Medical Imaging Pipeline

Yu-Ting Chen, Young-kyu Choi, Jason Cong, and Bingjun Xiao

Center for Domain-Specific Computing

Center for Future Architectures Research

Computer Science Department, UCLA

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Medical Imaging Pipeline3D images

Major Steps:1. Imaging reconstruction2. Denoise/deblur3. Image registration4. Image reconstruction

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Reconstruction Denoise Registration Segmentation

Data access pattern

Random in EMupdateStencil in TVupdate

Stencil Random in interp;Stencil in others

Stencil

Major computation

FP(mul, add) FP(mul, add, div, sqrt)

FP(mul, add) FP(mul, add, div, sqrt)

Summary of Application Characteristics

3D images

Data access pattens Most of the applications contain stencil computation

Some of them has random access pattens

Floating point computations Add, multiplication, division, sqrt

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Image Reconstruction

Forward & Backward Projection

• Coordinate & intersection length computation :

Do projection based on the updated images

Update images (voxels)

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Remove noises which under Rician distribution

Solve the minimization problem using gradient decent Iterative optimization (nested loop)

Stencil computation

Hardware opt. techniques Tiling

Data reuse design

Pipelining

Denoise

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Registration: fluid registration Align two images and capture the tumors

Fluid PDE equation updateU / update – stencil; updateV - sweeping

Segmentation: Try to find and segment an object of interest

gradient decent: stencil computation

Stencil computation patterns Apply similar techniques using in Denoise

Registration & Segmentation

Dataflow of registration

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Backup Slides

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Mapping to FPGA Architecture