Do Disinfectant Solutions during Gargling Reach the Pharynx?

INTRODUCTION: In times of COVID-19, gargling disinfectant is commonly used. Disinfectant solutions seem to decrease the infection's symptoms. For disinfection, several techniques are reported. So far, there are no data about the regions in the upper airways achieved by gargled fluid. METHODS: Ten healthy volunteers without any dysphagia were investigated with a high-sensitivity flexible endoscopic evaluation of swallowing (hsFEES®) during and after gargling colored water. One volunteer repeated the gargling process in fast and real-time MRI. RESULTS: In all cases, no color accumulation was detected on the posterior pharyngeal wall, epi- or hypopharynx during gargling. The MRI scans confirmed the results. CONCLUSIONS: hsFEES® and fast MRI provide an insight into the gargling pattern. Data show that during gargling, the fluid covers the soft tissue in the oral cavity and the anterior part of the soft palate, but not the posterior pharyngeal wall nor the epi- and hypopharynx.

Assessment of resolution and noise in magnetic resonance images reconstructed by data driven approaches.

INTRODUCTION: Deep learning-based approaches are increasingly being used for the reconstruction of accelerated MRI scans. However, presented analyses are frequently lacking in-detail evaluation of basal measures like resolution or signal-to-noise ratio. To help closing this gap, spatially resolved maps of image resolution and noise enhancement (g-factor) are determined and assessed for typical model- and data-driven MR reconstruction methods in this paper. METHODS: MR data from a routine brain scan of a patient were undersampled in retrospect at R = 4 and reconstructed using two data-driven (variational network (VN), U-Net) and two model based reconstructions methods (GRAPPA, TV-constrained compressed sensing). Local resolution was estimated by the width of the main-lobe of a local point-spread function, which was determined for every single pixel by reconstructing images with an additional small perturbation. G-factor maps were determined using a multiple replica method. RESULTS: GRAPPA showed good spatial resolution, but increased g-factors (1.43-1.84, 75% quartile) over all other methods. The images delivered from compressed sensing suffered most from low local resolution, in particular in homogeneous areas of the image. VN and U-Net show similar resolution with mostly moderate local blurring, slightly better for U-Net. For all methods except GRAPPA the resolution as well as the g-factors depend on the anatomy and the direction of undersampling. CONCLUSION: Objective image quality parameters, local resolution and g-factors have been determined. The examined data driven methods show less local blurring than compressed sensing. The noise enhancement for reconstructions using CS, VN and U-Net is elevated at anatomical contours but is drastically reduced with respect to GRAPPA. Overall, the applied framework provides the possibility for more detailed analysis of novel reconstruction approaches incorporating non-linear and non-stationary transformations.

Fast measurement of the gradient system transfer function at 7 T.

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.

Real-time cardiac MRI using an undersampled spiral k-space trajectory and a reconstruction based on a variational network.

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.

Evaluation of combined late gadolinium-enhancement and functional cardiac magnetic resonance imaging using spiral real-time acquisition.

The purpose of the current study was to implement and validate joint real-time acquisition of functional and late gadolinium-enhancement (LGE) cardiac magnetic resonance (MR) images during free breathing. Inversion recovery cardiac real-time images with a temporal resolution of 50 ms were acquired using a spiral trajectory (IR-CRISPI) with a pre-emphasis based on the gradient system transfer function during free breathing. Functional and LGE cardiac MR images were reconstructed using a low-rank plus sparse model. Late gadolinium-enhancement appearance, image quality, and functional parameters of IR-CRISPI were compared with clinical standard balanced steady-state free precession breath-hold techniques in 10 patients. The acquisition of IR-CRISPI in free breathing of the entire left ventricle took 97 s on average. Bland-Altman analysis and Wilcoxon tests showed a higher artifact level for the breath-hold technique (p = 0.003), especially for arrhythmic patients or patients with dyspnea, but an increased noise level for IR-CRISPI of the LGE images (p = 0.01). The estimated transmural extent of the enhancement differed by not more than 25% and did not show a significant bias between the techniques (p = 0.50). The ascertained functional parameters were similar for the breath-hold technique and IR-CRISPI, that is, with a minor, nonsignificant (p = 0.16) mean difference of the ejection fraction of 2.3% and a 95% confidence interval from -4.8% to 9.4%. IR-CRISPI enables joint functional and LGE imaging in free breathing with good image quality but distinctly shorter scan times in comparison with breath-hold techniques.

