Introduction

Increasing the speed of multispectral qMRI (MS-qMRI) acquisitions is of paramount importance for developing clinically practical quantitative protocols, particularly for high spatial resolution applications. Compressed sensing (CS) is a recently developed technique based on the premise that if “the underlying image exhibits transform sparsity, and if k-space undersampling results in incoherent artifacts in that transform domain, then the image can be recovered from randomly undersampled frequency domain data, provided an appropriate nonlinear recovery scheme is used”¹. In this work, we tested a commercially available CS implementation in combination with an ultra-high spatial resolution variant of the Tri-TSE pulse sequence to test for MS-qMRI accuracy relative to its non-CS counterpart.

Methods

A healthy male volunteer (35yo) was consented with an IRB approved research protocol. We tested CS-Tri-TSE at 3T (Philips, Ingenia Elition X) with a CS factor of 3 to generate self-coregistered maps of the relaxation times (T1 and T2) and proton density (PD) at a native voxel size of 0.4 x 0.4 x 1.2 mm³. Other selected parameters: TRlong=11,142ms, TRshort=550ms, TE1,2=13.75 and 110ms, Ns=130 slices, Scan time=17min. The scanner CS-reconstructed directly acquired dicom images were further processed with MS-qMRI algorithms programmed in Python 3.7 with the Enthought Deployment Manager. Two preparation steps are required and performed with Fiji: 1) manual editing of the segmented intracranial matter (ICM) segment and 2) manual delineation of the cerebellum’s superior aspect. MS-qMRI algorithms were derived as functions of pulse sequence parameters according to the Bloch equation model of the Tri-TSE, which is applicable across all MRI platforms (Eq. 1-3).
Eq. 1: $$$T_2=cf_3\frac{TE1_{eff}-TE2_{eff}}{ln\left(\frac{DA_2}{DA_1}\right)}$$$
Eq. 2: $$$PD=\frac{cf_1}{C_{coil}}\cdot\frac{DA_1\exp\left(\frac{TE1_{eff}}{T_2}\right)+DA_2\exp\left(\frac{TE2_{eff}}{T_2}\right)}{1-\exp\left({\frac{-\left(TR_{long}-TSEshot_{DE}\right)}{T_1}}\right)}$$$
Eq. 3: $$$T_1=Root_{hybr}\left\{T_1+\frac{TR_{short}}{ln\left(\frac{1-\left(\frac{DA_3}{DA_1}\right)\left[\left(1-\exp\left(\frac{-TR_{long}}{T_1}\right)\right)-cf_2\exp\left(\frac{-\left(TR_{long}-TSEshot_{DE}\right)}{T_1}\right)\right]}{1+cf_2\exp\left(\frac{TSEshot_{SE}}{T_1}\right)}\right)}\right\}$$$

Results

As shown in Fig. 1, the image quality of the CS-Tri-TSE images is nearly indistinguishable from the non-CS counterparts in terms of contrast and SNR. Remarkably, flow ghosting artifacts (not shown) were less severe in the CS-Tri-TSE images, specifically in the posterior fossa. Selected qMRI maps are shown in Fig. 2 with the corresponding pixel value scale bars showing good quantitative agreement with values reported in the literature² as further confirmed in the whole-brain histograms (Fig. 3). These histograms further show that there is less parameter spread in the CS-maps, possibly indicating less vulnerability to pulsation induced variability.

Discussion

We tested a three-fold acceleration factor and found that MS-qMRI accuracy and map quality are not affected. It is likely that further improvements in pulse sequence development and compressed sensing technologies could lead to routine ultra-high spatial resolution in the clinical context.

Conclusions

Compressed sensing does not appear to negatively impact MS-qMRI accuracy and opens the door to either, ultrafast brain MS-qMRI at current spatial resolution or to a new high spatial resolution standard in the clinical context. This work could have implications for protocol unification and standardization, and for ultra-high spatial resolution Synthetic-MRI and white matter fibrography.

Acknowledgements

No acknowledgement found.