publications
2024
- "Primer Shot" Fractionation with an Early Treatment Break is Theoretically Superior to Consecutive Weekday Fractionation Schemes for Early-Stage Non-Small Cell Lung CancerZ. A. R. Gouw, J. Jeong, A. Rimner, and 4 more authorsRadiotherapy and Oncology, Jan 2024
Radiotherapy is traditionally given in equally spaced weekday fractions. We hypothesize that heterogeneous interfraction intervals can increase radiosensitivity via reoxygenation. Through modeling, we investigate whether this minimizes local failures and toxicity for early-stage non-small cell lung cancer (NSCLC). Previously, a tumor dose-response model based on resource competition and cell-cycle-dependent radiosensitivity accurately predicted local failure rates for early-stage NSCLC cohorts. Here, the model mathematically determined non-uniform inter-fraction intervals minimizing local failures at similar normal tissue toxicity risk, i.e., iso-BED3 (iso-NTCP) for fractionation schemes 18Gyx3, 12Gyx4, 10Gyx5, 7.5Gyx8, 5Gyx12, 4Gyx15. Next, we used these optimized schedules to reduce toxicity risk (BED3) while maintaining stable local failures (TCP). Optimal schedules consistently favored a “primer shot” fraction followed by a 2-week break, allowing tumor reoxygenation. Increasing or decreasing the assumed baseline hypoxia extended or shortened this optimal break by up to one week. Fraction sizes of 7.5 Gy and up required a single primer shot, while smaller fractions needed one or two extra fractions for full reoxygenation. The optimized schedules, versus consecutive weekday fractionation, predicted absolute LF reductions of 4.6%-7.4%, except for the already optimal LF rate seen for 18Gyx3. Primer shot schedules could also reduce BED3 at iso-TCP with the biggest improvements for the shortest schedules (94.6Gy reduction for 18Gyx3). A validated simulation model clearly supports non-standard “primer shot” fractionation, reducing the impact of hypoxia-induced radioresistance. A limitation of this study is that primer-shot fractionation is outside prior clinical experience and therefore will require clinical studies for definitive testing.
2023
- Distributed and Scalable Optimization for Robust Proton Treatment PlanningA. Fu, V. T. Taasti, and M. ZarepishehMedical Physics, Jan 2023
The importance of robust proton treatment planning to mitigate the impact of uncertainty is well understood. However, its computational cost grows with the number of uncertainty scenarios, prolonging the treatment planning process. We developed a fast and scalable distributed optimization platform that parallelizes the robust proton treatment plan computation over the uncertainty scenarios. We modeled the robust proton treatment planning problem as a weighted least-squares problem. To solve it, we employed an optimization technique called the alternating direction method of multipliers with Barzilai–Borwein step size (ADMM-BB). We reformulated the problem in such a way as to split the main problem into smaller subproblems, one for each proton therapy uncertainty scenario. The subproblems can be solved in parallel, allowing the computational load to be distributed across multiple processors (e.g., CPU threads/cores). We evaluated ADMM-BB on four head-and-neck proton therapy patients, each with 13 scenarios accounting for 3 mm setup and 3.5% range uncertainties. We then compared the performance of ADMM-BB with projected gradient descent (PGD) applied to the same problem. For each patient, ADMM-BB generated a robust proton treatment plan that satisfied all clinical criteria with comparable or better dosimetric quality than the plan generated by PGD. However, ADMM-BB’s total runtime averaged about 6 to 7 times faster. This speedup increased with the number of scenarios. ADMM-BB is a powerful distributed optimization method that leverages parallel processing platforms, such as multicore CPUs, GPUs, and cloud servers, to accelerate the computationally intensive work of robust proton treatment planning. This results in (1) a shorter treatment planning process and (2) the ability to consider more uncertainty scenarios, which improves plan quality.
2022
- Operator Splitting for Adaptive Radiation Therapy with Nonlinear Health ConstraintsOptimization Methods and Software, Jun 2022
We present an optimization-based approach to radiation treatment planning over time. Our approach formulates treatment planning as an optimal control problem with nonlinear patient health dynamics derived from the standard linear-quadratic cell survival model. As the formulation is nonconvex, we propose a method for obtaining an approximate solution by solving a sequence of convex optimization problems. This method is fast, efficient, and robust to model error, adapting readily to changes in the patient’s health between treatment sessions. Moreover, we show that it can be combined with the operator splitting method ADMM to produce an algorithm that is highly scalable and can handle large clinical cases. We introduce an open-source Python implementation of our algorithm, AdaRad, and demonstrate its performance on several examples.
2021
2020
- Anderson Accelerated Douglas-Rachford SplittingSIAM Journal on Scientific Computing, Nov 2020
We consider the problem of nonsmooth convex optimization with linear equality constraints, where the objective function is only accessible through its proximal operator. This problem arises in many different fields such as statistical learning, computational imaging, telecommunications, and optimal control. To solve it, we propose an Anderson accelerated Douglas–Rachford splitting (A2DR) algorithm, which we show either globally converges or provides a certificate of infeasibility/unboundedness under very mild conditions. Applied to a block separable objective, A2DR partially decouples so that its steps may be carried out in parallel, yielding an algorithm that is fast and scalable to multiple processors. We describe an open-source implementation and demonstrate its performance on a wide range of examples.