A data-driven semantic segmentation model for direct cardiac functional analysis based on undersampled radial MR cine series.

PURPOSE: Image acquisition and subsequent manual analysis of cardiac cine MRI is time-consuming. The purpose of this study was to train and evaluate a 3D artificial neural network for semantic segmentation of radially undersampled cardiac MRI to accelerate both scan time and postprocessing. METHODS: A database of Cartesian short-axis MR images of the heart (148,500 images, 484 examinations) was assembled from an openly accessible database and radial undersampling was simulated. A 3D U-Net architecture was pretrained for segmentation of undersampled spatiotemporal cine MRI. Transfer learning was then performed using samples from a second database, comprising 108 non-Cartesian radial cine series of the midventricular myocardium to optimize the performance for authentic data. The performance was evaluated for different levels of undersampling by the Dice similarity coefficient (DSC) with respect to reference labels, as well as by deriving ventricular volumes and myocardial masses. RESULTS: Without transfer learning, the pretrained model performed moderately on true radial data [maximum number of projections tested, P = 196; DSC = 0.87 (left ventricle), DSC = 0.76 (myocardium), and DSC =0.64 (right ventricle)]. After transfer learning with authentic data, the predictions achieved human level even for high undersampling rates (P = 33, DSC = 0.95, 0.87, and 0.93) without significant difference compared with segmentations derived from fully sampled data. CONCLUSION: A 3D U-Net architecture can be used for semantic segmentation of radially undersampled cine acquisitions, achieving a performance comparable with human experts in fully sampled data. This approach can jointly accelerate time-consuming cine image acquisition and cumbersome manual image analysis.

Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction.

PURPOSE: To fully automatically derive quantitative parameters from late gadolinium enhancement (LGE) cardiac MR (CMR) in patients with myocardial infarction and to investigate if phase sensitive or magnitude reconstructions or a combination of both results in best segmentation accuracy. METHODS: In this retrospective single center study, a convolutional neural network with a U-Net architecture with a self-configuring framework ("nnU-net") was trained for segmentation of left ventricular myocardium and infarct zone in LGE-CMR. A database of 170 examinations from 78 patients with history of myocardial infarction was assembled. Separate fitting of the model was performed, using phase sensitive inversion recovery, the magnitude reconstruction or both contrasts as input channels. Manual labelling served as ground truth. In a subset of 10 patients, the performance of the trained models was evaluated and quantitatively compared by determination of the Sørensen-Dice similarity coefficient (DSC) and volumes of the infarct zone compared with the manual ground truth using Pearson's r correlation and Bland-Altman analysis. RESULTS: The model achieved high similarity coefficients for myocardium and scar tissue. No significant difference was observed between using PSIR, magnitude reconstruction or both contrasts as input (PSIR and MAG; mean DSC: 0.83 ±â€¯0.03 for myocardium and 0.72 ±â€¯0.08 for scars). A strong correlation for volumes of infarct zone was observed between manual and model-based approach (r = 0.96), with a significant underestimation of the volumes obtained from the neural network. CONCLUSION: The self-configuring nnU-net achieves predictions with strong agreement compared to manual segmentation, proving the potential as a promising tool to provide fully automatic quantitative evaluation of LGE-CMR.

Deep learning-based cardiac cine segmentation: Transfer learning application to 7T ultrahigh-field MRI.

