graded mesh change of variable in the vessel to allow for resolution of the boundary layer
DESCRIPTION
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. Algorithm. Numerical Results. - PowerPoint PPT PresentationTRANSCRIPT
Graded MeshChange of variable in the vessel to allow for resolution of the boundary layer
Alternating Directions Implicit (ADI) Discretize in time:
Split into operators on Cn+1 and Cn
and factor into differential operators in x and y
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 Use the following change of unknown
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
A Fast, Accurate Algorithm Enabling EfficientSolution of a Drug Delivery Problem
Catherine E. Beni, Oscar P. BrunoApplied and Computational Mathematics
California Institute of Technology
Introduction Algorithm
Conclusions
Framework
References
Numerical Results
The VMT convection-diffusion problem:
The parameters D, vx, and vy vary in each layer
VMT geometry:
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
“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)
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
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.
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:
Greatly disparate time-scales
Extremely steep boundary layers
Occurrence of very small diffusion coefficients
Tissue Membrane Vessel
D = .0001, vy = .0001, Ren = .01
COMSOL VMT SolverSpeed 36 hours < 5 minutes
Memory Requirements 32 GB 32.7 MB
→ 432 times faster.→ 1000 times reduction in memory requirements.
D = .00001, vy = .00001, Ren = .001
COMSOL VMT Solver
Speed N/A < 8 minutes
Memory Requirements > Available 32Gb 98.3 Mb
Future work 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
Approximate the Radon transform with its Fourier series:
where ak and bk are the Fourier coefficients
Compute derivative of Hilbert transform:
Approximate CT and ST, the Hilbert transforms of cosine and sine respectively, as follows:
Combine with the derivative of the Hilbert transform
and integrate to obtain the modified-Filtered Back Projection (mFBP) algorithm
A Noise-tolerant Fejér-based modified-FBP Reconstruction Algorithm (Fejér-mFBP) for Positron Emission Tomography
C.E. Beni, O.P. BrunoApplied and Computational Mathematics
California Institute of Technology
Reconstructed Images
Framework
Introduction Modified-FBP 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.
Both methods suffer in the presence of noise:
Direct methods amplify noise and show a dramatic loss of information in the reconstructed images
Iterative algorithms are not guaranteed to converge
Goal: to design a fast, accurate reconstruction algorithm that does not degrade substantially in the presence of noise
Radon Transform:
Geometry
Inverse Radon Transform:
h(½,µ) is the Hilbert transform of the Radon transform
Reconstructions using 711 values of ½ , 200 values of µ, and 200 Fourier modes → Unrealistic noise, unrealistically sensitive device
Original FBP Compute the Hilbert transform and its derivative as follows:
Integrate to obtain the inverse Radon transform
Fejér-mFBP
Numerical Results Both algorithms were implemented in C++
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
MATLAB’s ‘iradon’ mFBP Fejér-mFBP
MATLAB’s ‘iradon’ mFBP Fejér-mFBP
MATLAB’s ‘iradon’ mFBP Fejér-mFBP
MATLAB’s ‘iradon’ mFBP Fejér-mFBP
Conclusions
References
Developed a new reconstruction algorithm that, in presence of noise, yields iterative-solver-like quality at FBP computational costs.
“A Noise-Tolerant Fejér-based modified-FBP Reconstruction Algorithm (Fejér-mFBP) for Positron Emission Tomography”, C.E. Beni, O.P. Bruno (to be submitted)
Reconstructions using 100 values of ½ , 200 values of µ, and 200 Fourier modes → Unrealistic noise, realistically sensitive device
Reconstructions using 711 values of ½ , 200 values of µ, and 200 Fourier modes with 18.5% noise present → Realistic noise, unrealistically sensitive device
Reconstructions using 100 values of ½ , 200 values of µ, and 200 Fourier modes with 18.5% noise present → Realistic noise, realistically sensitive device
Fejér series of a given function:
By approximating the Radon transform with a Fejér series instead, we obtain the Fejér-mFBP algorithm:
Shepp-Logan phantom