Compression of Real-Time Cardiac MRI Video Sequences

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Compression of Real-Time Cardiac MRI Video
Sequences

• EE 368B Final Project
• December 8, 2000

Neal K. Bangerter and Julie C. Sabataitis
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Overview

• Real-time cardiac MRI imaging
• New technology
• 128 x 128 pixels, 18 frames / sec
• Compression of cardiac sequences for remote
  diagnosis
• Motivation
• What PSNR is necessary to preserve diagnostic
  utility of sequences?
• What compression techniques work best on these
  real-time cardiac sequences?
• What channel bit-rate is required for streaming
  of these sequences?

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Project Goals

• Implement video compression algorithm that
  supports
• Frame-difference encoding
• Motion Compensated Prediction (MCP)
• Long-term memory MCP
• Optimize MCP parameters for real-time cardiac MRI
  studies
• Determine acceptable PSNR for diagnosis
• Identify compression technique which yields
  lowest bit-rate at determined PSNR

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MCP with Long-Term Memory

• Wiegand, Zhang, Girod (1997) decrease prediction
  error by increasing block matching to search many
  previous frames
• Bit savings from better prediction should be
  larger than number of bits needed to send
  displacements (dx, dy, dt)
• MCP Parameters
• Block size
• Search range maximum absolute value of dx, dy
• Frame buffer size number of previous frames
  used for comparison

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Initial Exploration of MCP on Original Sequences
using Matlab
Displacement vectors

• MCP (long-term and single-frame) with uniform
  quantization of DCT coeff.
• Smaller displacement vectors for single-frame
  MCP, similar error images for both
• Block indices for time buffer frame selected was
  often previous frame
• Suggests strong frame-to-frame correlation

Long-term MCP
Single-frame MCP
Mesh plots of error images
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Exploration of Matlab MCP on Synthetic Periodic
Sequence
Displacement vectors

• Five frames of short-axis study repeated
• Expect three things of long-term MCP
• Time buffer indices should be 5 at each block
• Displacement vectors should be 0
• Error image should consist of only quantization
  noise

Long-term MCP
Single-frame MCP
Mesh plots of error images
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Matlab MCP on Temporally Sub-Sampled Sequences
Displacement vectors

• 2/3 of image data shared between successive
  frames
• Sampled sequences temporally to remove
  dependencies
• No data shared 6 fps
• 1/6 of data shared 9 fps

Long-term MCP
Single-frame MCP
Mesh plots of error images
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C Implementation

• Features
• Variable block size, search range, and frame
  buffer size
• Zig-zag and run-level encoding of 8x8 DCT blocks
• Lagrangian cost function using block MSE and bit
  cost of motion vectors (dx, dy, dt)
• Testing
• Periodic video sequence 10 frames repeated
• PSNR of predicted image should increase
  significantly beyond 11th frame
• MCP with buffer gt 10 frames should yield
  significant compression gains

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Optimizing MCP Parameters

• Try 35 different MCP parameter combinations
• 16x16, 8x8, and 4x4 block size
• 2, 4, and 8 pixel search range
• 1, 2, 4, 8, and 16 frame buffer size
• Run each at 7 different quantization levels to
  generate 35 PSNR curves
• Frame-difference and intra-frame PSNR curves also
  generated

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Optimization Results

• High PSNR
• Long-term MCP
• 4x4 blocks
• 4 pixel search range
• 16 frame buffer
• Low PSNR
• Frame-difference coding best

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Determination of Acceptable PSNR

• Presented videos at different PSNR to
  cardiologist
• 30 to 31 dB sufficient for current applications
  (wall motion assessment, coronary imaging)
• Very few cardiologists familiar with cardiac MRI
• New technology as quality increases, new
  applications will emerge that may have different
  PSNR requirements

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Conclusions

• Current applications require PSNR of 30-31 dB to
  preserve diagnostic utility
• At this PSNR, simple frame-difference coding
  yields best compression
• Original 2.3 Mbps
• Compressed 70 Kbps
• Current real-time cardiac MRI video experiences
  little to no gain in PSNR at a given bit-rate
  (generally lt 1 dB) when using long-term memory
  MCP vs. frame-difference encoding
• Strong frame to frame correlation
• Limited motion often confined to a small portion
  of the image

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Future Work

• Capabilities of real-time MRI likely to increase
• Revisit MCP techniques as images become less
  noisy and have higher resolution
• Development of metrics for evaluation of
  acceptable image distortion levels for various
  kinds of diagnostic studies
• Integration of video-compression techniques with
  remote-diagnosis systems
• Compression of spatial frequency MRI data prior
  to reconstruction

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Acknowledgements

• Markus Flierl for zig-zag DCT compression code
  and for his help whenever we showed up at his
  office
• Authors of the CIDS library of C functions for
  image processing and compression
• Bob Hu for evaluation of real-time sequences at
  various PSNR levels
• Krishna Nayak for providing real-time cardiac MRI
  sequences