Engineering topics

Craft, D

Publications and source records attributed to Craft, D.

9 records

DOE OSTI2016

SU-F-T-181: Proton Therapy Tissue-Equivalence of 3D Printed Materials

Purpose: This work investigated the proton tissue-equivalence of various 3D printed materials. Methods: Three 3D printers were used to create 5 cm cubic phantoms made of different plastics with varying percentages of infill. White resin, polylactic acid (PLA), and NinjaFlex plastics were used. The infills ranged from 15% to 100%. Each phantom was scanned with a CT scanner to obtain the HU value. The relative linear stopping power (RLSP) was then determined using a multi-layer ion chamber in a 200 MeV proton beam. The RLSP was measured both parallel and perpendicular to the print direction for each material. Results: The HU values of the materials ranged from lung-equivalent (−820 HU σ160) when using a low infill, to soft-tissue-equivalent 159 (σ12). The RLSP of the materials depended on the orientation of the beam relative to the print direction. When the proton beam was parallel to the print direction, the RLSP was generally higher than the RLSP in the perpendicular orientation, by up to 45%. This difference was smaller (less than 6%) for the materials with 100% infill. For low infill cubes irradiated parallel to the print direction, the SOBP curve showed extreme degradation of the beam in the distal region. The materials with 15–25% infill had wide-ranging agreement with a clinical HU-RLSP conversion curve, with some measurements falling within 1% of the curve and others deviating up to 45%. The materials with 100% infill all fell within 7% of the curve. Conclusion: While some materials tested fall within 1% of a clinical HU-RLSP curve, caution should be taken when using 3D printed materials with proton therapy, as the orientation of the beam relative to the print direction can result in a large change in RLSP. Further investigation is needed to measure how the infill pattern affects the material RLSP. This work was supported by PHS grant CA180803.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchical clustering, and a clustering is judged by how well it groups patients into distinct survival classes. The most effective pathway scoring/clustering combination, per clustering p-value, thus generates various ‘signatures’ for conventional and functional cancer classification. Results: PICS successfully regularized large dimension gene data, separated normal and cancerous tissues, and clustered a large patient cohort spanning six cancer types. Furthermore, PICS clustered patient cohorts into distinct, statistically-significant survival groups. For a suboptimally-debulked ovarian cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00127) showed significant improvement over that of a prior gene expression-classified study (p = .0179). For a pancreatic cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00141) showed significant improvement over that of a prior gene expression-classified study (p = .04). Pathway-based classification confirmed biomarkers for the pyrimidine, WNT-signaling, glycerophosphoglycerol, beta-alanine, and panthothenic acid pathways for ovarian cancer. Despite its robust nature, PICS requires significantly less run time than current pathway scoring methods. Conclusion: This work validates the PICS method to improve cancer classification using biological pathways. Patients are classified with greater specificity and physiological relevance as compared to current gene-specific approaches. Focus now moves to utilizing PICS for pan-cancer patient-specific treatment response prediction.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TU-H-BRC-08: Use and Validation of Flexible 3D Printed Tissue Compensators for Post-Mastectomy Radiation Therapy

Purpose: Patient-specific tissue equivalent compensators can be used for post-mastectomy radiation therapy (PMRT) to achieve homogenous dose distributions with single-field treatments. However, current fabrication methods are time consuming and expensive. 3D-printing technology could overcome these limitations. The purposes of this study were to [1] evaluate materials for 3D-printed compensators [2] design and print a compensator to achieve a uniform thickness to a clinical target volume (CTV), and [3] demonstrate that a single-field electron compensator plan is a clinically feasible treatment option for PMRT. Methods: Blocks were printed with three materials; print accuracy, density, Hounsfield units (HU), and percent depth doses (PDD) were evaluated. For a CT scan of an anthropomorphic phantom, we used a ray-tracing method to design a compensator that achieved uniform thickness from compensator surface to CTV. The compensator was printed with flexible tissue equivalent material whose physical and radiological properties were most similar to soft tissue. A single-field electron compensator plan was designed and compared with two standard-of-care techniques. The compensator plan was validated with thermoluminescent dosimeter (TLD) measurements. Results: We identified an appropriate material for 3D-printed compensators that had high print accuracy (99.6%) and was similar to soft tissue; density was 1.04, HU was - 45 ± 43, and PDD curves agreed with clinical curves within 3 mm. We designed and printed a compensator that conformed well to the phantom surface and created a uniform thickness to the CTV. In-house fabrication was simple and inexpensive (<$75). Compared with the two standard plans, the compensator plan resulted in overall more homogeneous dose distributions and performed similarly in terms of lung/heart doses and 90% isodose coverage of the CTV. TLD measurements agreed well with planned doses (within 5 %). Conclusions: We have demonstrated that 3D-printed compensators make single-field electron therapy a clinically feasible treatment option for PMRT.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-AB-209-10: Optimizing the Delivery of Sequential Fluence Maps for Efficient VMAT Delivery

