Purpose: To investigate and mitigate the influence of physiological and acquisition-related parameters on myocardial blood flow (MBF) measurements obtained with myocardial Arterial Spin Labeling (myoASL). Methods: A Flow-sensitive Alternating Inversion Recovery (FAIR) myoASL sequence with bSSFP and spoiled GRE (spGRE) readout is investigated for MBF quantification. Bloch-equation simulations and phantom experiments were performed to evaluate how variations in acquisition flip angle (FA), acquisition matrix size (AMS), heart rate (HR) and blood (Formula presented.) relaxation time ((Formula presented.)) affect quantification of myoASL-MBF. In vivo myoASL-images were acquired in nine healthy subjects. A corrected MBF quantification approach was proposed based on subject-specific (Formula presented.) values and, for spGRE imaging, subtracting an additional saturation-prepared baseline from the original baseline signal. Results: Simulated and phantom experiments showed a strong dependence on AMS and FA ((Formula presented.) >0.73), which was eliminated in simulations and alleviated in phantom experiments using the proposed saturation-baseline correction in spGRE. Only a very mild HR dependence ((Formula presented.) >0.59) was observed which was reduced when calculating MBF with individual (Formula presented.). For corrected spGRE, in vivo mean global spGRE-MBF ranged from 0.54 to 2.59 mL/g/min and was in agreement with previously reported values. Compared to uncorrected spGRE, the intra-subject variability within a measurement (0.60 mL/g/min), between measurements (0.45 mL/g/min), as well as the inter-subject variability (1.29 mL/g/min) were improved by up to 40% and were comparable with conventional bSSFP. Conclusion: Our results show that physiological and acquisition-related factors can lead to spurious changes in myoASL-MBF if not accounted for. Using individual (Formula presented.) and a saturation-baseline can reduce these variations in spGRE and improve reproducibility of FAIR-myoASL against acquisition parameters.

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Background: To develop a regression neural network for the reconstruction of lesion probability maps on Magnetic Resonance Fingerprinting using echo-planar imaging (MRF-EPI) in addition to T₁, T2∗, NAWM, and GM- probability maps. Methods: We performed MRF-EPI measurements in 42 patients with multiple sclerosis and 6 healthy volunteers along two sites. A U-net was trained to reconstruct the denoised and distortion corrected T₁ and T2∗ maps, and to additionally generate NAWM-, GM-, and WM lesion probability maps. Results: WM lesions were predicted with a dice coefficient of 0.61 ± 0.09 and a lesion detection rate of 0.85 ± 0.25 for a threshold of 33%. The network jointly enabled accurate T₁ and T2∗ times with relative deviations of 5.2% and 5.1% and average dice coefficients of 0.92 ± 0.04 and 0.91 ± 0.03 for NAWM and GM after binarizing with a threshold of 80%. Conclusion: DL is a promising tool for the prediction of lesion probability maps in a fraction of time. These might be of clinical interest for the WM lesion analysis in MS patients.

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Purpose: To implement a free-breathing sequence for simultaneous quantification of (Formula presented.), (Formula presented.), and (Formula presented.) for comprehensive tissue characterization of the myocardium in a single scan using a multi-gradient-echo readout with saturation and (Formula presented.) preparation pulses. Methods: In the proposed Saturation And (Formula presented.) -prepared Relaxometry with Navigator-gating (SATURN) technique, a series of multi-gradient-echo (GRE) images with different magnetization preparations was acquired during free breathing. A total of 35 images were acquired in 26.5 ± 14.9 seconds using multiple saturation times and (Formula presented.) preparation durations and with imaging at 5 echo times. Bloch simulations and phantom experiments were used to validate a 5-parameter fit model for accurate relaxometry. Free-breathing simultaneous (Formula presented.), (Formula presented.), and (Formula presented.) measurements were performed in 10 healthy volunteers and 2 patients using SATURN at 3T and quantitatively compared to conventional single-parameter methods such as SASHA for (Formula presented.), (Formula presented.) -prepared bSSFP, and multi-GRE for (Formula presented.). Results: Simulations confirmed accurate fitting with the 5-parameter model. Phantom measurements showed good agreement with the reference methods in the relevant range for in vivo measurements. Compared to single-parameter methods comparable accuracy was achieved. SATURN produced in vivo parameter maps that were visually comparable to single-parameter methods. No significant difference between (Formula presented.), (Formula presented.), and (Formula presented.) times acquired with SATURN and single-parameter methods was shown in quantitative measurements (SATURN (Formula presented.), (Formula presented.), (Formula presented.); conventional methods: (Formula presented.), (Formula presented.), (Formula presented.); (Formula presented.)). Conclusion: SATURN enables simultaneous quantification of (Formula presented.), (Formula presented.), and (Formula presented.) in the myocardium for comprehensive tissue characterization with co-registered maps, in a single scan with good agreement to single-parameter methods.

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Purpose: To evaluate the use of magnetic resonance fingerprinting (MRF) for simultaneous quantification of T₁ and T*₂ 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 T*₂. 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 T*₂ 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 T*₂ 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 T*₂. Baseline images in vivo were free of artifacts and relaxation times yielded good agreement with conventional methods and literature (deviation T₁:7 ± 4%, T*₂:6±3%). Conclusions: In this feasibility study, the proposed renal MRF sequence resulted in accurate T₁ and T*₂ quantification in a single breath-hold.

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Measurement of the bloodT1time using conventional myocardialT1mapping methods hasgained clinical significance in the context of extracellular volume (ECV) mapping and synthetichematocrit (Hct). However, its accuracy is potentially compromised by in-flow ofnon-inverted/non-saturated spins and in-flow of spins which are not partially saturated fromprevious imaging pulses.Bloch simulations were used to analyze various flow effects separately.T1measurements ofgadolinium doped water were performed using a flow phantom with adjustable flow velocities at3 T. Additionally,in vivobloodT1measurements were performed in 6 healthy subjects (26±5years, 2 female). To study theT1time as a function of the instantaneous flow velocity,T1timeswere evaluated in an axial imaging slice of the descending aorta. Velocity encoded cinemeasurements were performed to quantify the flow velocity throughout the cardiac cycle.Simulation results show more than 30% loss in accuracy for 10% non-prepared in-flowingspins. However, in- and out-flow to the imaging plane only demonstrated minor impact on theT1time. PhantomT1times were decreased by up to 200 ms in the flow phantom, due to in-flow ofnon-preparedspins.Highflowvelocitiescausein-flowofspinsthatlackpartialsaturationfromtheimaging pulses but only lead to negligibleT1time deviation (less than 30 ms).In vivomeasurements confirm a substantial variation of theT1time depending on the flow velocity. Thehighest aorticT1times are observed at the time point of minimal flow with increased flow velocityleading to reduction of the measuredT1time by up to130±49 ms at peak velocity.In this work we attempt to dissect the effects of flow onT1times, by using simulations,well-controlled, simplified phantom setup and the linear flow pattern in the descending aortain vivo.@en