PURPOSE: Artificial neural networks show promising performance in automatic segmentation of cardiac MRI. However, training requires large amounts of annotated data and generalization to different vendors, field strengths, sequence parameters, and pathologies is limited. Transfer learning addresses this challenge, but specific recommendations regarding type and amount of data required is lacking. In this study, we assess data requirements for transfer learning to experimental cardiac MRI at 7T where the segmentation task can be challenging. In addition, we provide guidelines, tools, and annotated data to enable transfer learning approaches by other researchers and clinicians. METHODS: A publicly available segmentation model was used to annotate a publicly available data set. This labeled data set was subsequently used to train a neural network for segmentation of left ventricle and myocardium in cardiac cine MRI. The network is used as starting point for transfer learning to 7T cine data of healthy volunteers (n = 22; 7873 images) by updating the pre-trained weights. Structured and random data subsets of different sizes were used to systematically assess data requirements for successful transfer learning. RESULTS: Inconsistencies in the publically available data set were corrected, labels created, and a neural network trained. On 7T cardiac cine images the model pre-trained on public imaging data, acquired at 1.5T and 3T, achieved DICELV = 0.835 and DICEMY = 0.670. Transfer learning using 7T cine data and ImageNet weight initialization improved model performance to DICELV = 0.900 and DICEMY = 0.791. Using only end-systolic and end-diastolic images reduced training data by 90%, with no negative impact on segmentation performance (DICELV = 0.908, DICEMY = 0.805). CONCLUSIONS: This work demonstrates and quantifies the benefits of transfer learning for cardiac cine image segmentation. We provide practical guidelines for researchers planning transfer learning projects in cardiac MRI and make data, models, and code publicly available.

Deep learning-based segmentation of the lung in MR-images acquired by a stack-of-spirals trajectory at ultra-short echo-times.

BACKGROUND: Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters. METHODS: In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the Sørensen-Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson's correlation coefficient and Bland Altman analysis. To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer. RESULTS: The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson's correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L. CONCLUSIONS: Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.

Cardiac real-time MRI using a pre-emphasized spiral acquisition based on the gradient system transfer function.

PURPOSE: Segmented Cartesian acquisition in breath hold represents the current gold standard for cardiac functional MRI. However, it is also associated with long imaging times and severe restrictions in arrhythmic or dyspneic patients. Therefore, we introduce a real-time imaging technique based on a spoiled gradient-echo sequence with undersampled spiral k-space trajectories corrected by a gradient pre-emphasis. METHODS: A fully automatic gradient waveform pre-emphasis based on the gradient system transfer function was implemented to compensate for gradient inaccuracies, to optimize fast double-oblique spiral MRI. The framework was tested in a phantom study and subsequently transferred to compressed sensing-accelerated cardiac functional MRI in real time. Spiral acquisitions during breath hold and free breathing were compared with this reference method for healthy subjects (N = 7) as well as patients (N = 2) diagnosed with heart failure and arrhythmia. Left-ventricular volumes and ejection fractions were determined and analyzed using a Wilcoxon signed-rank test. RESULTS: The pre-emphasis successfully reduced typical artifacts caused by k-space misregistrations. Dynamic cardiac imaging was possible in real time (temporal resolution < 50 ms) with high spatial resolution (1.34 × 1.34 mm2 ), resulting in a total scan time of less than 50 seconds for whole heart coverage. Comparable image quality, as well as similar left-ventricular volumes and ejection fractions, were observed for the accelerated and the reference method. CONCLUSION: The proposed technique enables high-resolution real-time cardiac MRI with no need for breath holds and electrocardiogram gating, shortening the duration of an entire functional cardiac exam to less than 1 minute.

Accelerated aortic 4D flow MRI with wave-CAIPI.

PURPOSE: The aim of this study was to investigate the acceleration potential of wave-CAIPI (controlled aliasing in parallel imaging) for 4D flow MRI, provided that image quality and precision of flow parameters are maintained. METHODS: The 4D flow MRIs with acceleration factor R = 2 were performed on 10 healthy volunteers, using both wave-CAIPI and standard Cartesian/2D-CAIPI sampling for reference. In addition, 1 patient with known aortic valve stenosis was examined. The flow rate ( Q ), net flow ( Qnet ), peak velocity vmax , and net average through-plane velocity ( v¯âŠ¥ ) were calculated in eight analysis planes in the ascending and descending aorta. The acquisitions were retrospectively undersampled (R = 6), and deviations of flow parameters and hemodynamic flow patterns were evaluated. RESULTS: Flow parameters measured with an undersampled wave-CAIPI trajectory showed considerably smaller deviations to the references than the 2D-CAIPI images. For vmax , the mean absolute differences were 6.02±2.08 cm/s versus 14.36±5.68 cm/s; for Qnet , the mean absolute differences were 3.67±1.40 ml versus 5.87±1.91 ml for wave-CAIPI versus 2D-CAIPI, respectively. Noise calculations indicate that the 2D-CAIPI sampling exhibits a 43±38% higher average noise level than the wave-CAIPI technique. Qualitative discrepancies in hemodynamic flow patterns, visualized through streamlines, particle traces and flow velocity vectors, could be reduced by using the undersampled wave-CAIPI trajectory. CONCLUSION: Use of wave-CAIPI instead of 2D-CAIPI sampling in retrospectively 6-fold accelerated 4D flow MRI enhances the precision of flow parameters. The acquisition time of 4D flow measurements could be reduced by a factor of 3, with minimal differences in flow parameters.

