Browsing by Author "Uecker, Martin"

Abstract Purpose In MRI, the magnetization of nuclear spins is spatially encoded with linear gradients and radiofrequency receivers sensitivity profiles to produce images, which inherently leads to a long scan time. Cartesian MRI, as widely adopted for clinical scans, can be accelerated with parallel imaging and rapid magnetic field modulation during signal readout. Here, by using an 8‐channel local coil array, the modulation scheme optimized for sampling efficiency is investigated to speed up 2D Cartesian scans. Theory and Methods An 8‐channel local coil array is made to carry sinusoidal currents during signal readout to accelerate 2D Cartesian scans. An MRI sampling theory based on reproducing kernel Hilbert space is exploited to visualize the efficiency of nonlinear encoding in arbitrary sampling duration. A field calibration method using current monitors for local coils and the ESPIRiT algorithm is proposed to facilitate image reconstruction. Image acceleration with various modulation field shapes, aliasing control, and distinct modulation frequencies are scrutinized to find an optimized modulation scheme. A safety evaluation is conducted. In vivo 2D Cartesian scans are accelerated by the local coils. Results For 2D Cartesian MRI, the optimal modulation field by this local array converges to a nearly linear gradient field. With the field calibration technique, it accelerates the in vivo scans (i.e., proved safe) by threefold and eightfold free of visible artifacts, without and with SENSE, respectively. Conclusion The nonlinear encoding analysis tool, the field calibration method, the safety evaluation procedures, and the in vivo reconstructed scans make significant steps to push MRI speed further with the local coil array.

Abstract In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed for volumetric analysis (via segmentation) of the left ventricle in real-time free-breathing CMR at rest and under exercise stress. Data from healthy volunteers (n = 15) for cine and real-time free-breathing CMR at rest and under exercise stress were analyzed retrospectively. Exercise stress was performed using an ergometer in the supine position. Segmentations of two deep learning methods, a commercially available technique (comDL) and an openly available network (nnU-Net), were compared to a reference model created via the manual correction of segmentations obtained with comDL. Segmentations of left ventricular endocardium (LV), left ventricular myocardium (MYO), and right ventricle (RV) are compared for both end-systolic and end-diastolic phases and analyzed with Dice’s coefficient. The volumetric analysis includes the cardiac function parameters LV end-diastolic volume (EDV), LV end-systolic volume (ESV), and LV ejection fraction (EF), evaluated with respect to both absolute and relative differences. For cine CMR, nnU-Net and comDL achieve Dice’s coefficients above 0.95 for LV and 0.9 for MYO, and RV. For real-time CMR, the accuracy of nnU-Net exceeds that of comDL overall. For real-time CMR at rest, nnU-Net achieves Dice’s coefficients of 0.94 for LV, 0.89 for MYO, and 0.90 for RV and the mean absolute differences between nnU-Net and the reference are 2.9 mL for EDV, 3.5 mL for ESV, and 2.6% for EF. For real-time CMR under exercise stress, nnU-Net achieves Dice’s coefficients of 0.92 for LV, 0.85 for MYO, and 0.83 for RV and the mean absolute differences between nnU-Net and reference are 11.4 mL for EDV, 2.9 mL for ESV, and 3.6% for EF. Deep learning methods designed or trained for cine CMR segmentation can perform well on real-time CMR. For real-time free-breathing CMR at rest, the performance of deep learning methods is comparable to inter-observer variability in cine CMR and is usable for fully automatic segmentation. For real-time CMR under exercise stress, the performance of nnU-Net could promise a higher degree of automation in the future.

