Model‐based super‐resolution reconstruction of T₂ maps

Abstract

Purpose: High-resolution isotropic T 2 mapping of the human brain with multi-echo spin-echo (MESE) acquisitions is challenging. When using a 2D sequence, the resolution is limited by the slice thickness. If used as a 3D acquisition, specific absorption rate limits are easily exceeded due to the high power deposition of nonselective refocusing pulses. A method to reconstruct 1-mm 3 isotropic T 2 maps is proposed based on multiple 2D MESE acquisitions. Data were undersampled (10-fold) to compensate for the prolonged scan time stemming from the super-resolution acquisition. Theory and Methods: The proposed method integrates a classical super-resolution with an iterative model-based approach to reconstruct quantitative maps from a set of undersampled low-resolution data. The method was tested on numerical and multipurpose phantoms, and in vivo data. T 2 values were assessed with a region-of-interest analysis using a single-slice spin-echo and a fully sampled MESE acquisition in a phantom, and a MESE acquisition in healthy volunteers. Results: Numerical simulations showed that the best trade-off between acceleration and number of low-resolution datasets is 10-fold acceleration with 4 acquisitions (acquisition time = 18 min). The proposed approach showed improved resolution over low-resolution images for both phantom and brain. Region-of-interest analysis of the phantom compartments revealed that at shorter T 2 , the proposed method was comparable with the fully sampled MESE. For the volunteer data, the T 2 values found in the brain structures were consistent across subjects (8.5-13.1 ms standard deviation). Conclusion: The proposed method addresses the inherent limitations associated with high-resolution T 2 mapping and enables the reconstruction of 1 mm 3 isotropic relaxation maps with a 10 times faster acquisition. K E Y W O R D Smodel-based reconstruction, parallel Imaging, super-resolution, T 2 mapping

Model-based super-resolution reconstruction with joint motion estimation for improved quantitative MRI parameter mapping

“…SRR has indeed been shown to provide a better trade-off between acquisition time, spatial resolution, and signal-to-noise ratio (SNR) than conventional direct HR acquisition (Plenge et al, 2012). Meanwhile, SRR has also been successfully applied to different qMRI modalities, including diffusion MRI (Poot et al, 2013;Fogtmann et al, 2014;Van Steenkiste et al, 2016), relaxometry (Van Steenkiste et al, 2017;Bano et al, 2020;Lajous et al, 2020) and arterial spin labeling (Bladt et al, 2020). In some of these approaches, HR images are individually reconstructed from a set of equally contrast-weighted LR images, prior to voxel-wise fitting a parametric qMRI signal model (e.g., a diffusion model or relaxation model) to these reconstructed HR images (Poot et al, 2013;Lajous et al, 2020), whereas in other approaches the qMRI signal model is included in the reconstruction and HR parameter maps are estimated directly from the LR images, without first reconstructing the individual HR contrast-weighted images (Fogtmann et al, 2014;Van Steenkiste et al, 2016Bano et al, 2020).…”

“…a relaxation model or diffusion model) to these reconstructed images (Poot et al, 2013;Lajous et al, 2020). Second, the joint estimation of the motion and the biophysical parameters of interest allows our method to outperform state-of-the-art qMRI SRR algorithms that either do not correct for motion (Bano et al, 2020), or work with decoupled motion estimation algorithms (Van Steenkiste et al, 2017). Third, unlike state-of-the-art SRR methods in qMRI that rely on orthogonal slice orientations and use the same set of contrast weightings for each slice orientation (e.g., Fogtmann et al, 2014), our method allows for arbitrary slice orientations and a different contrast weighting for each LR image, offering a much-increased imaging flexibility.…”

Efficient T2 mapping with blip‐up/down EPI and gSlider‐SMS (T2‐BUDA‐gSlider)

“…Other approaches including DESPOT 2 , 9 MR fingerprinting, 10,11 3D turbo spin-echo (TSE) T 2 mapping, 12,13 multi-echo acquisition, 14 and model-based acceleration [15][16][17] have been developed to enable faster T 2 parameter estimation. These techniques can reduce the scan time and improve clinical feasibility, but still require several minutes to provide whole-brain high-resolution T 2 maps (eg, 17 min for DESPOT 2 , 9 11-16 min for model-based T 2 mapping, 18 and 5-8 min for 3D MR fingerprinting) while also providing T 1 mapping. 11,19 Another potential solution is using SE-EPI to reduce the scan time.…”

T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions

“…In clinical routine, 2D thick slices are typically acquired in a few seconds using T2w multi-slice single-shot fast spin echo sequences [8]. We hypothesize that the combination of SR fetal brain MRI with the sensitivity of qMRI would enable reliable and robust 3D HR T2 relaxometry of the fetal brain [2,3]. In this context, we have explored the feasibility of repeatable, accurate and robust 3D HR T2 mapping from SR-reconstructed clinical fast T2w Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) with variable echo time (TE) on a quantitative MR phantom [12].…”