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A framework for optimizing the acquisition protocol multishell diffusion-weighted imaging for multimodel assessment.

Tommaso Ciceri1,2, Alberto De Luca3,4, Nivedita Agarwal5

  • 1Neuroimaging Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy.

NMR in Biomedicine
|March 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized acquisition scheme (OAS) for diffusion-weighted imaging (DWI) to efficiently study tissue microstructure. The OAS significantly reduces scan time while maintaining data quality for multiple DWI models.

Keywords:
MRIacquisition optimizationbraindiffusionmultiple model estimation

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Area of Science:

  • Neuroimaging
  • Biophysics
  • Medical Physics

Background:

  • Diffusion-weighted imaging (DWI) offers insights into tissue microstructure.
  • Current DWI protocols lack standardization for multi-model analysis within clinical time constraints.

Purpose of the Study:

  • To develop a flexible framework for optimizing DWI acquisition protocols for multiple diffusion models.
  • To determine an optimized acquisition scheme (OAS) using a data-driven approach.

Main Methods:

  • An extensive DWI protocol with 15 shells (10–3500 s/mm²) was acquired from 11 healthy subjects.
  • The OAS was developed to minimize model-estimated parameter errors across five models: DTI, free water, IVIM, DKI, and NODDI.
  • Voxel-level and ROI-level analyses were performed on white matter and fiber bundles.

Main Results:

  • The OAS reduced acquisition time to 14 minutes from 35 minutes for joint recommended protocols.
  • OAS-derived parameters showed comparable values, noise, and contrast to reference schemes.
  • Power analysis indicated OAS retains sensitivity for group differences, except for the free water model (R²=0.91).

Conclusions:

  • The proposed framework successfully optimizes DWI shell acquisition schemes for multiple models.
  • The OAS integrates low and high b-values, significantly reducing scan time.
  • This optimization facilitates efficient multi-model DWI analysis in clinical settings.