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A Fast, Accurate Algorithm Enabling Efficient Solution of a Drug Delivery Problem Catherine E. Beni, Oscar P. Bruno Applied and Computational Mathematics California Institute of Technology Introduction The goal of magnetic drug delivery is to use magnetic fields to direct and confine magnetically-responsive particles bound to therapeutic agents to specific regions in a patient’s body-- thus allowing for focused treatment in an area of interest. Algorithm Graded Mesh D = .0001, vy = .0001, Ren = .01 Change of variable in the vessel to allow for resolution of the boundary layer Tissue To design a method leading to confinement of the magnetically-responsive particles to a particular region of the body, a predictive capability must be used to evaluate the effects of external magnetic forces on the convection and diffusion of magnetic particles through the bloodstream and in membranes and tissue. The numerical solution of the Vessel-Membrane-Tissue (VMT) convection diffusion problem proposed by Grief and Richardson is highly challenging: Numerical Results Membrane COMSOL VMT Solver Speed 36 hours < 5 minutes Memory Requirements 32 GB 32.7 MB → 432 times faster. → 1000 times reduction in memory requirements. Vessel Alternating Directions Implicit (ADI) Discretize in time: Split into operators on Cn+1 and Cn Greatly disparate time-scales D = .00001, vy = .00001, Ren = .001 Extremely steep boundary layers Occurrence of very small diffusion coefficients and factor into differential operators in x and y COMSOL VMT Solver Speed N/A < 8 minutes Memory Requirements > Available 32Gb 98.3 Mb Framework The VMT convection-diffusion problem: The parameters D, vx, and vy vary in each layer VMT geometry: Solve the resulting scheme using Finite Difference methods On-and-Off Fluid Freezing Methodology Evolve algorithm until concentration in vessel reaches steady state “Freeze” concentration in vessel by not applying solver in vessel region Iterate twice using large time steps Reduce time step, unfreeze vessel, and iterate until concentration in vessel reaches steady state Repeat Change of Unknown Conclusions Developed a fast, efficient solver for a drug delivery problem 432 times faster than commercial package COMSOL Multiphysics 1000 times reduced memory requirements Allows for solution of previously intractable problems Use the following change of unknown Future work The VMT solver is based on a combination of Use of a graded mesh to adequately resolve boundary layers The Alternating Directions Implicit (ADI) method to overcome the overwhelmingly restrictive CFL condition imposed by the fine spatial discretization mentioned above An on-and-off fluid-freezing methodology that allows for efficient treatment of the multiple time-scales that coexist in the problem (whose equilibria arise through a complex balance of fluid-flow, magnetic-pull and diffusion effects) A change of unknown that enables evaluation of steady states in tissue and membrane layers through a highly accelerated time-stepping procedure to transform the differential operator in y in the membrane and tissue regions into the Helmholtz equation to make use of a previously known fast time-stepping method Finite Difference methods restrict us to a rectangular geometry Room for accuracy improvement These two problems will be fixed by solving the ODEs present in the ADI method with the new Fourier Continuation-Alternating Directions (FC-AD) methodology See the talk by O.P. Bruno for more details References “The Behaviors of Ferro-Magnetic Nano-Particles in Blood Vessels under Applied Magnetic Fields”, A. Nacev, C.E. Beni, O.P. Bruno, B. Shapiro (to be submitted) A Noise-tolerant Fejér-based modified-FBP Reconstruction Algorithm (Fejér-mFBP) for Positron Emission Tomography C.E. Beni, O.P. Bruno Applied and Computational Mathematics California Institute of Technology Introduction Images can be reconstructed from Positron Emission Tomography (PET) scanners via two methods: Iterative methods and Direct methods Direct methods, such as the well-known Filtered Back Projection (FBP) algorithm are fast, but reconstruct images that are low resolution. Iterative methods, such as ML-EM (Maximum Likelihood-Expectation Maximization) and OSEM (Ordered Subset Expectation Maximization), are much slower (each iteration takes the same amount of time as a full reconstruction using a direct method and approximately 20-30 iterations are required), but provide high quality reconstructions. Modified-FBP Approximate the Radon transform with its Fourier series: where ak and bk are the Fourier coefficients MATLAB’s ‘iradon’ Combine with the derivative of the Hilbert transform Shepp-Logan phantom Fejér-mFBP mFBP Fejér-mFBP Reconstructions using 711 values of ½ , 200 values of µ, and 200 Fourier modes with 18.5% noise present → Realistic noise, unrealistically sensitive device MATLAB’s ‘iradon’ Geometry mFBP Approximate CT and ST, the Hilbert transforms of cosine and sine respectively, as follows: Framework Radon Transform: MATLAB’s ‘iradon’ Reconstructions using 100 values of ½ , 200 values of µ, and 200 Fourier modes → Unrealistic noise, realistically sensitive device Direct methods amplify noise and show a dramatic loss of information in the reconstructed images Goal: to design a fast, accurate reconstruction algorithm that does not degrade substantially in the presence of noise Reconstructions using 711 values of ½ , 200 values of µ, and 200 Fourier modes → Unrealistic noise, unrealistically sensitive device Compute derivative of Hilbert transform: Both methods suffer in the presence of noise: Iterative algorithms are not guaranteed to converge Reconstructed Images mFBP Fejér-mFBP and integrate to obtain the modified-Filtered Back Projection (mFBP) algorithm Fejér-mFBP Fejér series of a given function: Inverse Radon Transform: Reconstructions using 100 values of ½ , 200 values of µ, and 200 Fourier modes with 18.5% noise present → Realistic noise, realistically sensitive device MATLAB’s ‘iradon’ h(½,µ) is the Hilbert transform of the Radon transform mFBP Fejér-mFBP By approximating the Radon transform with a Fejér series instead, we obtain the Fejér-mFBP algorithm: Original FBP Compute the Hilbert transform and its derivative as follows: Numerical Results Conclusions Both algorithms were implemented in C++ Integrate to obtain the inverse Radon transform Each reconstruction requires ~3.6 seconds, the same amount of time used by MATLAB’s built-in ‘iradon’ function All reconstructions shown here are of the well-known Shepp-Logan phantom generated using MATLAB’s built-in ‘phantom’ command Developed a new reconstruction algorithm that, in presence of noise, yields iterative-solver-like quality at FBP computational costs. References “A Noise-Tolerant Fejér-based modified-FBP Reconstruction Algorithm (Fejér-mFBP) for Positron Emission Tomography”, C.E. Beni, O.P. Bruno (to be submitted)