Search

Background Total skin electron therapy (TSET) utilizes high-energy electrons to treat malignancies on the entire body surface. The otherwise invisible radiation beam can be observed via the optical Cherenkov photons emitted from interactions between the high-energy electron beam and tissue. Methods and materials With a time-gated intensified camera system, the Cherenkov emission can be used to evaluate the dose uniformity on the surface of the patient in real time. Fifteen patients undergoing TSET in various conditions (whole body and half body) were imaged and analyzed. Each patient was monitored during TSET via in vivo detectors (IVD) in nine locations. For accurate Cherenkov imaging, a comparison between IVD and Cherenkov profiles was conducted using a polyvinyl chloride board to establish the perspective corrections. Results and discussion With proper corrections developed in this study including the perspective and inverse square corrections, the Cherenkov imaging provided two-dimensional maps proportional to dose and projected on patient skin. The results of ratio between chest and umbilicus points were in good agreement with in vivo point dose measurements, with a standard deviation of 2.4% compared to OSLD measurements. Conclusions Cherenkov imaging is a viable tool for validating patient-specific dose distributions during TSET.

Purpose Manual delineation of organs-at-risk (OARs) in radiotherapy is both time-consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve the segmentation accuracy and efficiency with a novel network named convolutional neural networks (CNN) Cascades. Methods CNN Cascades was a two-step, coarse-to-fine approach that consisted of a simple region detector (SRD) and a fine segmentation unit (FSU). The SRD first used a relative shallow network to define the region of interest (ROI) where the organ was located, and then, the FSU took the smaller ROI as input and adopted a deep network for fine segmentation. The imaging data (14,651 slices) of 100 head-and-neck patients with segmentations were used for this study. The performance was compared with the state-of-the-art single CNN in terms of accuracy with metrics of Dice similarity coefficient (DSC) and Hausdorff distance (HD) values. Results The proposed CNN Cascades outperformed the single CNN on accuracy for each OAR. Similarly, for the average of all OARs, it was also the best with mean DSC of 0.90 (SRD: 0.86, FSU: 0.87, and U-Net: 0.85) and the mean HD of 3.0 mm (SRD: 4.0, FSU: 3.6, and U-Net: 4.4). Meanwhile, the CNN Cascades reduced the mean segmentation time per patient by 48% (FSU) and 5% (U-Net), respectively. Conclusions The proposed two-step network demonstrated superior performance by reducing the input region. This potentially can be an effective segmentation method that provides accurate and consistent delineation with reduced clinician interventions for clinical applications as well as for quality assurance of a multicenter clinical trial.

Purpose: Potential collision between the patient/couch and the gantry could delay the start of the treatment and reduce clinical efficiency. The ability to accurately detect possible collisions during the treatment planning phase is desired. Such collision detection should account for the specific proton gantry design, the treatment beam configuration, couch orientation, and the patient specific geometry. In this paper the authors developed an approach to detect possible patient-machine collisions using patient treatment plan data. Methods: The geometry of the machine and the patient was reconstructed relative to the isocenter of the proton treatment room. The surface contour of the gantry was first captured from the proton computer aided design and reconstructed to account for specific gantry rotation, snout position, collimator rotation, and range compensator dimensions based on the patient treatment plan data. The patient body and couch contours were captured from the patient's CT DICOM structure file. They were reconstructed relative to the isocenter taking into account treatment couch rotation. For potential collision that occurs at body portions where no CT images exist, scout images are used to construct the body contour. A software program was developed using a ray casting algorithm that was applied to detect collisions by determiningmore » if any of the patient and couch contour points fall into the spatial polygons formed by the proton gantry surfaces. Results: Twenty-four patient plans with or without potential collisions were retrospectively identified and analyzed using the collision detection software. In addition, five collision cases were artificially generated using an anthropomorphic phantom. The program successfully detected the collisions in all cases. The calculation time for each case was within 20 s. The software program was implemented in the authors' clinic to detect patient-gantry or gantry-couch collisions in the treatment planning phase. Conclusions: The authors developed a fast and clinically feasible patient-specific collision detection program for proton therapy based on a ray casting algorithm. If incorporated during the treatment planning phase it may lead to improved clinical efficiency. This methodology could also be applied to patient collision detection in photon therapy.« less