Purpose: To develop an optimization model and solution approach for computing MLC leaf trajectories and dose rates for high quality matching of a set of optimized fluence maps to be delivered sequentially around a patient in a VMAT treatment. Methods: We formulate the fluence map matching problem as a nonlinear optimization problem where time is discretized but dose rates and leaf positions are continuous variables. For a given allotted time, which is allocated across the fluence maps based on the complexity of each fluence map, the optimization problem searches for the best leaf trajectories and dose rates such that the original fluence maps are closely recreated. Constraints include maximum leaf speed, maximum dose rate, and leaf collision avoidance, as well as the constraint that the ending leaf positions for one map are the starting leaf positions for the next map. The resulting model is non-convex but smooth, and therefore we solve it by local searches from a variety of starting positions. We improve solution time by a custom decomposition approach which allows us to decouple the rows of the fluence maps and solve each leaf pair individually. This decomposition also makes the problem easily parallelized. Results: We demonstrate method on a prostate case and a head-and-neck case and show that one can recreate fluence maps to high degree of fidelity in modest total delivery time (minutes). Conclusion: We present a VMAT sequencing method that reproduces optimal fluence maps by searching over a vast number of possible leaf trajectories. By varying the total allotted time given, this approach is the first of its kind to allow users to produce VMAT solutions that span the range of wide-field coarse VMAT deliveries to narrow-field high-MU sliding window-like approaches.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-CD-209-01: A Greedy Reassignment Algorithm for the PBS Minimum Monitor Unit Constraint

Purpose: To investigate a Greedy Reassignment algorithm in order to mitigate the effects of low weight spots in proton pencil beam scanning (PBS) treatment plans. Methods: To convert a plan from the treatment planning system’s (TPS) to a deliverable plan, post processing methods can be used to adjust the spot maps to meets the minimum MU constraint. Existing methods include: deleting low weight spots (Cut method), or rounding spots with weight above/below half the limit up/down to the limit/zero (Round method). An alternative method called Greedy Reassignment was developed in this work in which the lowest weight spot in the field was removed and its weight reassigned equally among its nearest neighbors. The process was repeated with the next lowest weight spot until all spots in the field were above the MU constraint. The algorithm performance was evaluated using plans collected from 190 patients (496 fields) treated at our facility. The evaluation criteria were the γ-index pass rate comparing the pre-processed and post-processed dose distributions. A planning metric was further developed to predict the impact of post-processing on treatment plans for various treatment planning, machine, and dose tolerance parameters. Results: For fields with a gamma pass rate of 90±1%, the metric has a standard deviation equal to 18% of the centroid value. This showed that the metric and γ-index pass rate are correlated for the Greedy Reassignment algorithm. Using a 3rd order polynomial fit to the data, the Greedy Reassignment method had 1.8 times better metric at 90% pass rate compared to other post-processing methods. Conclusion: We showed that the Greedy Reassignment method yields deliverable plans that are closest to the optimized-without-MU-constraint plan from the TPS. The metric developed in this work could help design the minimum MU threshold with the goal of keeping the γ-index pass rate above an acceptable value.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-T-614: Plan Averaging for Multi-Criteria Navigation of Step-And-Shoot IMRT

Purpose: Step-and-shoot IMRT is fundamentally discrete in nature, while multi-criteria optimization (MCO) is fundamentally continuous: the MCO planning consists of continuous sliding across the Pareto surface (the set of plans which represent the tradeoffs between organ-at-risk doses and target doses). In order to achieve close to real-time dose display during this sliding, it is desired that averaged plans share many of the same apertures as the pre-computed plans, since dose computation for apertures generated on-the-fly would be expensive. We propose a method to ensure that neighboring plans on a Pareto surface share many apertures. Methods: Our baseline step-and-shoot sequencing method is that of K. Engel (a method which minimizes the number of segments while guaranteeing the minimum number of monitor units), which we customize to sequence a set of Pareto optimal plans simultaneously. We also add an error tolerance to study the relationship between the number of shared apertures, the total number of apertures needed, and the quality of the fluence map re-creation. Results: We run tests for a 2D Pareto surface trading off rectum and bladder dose versus target coverage for a clinical prostate case. We find that if we enforce exact fluence map recreation, we are not able to achieve much sharing of apertures across plans. The total number of apertures for all seven beams and 4 plans without sharing is 217. With sharing and a 2% error tolerance, this number is reduced to 158 (73%). Conclusion: With the proposed method, total number of apertures can be decreased by 42% (averaging) with no increment of total MU, when an error tolerance of 5% is allowed. With this large amount of sharing, dose computations for averaged plans which occur during Pareto navigation will be much faster, leading to a real-time what-you-see-is-what-you-get Pareto navigation experience. Minghao Guo and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

SU-F-BRD-08: Guaranteed Epsilon-Optimal Treatment Plans with Minimum Number of Beams for SBRT Using RayStation

