Lothar R. Schad
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7 records found
1
Methods: DIR-labeling was implemented for double ECG-gated myoASLsequences and compared with conventional Flow-sensitive Alternating Inversion Recovery (FAIR) labeling using single inversions. In DIR-preparations, the FAIR-inversion pulses were immediately followed by an identical reinversion pulse, applied either slice-selectively or nonselectively. Bloch-equation-based simulation and phantom experiments were performed to evaluate the PN and SNR across a range of HR variabilities. Data from six healthy subjects were acquired to evaluate myocardial blood flow (MBF), PN, and SNR in vivo.
Results: Simulation experiments showed that the averageMBFvalues remained nearly constant across the range of HR variabilities and were comparable across all three sequences. However, DIR-labeling allowed for greater recovery of the myocardial background signal, which mitigates the sensitivity to HR-dependent changes in the inversion time. Consequently, PN in the presence of HR variability was substantially reduced with DIR-labeling. For HR variabilities corresponding to the mean value observed in vivo, this resulted in a simulated SNR gain of 1.79 ± 0.90 for selective and 1.55 ± 0.77 for nonselective DIR-labeling. In vivo, DIR-labeling showed reduced PN, with 53% (p < 0.05)/44% (p = 0.16) less PN compared with conventional FAIR-myoASL, leading to an average SNR gain of 1.47 ± 0.63 (p = 0.09)/1.32 ± 0.57 (p = 0.84) with selective/nonselective reinversions.
Conclusion: The proposed DIR-preparations reduce sensitivity to HR variations and alleviate PN in double ECG-gated myoASL, improving the precision of myoASL-based perfusion quantification.
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Methods: DIR-labeling was implemented for double ECG-gated myoASLsequences and compared with conventional Flow-sensitive Alternating Inversion Recovery (FAIR) labeling using single inversions. In DIR-preparations, the FAIR-inversion pulses were immediately followed by an identical reinversion pulse, applied either slice-selectively or nonselectively. Bloch-equation-based simulation and phantom experiments were performed to evaluate the PN and SNR across a range of HR variabilities. Data from six healthy subjects were acquired to evaluate myocardial blood flow (MBF), PN, and SNR in vivo.
Results: Simulation experiments showed that the averageMBFvalues remained nearly constant across the range of HR variabilities and were comparable across all three sequences. However, DIR-labeling allowed for greater recovery of the myocardial background signal, which mitigates the sensitivity to HR-dependent changes in the inversion time. Consequently, PN in the presence of HR variability was substantially reduced with DIR-labeling. For HR variabilities corresponding to the mean value observed in vivo, this resulted in a simulated SNR gain of 1.79 ± 0.90 for selective and 1.55 ± 0.77 for nonselective DIR-labeling. In vivo, DIR-labeling showed reduced PN, with 53% (p < 0.05)/44% (p = 0.16) less PN compared with conventional FAIR-myoASL, leading to an average SNR gain of 1.47 ± 0.63 (p = 0.09)/1.32 ± 0.57 (p = 0.84) with selective/nonselective reinversions.
Conclusion: The proposed DIR-preparations reduce sensitivity to HR variations and alleviate PN in double ECG-gated myoASL, improving the precision of myoASL-based perfusion quantification.
Methods: TRAFF2 mapping was performed using a breath-held ECG-gated acquisition of five images: one without preparation, three preceded by RAFF2 trains of varying duration, and one preceded by a saturation prepulse. Pixel-wise TRAFF2 maps were obtained after three-parameter exponential fitting. The repeatability of TRAFF2, T1, and T2 was assessed in phantom via the coefficient of variation (CV) across three repetitions. In seven healthy subjects, TRAFF2 was tested for precision, reproducibility, inter-subject variability, and image quality (IQ) on a Likert scale (1 = Nondiagnostic, 5 = Excellent). Additionally, TRAFF2 mapping was performed in three patients with suspected cardiovascular disease, comparing it to late gadolinium enhancement (LGE), native T1, T2, and ECV mapping.
Results: In phantom, TRAFF2 showed good repeatability (CV < 1.5%) while showing no (R2=0.09) and high (R2=0.99) correlation with T1 and T2, respectively. Myocardial TRAFF2 maps exhibited overall acceptable image quality (IQ = 3.0±1.0) with moderate artifact levels, stemming from off-resonances near the coronary sinus. Average TRAFF2 time across subjects and repetitions was 79.1 ± 7.3 ms. Good precision (7.6 ± 1.4%), reproducibility (1.0 ± 0.6%), and low inter-subject variability (10.0 ± 1.8%) were obtained. In patients, visual agreement of the infarcted area was observed in the TRAFF2 map and LGE.
Conclusion: Myocardial TRAFF2 quantification at 3 T was successfully achieved in a single breath-hold with acceptable image quality, albeit with residual off-resonance artifacts. Nonetheless, preliminary clinical data indicate potential sensitivity of TRAFF2 mapping to myocardial infarction detection without the need for contrast agents, but off-resonance artifacts mitigation warrants further investigation. ...
Methods: TRAFF2 mapping was performed using a breath-held ECG-gated acquisition of five images: one without preparation, three preceded by RAFF2 trains of varying duration, and one preceded by a saturation prepulse. Pixel-wise TRAFF2 maps were obtained after three-parameter exponential fitting. The repeatability of TRAFF2, T1, and T2 was assessed in phantom via the coefficient of variation (CV) across three repetitions. In seven healthy subjects, TRAFF2 was tested for precision, reproducibility, inter-subject variability, and image quality (IQ) on a Likert scale (1 = Nondiagnostic, 5 = Excellent). Additionally, TRAFF2 mapping was performed in three patients with suspected cardiovascular disease, comparing it to late gadolinium enhancement (LGE), native T1, T2, and ECV mapping.
Results: In phantom, TRAFF2 showed good repeatability (CV < 1.5%) while showing no (R2=0.09) and high (R2=0.99) correlation with T1 and T2, respectively. Myocardial TRAFF2 maps exhibited overall acceptable image quality (IQ = 3.0±1.0) with moderate artifact levels, stemming from off-resonances near the coronary sinus. Average TRAFF2 time across subjects and repetitions was 79.1 ± 7.3 ms. Good precision (7.6 ± 1.4%), reproducibility (1.0 ± 0.6%), and low inter-subject variability (10.0 ± 1.8%) were obtained. In patients, visual agreement of the infarcted area was observed in the TRAFF2 map and LGE.
Conclusion: Myocardial TRAFF2 quantification at 3 T was successfully achieved in a single breath-hold with acceptable image quality, albeit with residual off-resonance artifacts. Nonetheless, preliminary clinical data indicate potential sensitivity of TRAFF2 mapping to myocardial infarction detection without the need for contrast agents, but off-resonance artifacts mitigation warrants further investigation.
Improved reproducibility for myocardial ASL
Impact of physiological and acquisition parameters
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.
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.
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.
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.