Uncontrolled local growth is the cause of death in ∼ 30% of patients with unresectable pancreatic cancers. The addition of standard-dose radiotherapy to gemcitabine has been shown to confer a modest survival benefit in this population. Radiation dose escalation with three-dimensional planning is not feasible, but high-dose intensity-modulated radiation therapy (IMRT) has been shown to improve local control. Still, dose-escalation remains limited by gastrointestinal toxicity. In this study, the authors investigate the potential use of double scattering (DS) and pencil beam scanning (PBS) proton therapy in limiting dose to critical organs at risk.The authors compared DS, PBS, and IMRT plans in 13 patients with unresectable cancer of the pancreatic head, paying particular attention to duodenum, small intestine, stomach, liver, kidney, and cord constraints in addition to target volume coverage. All plans were calculated to 5500 cGy in 25 fractions with equivalent constraints and normalized to prescription dose. All statistics were by two-tailed paired t-test.Both DS and PBS decreased stomach, duodenum, and small bowel dose in low-dose regions compared to IMRT (p0.01). However, protons yielded increased doses in the mid to high dose regions (e.g., 23.6-53.8 and 34.9-52.4 Gy for duodenum using DS and PBS, respectively; p0.05). Protons also increased generalized equivalent uniform dose to duodenum and stomach, however these differences were small (5% and 10%, respectively; p0.01). Doses to other organs-at-risk were within institutional constraints and placed no obvious limitations on treatment planning.Proton therapy does not appear to reduce OAR volumes receiving high dose. Protons are able to reduce the treated volume receiving low-intermediate doses, however the clinical significance of this remains to be determined in future investigations.

Potential collision between the patient∕couch and the gantry could delay the start of the treatment and reduce clinical efficiency. The ability to accurately detect possible collisions during the treatment planning phase is desired. Such collision detection should account for the specific proton gantry design, the treatment beam configuration, couch orientation, and the patient specific geometry. In this paper the authors developed an approach to detect possible patient-machine collisions using patient treatment plan data.The geometry of the machine and the patient was reconstructed relative to the isocenter of the proton treatment room. The surface contour of the gantry was first captured from the proton computer aided design and reconstructed to account for specific gantry rotation, snout position, collimator rotation, and range compensator dimensions based on the patient treatment plan data. The patient body and couch contours were captured from the patient's CT DICOM structure file. They were reconstructed relative to the isocenter taking into account treatment couch rotation. For potential collision that occurs at body portions where no CT images exist, scout images are used to construct the body contour. A software program was developed using a ray casting algorithm that was applied to detect collisions by determining if any of the patient and couch contour points fall into the spatial polygons formed by the proton gantry surfaces.Twenty-four patient plans with or without potential collisions were retrospectively identified and analyzed using the collision detection software. In addition, five collision cases were artificially generated using an anthropomorphic phantom. The program successfully detected the collisions in all cases. The calculation time for each case was within 20 s. The software program was implemented in the authors' clinic to detect patient-gantry or gantry-couch collisions in the treatment planning phase.The authors developed a fast and clinically feasible patient-specific collision detection program for proton therapy based on a ray casting algorithm. If incorporated during the treatment planning phase it may lead to improved clinical efficiency. This methodology could also be applied to patient collision detection in photon therapy.

Purpose: Uncontrolled local growth is the cause of death in ∼30% of patients with unresectable pancreatic cancers. The addition of standard-dose radiotherapy to gemcitabine has been shown to confer a modest survival benefit in this population. Radiation dose escalation with three-dimensional planning is not feasible, but high-dose intensity-modulated radiation therapy (IMRT) has been shown to improve local control. Still, dose-escalation remains limited by gastrointestinal toxicity. In this study, the authors investigate the potential use of double scattering (DS) and pencil beam scanning (PBS) proton therapy in limiting dose to critical organs at risk. Methods: The authors compared DS, PBS, and IMRT plans in 13 patients with unresectable cancer of the pancreatic head, paying particular attention to duodenum, small intestine, stomach, liver, kidney, and cord constraints in addition to target volume coverage. All plans were calculated to 5500 cGy in 25 fractions with equivalent constraints and normalized to prescription dose. All statistics were by two-tailed paired t-test. Results: Both DS and PBS decreased stomach, duodenum, and small bowel dose in low-dose regions compared to IMRT (p < 0.01). However, protons yielded increased doses in the mid to high dose regions (e.g., 23.6–53.8 and 34.9–52.4 Gy for duodenum using DS and PBS, respectively; p < 0.05). Protons also increased generalized equivalent uniform dose to duodenum and stomach, however these differences were small (