Purpose: To develop a model-based nonlinear reconstruction for simultaneous water-specific $T_{1}$, $R_{2}^{*}$, $B_{0}$ field and/or fat fraction (FF) mapping using single-shot inversion-recovery (IR) multi-echo radial FLASH. Methods: The proposed model-based reconstruction jointly estimates water-specific $T_{1}$, $R_{2}^{*}$, $B_{0}$ field and/or FF maps, as well as a set of coil sensitivities directly from $k$-space obtained with a single-shot IR multi-echo radial FLASH sequence using blip gradients across echoes. Joint sparsity constraints are exploited on multiple quantitative maps to improve precision. Validations are performed on numerical and NIST phantoms and with in vivo studies of the human brain and liver at 3 T. Results: Numerical phantom studies demonstrate the effects of fat signals in $T_{1}$ estimation and confirm good quantitative accuracy of the proposed method for all parameter maps. NIST phantom results confirm good quantitative $T_{1}$ and $R_{2}^{*}$ accuracy in comparison to Cartesian references. Apart from good quantitative accuracy and precision for multiple parameter maps, in vivo studies show improved image details utilizing the proposed joint estimation. The proposed method can achieve simultaneous water-specific $T_{1}$, $R_{2}^{*}$, $B_{0}$ field and/or FF mapping for brain (0.81 $\times$ 0.81 $\times$ 5 mm$^{3}$) and liver (1.6 $\times$ 1.6 $\times$ 6 mm$^{3}$) imaging within four seconds. Conclusion: The proposed model-based nonlinear reconstruction, in combination with a single-shot IR multi-echo radial FLASH acquisition, enables joint estimation of accurate water-specific $T_{1}$, $R_{2}^{*}$, $B_{0}$ field and/or FF maps within four seconds. The present work is of potential value for specific clinical applications.

Purpose: To develop a generic radial sampling scheme that combines the advantages of golden ratio sampling with simplicity of equidistant angular patterns. The irrational angle between consecutive spokes in golden ratio based sampling schemes enables a flexible retrospective choice of temporal resolution, while preserving good coverage of k-space for each individual bin. Nevertheless, irrational increments prohibit precomputation of the point-spread function (PSF), can lead to numerical problems, and require more complex processing steps. To avoid these problems, a new sampling scheme based on a rational approximation of golden angles (RAGA) is developed. Methods: The theoretical properties of RAGA sampling are mathematically derived. Sidelobe-to-peak ratios (SPR) are numerically computed and compared to the corresponding golden ratio sampling schemes. The sampling scheme is implemented in the BART toolbox and in a radial gradient-echo sequence. Feasibility is shown for quantitative imaging in a phantom and a cardiac scan of a healthy volunteer. Results: RAGA sampling can accurately approximate golden ratio sampling and has almost identical PSF and SPR. In contrast to golden ratio sampling, each frame can be reconstructed with the same equidistant trajectory using different sampling masks, and the angle of each acquired spoke can be encoded as a small index, which simplifies processing of the acquired data. Conclusion: RAGA sampling provides the advantages of golden ratio sampling while simplifying data processing, rendering it a valuable tool for dynamic and quantitative MRI.

Abstract Purpose To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. Methods NLINV‐Net is a model‐based neural network architecture that directly estimates images and coil sensitivities from (radial) k‐space data via nonlinear inversion (NLINV). Combined with a training strategy using self‐supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real‐time cardiac imaging and (2) single‐shot subspace‐based quantitative T1 mapping. Furthermore, region‐optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the field of view and to focus the k‐space‐based SSDU loss on the region of interest. NLINV‐Net‐based reconstructions were compared with conventional NLINV and PI‐CS (parallel imaging + compressed sensing) reconstruction and the effect of the region‐optimized virtual coils and the type of training loss was evaluated qualitatively. Results NLINV‐Net‐based reconstructions contain significantly less noise than the NLINV‐based counterpart. ROVir coils effectively suppress streakings which are not suppressed by the neural networks while the ROVir‐based focused loss leads to visually sharper time series for the movement of the myocardial wall in cardiac real‐time imaging. For quantitative imaging, T1‐maps reconstructed using NLINV‐Net show similar quality as PI‐CS reconstructions, but NLINV‐Net does not require slice‐specific tuning of the regularization parameter. Conclusion NLINV‐Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.

Presentation slides used in the Research data management workshop of the CRC1456 from Monday, June 21st 2021 and Monday June 28th 2021