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Dose delivery uncertainty is a major concern in proton therapy, adversely affecting the treatment precision and outcome. Recently, a promising technique, proton-acoustic (PA) imaging, has been developed to provide real-timein vivo3D dose verification. However, its dosimetry accuracy is limited due to the limited-angle view of the ultrasound transducer. In this study, we developed a deep learning-based method to address the limited-view issue in the PA reconstruction. A deep cascaded convolutional neural network (DC-CNN) was proposed to reconstruct 3D high-quality radiation-induced pressures using PA signals detected by a matrix array, and then derive precise 3D dosimetry from pressures for dose verification in proton therapy. To validate its performance, we collected 81 prostate cancer patients' proton therapy treatment plans. Dose was calculated using the commercial software RayStation and was normalized to the maximum dose. The PA simulation was performed using the open-source k-wave package. A matrix ultrasound array with 64 × 64 sensors and 500 kHz central frequency was simulated near the perineum to acquire radiofrequency (RF) signals during dose delivery. For realistic acoustic simulations, tissue heterogeneity and attenuation were considered, and Gaussian white noise was added to the acquired RF signals. The proposed DC-CNN was trained on 204 samples from 69 patients and tested on 26 samples from 12 other patients. Predicted 3D pressures and dose maps were compared against the ground truth qualitatively and quantitatively using root-mean-squared-error (RMSE), gamma-index (GI), and dice coefficient of isodose lines. Results demonstrated that the proposed method considerably improved the limited-view PA image quality, reconstructing pressures with clear and accurate structures and deriving doses with a high agreement with the ground truth. Quantitatively, the pressure accuracy achieved an RMSE of 0.061, and the dose accuracy achieved an RMSE of 0.044, GI (3%/3 mm) of 93.71%, and 90%-isodose line dice of 0.922. The proposed method demonstrates the feasibility of achieving high-quality quantitative 3D dosimetry in PA imaging using a matrix array, which potentially enables the online 3D dose verification for prostate proton therapy.

The purpose of this work was to develop a deep learning (DL) based algorithm, Automatic intensity-modulated radiotherapy (IMRT) Planning via Static Field Fluence Prediction (AIP-SFFP), for automated prostate IMRT planning with real-time planning efficiency. The following method was adopted: AIP-SFFP generates a prostate IMRT plan through predictions of fluence maps using patient anatomy. No inverse planning is required. AIP-SFFP is centered on a custom-built deep learning (DL) neural network for fluence map prediction. Predictions are imported to a commercial treatment-planning system for dose calculation and plan generation. AIP-SFFP was demonstrated for prostate IMRT simultaneously-integrated-boost planning (58.8 Gy/70 Gy to PTV58.8 Gy/PTV70 Gy in 28 fx, PTV = Planning Target Volume). Training data was generated from 106 patients using a knowledge-based planning (KBP) plan generator. Two types of 2D projection images were designed to represent structures' sizes and locations, and a total of eight projections were utilized to describe targets and organs-at-risk. Projections at nine template beam angles were stacked as inputs for artificial intelligence (AI) training. 14 patients were used as independent tests. The generated test plans were compared with the plans from the KBP training plan generator and clinic practice. The following results were obtained: After normalization (PTV70 Gy V70 Gy = 95%), all 14 AI plans met institutional criteria. The coverage of PTV58.8 Gy in the AI plans was comparable to KBP and clinic plans without statistical significance. The whole body (BODY) D1cc and rectum D0.1cc of AI plans were slightly higher (<1 Gy) compared to KBP and clinic plans; in contrast, the bladder D1cc and other rectum and bladder low doses in the AI plans were slightly improved without clinical relevance. The overall isodose distribution in the AI plans was comparable with KBP plans and clinical plans. AIP-SFFP generated each test plan within 20s including the prediction and the dose calculation. In conclusion, AIP-SFFP was successfully developed for prostate IMRT planning. AIP-SFFP demonstrated good overall plan quality and real-time efficiency. Showing great promise, AIP-SFFP will be investigated for immediate clinical application. [ABSTRACT FROM AUTHOR]

