Figure 2 A random signal (aka noise) is not sparse. Most images have some level of structure to them and can take advantage of compressed sensing. (Claude Monet, “Young Girl in the Garden, Giverny”, 1888; public domain)
Figure 3 (Left) Traditional sensing samples at regular intervals. (Right) Compressed sensing samples at random (incoherent) intervals. (Claude Monet, "Young Girl in the Garden, Giverny", 1888; public domain)
Figure 4 (Left) A random sampling of the original MRI space. (Middle) The image reconstructed without AI. (Right) The image reconstructed with AI. The combination of CS and AI allows good image reconstruction with only a fraction of the typical data samples.
CS techniques have been integrated into clinical CT and MR scanners over the last several years. More recently, deep learning AI algorithms have been combined with CS to improve the quality of the final reconstructed image. Figure 4 shows a combined CS/AI approach called W-Net that yields a good quality MRI and is five times faster than the traditional approach (Souza and Frayne, 2018).
Intel plays a vital role in the adoption of AI into the medical imaging industry. Intel CPUs are a key component in most of the medical imaging scanners today. We work directly with healthcare companies to make sure their workloads run optimally on Intel hardware. With the emergence of AI, Intel has made great strides in bringing cutting edge techniques like CS/AI to medical imaging devices.
In fact, Intel recently partnered with Philips Healthcare to optimize a Philips AI algorithm for CS that resulted in a 54x speedup on the same Intel CPU by using the Intel® Distribution of the OpenVINO toolkit (Figure 5). We invite you to read about the Intel-Philips collaboration and learn more about how Compressed Sensing and Artificial Intelligence can allow medical scanners to perform faster high-quality images.
Figure 5 The Adaptive-CS-Net architecture used by Philips Healthcare. This AI algorithm reconstructs the original image with far fewer samples than would traditionally be necessary without a CS approach.
- Souza R and Frayne R. “A Hybrid Frequency-domain/Image-domain Deep Network for Magnetic Resonance Image Reconstruction”, arXiv 1810.12473, 2018.
- Pezzotti N, de Weerdt E, Yousefi S, Elmahdy MS, van Gemert J, Schülke C, Doneva M, Nielsen T, and Kastryulin S, Lelieveldt BPF, van Osch MJP, and Staring M. “Adaptive-CS-Net: FastMRI with Adaptive Intelligence”, arXiv 1912.12259, 2019.
- Reina GA, Stassen M, Pezzotti N, Moolenaar D, Kurtaev D, Khowala A. “Philips Healthcare Uses the Intel® Distribution of OpenVINO™ Toolkit and the Intel® DevCloud for the...”, 2020.
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