Spin-lock MRI is a valuable diagnostic imaging technology, as it can be used to probe the macromolecule environment of tissues. Quantitative T imaging is one application of spin-lock MRI that is reported to be promising for a number of clinical applications. Spin-lock is often performed with a continuous RF wave at a constant RF amplitude either on resonance or off resonance. However, both on- and off-resonance spin-lock approaches are susceptible to B and B inhomogeneities, which results in image artifacts and quantification errors. In this work, we report a continuous wave constant amplitude spin-lock approach that can achieve negligible image artifacts in the presence of B and B inhomogeneities for both on- and off-resonance spin-lock. Under the adiabatic condition, by setting the maximum B amplitude of the adiabatic pulses equal to the B amplitude of spin-lock RF pulse, the spins are ensured to align along the effective field throughout the spin-lock process. We show that this results in simultaneous compensation of B and B inhomogeneities for both on- and off-resonance spin-lock. The relaxation effect during the entire adiabatic half passage (AHP) and reverse AHP, and the stationary solution of the Bloch-McConnell equation present at off-resonance frequency offset, are considered in the revised relaxation model. We demonstrate that these factors create a direct current component to the conventional relaxation model. In contrast to the previously reported dual-acquisition method, the revised relaxation model just requires one acquisition to perform quantification. The simulation, phantom, and in vivo experiments demonstrate that the proposed approach achieves superior image quality compared with the existing methods, and the revised relaxation model can perform T quantification with one acquisition instead of two.

Purpose In MRI, the macromolecular proton fraction (MPF) is a key parameter of magnetization transfer (MT). It represents the relative amount of immobile protons associated with semi‐solid macromolecules involved in MT with free water protons. We aim to quantify MPF based on spin‐lock MRI and explore its advantages over the existing MPF‐mapping methods. Methods In the proposed method, termed MPF quantification based on spin‐lock (MPF‐SL), off‐resonance spin‐lock is used to sensitively measure the MT effect. MPF‐SL is designed to measure a relaxation rate (Rmpfsl) that is specific to the MT effect by removing the R1ρ relaxation due to the mobile water and chemical exchange pools. A theory is derived to quantify MPF from the measured Rmpfsl. No prior knowledge of tissue relaxation parameters, including T1 or T2, is needed to quantify MPF using MPF‐SL. The proposed approach is validated with Bloch‐McConnell simulations, phantom, and in vivo liver studies at 3.0T. Results Both Bloch‐McConnell simulations and phantom experiments show that MPF‐SL is insensitive to variations of the mobile water pool and the chemical exchange pool. MPF‐SL is specific to the MT effect and can measure MPF reliably. In vivo liver studies show that MPF‐SL can be used to detect collagen deposition in patients with liver fibrosis. Conclusion A novel MPF imaging method based on spin‐lock MRI is proposed. The confounding factors are removed, and the measurement is specific to the MT effect. It holds promise for MPF‐sensitive diagnostic imaging in clinical settings.

Objective: T1ρ mapping is a promising quantitative MRI technique for the non-invasive assessment of tissue properties. Learning-based approaches can map T1ρ from a reduced number of T1ρ weighted images but requires significant amounts of high-quality training data. Moreover, existing methods do not provide the confidence level of the T1ρ estimation. We aim to develop a learning-based liver T1ρ approach that can map T1ρ with a reduced number of images and provide uncertainty estimation. Approach: We proposed a self-supervised learning neural network that learns a T1ρ mapping using the relaxation constraint in the learning process. Epistemic uncertainty and aleatoric uncertainty are modelled for the T1ρ quantification network to provide a Bayesian confidence estimation of the T1ρ mapping. The uncertainty estimation can also regularize the model to prevent it from learning imperfect data. Main Results: We conducted experiments on T1ρ data collected from 52 patients with non-alcoholic fatty liver disease. The results showed that when only collecting two T1ρ-weighted images, our method outperformed the existing methods for T1ρ quantification of the liver. Our uncertainty estimation can further regularize the model to improve the performance of the model and it is consistent with the confidence level of liver T1ρ values. Significance: Our method demonstrates the potential for accelerating the T1ρ mapping of the liver by using a reduced number of images. It simultaneously provides uncertainty of T1ρ quantification which is desirable in clinical applications.

BACKGROUND AND PURPOSE: T1r imaging is a new quantitative MR imaging pulse sequence with the potential to discriminate between malignant and benign tissue. In this study, we evaluated the capability of T1r imaging to characterize tissue by applying T1r imaging to malignant and benign tissue in the nasopharynx and to normal tissue in the head and neck.MATERIALS AND METHODS: Participants with undifferentiated nasopharyngeal carcinoma and benign hyperplasia of the nasopharynx prospectively underwent T1r imaging. T1r measurements obtained from the histogram analysis for nasopharyngeal carcinoma in 43 participants were compared with those for benign hyperplasia and for normal tissue (brain, muscle, and parotid glands) in 41 participants using the Mann-Whitney U test. The area under the curve of significant T1r measurements was calculated and compared using receiver operating characteristic analysis and the Delong test, respectively. A P , . 05 indicated statistical significance.RESULTS: There were significant differences in T1r measurements between nasopharyngeal carcinoma and benign hyperplasia and between nasopharyngeal carcinoma and normal tissue (all, P , . 05). Compared with benign hyperplasia, nasopharyngeal carcinoma showed a lower T1r mean (62.14 versus 65.45 Â ms), SD (12.60 versus 17.73 Â ms), and skewness (0.61 versus 0.76) (all P , .05), but no difference in kurtosis (P ¼ . 18). The T1r SD showed the highest area under the curve of 0.95 compared with the T1r mean (area under the curve ¼ 0.72) and T1r skewness (area under the curve ¼ 0.72) for discriminating nasopharyngeal carcinoma and benign hyperplasia (all, P , .05). CONCLUSIONS:Quantitative T1r imaging has the potential to discriminate malignant from benign and normal tissue in the head and neck. ABBREVIATIONS: AHP ¼ adiabatic half passage; AUC ¼ area under the curve; NPC ¼ nasopharyngeal carcinoma; rAHP ¼ reverse adiabatic half passage; TSL ¼ time of spin-lock; PSNR ¼ peak signal-to-noise ratio