To develop a fast and reliable spin-locking method to assess and quantify the longitudinal relaxation time in therotating frame (T_1ρ) in the human brain at high image resolution (whole brain acquisition, resolution 1.5mm³ isotropic). The implemented sequence and protocol optimizations (fluid suppression, image deblurring, and scan time reduction) lead to a decrease in the overall SNR of the raw MR images. To counteract the SNR loss, image denoising was investigated in order to increase the accuracy and precision of the resulting T_1ρ data.An in-house developed, extended phase graph (EPG) based based variable flip angle-TSE sequence [1] was equipped with a fluid attenuated T_1ρ preparation, combining previous approaches from Borthakur and Li [2,3] (figure 1). At the beginning of each TR a saturation recovery preparation is applied in order to reset the longitudinal magnetization. At the end of the saturation time T_SAT an inversion pulse is applied to perform a fluid suppression.After the inversion time T_INV, a B₀ and B₁ compensated T_1ρ module is applied [4], followed by the imaging readout. For fat-suppression a water excitation (utilizing binomial pulses) was employed. A centric radial reordering was used to maintain T_1ρ weighting in the center of k-space. The refocussing angles along the echo trains were calculated to acquire a constant signal shape for point-spread function (PSF) optimization. All experiments were performed in healthy volunteers on a MAGNETOM Skyra scanner (Siemens GmbH, Healthcare Sector,Erlangen, Germany) using a 32 channel head coil using the following parameters: T_SAT/T_INV/TR = 1000/811/2600 ms, Turbofactor TF = 176, 1.2 mm$$$^3$$$ isotropic resolution (whole brain coverage), 2D-GRAPPA factor 6 (3x2). Sequence parameters (T_SAT/T_INV/TF) were optimized via simulations (utilizing Bloch equations and the EPG algorithm). In total five T_1ρ weighted whole-brain volumes were acquired. The spin-lock sampling scheme was based on the Cramer Rao lower bounds theory [5] (spin-lock times TSL = 1, 25, 50, 75, 100 ms). The total scan time was 3:45min. Before curve fitting a motion correction of the individual T_1ρ scans [6] and image denoising [7] was performed. In order to legitimate the denoising approach simulations on synthetic data were carried out using noise level and distribution similar to the actual experiments.Figure 2 depicts the curve fitting results for synthetic data after adding Rician noise (left: Ground truth). T_1ρ maps are shown with (middle) and without (right) application of denoising before the curve fitting. The fitting error is significantly reduced for the denoised data, resulting in both, a higher precision and a higher accuracy of the T_1ρ values (see figure 3). Figure 4 shows acquired, native T_1ρ weighted images before and after denoising. The noise level is significantly lower after denoising, while keeping the image contrast and features, as well as the relative signal intensity between the various spin-lock weightings. Additionally, signal from CSF is effectively suppressed in all T_1ρ images, allowing a more accurate T_1ρ quantification at tissue/CSF boundaries. Figure 5 depicts a T_1ρ map after denoising (left) and the corresponding fitting error (right). It was shown that whole-brain, fluid-suppressed T_1ρ imaging in short scan times is possible. By implementing a fast and SAR efficient imaging sequence the total scan time was shortened significantly. The loss in SNR, induced during the sequence optimization process (magnetization reset, fluid suppression, image deblurring, and scan time reduction), was compensated by the application of image denoising. The described method does not introduce or remove any features in the images. Hence, an increase in accuracy and precision could be achieved in the quantification process. The achieved gain in acquisition speed is essential, as motion during the the acquisition corrupts the T_1ρ quantification.This is especially important when targeting neurodegenerative diseases, where subject movement during the scans often occurs.