A bootstrapping residuals approach to determine the error in quantitative functional lung imaging

Area of Science:

• Medical Imaging
• Pulmonary Medicine
• Quantitative MRI

Background:

• Functional lung MRI (fLMRI) provides quantitative insights into lung function.
• Accurate assessment of statistical errors is crucial for reliable fLMRI interpretation.
• Traditional error assessment often requires repeated measurements, which is time-consuming.

Purpose of the Study:

• To implement and validate a novel algorithm for determining statistical errors in self-gated, non-contrast-enhanced functional lung imaging.
• To assess the precision, accuracy, and reproducibility of the proposed error quantification method.

Main Methods:

• A bootstrapping residuals approach was developed to quantify errors in quantitative fLMRI.
• The algorithm's precision, accuracy, and reproducibility were evaluated in 7 healthy volunteers.
• The method was also applied to fLMRI data from a patient with cystic fibrosis.

Main Results:

• Bootstrapping-derived error maps were comparable to those from repeated measurements.
• Median absolute error values showed similar results for both methods, even with reduced averages.
• In a volunteer, error metrics for ventilation, perfusion amplitude, and timing were highly precise and accurate.

Conclusions:

• The bootstrapping residuals method enables error determination in functional lung MRI without repeated measurements.
• The approach yields reproducible error values, suitable for quality control in fLMRI.
• This method can help differentiate true lung defects from imaging noise, as demonstrated in a cystic fibrosis patient.