Background and Purpose: To report single institution's IGRT and dosimetry analysis on the 37 Gy/5 fraction prostate SBRT clinical trial.Materials/methods: The IRB (Duke University Medical Center) approved clinical trial has treated 28 patients with stage T1-T2c prostate cancer with a regimen of 37 Gy in 5 fractions using IMRT and IGRT protocols since 2009. The clinical trial protocol requires CT/MRI imaging for the prostate delineation; a margin of 3 mm in posterior direction and 5 mm elsewhere for planning target volume (PTV); and strict dose constraints for primary organs-at-risks (OARs) including the bladder, the rectum, and the femoral heads. Rigid IGRT process is also an essential part of the protocol. Precise patient and prostate positioning and dynamic tracking of prostate motion are performed with electromagnetic localization device (Calypso) and on-board imaging (OBI) system. Initial patient and target alignment is performed based on fiducials with OBI imaging system and Calypso system. Prior to treatment, cone-beam CT (CBCT) is performed for soft tissue alignment verification. During treatment, per-beam corrections for target motion using translational couch movements is performed before irradiating each field, based on electromagnetic localization or on-board imaging localization. Dosimetric analysis on target coverage and OAR sparing is performed based on key DVH parameters corresponding to protocol guidance. IGRT analysis is focused on the average frequency and magnitude of corrections during treatment, and overall intra-fractional target drift. A margin value is derived using actual target motion data and the margin recipe from Van Herk et al., and is compared to the current one in practice. In addition, cumulative doses with and without per-beam IGRT corrections are compared to assess the benefit of online IGRT.Results: 1. No deviation has been found in 10 of 14 dosimetric constraints, with minor deviations in the rest 4 constraints.2. Online IGRT techniques including Calypso, OBI and CBCT supplement each other to create an effective and reliable system on tracking target and correcting intra-fractional motion.3. On average ½ corrections have been performed per fraction, with magnitude of (0.22 ± 0.11) cm. Average target drift magnitude is (0.7 ± 1.3) mm in one direction during each fraction.4. Benefit from per-beam correction in overall review is small: most differences from no correction are < 0.1 Gy for PTV D1cc/Dmean and < 1%/1.5 cc for OAR parameters. Up to 1.5 Gy reduction was seen in PTV D99% without online correction. Largest differences for OARs are -4.1 cc and +1.6 cc in the V50% for the bladder and the rectum, respectively. However, online IGRT helps to catch unexpected significant target motion.5. Margin derived from actual target motion is 2.5 mm isotropic, consist with current practice.Conclusions: Clinical experience of the 37 Gy/5-fraction prostate SBRT from a single institution is reported. Dosimetric analysis demonstrated excellent target coverage and OAR sparing for our first 28 patients in this trial. Online IGRT techniques implemented are both effective and reliable. Per-beam correction in general provides a small benefit in dosimetry. Target motion measured by online localization devices confirms that current margin selection is adequate. [ABSTRACT FROM AUTHOR]

Purpose: To ensure plan quality for adaptive IMRT of the prostate, we developed a quantitative evaluation tool using a machine learning approach. This tool generates dose volume histograms (DVHs) of organs-at-risk (OARs) based on prior plans as a reference, to be compared with the adaptive plan derived from fluence map deformation. Methods: Under the same configuration using seven-field 15 MV photon beams, DVHs of OARs (bladder and rectum) were estimated based on anatomical information of the patient and a model learned from a database of high quality prior plans. In this study, the anatomical information was characterized by the organ volumes and distance-to-target histogram (DTH). The database consists of 198 high quality prostate plans and was validated with 14 cases outside the training pool. Principal component analysis (PCA) was applied to DVHs and DTHs to quantify their salient features. Then, support vector regression (SVR) was implemented to establish the correlation between the features of the DVH and the anatomical information. Results: DVH/DTH curves could be characterized sufficiently just using only two or three truncated principal components, thus, patient anatomical information was quantified with reduced numbers of variables. The evaluation of the model using the test data set demonstrated its accuracy ∼80% in prediction and effectiveness in improving ART planning quality. Conclusions: An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART. [ABSTRACT FROM AUTHOR]

Purpose: Dosimetric results and treatment delivery efficiency of RapidArc plans to those of conventional intensity-modulated radiotherapy (IMRT) plans were compared using the Eclipse treatment planning system for high-risk prostate cancer. Materials and Methods: This study included 10 patients. The primary planning target volume (PTVP) contained prostate, seminal vesicles, and pelvic lymph nodes with a margin. The boost PTV (PTVB) contained prostate and seminal vesicles with a margin. The total prescription dose was 75.6 Gy (46.8 Gy to PTVP and an additional 28.8 Gy to PTVB; 1.8 Gy/fraction). Three plans were generated for each PTV: Multiple-field IMRT, one-arc RapidArc (1ARC), and two-arc RapidArc (2ARC). Results: In the primary IMRT with PTVP, average mean doses to bladder, rectum and small bowel were lower by 5.9%, 7.7% and 4.3%, respectively, than in the primary 1ARC and by 3.6%, 4.8% and 3.1%, respectively, than in the primary 2ARC. In the boost IMRT with PTVB, average mean doses to bladder and rectum were lower by 2.6% and 4.8% than with the boost 1ARC and were higher by 0.6% and 0.2% than with the boost 2ARC. Integral doses were 7% to 9% higher with RapidArc than with IMRT for both primary and boost plans. Treatment delivery time was reduced by 2-7 minutes using RapidArc. Conclusion: For PTVs including prostate, seminal vesicles, and lymph nodes, IMRT performed better in dose sparing for bladder, rectum, and small bowel than did RapidArc. For PTVs including prostate and seminal vesicles, RapidArc with two arcs provided plans comparable to those for IMRT. The treatment delivery is more efficient with RapidArc. [Copyright &y& Elsevier]

