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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Multifrequency reconstruction for frequency-modulated bSSFP.

PURPOSE: Banding artifacts in images acquired by balanced steady-state free precession (bSSFP) remain a challenge in MRI as they considerably reduce image quality, and diagnostic value deteriorates accordingly. As the steady-state tolerates small shifts in frequency, it is possible to acquire frequency-modulated bSSFP. Unfortunately, standard reconstructions of such measurements suffer from signal loss. Our study proposes a multifrequency reconstruction and demonstrates its capability of suppressing banding artifacts while retaining the high signal level of standard bSSFP. METHODS: Numerical simulations in vitro and in vivo measurements were performed using both standard bSSFP and frequency-modulated bSSFP. The modulated data were reconstructed using a multifrequency approach consisting of three steps: phase correction, multiple reconstructions for different assumed frequencies, and maximum intensity projection. RESULTS: Although standard bSSFP measurements showed banding artifacts that compromised the image quality, standard reconstructions of frequency-modulated acquisitions suffered from signal loss. In contrast, images reconstructed from frequency-modulated data using the proposed multifrequency reconstruction showed no visual bandings and featured a higher signal-to-noise ratio (SNR). The SNR gain for phantom and in vivo measurements ranged from 1.23 to 1.49. CONCLUSIONS: The presented multifrequency reconstruction for frequency-modulated bSSFP provides images showing no bandings and featuring high SNR in short scan times. Magn Reson Med 78:2226-2235, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

An intravoxel oriented flow model for diffusion-weighted imaging of the kidney.

By combining intravoxel incoherent motion (IVIM) and diffusion tensor imaging (DTI) we introduce a new diffusion model called intravoxel oriented flow (IVOF) that accounts for anisotropy of diffusion and the flow-related signal. An IVOF model using a simplified apparent flow fraction tensor (IVOFf ) is applied to diffusion-weighted imaging of human kidneys. The kidneys of 13 healthy volunteers were examined on a 3 T scanner. Diffusion-weighted images were acquired with six b values between 0 and 800 s/mm(2) and 30 diffusion directions. Diffusivity and flow fraction were calculated for different diffusion models. The Akaike information criterion was used to compare the model fit of the proposed IVOFf model to IVIM and DTI. In the majority of voxels the proposed IVOFf model with a simplified apparent flow fraction tensor performs better than IVIM and DTI. Mean diffusivity is significantly higher in DTI compared with models that account for the flow-related signal. The fractional anisotropy of diffusion is significantly reduced when flow fraction is considered to be anisotropic. Anisotropy of the apparent flow fraction tensor is significantly higher in the renal medulla than in the cortex region. The IVOFf model describes diffusion-weighted data in the human kidney more accurately than IVIM or DTI. The apparent flow fraction in the kidney proved to be anisotropic.

Anti-CCP status determines the power Doppler oscillation pattern in rheumatoid arthritis: a prospective study.

Rheumatoid arthritis (RA) is a chronic inflammatory disease leading to joint destruction. Serologically, it can be differentiated according to rheumatoid factor (RF), anti-cyclic citrullinated peptide antibodies (anti-CCP), or both. This differentiation is prognostically and therapeutically relevant. No method has been described to separate the two forms phenotypically. We hypothesize that a differentiation is possible by evaluating oscillation patterns in power Doppler sonography (PDS). In a prospective study, 20 patients with anti-CCP-positive RA and 20 patients with anti-CCP-negative RA with active wrist synovitis were examined. A PDS scan was performed, and perfusion maxima (P max) and minima (P min) as well as the amplitude (ΔP) were determined by a blinded study member. The amplitude was standardized (sΔP) by dividing by P max, and the anti-CCP-positive and anti-CCP-negative patients as well as the RF-positive and RF-negative were compared to each other. In the ultrasonographic evaluation, we found a highly significant difference in sΔP between CCPp and CCPn patients (median 19.0 vs. 42.9 %, p < 0.0001). sΔP is independent of disease activity. The absolute amplitude ΔP did not differ between the groups. Also, in anti-CCP-positive patients there was a completely linear correlation between P max and P min, and this was far less marked in anti-CCP-negative patients. Anti-CCP-positive and anti-CCP-negative RA display different PDS oscillation patterns. This constitutes a nonserological parameter to differentiate between the two forms. The difference in PDS oscillation patterns suggests that the underlying pathological process differs between the forms.