- CVXR: An R Package for Disciplined Convex OptimizationA. Fu, B. Narasimhan, and S. BoydJournal of Statistical Software, Sep 2020
CVXR is an R package that provides an object-oriented modeling language for convex optimization, similar to CVX, CVXPY, YALMIP, and Convex.jl. It allows the user to formulate convex optimization problems in a natural mathematical syntax rather than the restrictive form required by most solvers. The user specifies an objective and set of constraints by combining constants, variables, and parameters using a library of functions with known mathematical properties. CVXR then applies signed disciplined convex programming (DCP) to verify the problem’s convexity. Once verified, the problem is converted into standard conic form using graph implementations and passed to a cone solver such as ECOS or SCS. We demonstrate CVXR’s modeling framework with several applications.
2019
- A Convex Optimization Approach to Radiation Treatment Planning with Dose ConstraintsOptimization and Engineering, Mar 2019
We present a method for handling dose constraints as part of a convex programming framework for inverse treatment planning. Our method uniformly handles mean dose, maximum dose, minimum dose, and dose-volume (i.e., percentile) constraints as part of a convex formulation. Since dose-volume constraints are non-convex, we replace them with a convex restriction. This restriction is, by definition, conservative; to mitigate its impact on the clinical objectives, we develop a two-pass planning algorithm that allows each dose-volume constraint to be met exactly on a second pass by the solver if its corresponding restriction is feasible on the first pass. In another variant, we add slack variables to each dose constraint to prevent the problem from becoming infeasible when the user specifies an incompatible set of constraints or when the constraints are made infeasible by our restriction. Finally, we introduce ConRad, a Python-embedded open-source software package for convex radiation treatment planning. ConRad implements the methods described above and allows users to construct and plan cases through a simple interface.
2009
- Dielectric-Substrate-Induced Surface-Enhanced Raman ScatteringO. J. Glembocki, R. W. Rendell, D. A. Alexon, and 3 more authorsPhysical Review B, Aug 2009
It is shown through experimental mapping of surface-enhanced Raman scattering (SERS) from dielectric core nanowires exposed to benzene thiol that any dielectric substrate plays a critical role in the SERS enhancement. Theoretical calculations of the plasmonic enhancement using finite element methods confirms the role that the substrate plays in increasing the intensity and spatial extent of the SERS enhancement. It is shown that because of the cylindrical shape of the nanowires, significant SERS hot spots form not only between crossed nanowires but also at the point of contact between the nanowires and the substrate on which they are placed. This result also applies to any structure whose geometry results in a point or line contact with an underlying substrate.
2006
- The Effect of Small Metal Additions (Co, Cu, Ga, Mn, Al, Bi, Sn) on the Magnetocaloric Properties of the Gd5Ge2Si2 AlloyJournal of Applied Physics, Apr 2006
The structural and magnetic properties of arc-melted and homogenized (1300 C, 1 h) alloys of Gd5Ge1.9Si2X0.1 (X = Cu, Co, Ga, Mn, Al, Bi, or Sn) were investigated by powder x-ray diffraction, scanning electron microscopy, energy dispersive spectroscopy, and magnetometry. The addition of Cu, Ga, Mn, and Al completely eliminated the large hysteresis losses present in the undoped Gd5Ge2Si2 alloy between 270 and 330 K, broadened the magnetic entropy change ∆Sm peak, and shifted its peak value from 275 to 305 K similar to that observed earlier for Gd5Ge1.9Si2Fe0.1. The addition of Bi or Sn had a negligible effect on either the alloy hysteresis losses or the characteristics of the ∆Sm vs T peak. The microstructure of the alloy doped with Cu, Co, Ga, Mn, or Al consisted of a majority phase (depleted of silicon) and a minor intergranular phase (rich in silicon and of the corresponding metal additive). For Bi or Sn doping, the microstructure consisted of only the Gd5Ge2Si2 phase. Low temperature x-ray diffraction data on an Fe-doped sample showed the same spectra at 245 and 300 K, consistent with the majority phase possessing an orthorhombic structure. Refrigeration capacity calculations show that Gd5Ge1.9Si2X0.1 (X = Fe, Cu, Co, Ga, Mn, or Al) alloys are superior magnetic refrigerants compared to the undoped Gd5Ge2Si2 alloy.
2005
- High-Magnetic Field X-ray Diffraction Studies on Gd5(Ge(2-x)Fe(x))Si2 (x = 0.05 and 0.2)J. L. Her, K. Koyama, K. Watanabe, and 4 more authorsMaterials Transactions, Sep 2005
We performed the powder X-ray diffraction measurements in magnetic fields up to 5 T for Gd5(Ge1.95Fe0.05)Si2 and Gd5(Ge1.8Fe0.2)Si2. With heating from 8 K, the matrix of Gd5(Ge1.95Fe0.05)Si2 clearly shows a structural transition from an orthorhombic to a monoclinic structure at the Curie temperature (TC 1/4 276 K). On the other hand, the matrix of Gd5(Ge1.8Fe0.2)Si2 with the orthorhombic structure in the ferromagnetic state shows two-phases co-existence of the orthorhombic and the monoclinic structures above TC 1/4 303 K, indicating that a small amount of the matrix participates in the transformation. For both samples, the monoclinic structure is suppressed but the orthorhombic structure is enhanced just above TC by applying a magnetic field, which closely relates to the magnetization process.