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At least 1,279 records · Page 71
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DOE OSTI2014

Poster — Thur Eve — 61: A new framework for MPERT plan optimization using MC-DAO

This work combines the inverse planning technique known as Direct Aperture Optimization (DAO) with Intensity Modulated Radiation Therapy (IMRT) and combined electron and photon therapy plans. In particular, determining conditions under which Modulated Photon/Electron Radiation Therapy (MPERT) produces better dose conformality and sparing of organs at risk than traditional IMRT plans is central to the project. Presented here are the materials and methods used to generate and manipulate the DAO procedure. Included is the introduction of a powerful Java-based toolkit, the Aperture-based Monte Carlo (MC) MPERT Optimizer (AMMO), that serves as a framework for optimization and provides streamlined access to underlying particle transport packages. Comparison of the toolkit's dose calculations to those produced by the Eclipse TPS and the demonstration of a preliminary optimization are presented as first benchmarks. Excellent agreement is illustrated between the Eclipse TPS and AMMO for a 6MV photon field. The results of a simple optimization shows the functioning of the optimization framework, while significant research remains to characterize appropriate constraints.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Asymmetric dose–volume optimization with smoothness control for rotating-shield brachytherapy

Purpose: It is important to reduce fluence map complexity in rotating-shield brachytherapy (RSBT) inverse planning to improve delivery efficiency while maintaining plan quality. This study proposes an efficient and effective RSBT dose optimization method which enables to produce smooth fluence maps. Methods: Five cervical cancer patients each with a high-risk clinical-target-volume (HR-CTV) larger than 40 cm{sup 3} were considered as the test cases. The RSBT source was a partially shielded electronic brachytherapy source (Xoft Axxent™). The anchor RSBT plans generated by the asymmetric dose–volume optimization with smoothness control (ADOS) method were compared against those produced by the dose–surface optimization (DSO) method and inverse-planning with simulated annealing (IPSA). Either L{sub 1}-norm or L{sub 2}-norm was used to measure the smoothness of a fluence map in the proposed ADOS method as one weighted term of the objective function. Uniform dwell-time scaling was applied to all plans such that HR-CTV D{sub 90} was maximized without violating the D{sub 2cc} tolerances of the rectum, bladder, and sigmoid colon. The quality of the anchor plans was measured with HR-CTV D{sub 90} of the anchor plans. Single-shielded RSBT [(S-RSBT), RSBT with single, fix sized delivery window] and dynamic-sheilded RSBT [(D-RSBT), RSBT with dynamically varying sized delivery window] delivery plans generated based on the anchor plans were also measured, with delivery time constraints of 10, 20, and 30 min/fraction (fx). Results: The average HR-CTV D{sub 90} values of the anchor plans achieved by the ADOS, DSO, and IPSA methods were 111.5, 94.2, and 107.4 Gy, respectively, where the weighting parameter β used in ADOS with L{sub 2}-norm was set to be 100. By using S-RSBT sequencing and 20 min/fx delivery time, the corresponding D{sub 90} values were 88.8, 81.9, and 83.4 Gy; while using D-RSBT sequencing with 20 min/fx delivery time, the corresponding D{sub 90} values were 91.4, 88.3, and 78.9 Gy, respectively. The average optimization times for ADOS, DSO, and IPSA were, respectively, 77, 4, and 1800 s. By using L{sub 1}-norm instead of L{sub 2}-norm in the ADOS method, the optimization time was increased by 20 s, while the D{sub 90} was reduced by 6.8 Gy on average. ADOS-L1 was found to be more sensitive to the weighting parameter than ADOS-L2. If β was increased to 10 000, the D{sub 90} drop with ADOS-L1 was 38 Gy, while the drop with ADOS-L2 is 13 Gy. Conclusions: The ADOS method had a reasonable optimization time cost, while achieving comparable RSBT dose plans as the IPSA method, which is of much higher time complexity. Compared to the DSO and IPSA methods, ADOS is able to generate anchor plans which are more suitable for RSBT delivery while preserving the high quality of the original plans. Compared to ADOS-L1, ADOS-L2 is able to achieve better quality of anchor plans more efficiently.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-07: 4DCT Robust Optimization for Esophageal Cancer Using Intensity Modulated Proton Therapy

