Introduction to the Technical Aspects of Computed Diffusion-weighted Imaging for Radiologists

Area of Science:

• Magnetic Resonance Imaging
• Medical Imaging Physics

Background:

• Diffusion-weighted (DW) imaging is crucial for diagnosing acute cerebral infarction and in oncologic imaging.
• Current MR imaging units have limitations in achieving higher diffusion effects.
• There is a need for improved DW imaging techniques with reduced imaging times and enhanced signal-to-noise ratios.

Purpose of the Study:

• To introduce and explain the principles of computed DW imaging.
• To highlight the advantages of computed DW imaging over conventional methods.
• To discuss the methods and considerations for generating accurate computed DW images.

Main Methods:

• Synthesizing arbitrary b-value DW images from measured b-value images using voxelwise fitting.
• Employing mathematical models (mono-, bi-, or triexponential equations) for image generation.
• Utilizing image registration techniques to correct misalignment of input data and reduce artifacts.

Main Results:

• Computed DW imaging generates images with higher diffusion effects than currently achievable with MR units.
• This method can reduce imaging time while improving the signal-to-noise ratio compared to acquired DW images.
• Fitting input images at lower b-values and shorter echo times results in computed DW images closer to the ideal case.

Conclusions:

• Computed DW imaging offers significant advantages for MRI, including enhanced diffusion effects and signal quality.
• Accurate generation of computed DW images requires appropriate model selection and parameter choices.
• Image registration is essential for minimizing artifacts and ensuring the accuracy of computed DW images.