This study compares three online image guidance techniques (IGRT) for prostate IMRT treatment: bony-anatomy matching, soft-tissue matching, and online replanning. Six prostate IMRT patients were studied. Five daily CBCT scans from the first week were acquired for each patient to provide representative “snapshots” of anatomical variations during the course of treatment. Initial IMRT plans were designed for each patient with seven coplanar 15 MV beams on a Eclipse treatment planning system. Two plans were created, one with a PTV margin of 10 mm and another with a 5 mm PTV margin. Based on these plans, the delivered dose distributions to each CBCT anatomy was evaluated to compare bony-anatomy matching, soft-tissue matching, and online replanning. Matching based on bony anatomy was evaluated using the 10 mm PTV margin (“bone10”). Soft-tissue matching was evaluated using both the 10 mm (“soft10”) and 5 mm (“soft5”) PTV margins. Online reoptimization was evaluated using the 5 mm PTV margin (“adapt”). The replanning process utilized the original dose distribution as the basis and linear goal programming techniques for reoptimization. The reoptimized plans were finished in less than 2 min for all cases. Using each IGRT technique, the delivered dose distribution was evaluated on all 30 CBCT scans (6 patients×5CBCT/patient). The mean minimum dose (in percentage of prescription dose) to the CTV over five treatment fractions were in the ranges of 99%–100% (SD=0.1%–0.8%), 65%–98% (SD=0.4%–19.5%), 87%–99% (SD=0.7%–23.3%), and 95%–99% (SD=0.4%–10.4%) for the adapt, bone10, soft5, and soft10 techniques, respectively. Compared to patient position correction techniques, the online reoptimization technique also showed improvement in OAR sparing when organ motion/deformations were large. For bladder, the adapt technique had the best (minimum) D90, D50, and D30 values for 24, 17, and 15 fractions out of 30 total fractions, while it also had the best D90, D50, and D30 values for the rectum for 25, 16, and 19 fractions, respectively. For cases where the adapt plans did not score the best for OAR sparing, the gains of the OAR sparing in the repositioning-based plans were accompanied by an underdosage in the target volume. To further evaluate the fast online replanning technique, a gold-standard plan (“new” plan) was generated for each CBCT anatomy on the Eclipse treatment planning system. The OAR sparing from the online replanning technique was compared to the new plan. The differences in D90, D50, and D30 of the OARs between the adapt and the new plans were less than 5% in 3 patients and were between 5% and 10% for the remaining three. In summary, all IGRT techniques could be sufficient to correct simple geometrical variations. However, when a high degree of deformation or differential organ position displacement occurs, the online reoptimization technique is feasible with less than 2 min optimization time and provides improvements in both CTV coverage and OAR sparing over the position correction techniques. For these cases, the reoptimization technique can be a highly valuable online IGRT tool to correct daily treatment uncertainties, especially when hypofractionation scheme is applied and daily correction, rather than averaging over many fractions, is required to match the original plan. [ABSTRACT FROM AUTHOR]

Purpose: To provide a benchmark for seminal vesicle (SV) margin selection to account for intrafractional motion and to investigate the effectiveness of 2 motion surrogates in predicting intrafractional SV coverage.Methods and Materials: Fifteen prostate patients were studied. Each patient had 5 pairs (1 patient had 4 pairs) of pretreatment and posttreatment cone beam CTs (CBCTs). Each pair of CBCTs was registered on the basis of prostate fiducial markers. All pretreatment SVs were expanded with 1-, 2-, 3-, 4-, 5-, and 8-mm isotropic margins to form a series of planning target volumes, and their intrafractional coverage to the posttreatment SV determined the "ground truth" for exact coverage. Two motion surrogates, the center of mass (COM) and the border of contour, were evaluated by the use of Pearson product-moment correlation coefficient and exponential fitting for predicting SV underdosage. Action threshold of each surrogate was calculated. The margin for each surrogate was calculated according to a traditional margin recipe.Results: Ninety-five percent posttreatment SV coverage was achieved in 9%, 53%, 73%, 86%, 95%, and 97% of fractions with 1-, 2-, 3-, 4-, 5-, and 8-mm margins, respectively. The 5-mm margins provided 95% intrafractional SV coverage in over 90% of fractions. The correlation between the COM and border was weak, moderate, and strong in the left-right (L-R), anterior-posterior (A-P), and superior-inferior (S-I) directions, respectively. Exponential fitting gave the underdosage threshold of 4.5 and 7.0 mm for the COM and border. The Van Herk margin recipe recommended 0-, 0.5-, and 0.8-mm margins in the L-R, A-P, and S-I directions based on the COM, and 1.2-, 3.9-, and 2.5-mm margins based on the border.Conclusions: Five-millimeter isotropic margins for the SV constitute the minimum required to mitigate the intrafractional motion. Both the COM and the border are acceptable predictors for SV underdosage with 4.5- and 7.0-mm action threshold. Traditional margin based on the COM or border underestimates the margin. [ABSTRACT FROM AUTHOR]