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Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing
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Flexible and efficient optimization of quantitative sequences using automatic differentiation of Bloch simulations.

Philip K Lee1,2, Lauren E Watkins1,3, Timothy I Anderson2

  • 1Radiology, Stanford University, Stanford, California.

Magnetic Resonance in Medicine
|May 28, 2019
PubMed
Summary

Automatic differentiation optimizes quantitative MRI sequences by improving Cramér-Rao Lower Bound (CRLB) without approximations. This method enhances Magnetic Resonance Fingerprinting (MRF) performance in noisy conditions and is computationally efficient.

Keywords:
Cramér-Rao lower boundautomatic differentiationmagnetic resonance fingerprintingoptimal experiment designquantitative imaging

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Quantitative Imaging
  • Computational Methods

Background:

  • Optimizing quantitative MRI sequences is crucial for accurate parameter estimation.
  • Conventional methods often rely on approximations or complex analytical expressions.
  • The Cramér-Rao Lower Bound (CRLB) provides a theoretical limit for estimation accuracy.

Purpose of the Study:

  • To develop and validate a computationally efficient method for optimizing the CRLB of quantitative MRI sequences.
  • To avoid approximations or analytical signal expressions in the optimization process.

Main Methods:

  • Applied automatic differentiation to Bloch simulations for sequence optimization.
  • Validated the approach using multi-echo spin echo and Double Echo Spoiled Gradient Recalled Echo (DESPO) sequences.
  • Optimized the CRLB for Magnetic Resonance Fingerprinting (MRF) sequences.

Main Results:

  • Achieved sequence parameters matching conventional methods for benchmark sequences.
  • Demonstrated improved in vivo MRF performance in the presence of white noise.
  • MRF CRLB optimization converged efficiently with scalable runtime.

Conclusions:

  • Automatic differentiation offers a robust method for CRLB optimization of quantitative MRI sequences.
  • The approach is computationally efficient for complex sequences like MRF.
  • Enables investigation of confounding factors and extended MRF sequence designs.