Purpose: To develop a 4DCT robust optimization method to reduce the dosimetric impact from respiratory motion in intensity modulated proton therapy (IMPT) for esophageal cancer. Methods: Four esophageal cancer patients were selected for this study. The different phases of CT from a set of 4DCT were incorporated into the worst-case dose distribution robust optimization algorithm. 4DCT robust treatment plans were designed and compared with the conventional non-robust plans. Result doses were calculated on the average and maximum inhale/exhale phases of 4DCT. Dose volume histogram (DVH) band graphic and ΔD95%, ΔD98%, ΔD5%, ΔD2% of CTV between different phases were used to evaluate the robustness of the plans. Results: Compare to the IMPT plans optimized using conventional methods, the 4DCT robust IMPT plans can achieve the same quality in nominal cases, while yield a better robustness to breathing motion. The mean ΔD95%, ΔD98%, ΔD5% and ΔD2% of CTV are 6%, 3.2%, 0.9% and 1% for the robustly optimized plans vs. 16.2%, 11.8%, 1.6% and 3.3% from the conventional non-robust plans. Conclusion: A 4DCT robust optimization method was proposed for esophageal cancer using IMPT. We demonstrate that the 4DCT robust optimization can mitigate the dose deviation caused by the diaphragm motion.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-574: Novel Chance-Constrained Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

Purpose: We propose to apply a probabilistic framework, namely chanceconstrained optimization, in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to hedge against the influence of uncertainties and improve robustness of treatment plans. Methods: IMPT plans were generated for a typical prostate patient. Nine dose distributions are computed — the nominal one and one each for ±5mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. These nine dose distributions are supplied to the solver CPLEX as chance constraints to explicitly control plan robustness under these representative uncertainty scenarios with certain probability. This probability is determined by the tolerance level. We make the chance-constrained model tractable by converting it to a mixed integer optimization problem. The quality of plans derived from this method is evaluated using dose-volume histogram (DVH) indices such as tumor dose homogeneity (D5% – D95%) and coverage (D95%) and normal tissue sparing like V70 of rectum, V65, and V40 of bladder. We also compare the results from this novel method with the conventional PTV-based method to further demonstrate its effectiveness Results: Our model can yield clinically acceptable plans within 50 seconds. The chance-constrained optimization produces IMPT plans with comparable target coverage, better target dose homogeneity, and better normal tissue sparing compared to the PTV-based optimization [D95% CTV: 67.9 vs 68.7 (Gy), D5% – D95% CTV: 11.9 vs 18 (Gy), V70 rectum: 0.0 % vs 0.33%, V65 bladder: 2.17% vs 9.33%, V40 bladder: 8.83% vs 21.83%]. It also simultaneously makes the plan more robust [Width of DVH band at D50%: 2.0 vs 10.0 (Gy)]. The tolerance level may be varied to control the tradeoff between plan robustness and quality. Conclusion: The chance-constrained optimization generates superior IMPT plan compared to the PTV-based optimization with explicit control of plan robustness. NIH/NCI K25CA168984, Eagles Cancer Research Career Development, The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, Mayo ASU Seed Grant, and The Kemper Marley Foundation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

SU-E-T-590: Optimizing Magnetic Field Strengths with Matlab for An Ion-Optic System in Particle Therapy Consisting of Two Quadrupole Magnets for Subsequent Simulations with the Monte-Carlo Code FLUKA

