delivery time of the LMA-reduced plans was shortened from 34.5 to 8.6 s for prostate cases and from 163.8 to 53.6 s for nasopharyngeal cases. The LMA-reduced plans had comparable robustness to the spot monitor unit (MU) error compared with the standard plans, but the LMA-reduced plans became more sensitive to spot position uncertainty. CONCLUSION: The delivery efficiency can be significantly improved using the LMA beamline and spots and energy layers reduction strategies. The method is promising to improve the efficiency of motion mitigation strategies for treating moving tumors.

Feasibility study of fast intensity-modulated proton therapy dose prediction method using deep neural networks for prostate cancer.

PURPOSE: Compared to the pencil-beam algorithm, the Monte-Carlo (MC) algorithm is more accurate for dose calculation but time-consuming in proton therapy. To solve this problem, this study uses deep learning to provide fast 3D dose prediction for prostate cancer patients treated with intensity-modulated proton therapy (IMPT). METHODS: A novel recurrent U-net (RU-net) architecture was trained to predict the 3D dose distribution. Doses, CT images, and beam spot information from IMPT plans were used to train the RU-net with a five-fold cross-validation. However, predicting the complicated dose properties of the IMPT plan is difficult for neural networks. Instead of the peak-monitor unit (MU) model, this work develops the multi-MU model that adopted more comprehensive inputs and was trained with a combinational loss function. The dose difference between the prediction dose and Monte Carlo (MC) dose was evaluated with gamma analysis, dice similarity coefficient (DSC), and dose-volume histogram (DVH) metrics. The MC dropout was also added to the network to quantify the uncertainty of the model. RESULTS: Compared to the peak-MU model, the multi-MU model led to smaller mean absolute errors (3.03% vs. 2.05%, p = 0.005), higher gamma-passing rate (2 mm, 3%: 97.42% vs. 93.69%, p = 0.005), higher dice similarity coefficient, and smaller relative DVH metrics error (clinical target volume (CTV) D98% : 3.03% vs. 6.08%, p = 0.017; in Bladder V30: 3.08% vs. 5.28%, p = 0.028; and in Bladder V20: 3.02% vs. 4.42%, p = 0.017). Considering more prior knowledge, the multi-MU model had better-predicted accuracy with a prediction time of less than half a second for each fold. The mean uncertainty value of the multi-MU model is 0.46%, with a dropout rate of 10%. CONCLUSION: This method was a nearly real-time IMPT dose prediction algorithm with accuracy comparable to the pencil beam (PB) analytical algorithms used in prostate cancer. This RU-net might be used in plan robustness optimization and robustness evaluation in the future.

Severity and prevalence of behavioral and psychological symptoms among patients of different dementia stages in Taiwan

Abstract Background To better understand the trends of behavioral and psychological symptoms of dementia (BPSD) over the disease progression is important to provide psychoeducation for dementia caregivers. Objective This study examined the severity and occurrence of BPSD across the various degrees of the disease. Methods This study was a cross-sectional design. Patients (N = 276) who had dementia from July 2001 to October 2008 were surveyed and assessed for dementia stage, using the clinical dementia rating scale (CDR). BPSD was evaluated using the Neuropsychiatric Inventory (NPI). We examined the differences between the severities and occurrence of the individual's BPSD among various CDR stages with the Kruskal-Wallis test and Chi-square test. Results Delusion (p = 0.01), agitation/aggression (p = 0.033), apathy/indifference (p = 0.009), aberrant motor behavior (p < 0.001), nighttime behavior disturbances (p < 0.001), and eating abnormalities (p = 0.001) were significantly different among stages of dementia. The severity of BPSD became exacerbated over the course of the disease, and was highest in moderate (CDR = 2) or severe (CDR = 3) dementia. The occurrence of BPSD was highest when the CDR equaled 2 (97.5%). Discussion The association of global (or certain) BPSD, across different stages of dementia, is a non-linear relationship. These findings suggest the importance of taking into account clinical dementia stage for managing BPSD.