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 MCPSingle-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 MCPSingle-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 MCPSingle-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 11 th 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 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