Quantitative Magnetic Resonance Imaging (MRI) can enable early diagnosis of knee cartilage damage if imaging is performed during the application of load. Mechanical loading via ropes, pulleys and suspended weights can be obstructive and require adaptations to the patient table. In this paper, a new lightweight MRI-compatible elastic loading mechanism is introduced. The new device showed sufficient linearity (|α/β| = 0.42 ± 0.25), reproducibility (CoV = 5 ± 2%), and stability (CoV = 0.5 ± 0.1%). In vivo and ex vivo scans confirmed the ability of the device to exert sufficient force to study the knee cartilage under loading conditions, inducing up to a 29% decrease in T2 of the central medial cartilage. With this device mechanical loading can become more accessible for researchers and clinicians, thus facilitating the translational use of MRI biomarkers for the detection of cartilage deterioration.@en

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.@en

Purpose: To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning. Methods: MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of (Formula presented.) and (Formula presented.) in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF (Formula presented.) and (Formula presented.) parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the (Formula presented.) and (Formula presented.) parametric maps, and the WM and GM probability maps. Results: Deep learning-based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for (Formula presented.) (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for (Formula presented.) (deviations 6.0%). Conclusions: MRF is a fast and robust tool for quantitative (Formula presented.) and (Formula presented.) mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.@en

T2-hyperintense lesions are the key imaging marker of multiple sclerosis (MS). Previous studies have shown that the white matter surrounding such lesions is often also affected by MS. Our aim was to develop a new method to visualize and quantify the extent of white matter tissue changes in MS based on relaxometry properties. We applied a fast, multi-parametric quantitative MRI approach and used a multi-component MR Fingerprinting (MC-MRF) analysis. We assessed the differences in the MRF component representing prolongedrelaxation time between patients with MS and controls and studied the relation between this component's volume and structural white matter damage identified on FLAIR MRI scans in patients with MS. A total of 48 MS patients at two different sites and 12 healthy controls were scanned with FLAIR and MRF-EPI MRI scans. MRF scans were analyzed with a joint-sparsity multi-component analysis to obtain magnetization fraction maps of different components, representing tissues such as myelin water, white matter, gray matter and cerebrospinal fluid. In the MS patients, an additional component was identified with increased transverse relaxation times compared to the white matter, likely representing changes in free water content. Patients with MS had a higher volume of the long- component in the white matter of the brain compared to healthy controls (B (95%-CI) = 0.004 (0.0006–0.008), p = 0.02). Furthermore, this MRF component had a moderate correlation (correlation coefficient R 0.47) with visible structural white matter changes on the FLAIR scans. Also, the component was found to be more extensive compared to structural white matter changes in 73% of MS patients. In conclusion, our MRF acquisition and analysis captured white matter tissue changes in MS patients compared to controls. In patients these tissue changes were more extensive compared to visually detectable white matter changes on FLAIR scans. Our method provides a novel way to quantify the extent of white matter changes in MS patients, which is underestimated using only conventional clinical MRI scans.@en

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.@en

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.@en

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

We propose a novel multi-component analysis for MR fingerprinting that enables detection of small lesions, while taking partial volume effects into account. The algorithm uses a joint sparsity constraint limiting the number of components in local regions. It is evaluated in simulations and on MRF-EPI data from a patient with multiple sclerosis (MS). MS-lesions are separated from other tissues based on having increased T2* relaxation times. The improved sensitivity to multiple components makes it possible to detect components with long relaxation times within the lesion, possibly increasing our insight into these small pathologies.@en