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GPGPU in Medical Imaging Applications

GPGPU in Medical Imaging Applications. Kevin Gorczowski. Overview. Random Walks for Interactive Organ Segmentation in Two and Three Dimensions Interactive, GPU-Based Level Sets for 3D Brain Tumor Segmentation Image Registration by a Regularized Gradient Flow

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GPGPU in Medical Imaging Applications

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  1. GPGPU in Medical Imaging Applications Kevin Gorczowski

  2. Overview • Random Walks for Interactive Organ Segmentation in Two and Three Dimensions • Interactive, GPU-Based Level Sets for 3D Brain Tumor Segmentation • Image Registration by a Regularized Gradient Flow • Accelerating Popular Tomographic Reconstruction Algorithms on Commodity PC Graphics Hardware

  3. GPUs and Medical Imaging • Computations involving 2D and 3D images • Already a natural fit for use of GPUs • Often involve numerical computations • ODEs and PDEs • Numerical accuracy not always top priority • Visually correct and speed most important

  4. Image Segmentation • Find the boundary or interior of a specific organ or structure • Hand-tracing still a standard clinical practice • Example use: radiation cancer treatment • Take CT scan on initial day • Segment organ • Use segmentation to aim radiation • Problem: organs move between days! • Hand-tracing much too time consuming to do each day

  5. Random Walks for Interactive Organ Segmentation in Two and Three Dimensions (2005) • Goal: Segmentation using only small amount of user interaction • User specifies seed points • Seeds labeled according to organ/structure • Segmentation propagates toward seed points

  6. Seed and Segmentation Examples Seeds: gray Segmentations: black

  7. Methodology • Unlabeled pixels (non-seeds) • Send out random walker • Determine probability that random walker will reach a labeled pixel first, ahead of all other labels • Assign pixel maximum probability label

  8. Random Walkers on Graph

  9. Formulating Images as Weighted Graphs • Nodes: pixels • Edges: between neighboring pixels • Edge weight: function of intensity difference between pixel and its neighbor • Weight function • g: pixel intensity, β: free parameter • Able to use same β throughout

  10. Solution • Probability of random walker reaching seed point same as solution to Dirichlet problem • Partial differential equation on interior of region with conditions at boundary of region • Boundary conditions at seed pixels • 1 if seed pixel belongs to label being searched for • 0 otherwise • Computation of solution involves solving linear system with graph Laplacian matrix

  11. GPU Implementation • Linear system LX = -BM must be solved for each seed label • Only M, seed boundary conditions, changes • Can use RGBA channels to solve for 4 labels simultaneously • Progress of segmentation can be updated on screen

  12. GPU Implementation • L is symmetric and diagonal is determined by off-diagonal elements • Can store L as size n = # of pixels, instead of n x n • Textures are same size as original images • Linear system solved using conjugate gradient • Only matrix-vector product and vector inner-product required

  13. Interactive, GPU-Based Level Sets for 3D Brain Tumor Segmentation (2003) • Uses deformable model approach • Before, started with pixel seeds and labeled pixels individually • Here, start with template model of organ/structure • Deform model to fit target image • Resulting model represents boundary

  14. Deformable Models • Two classes of deformable shape models • Parametric • Parameterized curves or surfaces • Spherical harmonics, wavelets • Geometric • Curves as embedded, implicit level sets of higher-dimensional functions • Can change topology (may or may not be advantage depending on application)

  15. Level Sets • Curve or surface: all points such that some function φ(x) = 0 • φ: R3 -> R, x: position • Can describe motion (deformation) as PDE • v(t): pixel-wise velocities • Implement deformations by choosing v’s

  16. Velocities for Segmentation • Velocities usually have two components when used for segmentation • Data term • Smoothness term • Data term drives curve toward boundaries • Typically areas of high contrast in image • Smoothness term constrains curve from becoming too irregular • Prevents “leaking” out of small discontinuities in image edges

