Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group

Abstract

Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton-density fat fraction, and cardiac T 1 . These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The | 1185 WEINGÄRTNER et al.

Editorial for “Noninvasive Assessment of APAP‐Induced Hepatotoxicity Using Multiple MR Parameters in an Experimental Rat Model”

“…To conclude, if such an animal study, confirming similar prior results, must of course be completed by new experimental and clinical efforts based on methodological approach such as that recommended by the International Society for Magnetic Resonance in Medicine (ISMRM) quantitative MR study group, 6 the work of Chen et al is of real interest to identify biomarkers and improve drug development process as well as DILI diagnosis and management.…”

Fat-Corrected Pancreatic R2* Relaxometry from Multi-Echo Gradient-Recalled Echo Sequence Using Convolutional Neural Network

“…CNN's approach slightly underestimated the FF value. Although the GT technique is well validated for contemporary R2* and FF evaluation in the liver [45,46], few reports are available for the pancreas [20], so a comparison of the obtained results with the reproducibility in the clinical setting is difficult. FF value estimation in the pancreas was reported to be challenging, with an estimated CoV among observers of about 11% [47], similar to the one assessed in the study.…”

Motion compensated renal diffusion weighted imaging

“…In each subject, the mean ADC over cardiac trigger delays, ADC mean_tds , the SD of the ADC over cardiac trigger delays, ADC SD_tds , and the coefficient of variation across cardiac trigger delays CV tds were also computed for characterizing the variation along the cardiac trigger delay dimension separately for each kidney. A global CV tds_global was finally determined by taking the RMS average over all subjects, from the equation [47]: $$$$ {CV}_{tds\_ global}=\frac{\sqrt{\sum \limits_{j=1}^m\frac{{{\mathrm{ADC}}_{\mathrm{SD}\_\mathrm{tds}}}_j^2}{m}}}{\sum_{j=1}^m\frac{{\mathrm{ADC}}_{\mathrm{mean}\_{\mathrm{tds}}_j}}{m}}, $$$$ where m is the number of subjects, $$$ {{\mathrm{ADC}}_{\mathrm{SD}\_\mathrm{tds}}}_j $$$ is the SD of ADC across trigger delays for subject j and $$$ {{\mathrm{ADC}}_{\mathrm{mean}\_\mathrm{tds}}}_j $$$ is the mean ADC across delays for subject j . The global ADC SD_tds_global s were found from the RMS average over all subjects, and the global ADC mean_tds_global s were found from averaging over all subjects.…”