Purpose: To efficiently find quality-guaranteed treatment plans with the minimum number of beams for stereotactic body radiation therapy using RayStation. Methods: For a pre-specified pool of candidate beams we use RayStation (a treatment planning software for clinical use) to identify the deliverable plan which uses all the beams with the minimum dose to organs at risk (OARs) and dose to the tumor and other structures in specified ranges. Then use the dose matrix information for the generated apertures from RayStation to solve a linear program to find the ideal plan with the same objective and constraints allowing use of all beams. Finally we solve a mixed integer programming formulation of the beam angle optimization problem (BAO) with the objective of minimizing the number of beams while remaining in a predetermined epsilon-optimality of the ideal plan with respect to the dose to OARs. Since the treatment plan optimization is a multicriteria optimization problem, the planner can exploit the multicriteria optimization capability of RayStation to navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus OARs sparing, and then use the proposed technique to reduce the number of beams while guaranteeing quality. For the numerical experiments two liver cases and one lung case with 33 non-coplanar beams are considered. Results: The ideal plan uses an impractically large number of beams. The proposed technique reduces the number of beams to the range of practical application (5 to 9 beams) while remaining in the epsilon-optimal range of 1% to 5% optimality gap. Conclusion: The proposed method can be integrated into a general algorithm for fast navigation of the ideal dose distribution Pareto surface and finding the treatment plan with the minimum number of beams, which corresponds to the delivery time, in epsilon-optimality range of the desired ideal plan. The project was supported by the Federal Share of program income earned by Massachusetts General Hospital on C06 CA059267, Proton Therapy Research and Treatment Center and partially by RaySearch Laboratories.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

TH-E-BRF-01: Exploiting Tumor Shrinkage in Split-Course Radiotherapy

Purpose: In split-course radiotherapy, a patient is treated in several stages separated by weeks or months. This regimen has been motivated by radiobiological considerations. However, using modern image-guidance, it also provides an approach to reduce normal tissue dose by exploiting tumor shrinkage. In this work, we consider the optimal design of split-course treatments, motivated by the clinical management of large liver tumors for which normal liver dose constraints prohibit the administration of an ablative radiation dose in a single treatment. Methods: We introduce a dynamic tumor model that incorporates three factors: radiation induced cell kill, tumor shrinkage, and tumor cell repopulation. The design of splitcourse radiotherapy is formulated as a mathematical optimization problem in which the total dose to the liver is minimized, subject to delivering the prescribed dose to the tumor. Based on the model, we gain insight into the optimal administration of radiation over time, i.e. the optimal treatment gaps and dose levels. Results: We analyze treatments consisting of two stages in detail. The analysis confirms the intuition that the second stage should be delivered just before the tumor size reaches a minimum and repopulation overcompensates shrinking. Furthermore, it was found that, for a large range of model parameters, approximately one third of the dose should be delivered in the first stage. The projected benefit of split-course treatments in terms of liver sparing depends on model assumptions. However, the model predicts large liver dose reductions by more than a factor of two for plausible model parameters. Conclusion: The analysis of the tumor model suggests that substantial reduction in normal tissue dose can be achieved by exploiting tumor shrinkage via an optimal design of multi-stage treatments. This suggests taking a fresh look at split-course radiotherapy for selected disease sites where substantial tumor regression translates into reduced target volumes.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

SU-E-T-478: Sliding Window Multi-Criteria IMRT Optimization

Purpose: To demonstrate a method for what-you-see-is-what-you-get multi-criteria Pareto surface navigation for step and shoot IMRT treatment planning. Methods: We show mathematically how multiple sliding window treatment plans can be averaged to yield a single plan whose dose distribution is the dosimetric average of the averaged plans. This is incorporated into the Pareto surface navigation based approach to treatment planning in such a way that as the user navigates the surface, the plans he/she is viewing are ready to be delivered (i.e. there is no extra ‘segment the plans’ step that often leads to unacceptable plan degradation in step and shoot Pareto surface navigation). We also describe how the technique can be applied to VMAT. Briefly, sliding window VMAT plans are created such that MLC leaves paint out fluence maps every 15 degrees or so. These fluence map leaf trajectories are averaged in the same way the static beam IMRT ones are. Results: We show mathematically that fluence maps are exactly averaged using our leaf sweep averaging algorithm. Leaf transmission and output factor corrections effects, which are ignored in this work, can lead to small errors in terms of the dose distributions not being exactly averaged even though the fluence maps are. However, our demonstrations show that the dose distributions are almost exactly averaged as well. We demonstrate the technique both for IMRT and VMAT. Conclusions: By turning to sliding window delivery, we show that the problem of losing plan fidelity during the conversion of an idealized fluence map plan into a deliverable plan is remedied. This will allow for multicriteria optimization that avoids the pitfall that the planning has to be redone after the conversion into MLC segments due to plan quality decline. David Craft partially funded by RaySearch Laboratories.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