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Compression of Real-Time Cardiac MRI Video Sequences

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:

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Compression of Real-Time Cardiac MRI Video Sequences

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  1. Compression of Real-Time Cardiac MRI Video Sequences EE 368B Final Project December 8, 2000 Neal K. Bangerter and Julie C. Sabataitis

  2. 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?

  3. 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

  4. 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

  5. Initial Exploration of MCP on Original Sequences using Matlab Displacementvectors • 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

  6. Exploration of Matlab MCP on Synthetic Periodic Sequence Displacementvectors • 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

  7. Matlab MCP on Temporally Sub-Sampled Sequences Displacementvectors • 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

  8. 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

  9. 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

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

  11. 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

  12. 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

  13. 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

  14. 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

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