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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

High-performance 3D compressive sensing MRI reconstruction.

Daehyun Kim¹, Joshua D Trzasko, Mikhail Smelyanskiy

¹Throughput Computing Lab, Intel Corporation, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 25, 2010

Summary

This summary is machine-generated.

Compressive Sensing (CS) in MRI significantly speeds up image reconstruction. Optimized algorithms on parallel architectures like GPUs and Intel MIC achieve clinically viable scan times for neurovascular imaging.

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Area of Science:

Medical Imaging
Computational Science
Signal Processing

Background:

Compressive Sensing (CS) offers reduced sampling in Magnetic Resonance Imaging (MRI).
High computational cost of CS reconstructions limits clinical adoption.
Current clinical practice often uses lower-quality images due to reconstruction time.

Purpose of the Study:

Optimize a quasi-Newton CS reconstruction algorithm for speed.
Evaluate performance on modern parallel computing architectures (CPUs, GPUs, Intel MIC).
Assess the clinical viability of optimized CS reconstruction for neurovascular imaging.

Main Methods:

Developed and optimized an inexact quasi-Newton CS reconstruction algorithm.
Implemented and tested the algorithm on CPUs (quad-core, six-core), GPUs (NVIDIA GTX480), and Intel MIC (Knights Ferry).
Reconstructed a 10x undersampled 8-channel neurovascular dataset (256x160x80).

Main Results:

Optimized baseline on quad-core CPU: 56 seconds.
Reduced reconstruction time to 32 seconds on a six-core CPU.
Achieved 16 seconds on NVIDIA GTX480 (GPU) and 12 seconds on Intel MIC (Knights Ferry).

Conclusions:

CS reconstruction algorithms significantly benefit from parallel processing architectures.
Optimized CS reconstruction is now feasible within clinically relevant timeframes for neurovascular MRI.
This advancement can enable faster MRI scans without compromising image quality.