Using self-consistency for an iterative trajectory adjustment (SCITA).

PURPOSE: To iteratively correct for deviations in radial trajectories with no need of additionally performed calibration scans. THEORY AND METHODS: Radially acquired data sets-even when undersampled to a certain extend-inherently feature an oversampled area in the center of k-space. Thus, for a perfectly measured trajectory and neglecting noise, information is consistent between multiple measurements gridded to the same Cartesian position within this region. In the case of erroneous coordinates, this accordance-and therefore a correction of the trajectory-can be enforced by an algorithm iteratively shifting the projections with respect to each other by applying the GRAPPA operator. The method was validated in numerical simulations, as well as in radial acquisitions of a phantom and in vivo images at 3T. The results of the correction were compared to a previously proposed correction method. RESULTS: The newly introduced technique allowed for a reliable trajectory correction in each of the presented examples. The method was able to remove artifacts as effectively as methods that are based on data from additional calibration scans. CONCLUSION: The iterative technique introduced in this paper allows for a correction of trajectory errors in radial imaging with no need for additional calibration data.

High resolution myocardial first-pass perfusion imaging with extended anatomic coverage.

PURPOSE: To evaluate and to compare Parallel Imaging and Compressed Sensing acquisition and reconstruction frameworks based on simultaneous multislice excitation for high resolution contrast-enhanced myocardial first-pass perfusion imaging with extended anatomic coverage. MATERIALS AND METHODS: The simultaneous multislice imaging technique MS-CAIPIRINHA facilitates imaging with significantly extended anatomic coverage. For additional resolution improvement, equidistant or random undersampling schemes, associated with corresponding reconstruction frameworks, namely Parallel Imaging and Compressed Sensing can be used. By means of simulations and in vivo measurements, the two approaches were compared in terms of reconstruction accuracy. Comprehensive quality metrics were used, identifying statistical and systematic reconstruction errors. RESULTS: The quality measures applied allow for an objective comparison of the frameworks. Both approaches provide good reconstruction accuracy. While low to moderate noise enhancement is observed for the Parallel Imaging approach, the Compressed Sensing framework is subject to systematic errors and reconstruction induced spatiotemporal blurring. CONCLUSION: Both techniques allow for perfusion measurements with a resolution of 2.0 × 2.0 mm(2) and coverage of six slices every heartbeat. Being not affected by systematic deviations, the Parallel Imaging approach is considered to be superior for clinical studies.

Measurement accuracy of different active tracking sequences for interventional MRI.

PURPOSE: To compare the spatial accuracy of three typical active tracking sequences using a new, unique phantom design. MATERIALS AND METHODS: Three different tracking sequences (Single Echo, Dual Echo and Hadamard Multiplexed) were compared with each other in a phantom study with respect to their positional accuracy. A custom-built phantom was constructed to conduct the experiments with precise framework conditions which facilitated sufficient measurement accuracy. An electrophysiology catheter incorporating four micro-coils was used as an interventional device. Tracking profiles were acquired in all three spatial dimensions and validated against the distances that were measured by a Vernier caliper in combination with a three-dimensional reference scan. RESULTS: The Bland-Altman plots demonstrated that all three sequences show < 1.5 mm positional error. The measurement accuracy of Single Echo and Double Echo are prone to off-resonances, whereas Hadamard Encoding is immune to it. CONCLUSION: The developed phantom enabled the performance of objective measurements of the accuracy of different active tracking sequences. The proposed setup allows for objectively contrasting different methods for interventional procedures.