Magnetic resonance fingerprinting for simultaneous renal T₁ and T₂^* mapping in a single breath‐hold

Abstract

Purpose To evaluate the use of magnetic resonance fingerprinting (MRF) for simultaneous quantification of T1 and T2∗ in a single breath‐hold in the kidneys. Methods The proposed kidney MRF sequence was based on MRF echo‐planar imaging. Thirty‐five measurements per slice and overall 4 slices were measured in 15.4 seconds. Group matching was performed for in‐line quantification of T1 and T2∗. Images were acquired in a phantom and 8 healthy volunteers in coronal orientation. To evaluate our approach, region of interests were drawn in the kidneys to calculate mean values and standard deviations of the T1 and T2∗ times. Precision was calculated across multiple repeated MRF scans. Gaussian filtering is applied on baseline images to improve SNR and match stability. Results T1 and T2∗ times acquired with MRF in the phantom showed good agreement with reference measurements and conventional mapping methods with deviations of less than 5% for T1 and less than 10% for T2∗. Baseline images in vivo were free of artifacts and relaxation times yielded good agreement with conventional methods and literature (deviation T1:7±4%, T2∗:6±3%). Conclusions In this feasibility study, the proposed renal MRF sequence resulted in accurate T1 and T2∗ quantification in a single breath‐hold.

Accelerated white matter lesion analysis based on simultaneous T1 and T2∗ quantification using magnetic resonance fingerprinting and deep learning

“…Recently, Marchenko–Pastur principal component analysis (MPPCA) was proposed to denoise EPI diffusion MRI images 14 . This is of particular interest, as a recent study demonstrated the value of denoising the acquired MRF magnitude images to improve the quality of the quantitative maps 10 . The large number of magnitude images in an MRF acquisition leads to long reconstruction times, which has been acknowledged as one of the drawbacks of the MRF methodology 7,15 .…”

Streamlined Magnetic Resonance Fingerprinting: Fast Whole-brain Coverage with Deep-learning Based Parameter Estimation

“…First, T2 estimation was added to the previously introduced EPI-based MRF, making it possible to estimate all three main relaxation parameters in a single scan (T1, T2, and T2*). As also noted in previous work (Hermann et al, 2020;Rieger et al, 2018Rieger et al, , 2017, EPI-based acquisitions make use of online instantaneous image reconstruction and produce images without noticeable under-sampling artifacts. Second, the utilization of simultaneous multi-slice (SMS) acceleration combined with slice interleaving makes this method capable of acquiring each slice in a few seconds, consequently enabling whole-brain coverage in just a few minutes (3 minutes here with SMS factor of 4).…”

“…As the measured signals resemble the simulated ones, we were able to simplify the training process by not needing to acquire a large sample of undersampled data for realism of training. Furthermore, as motion correction has been recently raised as a means to improve MRF accuracy, the absence of undersampling artifacts in EPI images facilitates image-domain motion correction (Hermann et al, 2020). The high image quality also lends itself to possibly more accurate partial volume estimation through solving the inverse problem (Deshmane et al, 2019; Fang et al, 2019b).…”

“…Spiral acquisitions usually require an offline image reconstruction, including regridding and sampling density compensation and are more vulnerable to field imperfections as compared to Cartesian readouts. Based on the latter, MRF using echo-planar imaging (EPI) was introduced recently (Hermann et al, 2020;Rieger et al, 2018Rieger et al, , 2017, but so far can only provide T1 and T2* estimates.…”

“…Despite MRF’s great promise, there remain several bottlenecks: Spiral acquisitions usually require an offline image reconstruction, including regridding and sampling density compensation and are more vulnerable to field imperfections as compared to Cartesian readouts. Based on the latter, MRF using echo-planar imaging (EPI) was introduced recently (Hermann et al, 2020; Rieger et al, 2018, 2017), but so far can only provide T1 and T2* estimates. The original spiral-based MRF framework has the opposite issue --- it was designed for T1 and T2 quantification but not T2*. The current spiral-based approaches that offer whole-brain simultaneous T1, T2 and T2* estimation (Hong et al, 2018; C. Y.…”