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<p>This manuscript presents a novel combination of technologies that yield images of improved quality for accelerated Magnetic Resonance Imaging (MRI). Two established methods for accelerating MRI include <i>parallel imaging</i> and <i>compressed sensing</i>. Two types of parallel imaging include <i>linear predictability</i>, which assumes that the Fourier samples are linearly related, and <i>sensitivity encoding</i>, which incorporates <i>a priori</i> knowledge of the sensitivity maps. In this work, we combine compressed sensing with both types of parallel imaging using a novel regularization term: <i>SPIRiT regularization</i>. For a given number of samples, the images reconstructed with SPIRiT regularization are improved over those reconstructed without it. We demonstrate these improvements on data of a knee, a brain, and an ankle.</p>

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SPIRiT Regularization: Parallel MRI with a Combination of Sensitivity Encoding and Linear Predictability

  • 期刊论文

  • 发表日期：

    2026年7月19日

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  • Nicholas Dwork,
  • Alex McManus,
  • Stephen Becker,
  • Gennifer T. Smith

摘要

This manuscript presents a novel combination of technologies that yield images of improved quality for accelerated Magnetic Resonance Imaging (MRI). Two established methods for accelerating MRI include parallel imaging and compressed sensing. Two types of parallel imaging include linear predictability, which assumes that the Fourier samples are linearly related, and sensitivity encoding, which incorporates a priori knowledge of the sensitivity maps. In this work, we combine compressed sensing with both types of parallel imaging using a novel regularization term: SPIRiT regularization. For a given number of samples, the images reconstructed with SPIRiT regularization are improved over those reconstructed without it. We demonstrate these improvements on data of a knee, a brain, and an ankle.