Normalized averaged range (nAR), a robust quantification method for MPIO-content.

Area of Science:

• Biomedical Imaging
• Nanotechnology
• Medical Physics

Background:

• Micron-sized paramagnetic iron oxide particles (MPIO) are vital MRI contrast agents, but their in vivo quantification is hindered by blooming artifacts and bias fields.
• Existing methods struggle with accuracy due to signal distortions, limiting reliable MPIO detection and measurement.

Purpose of the Study:

• To introduce and validate a novel quantification method, the normalized average range (nAR), for MPIO in magnetic resonance imaging (MRI).
• To assess the efficacy of nAR across different contrast types (positive and negative) and under varying imaging conditions (biased and unbiased).

Main Methods:

• Developed the normalized average range (nAR) method, comparing average values in test regions of interest (ROIs) to a control ROI in range-filtered images.
• Applied nAR to agar phantoms with varying MPIO concentrations and to a mouse model tracking MPIO-labeled stem cells.
• Quantified MPIO in both positive and negative contrast MRI images, with and without bias field correction, and validated findings with histology.

Main Results:

• The nAR method reliably indicates the presence and relative content of MPIO in both negative and positive contrast MRI images.
• Optimized positive contrast images demonstrated slightly higher sensitivity for nAR compared to negative contrast images.
• The bias field had a minimal impact on MPIO quantification using nAR, reducing the need for extensive debiasing procedures.
• Histological analysis confirmed the correlation between nAR values and MPIO content.

Conclusions:

• The normalized average range (nAR) offers a robust and reliable tool for quantifying MPIO content in vivo, particularly in mouse models.
• The nAR method enhances accuracy and simplifies MPIO quantification, overcoming limitations of traditional approaches.
• The technique's effectiveness across different contrast types and minimal sensitivity to bias fields make it a versatile solution for MPIO research.