An Efficient Method for Multi-Parameter Mapping in Quantitative MRI Using B-Spline Interpolation

EUR Research Repository (Erasmus University Rotterdam)

Abstract

Quantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated Bloch simulations can be avoided by matching the measured signal with a precomputed signal dictionary on a discrete parameter grid (i.e. lookup table) as used in MR Fingerprinting. However, accurate estimation requires discretizing each parameter with a high resolution and consequently high computational and memory costs for dictionary generation, storage, and matching. Here, we reduce the required parameter resolution by approximating the signal between grid points through B-spline interpolation. The interpolant and its gradient are evaluated efficiently which enables a least-squares fitting method for parameter mapping. The resolution of each parameter was minimized while obtaining a user-specified interpolation accuracy. The method was evaluated by phantom and in-vivo experiments using fully-sampled and undersampled unbalanced (FISP) MR fingerprinting acquisitions. Bloch simulations incorporated relaxation effects (T-1, T-2), proton density (PD), receiver phase ( phi(0)), transmit field inhomogeneity (B-1(+)), and slice profile. Parametermapswere comparedwith those obtained from dictionary matching, where the parameter resolution was chosen to obtain similar signal (interpolation) accuracy. For both the phantom and the in-vivo acquisition, the proposed method approximated the parameter maps obtained through dictionary matching while reducing the parameter resolution in each dimension (T-1, T-2, B-1(+)) by - on average - an order of magnitude. In effect, the applied dictionary was reduced from 1.47GB to 464KB. Furthermore, the proposed method was equally robust against undersampling artifacts as dictionarymatching. Dictionary fittingwith B-spline interpolation reduces the computational and memory costs of dictionary-based methods and is therefore a promising method for multi- parametric mapping.

Authors 6

1. Willem van Valenberg

   Erasmus MC · Delft University of Technology

   Affiliation as printed

   Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands

   Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands

   Department of Radiology, Erasmus MC, Rotterdam, The Netherlands

2. Stefan Klein

   Erasmus MC

   Affiliation as printed

   Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands

   Department of Radiology, Erasmus MC, Rotterdam, The Netherlands

3. Frans M. Vos

   Amsterdam University Medical Centers · Delft University of Technology

   Affiliation as printed

   Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands

   Department of Radiology, Amsterdam UMC, Amsterdam, The Netherlands

4. Kirsten Koolstra Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands

5. Lucas J. van Vliet

   Delft University of Technology

   Affiliation as printed

   Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands

6. Dirk H. J. Poot

   Erasmus MC · Delft University of Technology

   Affiliation as printed

   Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands

   Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands

   Department of Radiology, Erasmus MC, Rotterdam, The Netherlands