Analysis of right ventricular mass from magnetic resonance imaging data: a simple post-processing algorithm for...

Area of Science:

• Cardiovascular Imaging
• Medical Physics
• Biomedical Engineering

Background:

• Accurate assessment of right ventricle (RV) mass via cardiac Magnetic Resonance Imaging (MRI) is crucial for diagnosing cardiovascular conditions.
• The complex geometry of the RV, particularly its crescent shape, leads to partial-volume effects that compromise measurement accuracy.
• Existing methods for RV mass (RVm) quantification using MRI are susceptible to these geometric challenges and partial-volume errors.

Purpose of the Study:

• To develop and evaluate a novel postprocessing algorithm for correcting partial-volume effects in RV mass (RVm) estimation from standard cardiac MRI cine images.
• To assess the improved reliability and accuracy of this self-correcting RVm measurement method in both clinical (human) and preclinical (rat) datasets.
• To validate the algorithm's performance against conventional RVm measurement techniques and ex vivo measurements.

Main Methods:

• A novel self-correcting postprocessing algorithm was developed to address partial-volume effects in RV mass (RVm) analysis from MRI cine images.
• The algorithm was applied to standard MRI data from 16 human patients and 17 Wistar rats with induced pulmonary congestion.
• Measurements from the self-correcting algorithm were compared to conventional RVm measurements, and preclinical RVm was compared to ex vivo RV weight (RVw) for accuracy assessment.

Main Results:

• The self-correcting algorithm significantly improved the reliability of RVm measurements, evidenced by narrower limits of agreement (LOAs) and lower coefficients of variation (CoVs) in both clinical and preclinical data.
• Clinical data showed improved LOAs (-1.8 ± 8.6g vs. 5.8 ± 7.8g) and CoVs (7.0% vs. 14.3%) compared to conventional methods.
• Preclinical data demonstrated similar improvements in LOAs (21 ± 46mg vs. 64 ± 89mg) and CoVs (9.0% vs. 17.4%), with better correspondence to ex vivo RV weight.

Conclusions:

• The proposed self-correcting algorithm effectively improves the reliability and accuracy of right ventricle mass (RVm) quantification using cardiac MRI.
• This method offers a simple, easily implementable solution to mitigate partial-volume effects without requiring additional imaging data.
• The algorithm shows significant potential for enhancing diagnostic information derived from RV MRI in both clinical practice and research settings.