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

Abstract

<jats:title>Abstract</jats:title><jats:sec><jats:title>Purpose</jats:title><jats:p>To accelerate the acquisition of free-breathing 3D saturation-recovery-based (SASHA) myocardial T1 mapping by acquiring fewer saturation points in combination with a novel post-processing 3D denoising technique to maintain high accuracy and precision.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>3D 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 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 T1-weighted images acquired between three and nine, both prospectively and retrospectively. 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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In 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 (<jats:italic>P</jats:italic>&lt;0.05) but was independent on the number of T1-weighted images after denoising.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>We demonstrate the feasibility of accelerating 3D SASHA T1 mapping by reducing the number of acquired T1-weighted images in combination with an efficient 3D denoising, without affecting accuracy and precision of T1 values.</jats:p></jats:sec>