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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
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?
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
• 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
MCP with Long-Term Memory
Initial Exploration of MCP on Original Sequences using Matlab
• 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
Displacement vectors
Long-term MCP Single-frame MCP
Mesh plots of error images
Exploration of Matlab MCP on Synthetic Periodic Sequence
• 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
Displacement vectors
Long-term MCP Single-frame MCP
Mesh plots of error images
Matlab MCP on Temporally Sub-Sampled Sequences
• 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
Displacement vectors
Long-term MCP Single-frame MCP
Mesh plots of error images
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 >= 10 frames should yield significant compression gains
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
• High PSNR• Long-term MCP• 4x4 blocks• 4 pixel search
range• 16 frame buffer
• Low PSNR• Frame-
difference coding best
Optimization Results
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
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 < 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
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
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