Purpose: Aim of this study was to optimize the magnetic field strengths of two quadrupole magnets in a particle therapy facility in order to obtain a beam quality suitable for spot beam scanning. Methods: The particle transport through an ion-optic system of a particle therapy facility consisting of the beam tube, two quadrupole magnets and a beam monitor system was calculated with the help of Matlab by using matrices that solve the equation of motion of a charged particle in a magnetic field and field-free region, respectively. The magnetic field strengths were optimized in order to obtain a circular and thin beam spot at the iso-center of the therapy facility. These optimized field strengths were subsequently transferred to the Monte-Carlo code FLUKA and the transport of 80 MeV/u C12-ions through this ion-optic system was calculated by using a user-routine to implement magnetic fields. The fluence along the beam-axis and at the iso-center was evaluated. Results: The magnetic field strengths could be optimized by using Matlab and transferred to the Monte-Carlo code FLUKA. The implementation via a user-routine was successful. Analyzing the fluence-pattern along the beam-axis the characteristic focusing and de-focusing effects of the quadrupole magnets could be reproduced. Furthermore the beam spot at the iso-center was circular and significantly thinner compared to an unfocused beam. Conclusion: In this study a Matlab tool was developed to optimize magnetic field strengths for an ion-optic system consisting of two quadrupole magnets as part of a particle therapy facility. These magnetic field strengths could subsequently be transferred to and implemented in the Monte-Carlo code FLUKA to simulate the particle transport through this optimized ion-optic system.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

SU-E-T-780: Use Robustness Optimization (RO) Method to Improve the Planning Efficiency for Pencil Beam Scanning Cranial Spinal Irradiation

Purpose: We evaluate the feasibility of using robustness optimization (RO) function to improve the planning efficiency of pencil beam scanning (PBS) craniospinal irradiation (CSI) with gradient matching technique. Methods: A CSI patient was planned with 2 lateral brain fields and 4 posterior fields to cover the entire spine to maximal field of 24 cm × 20 cm on a compact PBS gantry, ProteusONE. CSI plans were generated using traditional volumetric gradient dose optimization (VGDO) and robustness optimization (RO) method respectively. In traditional VGDO, besides the sectioned spine target volumes, gradient volume (GV) were generated as 4 equally spaced structures e.g. 80%, 60%, 40%, and 20% of prescription dose. In RO method, only sectioned spine target volumes with an overlap of 4cm were created. In the robustness optimization settings, 5mm uncertainty in superior and inferior direction was defined for auto gradient optimization. Dosimetric metrics of conformity number (CN), homogeneity index (HI), and maximal cord doses were compared in Raystation version 4.6.100.6. Results: In VGDO method, total 16 GV structures and five 100% dose level target structures were contoured compared to total 5 target structures in RO method which saves 30 min in contour. With the same PTV coverage (95% volume receive 30.6Gy prescription dose), maximum cord dose is 32.64Gy in VGDO and 31.94Gy in RO. HI is 1.03 and 1.04 for VGDO and RO respectively. CN is 0.93 and 0.94 for VGDO and RO respectively. Conclusions: The dosimetric comparison demonstrated both methods are equivalent in terms of plan quality. With robust optimization for CSI gradient matching, it efficiently reduces the amount of planning target contour structure by factor of 4 and thus improves the planning efficiency especially for 4 or more gradient junction area.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-FG-204-08: Optimization-Based Image Reconstruction From Unevenly Distributed Sparse Projection Views

Purpose: Optimization-based reconstruction has been proposed and investigated for reconstructing CT images from sparse views, as such the radiation dose can be substantially reduced while maintaining acceptable image quality. The investigation has so far focused on reconstruction from evenly distributed sparse views. Recognizing the clinical situations wherein only unevenly sparse views are available, e.g., image guided radiation therapy, CT perfusion and multi-cycle cardiovascular imaging, we investigate the performance of optimization-based image reconstruction from unevenly sparse projection views in this work. Methods: The investigation is carried out using the FORBILD and an anthropomorphic head phantoms. In the study, 82 views, which are evenly sorted out from a full (360°) axial CT scan consisting of 984 views, form sub-scan I. Another 82 views are sorted out in a similar manner to form sub-scan II. As such, a CT scan with sparse (164) views at 1:6 ratio are formed. By shifting the two sub-scans relatively in view angulation, a CT scan with unevenly distributed sparse (164) views at 1:6 ratio are formed. An optimization-based method is implemented to reconstruct images from the unevenly distributed views. By taking the FBP reconstruction from the full scan (984 views) as the reference, the root mean square (RMS) between the reference and the optimization-based reconstruction is used to evaluate the performance quantitatively. Results: In visual inspection, the optimization-based method outperforms the FBP substantially in the reconstruction from unevenly distributed, which are quantitatively verified by the RMS gauged globally and in ROIs in both the FORBILD and anthropomorphic head phantoms. The RMS increases with increasing severity in the uneven angular distribution, especially in the case of anthropomorphic head phantom. Conclusion: The optimization-based image reconstruction can save radiation dose up to 12-fold while providing acceptable image quality for advanced clinical applications wherein only unevenly distributed sparse views are available. Research Grants: W81XWH-12-1-0138 (DoD), Sinovision Technologies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-CD-304-10: Development and Optimization of “Compton Lens” Collimator Design for Increased Dose Rate in SRS