Free-breathing self-gated 4D lung MRI using wave-CAIPI.

PURPOSE: The aim of this study was to compare the wave-CAIPI (controlled aliasing in parallel imaging) trajectory to the Cartesian sampling for accelerated free-breathing 4D lung MRI. METHODS: The wave-CAIPI k-space trajectory was implemented in a respiratory self-gated 3D spoiled gradient echo pulse sequence. Trajectory correction applying the gradient system transfer function was used, and images were reconstructed using an iterative conjugate gradient SENSE (CG SENSE) algorithm. Five healthy volunteers and one patient with squamous cell carcinoma in the lung were examined on a clinical 3T scanner, using both sampling schemes. For quantitative comparison of wave-CAIPI and standard Cartesian imaging, the normalized mutual information and the RMS error between retrospectively accelerated acquisitions and their respective references were calculated. The SNR ratios were investigated in a phantom study. RESULTS: The obtained normalized mutual information values indicate a lower information loss due to acceleration for the wave-CAIPI approach. Average normalized mutual information values of the wave-CAIPI acquisitions were 10% higher, compared with Cartesian sampling. Furthermore, the RMS error of the wave-CAIPI technique was lower by 19% and the SNR was higher by 14%. Especially for short acquisition times (down to 1 minute), the undersampled Cartesian images showed an increased artifact level, compared with wave-CAIPI. CONCLUSION: The application of the wave-CAIPI technique to 4D lung MRI reduces undersampling artifacts, in comparison to a Cartesian acquisition of the same scan time. The benefit of wave-CAIPI sampling can therefore be traded for shorter examinations, or enhancing image quality of undersampled 4D lung acquisitions, keeping the scan time constant.

The temperature dependence of gradient system response characteristics.

PURPOSE: The gradient system transfer function (GSTF) characterizes the frequency transfer behavior of a dynamic gradient system and can be used to correct non-Cartesian k-space trajectories. This study analyzes the impact of the gradient coil temperature of a 3T scanner on the GSTF. METHODS: GSTF self- and B0 -cross-terms were acquired for a 3T Siemens scanner (Siemens Healthcare, Erlangen, Germany) using a phantom-based measurement technique. The GSTF terms were measured for various temperature states up to 45°C. The gradient coil temperatures were measured continuously utilizing 12 temperature sensors which are integrated by the vendor. Different modeling approaches were applied and compared. RESULTS: The self-terms depend linearly on temperature, whereas the B0 -cross-term does not. Effects induced by thermal variation are negligible for the phase response. The self-terms are best represented by a linear model including the three gradient coil sensors that showed the maximum temperature dependence for the three axes. The use of time derivatives of the temperature did not lead to an improvement of the model. The B0 -cross-terms can be modeled by a convolution model which considers coil-specific heat transportation. CONCLUSION: The temperature dependency of the GSTF was analyzed for a 3T Siemens scanner. The self- and B0 -cross-terms can be modeled using a linear and convolution modeling approach based on the three main temperature sensor elements.

A compressed sensing accelerated radial MS-CAIPIRINHA technique for extended anatomical coverage in myocardial perfusion studies on PET/MR systems.

