Herbert Köstler

Purpose In this work, a new method to determine the gradient system transfer function (GSTF) with high frequency resolution and high SNR is presented, using fast and simple phantom measurements. The GSTF is an effective instrument for hardware characterization and calibration, which can be used to correct for gradient distortions, or enhance gradient fidelity. Methods The thin‐slice approach for phantom‐based measurements of the GSTF is expanded by adding excitations that are shifted after the application of the probing gradient, to capture long‐lasting field fluctuations with high SNR. A physics‐informed regularization procedure is implemented to derive high‐quality transfer functions from a small number of measurements. The resulting GSTFs are evaluated by means of gradient time‐course estimation and pre‐emphasis of a trapezoidal test gradient on a 7T scanner. Results The GSTFs determined with the proposed method capture sharp mechanical resonances with a high level of detail. The measured trapezoidal gradient progressions are authentically reproduced by the GSTF estimations on all three axes. The GSTF‐based pre‐emphasis considerably improves the gradient fidelity in the plateau phase of the test gradient and almost completely eliminates lingering field oscillations. Conclusion The presented approach allows fast and simple characterization of gradient field fluctuations caused by long‐living eddy current and vibration effects, which become more pronounced at ultrahigh field strengths.

Purpose Cardiac MRI represents the gold standard to determine myocardial function. However, the current clinical standard protocol, a segmented Cartesian acquisition, is time‐consuming and can lead to compromised image quality in the case of arrhythmia or dyspnea. In this article, a machine learning–based reconstruction of undersampled spiral k‐space data is presented to enable free breathing real‐time cardiac MRI with good image quality and short reconstruction times. Methods Data were acquired in free breathing with a 2D spiral trajectory corrected by the gradient system transfer function. Undersampled data were reconstructed by a variational network (VN), which was specifically adapted to the non‐Cartesian sampling pattern. The network was trained with data from 11 subjects. Subsequently, the imaging technique was validated in 14 subjects by quantifying the difference to a segmented reference acquisition, an expert reader study, and by comparing derived volumes and functional parameters with values obtained using the current clinical gold standard. Results The scan time for the entire heart was below 1 min. The VN reconstructed data in about 0.9 s per image, which is considerably shorter than conventional model‐based approaches. The VN furthermore performed better than a U‐Net and not inferior to a low‐rank plus sparse model in terms of achieved image quality. Functional parameters agreed, on average, with reference data. Conclusions The proposed VN method enables real‐time cardiac imaging with both high spatial and temporal resolution in free breathing and with short reconstruction time.

Background Sodium homeostasis is disrupted in many cardiovascular diseases, which makes non-invasive sodium storage assessment desirable. In this regard, sodium MRI has shown its potential to reveal differences in sodium content between healthy and diseased tissues as well as treatment-related changes of sodium content. When different tissues are affected disparately, simultaneous assessment of these compartments is expected to provide better information about sodium distribution, reduce examination time, and improve clinical efficiency. Objectives The objectives were (1) to investigate sodium storage levels in calf and pectoral muscle in healthy controls and patients and quantify changes following medical treatment and (2) to demonstrate homogeneous disruption in skeletal muscle sodium storage in patients with primary hyperaldosteronism (PHA). Methods We assessed sodium storage levels (relative sodium signal intensity, rSSI) in the calf and pectoral muscles of eight patients with PHA prior and after treatment and 12 age- and sex-matched healthy volunteers. Results Calf and pectoral muscle compartments exhibited similar sodium content both in healthy subjects (calf vs pectoral rSSI: 0.14 ± 0.01 vs 0.14 ± 0.03) and PHA patients (calf vs pectoral rSSI: 0.19 ± 0.03 vs 0.18 ± 0.03). Further, we observed similar treatment-related changes in pectoral and calf muscles in the patients (proportional rSSI change calf: 26%; pectoral: 28%). Conclusion We found that sodium was distributed uniformly and behaved equally in different skeletal muscles in Conn’s syndrome. This allows to measure both heart and skeletal muscle sodium signals simultaneously by a single measurement without repositioning the patient. This increases 23 Na-MRI’s clinical feasibility as an innovative technique to monitor sodium storage.