Purpose: To provide a proof of concept for a new collimator design to increase the dose rate at isocenter for stereotactic radiosurgery (SRS) treatments by taking advantage of off axis Compton scattered photons which are attenuated in current collimators. Methods: A fundamentally new collimator design was developed and optimized by introducing a series of slits to a standard block collimator. The introduction of slits allowed for initially off axis radiation that was scattered in the direction of the target to contribute to the target dose. For optimization, the design was broken into two parts: an upper interaction plate where primary scattering occurs, and a lower “Compton slit” region which allows for scattered photons traveling toward the target to reach isocenter. To optimize the design, a series of simulations were performed using MCNP6 in which several key parameters were adjusted and the output was compared to a standard collimator. Key parameters modified included the collimator material, cone size, and interaction plate thickness. The effects of using energies different than the traditional 6 MV beam were also explored. Results: An optimized collimator design utilizing a solid interaction plate with a Cesium-137 beam and a 4 mm cone size resulted in a dose rate increase on the order of 5% relative to standard collimators in use. Currently, designs incorporating a Cesium-137 source are the most feasible due to necessary size and weight concerns for 6 MV beams. Conclusion: Preliminary designs provide a proof of concept and indicate a potential to improve upon the dose rate of current collimators while not largely compromising the sharp dose falloff inherent to SRS. Further optimization into the geometry and positioning of the interaction plate, as well as slit optimization, will likely lead to further dose rate increases than were observed in this study. Funding for this work was provided by the Wisconsin Alumni Research Foundation (WARF). Authors have a conflict of interest as the technology discussed is patent pending, and thus there is a financial interest in the work.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-EF-BRA-07: High Performance Preclinical Irradiation Through Optimized Dual Focal Spot Dose Painting and Online Virtual Isocenter Radiation Field Targeting

Purpose: Advances in radiotherapy practice facilitated by collimation systems to shape radiation fields and image guidance to target these conformal beams have motivated proposals for more complex dose patterns to improve the therapeutic ratio. Recent progress in small animal radiotherapy platforms has provided the foundation to validate the efficacy of such interventions, but robustly delivering heterogeneous dose distributions at the scale and accuracy demanded by preclinical studies remains challenging. This work proposes a dual focal spot optimization method to paint spatially heterogeneous dose regions and an online virtual isocenter targeting method to accurately target the dose distributions. Methods: Two-dimensional dose kernels were empirically measured for the 1 mm diameter circular collimator with radiochromic film in a solid water phantom for the small and large x-ray focal spots on the X-RAD 225Cx microirradiator. These kernels were used in an optimization framework which determined a set of animal stage positions, beam-on times, and focal spot settings to optimally deliver a given desired dose distribution. An online method was developed which defined a virtual treatment isocenter based on a single image projection of the collimated radiation field. The method was demonstrated by optimization of a 6 mm circular 2 Gy target adjoining a 4 mm semicircular avoidance region. Results: The dual focal spot technique improved the optimized dose distribution with the proportion of avoidance region receiving more than 0.5 Gy reduced by 40% compared to the large focal spot technique. Targeting tests performed by irradiating ball bearing targets on radiochromic film pieced revealed the online targeting method improved the three-dimensional accuracy from 0.48 mm to 0.15 mm. Conclusion: The dual focal spot optimization and online virtual isocenter targeting framework is a robust option for delivering dose at the preclinical level and provides a new experimental option for unique radiobiological investigations This work is supported, in part, by the Natural Sciences and Engineering Research Council of Canada and a Mitacs-Accelerate fellowship. P.E. Lindsay, and D.A. Jaffray are listed as inventors of the system described herein. This system has been licensed to Precision X-Ray Inc. for commercial development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-347: An Absolute Dose-Volume Constraint Based Deterministic Optimization Framework for Multi-Co60 Source Focused Radiotherapy