PURPOSE: Simultaneous acquisition of myocardial first-pass perfusion MRI and 18F-FDG PET viability imaging on integrated whole-body PET/MR hybrid systems synergistically delivers both functional and metabolic information on the tissue state. While PET viability scans are inherently three-dimensional, conventional MR myocardial perfusion imaging is typically performed using only three short-axis slices with a temporal resolution of one RR-interval. To improve the integrated diagnostics, an acquisition and image reconstruction method based on "Multi-Slice Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration (MS-CAIPIRINHA)" was developed extending anatomical coverage for MR perfusion imaging to six short-axis slices per RR-interval. METHODS: An ECG-gated radial TurboFLASH MR pulse sequence with dual band excitation was implemented on an integrated whole-body PET/MR system and a model-based reconstruction technique was developed to fully reconstruct the undersampled CAIPIRINHA acquisitions. An 18F-FDG viability PET scan was performed simultaneously to the MR protocol, additionally complemented by a late enhancement MRI acquisition (LGE). RESULTS AND CONCLUSION: The developed imaging technique was tested in five patients with known collateralized coronary total occlusions, resulting in improved characterization of perfusion across areas of decreased tissue viability as indicated by the simultaneously determined 18F-FDG uptake. While conventional MR perfusion with only three slice positions was occasionally missing substantial parts of the viable area, the new approach achieved LV coverage only slightly inferior to LGE imaging and therefore better comparable to PET results. The quality of first-pass enhancement curves was comparable between conventional and radial MS-CAIPIRINHA-based acquisitions.

Real-time Triggered RAdial Single-Shot Inversion recovery for arrhythmia-insensitive myocardial T1 mapping: motion phantom validation and in vivo comparison.

PURPOSE: Cardiac T1 mapping has become an increasingly important imaging technique, contributing novel diagnostic options. However, currently utilized methods are often associated with accuracy problems because of heart rate variations and cardiac arrhythmia, limiting their value in clinical routine. This study aimed to introduce an improved arrhythmia-related robust T1 mapping sequence called RT-TRASSI (real-time Triggered RAdial Single-Shot Inversion recovery). METHODS: All measurements were performed on a 3.0T whole-body imaging system. A real-time feedback algorithm for arrhythmia detection was implemented into the previously described pulse sequence. A programmable motion phantom was constructed and measurements with different simulated arrhythmias arranged. T1 mapping accuracy and susceptibility to artifacts were analyzed. In addition, in vivo measurements and comparisons with 3 prevailing T1 mapping sequences (MOLLI, ShMOLLI, and SASHA) were carried out to investigate the occurrence of artifacts. RESULTS: In the motion phantom measurements, RT-TRASSI showed excellent agreement with predetermined reference T1 values. Percentage scattering of the T1 values ranged from -0.6% to +1.9% in sinus rhythm and -1.0% to +3.1% for high-grade arrhythmias. In vivo, RT-TRASSI showed diagnostic image quality with only 6% of the acquired T1 maps including image artifacts. In contrast, more than 40% of the T1 maps acquired with MOLLI, ShMOLLI, or SASHA included motion artifacts. CONCLUSION: Accuracy issues because of heart rate variability and arrhythmia are a prevailing problem in current cardiac T1 mapping techniques. With RT-TRASSI, artifacts can be minimized because of the short acquisition time and effective real-time feedback, avoiding potential data acquisition during systolic heart phase.

Frequency-modulated bSSFP for phase-sensitive separation of water and fat.

Our study proposes the use of a frequency-modulated acquisition which suppresses banding artefacts in combination with a phase-sensitive water-fat separation algorithm. The performance of the phase-sensitive separation for standard bSSFP, complex sum combination thereof, and frequency-modulated bSSFP were compared in in vivo measurements of the upper and lower legs at 1.5 and 3 T. It is shown, that the standard acquisition suffered from banding artefacts and major swaps between tissues. The dual-acquisition bSSFP could alleviate banding artefacts and only minor swaps occurred, but it comes at the expense of a doubled acquisition. In the frequency-modulated acquisitions all banding artefacts and the associated phase jumps were eliminated and no swaps between tissues occurred. It therefore provides a means to robustly separate water and fat, in one single radial bSSFP scan, using the phase-sensitive approach, even in the presence of high field inhomogeneities.

Robust motion correction in CEST imaging exploiting low-rank approximation of the z-spectrum.