  17. Leaking without Smoothness Term

  18. Problems • Data term usually introduces free parameters • Their data term: • I: image intensity • ε: determines range of values considered inside object • T: determines how bright object is • Basically, T is mean intensity and ε is variance • User has to choose these free parameters • If segmentation runs at interactive speed (GPU), user can be much more productive

  19. GPU Implementation Issues • For stability, curve must move at most one pixel at each iteration • Results in many iterations before solution • Need to keep as much on GPU as possible • Major speedup if only places where φ is close to zero are considered • How to pack for GPU • Changes after each iteration

  20. Data Packing • Subdivide data into 16x16 tiles • Only tiles with non-zero derivatives sent to GPU

  21. Data Packing • Evaluation of PDE requires discrete derivatives • Pixels need to access neighbors • CPU calculates and sends texture coordinates of neighbor pixels in packed format • GPU does neighbor lookups and finite differences used for gradient and curvature • CPU also sends vertices of active tiles for quad rendering

  22. Data Packing • CPU needs to active tiles for each iteration so it can calculate neighbor pixels • GPU writes out a small (<64KB), encoded texture telling which tiles are active • Checks if a tile boundary has been crossed • Limits CPU <-> GPU bus use

  23. Performance • CPU: 1.7GHz Xeon • 7-8 iterations/sec • “Highly-optimized, sparse-field, CPU-based solver" • Radeon 9700 Pro • 60-70 iteration/sec • Running time of GPU implementation scales linearly with number of active voxels • Overhead for feedback image calculation about 15% of total GPU time

  24. Results Semi-automatic result has less discontinuities across slices than hand-traced segmentation

  25. Movies

  26. Image Registration by a Regularized Gradient Flow (2004) • Image registration • Try to get intensities in multiple images to match spatially • Simple case: use similarity transforms to align images as best as possible • Complex case: non-rigid registration • Each pixel has its own displacement

  27. Application: Atlas Formation • Lorenzen (UNC) created sharp “mean” images by finding mean deformation

  28. Registration Formulation • Must define a measure of how closely two images match • Can formulate as an energy function

  29. Registration as Optimization • Given formulation as energy function • Problem becomes a global optimization of an objective function • Find minimum of energy function

  30. Uniqueness of Solution

  31. Discretization

  32. Algorithm Pseudocode

  33. GPU Implementation

  34. Results

  35. Results

  36. Results

  37. Results

  38. Performance

  39. Accelerating Popular Tomographic Reconstruction Algorithms on Commodity Graphics Hardware • Computed Tomography • CT scan, used to be CAT scan • X-ray source • Object attenuates x-rays • Collector (2D sheet) measures left-over radiation • Source and collector rotate around object

  40. Reconstruction • Only information acquired through scan is 2D “image” at collector • Have collector image for each angle φ(position of source/collector on circle) • Must solve for attenuation of scanned object at points on 3D grid

  41. Formulation • Amount of radiation collected at pixel (u,v) of collector for angle φ • μ: attenuation (unknown) • Q0: original x-ray energy at source • L: distance between source and collector • Integrate attenuation along x-ray

  42. Formulation • Rewrite • i: pixel index of collector • Voxel form (loop through object voxel Grid)

  43. Formulation • wij : weight of voxel j’s contribution to detector pixel i • Determined ahead of time by interpolation and integration rules

  44. Solving for Attenuation • Mφ : number of pixels in collector for angle φ • Know qi and wij, solve for attenuation (backprojection)

  45. Final Image Reconstruction • Feldkamp algorithm • SART iterative algorithm • OS-EM algorithm

  46. GPU Implementation • Each iteration requires at least one backprojection and projection • Each backprojection is O(n3) • No real way around complexity • Must use brute force speed • Many CT machines use FPGAs or ASICs • Expensive • Inflexible

  47. GPU Implementation • Volume data stored at stacks of textures • Discrete form of projection and backprojection operations • Updates of current volume attenuation performed through texture blends

  48. GPU Implementation • Can use RGBA channels to compute orthogonal projections • Projection matrices are equal • Four 90° increments of φ • Can’t do this in SART because of incremental volume updates • Instead, fold volume in half (RG) • Projection matrices are same except for reflection

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