Purpose: Treatment plan optimization in multi-Co60 source focused radiotherapy with multiple isocenters is challenging, because dose distribution is normalized to maximum dose during optimization and evaluation. The objective functions are traditionally defined based on relative dosimetric distribution. This study presents an alternative absolute dose-volume constraint (ADC) based deterministic optimization framework (ADC-DOF). Methods: The initial isocenters are placed on the eroded target surface. Collimator size is chosen based on the area of 2D contour on corresponding axial slice. The isocenter spacing is determined by adjacent collimator sizes. The weights are optimized by minimizing the deviation from ADCs using the steepest descent technique. An iterative procedure is developed to reduce the number of isocenters, where the isocenter with lowest weight is removed without affecting plan quality. The ADC-DOF is compared with the genetic algorithm (GA) using the same arbitrary shaped target (254cc), with a 15mm margin ring structure representing normal tissues. Results: For ADC-DOF, the ADCs imposed on target and ring are (D100>10Gy, D50,10, 0<12Gy, 15Gy and 20Gy) and (D40<10Gy). The resulting D100, 50, 10, 0 and D40 are (9.9Gy, 12.0Gy, 14.1Gy and 16.2Gy) and (10.2Gy). The objectives of GA are to maximize 50% isodose target coverage (TC) while minimize the dose delivered to the ring structure, which results in 97% TC and 47.2% average dose in ring structure. For ADC-DOF (GA) techniques, 20 out of 38 (10 out of 12) initial isocenters are used in the final plan, and the computation time is 8.7s (412.2s) on an i5 computer. Conclusion: We have developed a new optimization technique using ADC and deterministic optimization. Compared with GA, ADC-DOF uses more isocenters but is faster and more robust, and achieves a better conformity. For future work, we will focus on developing a more effective mechanism for initial isocenter determination.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-BRC-05: Stereotactic Radiosurgery Optimized with Orthovoltage Beams

Purpose: To achieve improved stereotactic radiosurgery (SRS) dose distributions using orthovoltage energy fluence modulation with inverse planning optimization techniques. Methods: A pencil beam model was used to calculate dose distributions from the institution’s orthovoltage unit at 250 kVp. Kernels for the model were derived using Monte Carlo methods as well as measurements with radiochromic film. The orthovoltage photon spectra, modulated by varying thicknesses of attenuating material, were approximated using open-source software. A genetic algorithm search heuristic routine was used to optimize added tungsten filtration thicknesses to approach rectangular function dose distributions at depth. Optimizations were performed for depths of 2.5, 5.0, and 7.5 cm, with cone sizes of 8, 10, and 12 mm. Results: Circularly-symmetric tungsten filters were designed based on the results of the optimization, to modulate the orthovoltage beam across the aperture of an SRS cone collimator. For each depth and cone size combination examined, the beam flatness and 80–20% and 90–10% penumbrae were calculated for both standard, open cone-collimated beams as well as for the optimized, filtered beams. For all configurations tested, the modulated beams were able to achieve improved penumbra widths and flatness statistics at depth, with flatness improving between 33 and 52%, and penumbrae improving between 18 and 25% for the modulated beams compared to the unmodulated beams. Conclusion: A methodology has been described that may be used to optimize the spatial distribution of added filtration material in an orthovoltage SRS beam to result in dose distributions at depth with improved flatness and penumbrae compared to standard open cones. This work provides the mathematical foundation for a novel, orthovoltage energy fluence-modulated SRS system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-DE-207B-01: Optimization for Contrast-Enhanced Spectral Mammography Based On Photon-Counting Detectors