PURPOSE: To introduce and evaluate an image registration technique for robust quantification of CEST acquisitions corrupted by motion. METHODS: The proposed iterative algorithm exploits a low-rank approximation of the z-spectrum (LRAZ), to gradually separate the contrast variation due to saturation at different off-resonance frequencies and accompanying motion. This registration method was first tested in a creatine CEST analysis of a phantom with simulated rigid motion. Subsequently, creatine CEST acquisitions in the human thigh during exercise were exemplarily corrected. RESULTS: The z-spectrum obtained by applying LRAZ to the corrupted phantom series exhibited a normalized RMS error with respect to the noncorrupted gold standard series of less than 4%. The corresponding creatine map resulting from an asymmetry analysis of the registered data showed only little difference with regard to the noncorrupted determination, too. A comparable performance was observed exploiting LRAZ for the correction of nonrigid motion within the dynamic CEST acquisitions in skeletal muscles. While for the phantom simulations, high-quality registration was also possible by using a single reference image for the whole series and mutual information as similarity metric, this conventional approach resulted in inappropriate correction of the more complicated motion of the human thigh. CONCLUSION: The newly introduced method allows for a robust registration of CEST image series, which are corrupted by rigid and nonrigid motion of the investigated organ. The technique therefore improves the diagnostic value in various applications of CEST.

Gradient waveform pre-emphasis based on the gradient system transfer function.

PURPOSE: The gradient system transfer function (GSTF) has been used to describe the distorted k-space trajectory for image reconstruction. The purpose of this work was to use the GSTF to determine the pre-emphasis for an undistorted gradient output and intended k-space trajectory. METHODS: The GSTF of the MR system was determined using only standard MR hardware without special equipment such as field probes or a field camera. The GSTF was used for trajectory prediction in image reconstruction and for a gradient waveform pre-emphasis. As test sequences, a gradient-echo sequence with phase-encoding gradient modulation and a gradient-echo sequence with a spiral read-out trajectory were implemented and subsequently applied on a structural phantom and in vivo head measurements. RESULTS: Image artifacts were successfully suppressed by applying the GSTF-based pre-emphasis. Equivalent results are achieved with images acquired using GSTF-based post-correction of the trajectory as a part of image reconstruction. In contrast, the pre-emphasis approach allows reconstruction using the initially intended trajectory. CONCLUSION: The artifact suppression shown for two sequences demonstrates that the GSTF can serve for a novel pre-emphasis. A pre-emphasis based on the GSTF information can be applied to any arbitrary sequence type.

Single-shot late Gd enhancement imaging of myocardial infarction with retrospectively adjustable contrast and heart-phase.

OBJECT: To enable a retrospective adjustment of image contrast and heart phase in inversion recovery prepared late gadolinium enhancement (LE) imaging in the myocardium. MATERIALS AND METHODS: After one inversion pulse, unsegmented data were acquired over multiple cardiac cycles using a radial spoiled gradient-echo sequence with golden angle increments between subsequent readouts. Model-based acceleration of parameter mapping (MAP) was combined with an image registration technique ("MOCO-MAP") to enable the reconstruction of images with arbitrary inversion time TI. RESULTS: MOCO-MAP allowed the reconstruction of LE images with arbitrary TI for arbitrary cardiac phases in four patients suffering from myocardial infarction. Regions with LE agreed well between the MOCO-MAP and the segmented techniques typically applied in clinical routine. CONCLUSIONS: MOCO-MAP delivers LE images with arbitrary and thus retrospectively optimized contrast between vital and diseased tissue, without the need for time-consuming TI scouting.

Comparison of Turbo Spin Echo and Echo Planar Imaging for intravoxel incoherent motion and diffusion tensor imaging of the kidney at 3Tesla.

Echo Planar Imaging (EPI) is most commonly applied to acquire diffusion-weighted MR-images. EPI is able to capture an entire image in very short time, but is prone to distortions and artifacts. In diffusion-weighted EPI of the kidney severe distortions may occur due to intestinal gas. Turbo Spin Echo (TSE) is robust against distortions and artifacts, but needs more time to acquire an entire image compared to EPI. Therefore, TSE is more sensitive to motion during the readout. In this study we compare diffusion-weighted TSE and EPI of the human kidney with regard to intravoxel incoherent motion (IVIM) and diffusion tensor imaging (DTI). Images were acquired with b-values between 0 and 750s/mm2 with TSE and EPI. Distortions were observed with the EPI readout in all volunteers, while the TSE images were virtually distortion-free. Fractional anisotropy of the diffusion tensor was significantly lower for TSE than for EPI. All other parameters of DTI and IVIM were comparable for TSE and EPI. Especially the main diffusion directions yielded by TSE and EPI were similar. The results demonstrate that TSE is a worthwhile distortion-free alternative to EPI for diffusion-weighted imaging of the kidney at 3Tesla.