Purpose: To assess the feasibility of proton pencil beam scanning (PBS) for the treatment of mediastinal lymphoma. Methods: A group of 6 patients were planned using an anterior field with PBS. Spots with ∼5 mm σ were used for all patients, while large spots (∼10 mm σ) were employed for patients with motion perpendicular to the beam (≥5 mm). We considered volumetric repainting such that, in each fraction, the same field would be delivered twice. Four-dimensional dose was calculated on initial and verification 4-dimensional computed tomography (4D-CT) scans (2—3) based on respiratory trace and beam delivery sequence. This was implemented by binning the spots into separate plans on each 4D-CT phase respectively. Four starting phases were sampled for each painting and 4 energy switching times (0.5 s, 1 s, 3 s, and 5 s) were tested, resulting in 2560 dose distributions for the cohort. Plan robustness was measured for target and critical structures in terms of the percentage difference between delivered dose and planned dose. Results: For 5 of the 6 patients, the ITV (internal target volume) D98% was degraded by

Purpose: To quantify intrafraction prostate motion between patient groups treated with and without daily endorectal balloon (ERB) employed during prostate radiotherapy and establish the effectiveness of the ERB. Methods: Real time intrafraction prostate motion from 29 non‐ERB (1061 sessions) and 30 ERB (1008 sessions) patients was evaluated based on three‐dimensional (3D), left, right, cranial, caudal, anterior and posterior displacements. The average percentage of time with 3D and unidirectional prostate displacements > 2, 3, 4, 5, 6, 7, 8, 9, and 10mm in 1 minute intervals was calculated for up to 6 minutes of treatment time. The Kolmogorov‐Smirnov method was used to evaluate the intrafraction prostate motion pattern between both groups. Results: 3D motion >=1 cm was observed for the non‐ERB group only. The motion increased as a function of elapsed time for displacements > 2 to 8mm for the non‐ERB group and > 2 to 4 mm for the ERB group (p 5 mm (p < 0.05). The 3D internal margin (IM) covering 95% treatment time can be reduced from 5 to 3 mm (40% reduction) while the asymmetrical IM can be reduced from 3 to 2 mm (33% reduction) in cranial, caudal, anterior, and posterior for 6 minutes treatment, when ERB is employed. Beyond 6 minutes, the 3D and cranial, caudal, anterior, and posterior IMs can be reduced from 9, 4, 7, 7, and 8 to 5, 2, 5, 3, and 4 mm, respectively (up to 57% reduction). The calculated IMs apply to the worst case scenario patient as well. Conclusions: The ERB effectively reduces prostate motion and allows for smaller IMs. Therefore, daily ERB has the potential to further improve toxicity profiles in prostate radiotherapy.

Purpose: To study the feasibility of using convolution method of a motion kernel to correct tumor motion when treating pancreatic cancer with IMRT.Method and Materials: Five pancreatic IMRT plans were selected in this study. The Mapcheck device was placed on a moving phantom that simulated the 3D tumor motion: a sinusoid wave with a period of 4s. The IMRT plans were delivered under this condition, and the motion‐applied measurements were obtained. A convolution method incorporating the 3D pancreatic tumor motion was applied to correct tumor motion. The motion‐corrected verification fluence maps were extracted from the motion‐corrected 3D dose, and the simulated Mapcheck measurements were derived. Motion‐corrected verification fluence maps were compared with the measured ones, and simulated measurements were compared with the static verification fluence maps. The IMRT QA passing rates for those comparisons were obtained using the most stringent criterion (90% passing rate at 3%/ 3mm DTA, TH=10%). Results: The agreement between the measured and motion‐corrected verification fluence maps was ideal (the IMRT QA passing rates ≥ 98.5%). The magnitude of pancreatic tumor motion >1.0 cm caused a significant dosimetric effect as indicated by the IMRT QA passing rate. The IMRT QA passing rates dropped as low as 61% with the maximum magnitude of motion (2.4 cm). Conclusion: The motion correction by using motion kernel convolution is a reliable way to predict the actual dose to the moving target for IMRT plan. The motion convolved dose distribution should be used for accurate dose evaluation as the static dose distribution provided by TPS may provide erroneous dose prediction. A universal margin to compensate for tumor motion should be avoided in the clinical practice, and 4D MRI or 4D CT should be employed to evaluate the extent of pancreatic tumor motion for each patient.