Purpose: To investigate the feasibility of optimizing the imaging parameters for contrast-enhanced spectral mammography based on Si strip photon-counting detectors. Methods: A computer simulation model using polyenergetic spectra from a tungsten anode x-ray tube and a Si-based photon-counting detector was evaluated for contrast-enhanced spectral mammography. The simulation traces the emission of photons from the x-ray source, attenuation through the breast and subsequent absorption in the detector. The breast was modeled as a mixture of adipose and mammary gland tissues with a breast density of 30%. A 4 mm iodine signal with a concentration of 4 mg/ml was used to simulate the enhancement of a lesion. Quantum efficiency of the detector was calculated based on the effective attenuation length in the Si strips. The figure-of-merit (FOM), which was defined as the decomposed iodine signal-to-noise ratio (SNR) with respect to the square root of the mean glandular dose (MGD), was chosen to optimize the imaging parameters, in terms of beam energy, splitting energy, and pre-filtrations for breast of various thicknesses and densities. Results: The optimal imaging parameters, which lead to the highest FOM, were found at a beam energy of 45 kVp with a splitting energy at 34 keV for an averaged breast thickness of 4 cm with a standard 0.75 mm Al pre-filtration. The optimal tube voltage varied slightly from 46 to 44 kVp as the breast thickness increases from 2 to 8 cm. The optimal tube voltage decreased to 42 kVp when the Al pre-filtration was increased to 3 mm. Conclusion: This simulation study predicted the optimal imaging parameters for application of photon-counting spectral mammography to contrast-enhanced imaging. The simulation results laid the ground work for future phantom and clinical studies. Grant funding from Philips Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-AB-202-09: Direct-Aperture Optimization for Combined MV+kV Dose Planning in Fluoroscopic Real-Time Tumor-Tracking Radiation Therapy

Purpose: Real-time kV fluoroscopic tumor tracking has the benefit of direct tumor position monitoring. However, there is clinical concern over the excess kV imaging dose cost to the patient when imaging in continuous fluoroscopic mode. This work addresses this specific issue by proposing a combined MV+kV direct-aperture optimization (DAO) approach to integrate the kV imaging beam into a treatment planning such that the kV radiation is considered as a contributor to the overall dose delivery. Methods: The combined MV+kV DAO approach includes three algorithms. First, a projected Quasi-Newton algorithm (L-BFGS) is used to find optimized fluence with MV+kV dose for the best possible dose distribution. Then, Engel’s algorithm is applied to optimize the total number of monitor units and heuristically optimize the number of apertures. Finally, an aperture shape optimization (ASO) algorithm is applied to locally optimize the leaf positions of MLC. Results: Compared to conventional DAO MV plans with continuous kV fluoroscopic tracking, combined MV+kV DAO plan leads to a reduction in the total number of MV monitor units due to inclusion of kV dose as part of the PTV, and was also found to reduce the mean and maximum doses on the organs at risk (OAR). Compared to conventional DAO MV plan without kV tracking, the OAR dose in the combined MV+kV DAO plan was only slightly higher. DVH curves show that combined MV+kV DAO plan provided about the same PTV coverage as that in the conventional DAO plans without kV imaging. Conclusion: We report a combined MV+kV DAO approach that allows real time kV imager tumor tracking with only a trivial increasing on the OAR doses while providing the same coverage to PTV. The approach is suitable for clinic implementation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-CD-209-06: LET-Based Adjustment of IMPT Plans Using Prioritized Optimization

Purpose: In-vitro experiments suggest an increase in proton relative biological effectiveness (RBE) towards the end of range. However, proton treatment planning and dose reporting for clinical outcome assessment has been based on physical dose and constant RBE. Therefore, treatment planning for intensity-modulated proton therapy (IMPT) is unlikely to transition radically to pure RBE-based planning. We suggest a hybrid approach where treatment plans are initially created based on physical dose constraints and prescriptions, and are subsequently altered to avoid high linear energy transfer (LET) in critical structures while limiting the degradation of the physical dose distribution. Methods: To allow fast optimization based on dose and LET we extended a GPU-based Monte-Carlo code towards providing dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of physical dose and LET (LETxD). To first approximation, LETxD represents a measure of the additional biological dose that is caused by high LET. Regarding optimization techniques, LETxD has the advantage of being a linear function of the pencil beam intensities. Results: The method is applicable to treatments where serial critical structures with maximum dose constraint are located in or near the target. We studied intra-cranial tumors (high-grade meningiomas, base-of-skull chordomas) where the target (CTV) overlaps with the brainstem and optic structures. Often, high LETxD in critical structures can be avoided while minimally compromising physical dose planning objectives. Conclusion: LET-based re-optimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based and RBE-based planning. It can be realized with fast GPU-based Monte-Carlo dose calculation. The project was in part supported by NIH grants U19 CA 021239-36 and C06 CA 059267.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems

We propose optimal dimensionality reduction techniques for the solution of goal-oriented linear-Gaussian inverse problems, where the quantity of interest (QoI) is a function of the inversion parameters. These approximations are suitable for large-scale applications. In particular, we study the approximation of the posterior covariance of the QoI as a low-rank negative update of its prior covariance and prove optimality of this update with respect to the natural geodesic distance on the manifold of symmetric positive definite matrices. Assuming exact knowledge of the posterior mean of the QoI, the optimality results extend to optimality in distribution with respect to the Kullback--Leibler divergence and the Hellinger distance between the associated distributions. We also propose the approximation of the posterior mean of the QoI as a low-rank linear function of the data and prove optimality of this approximation with respect to a weighted Bayes risk. Both of these optimal approximations avoid the explicit computation of the full posterior distribution of the parameters and instead focus on directions that are well informed by the data and relevant to the QoI. These directions stem from a balance among all the components of the goal-oriented inverse problem: prior information, forward model, measurement noise, and ultimate goals. We illustrate the theory using a high-dimensional inverse problem in heat transfer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Randomized Sketching Algorithms for Low-Memory Dynamic Optimization

This paper develops a novel limited-memory method to solve dynamic optimization problems. The memory requirements for such problems often present a major obstacle, particularly for problems with PDE constraints such as optimal flow control, full waveform inversion, and optical tomography. In these problems, PDE constraints uniquely determine the state of a physical system for a given control; the goal is to find the value of the control that minimizes an objective. While the control is often low dimensional, the state is typically more expensive to store. This paper suggests using randomized matrix approximation to compress the state as it is generated and shows how to use the compressed state to reliably solve the original dynamic optimization problem. Concretely, the compressed state is used to compute approximate gradients and to apply the Hessian to vectors. The approximation error in these quantities is controlled by the target rank of the sketch. This approximate first- and second-order information can readily be used in any optimization algorithm. As an example, we develop a sketched trust-region method that adaptively chooses the target rank using a posteriori error information and provably converges to a stationary point of the original problem. Numerical experiments with the sketched trust-region method show promising performance on challenging problems such as the optimal control of an advection-reaction-diffusion equation and the optimal control of fluid flow past a cylinder.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

An Optimization-Based Coupling of Reduced Order Models with an Efficient Reduced Adjoint Basis Generation Approach

Optimization-based coupling (OBC) is an attractive alternative to traditional Lagrange multiplier approaches in multiple modeling and simulation contexts. However, application of OBC to time-dependent problems has been hindered by the computational cost of finding the stationary points of the associated Lagrangian, which requires primal and adjoint solves. This issue can be mitigated by using OBC in conjunction with computationally efficient reduced order models (ROMs). To demonstrate the potential of this combination, in this paper, we develop an optimization-based ROM-ROM coupling for a transient advection-diffusion transmission problem. We pursue the “optimize-then-reduce” path toward solving the minimization problem at each time step and solve reduced space adjoint system of equations, where the main challenge in this formulation is the generation of adjoint snapshots and reduced bases for the adjoint systems required by the optimizer. One of the main contributions of the paper is a new technique for an efficient adjoint snapshot collection for gradient-based optimizers in the context of optimization-based ROM-ROM couplings. In conclusion, we present numerical studies demonstrating the accuracy of the approach along with comparison between various approaches for selecting a reduced order basis for the adjoint systems, including decay of snapshot energy, average iteration counts, and timings.

coupled problems↗
DOE OSTI2026

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