Abstract

The computation of T1 maps from MR datasets represents an important step toward the precise characterization of kidney disease models in small animals. Here the main strategies to analyze renal T1 mapping datasets derived from small rodents are presented. Suggestions are provided with respect to essential software requirements, and advice is provided as to how dataset completeness and quality may be evaluated. The various fitting models applicable to T1 mapping are presented and discussed. Finally, some methods are proposed for validating the obtained results.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure.

Highlights

  • The computation of T1 maps from MR datasets represents an important step towards the precise characterization of kidney disease models in the small animal

  • Some concepts pertaining to T1 mapping are relatively general, there are some important points that need to be observed for each different acquisition strategy

  • Synthetic datasets of various complexities should be generated to test the validity of the processing solution

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Summary

Introduction

The computation of T1 maps from MR datasets represents an important step towards the precise characterization of kidney disease models in the small animal. Some concepts pertaining to T1 mapping are relatively general, there are some important points that need to be observed for each different acquisition strategy. We will describe in more detail the three main classes of data fitting procedures corresponding to saturation recovery, inversion recovery, and variable flip angle protocols. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure, which are part of this book.

Physiologic Motion Check
Acquisition Geometry Check
Acquisition Parameters Consistency Check (Multiple Scans Case Only)
Receiver Gain Consistency Check (Multiple Scans Case Only)
Data Intensity Scaling Consistency Check (Multiple Scans Case Only)
Data Coregistration
Fitting Data for Inversion Recovery Experiments
Fitting Data for Variable Flip Angle Experiments
Evaluation of Analysis Errors and Variability Using Synthetic Data
Comparison with Reference Values from the Literature
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