Faster 3D saturation-recovery based myocardial T1 mapping using a reduced number of saturation points and denoising

Abstract

PurposeTo accelerate the acquisition of free-breathing 3D saturation-recovery-based (SASHA) myocardial T1 mapping by acquiring fewer saturation points in combination with a post-processing 3D denoising technique to maintain high accuracy and precision. Methods3D SASHA T1 mapping acquires nine T1-weighted images along the saturation recovery curve, resulting in long acquisition times. In this work, we propose to accelerate conventional cardiac T1 mapping by reducing the number of saturation points. High T1 accuracy and low standard deviation (as a surrogate for precision) is maintained by applying a 3D denoising technique to the T1-weighted images prior to pixel-wise T1 fitting. The proposed approach was evaluated on a T1 phantom and 20 healthy subjects, by varying the number of T1weighted images acquired between three and nine, both prospectively and retrospectively. Following the results from the healthy subjects, three patients with suspected cardiovascular disease were acquired using five T1-weighted images. T1 accuracy and precision was determined for all the acquisitions before and after denoising. ResultsIn the T1 phantom, no statistical difference was found in terms of accuracy and precision for the different number of T1-weighted images before or after denoising (P = 0.99 and P = 0.99 for accuracy, P = 0.64 and P = 0.42 for precision, respectively). In vivo, both prospectively and retrospectively, the precision improved considerably with the number of T1-weighted images employed before denoising (P<0.05) but was independent on the number of T1weighted images after denoising.

3D model-based super-resolution motion-corrected cardiac T1 mapping

“…The proposed approach was not compared to previously published 3D T1 mapping frameworks as for example MR multitasking 50 or MR fingerprinting 51 . Nonetheless, the A c c e p t e d M a n u s c r i p t publications in this field have either not resulted in isotropic voxel sizes [52][53][54][55][56][57][58][59] or a longer total scan time 6,7,60 compared to the proposed approach.…”

Cartesian dictionary‐based native T1and T2mapping of the myocardium

“…Nevertheless, the in vivo precision of Multimapping for quantifying myocardial T 1 , as indicated by the spatial variability, was comparable to MOLLI, which is considered the most precise of the widely used T 1 mapping methods 11 . There are several factors that influence the precision of a cardiac T 1 mapping method, including which kind of pre‐pulse is used (inversion or saturation) and how many source images are used for the curve‐fitting or dictionary‐matching procedure 11,46–48 . In both these aspects, MOLLI and Multimapping are very similar, which could explain the near equivalence in T 1 precision.…”

“…11 There are several factors that influence the precision of a cardiac T 1 mapping method, including which kind of pre-pulse is used (inversion or saturation) and how many source images are used for the curve-fitting or dictionary-matching procedure. 11,[46][47][48] In both these aspects, MOLLI and Multimapping are very similar, which could explain the near equivalence in T 1 precision. For T 2 , there was a significant difference in mean values between Multimapping and the reference technique of ˗7.6 ms.…”

Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors

“…[14][15][16] Other approaches address MRI signal denoising before the tissue parameter reconstruction, for example, with Marchenko-Pastur Principal Component Analysis 17 or Beltrami Denoising. [18][19][20] More modern data-driven deep learning approaches include mapping relaxation parameters with residual networks, 21,22 frameworks with physical model constraints, [23][24][25][26] supervised 27 and unsupervised 28 intravoxel incoherent motion estimation and models that address uncertainty estimation in dynamic contrast-enhanced-MRI 29 and ADC mapping. 30 To some degree, any method that incorporates denoising will rely on specific prior knowledge rather than the data.…”