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The adoption of machine learning (ML) and, more specifically, deep learning (DL) applications into all major areas of our lives is underway. The development of trustworthy AI is especially important in medicine due to the large implications for patients' lives. While trustworthiness concerns various aspects including ethical, transparency and safety requirements, we focus on the importance of data quality (training/test) in DL. Since data quality dictates the behaviour of ML products, evaluating data quality will play a key part in the regulatory approval of medical ML products. We perform a systematic review following PRISMA guidelines using the databases Web of Science, PubMed and ACM Digital Library. We identify 5408 studies, out of which 120 records fulfil our eligibility criteria. From this literature, we synthesise the existing knowledge on data quality frameworks and combine it with the perspective of ML applications in medicine. As a result, we propose the METRIC-framework, a specialised data quality framework for medical training data comprising 15 awareness dimensions, along which developers of medical ML applications should investigate the content of a dataset. This knowledge helps to reduce biases as a major source of unfairness, increase robustness, facilitate interpretability and thus lays the foundation for trustworthy AI in medicine. The METRIC-framework may serve as a base for systematically assessing training datasets, establishing reference datasets, and designing test datasets which has the potential to accelerate the approval of medical ML products.
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PURPOSE: This study aims to map the transmit magnetic field ( B 1 + $$ {B}_1^{+} $$ ) in the human body at 7T using MR fingerprinting (MRF), with a focus on achieving high accuracy and precision across a large dynamic range, particularly at low flip angles (FAs). METHODS: A FLASH-based MRF sequence (B1-MRF) with high B 1 + $$ {B}_1^{+} $$ sensitivity was developed. Phantom and in vivo abdominal imaging were performed at 7T, and the results were compared with established reference methods, including a slow but precise preparation-based method (PEX), saturated TurboFLASH (satTFL), actual flip angle imaging (AFI) and Bloch-Siegert shift (BSS). RESULTS: The MRF signal curve was highly sensitive to B 1 + $$ {B}_1^{+} $$ , while T1 sensitivity was comparatively low. The phantom experiment showed good agreement of B 1 + $$ {B}_1^{+} $$ to PEX for a T1 range of 204-1691 ms evaluated at FAs from 0° to 70°. Compared to the references, a dynamic range increase larger than a factor of two was determined experimentally. In vivo liver scans showed a strong correlation between B1-MRF, satTFL, and RPE-AFI in a low body mass index (BMI) subject (18.1 kg/m2). However, in larger BMI subjects (≥25.5 kg/m2), inconsistencies were observed in low B 1 + $$ {B}_1^{+} $$ regions for satTFL and RPE-AFI, while B1-MRF still provided consistent results in these regions. CONCLUSION: B1-MRF provides accurate and precise B 1 + $$ {B}_1^{+} $$ maps over a wide range of FAs, surpassing the capabilities of existing methods in the FA range < 60°. Its enhanced sensitivity at low FAs is advantageous for various applications requiring precise B 1 + $$ {B}_1^{+} $$ estimates, potentially advancing the frontiers of ultra-high field (UHF) body imaging at 7T and beyond.
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OBJECTIVE: The purpose of this study was to investigate an approach for motion-corrected T1 mapping of the abdomen that allows for free breathing data acquisition with 100% scan efficiency. MATERIALS AND METHODS: Data were acquired using a continuous golden radial trajectory and multiple inversion pulses. For the correction of respiratory motion, motion estimation based on a surrogate was performed from the same data used for T1 mapping. Image-based self-navigation allowed for binning and reconstruction of respiratory-resolved images, which were used for the estimation of respiratory motion fields. Finally, motion-corrected T1 maps were calculated from the data applying the estimated motion fields. The method was evaluated in five healthy volunteers. For the assessment of the image-based navigator, we compared it to a simultaneously acquired ultrawide band radar signal. Motion-corrected T1 maps were evaluated qualitatively and quantitatively for different scan times. RESULTS: For all volunteers, the motion-corrected T1 maps showed fewer motion artifacts in the liver as well as sharper kidney structures and blood vessels compared to uncorrected T1 maps. Moreover, the relative error to the reference breathhold T1 maps could be reduced from up to 25% for the uncorrected T1 maps to below 10% for the motion-corrected maps for the average value of a region of interest, while the scan time could be reduced to 6-8 s. DISCUSSION: The proposed approach allows for respiratory motion-corrected T1 mapping in the abdomen and ensures accurate T1 maps without the need for any breathholds.
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Purpose: To provide a simulation framework for routine neuroimaging test data, which allows for "stress testing" of deep segmentation networks against acquisition shifts that commonly occur in clinical practice for T2 weighted (T2w) fluid-attenuated inversion recovery magnetic resonance imaging protocols. Approach: The approach simulates "acquisition shift derivatives" of MR images based on MR signal equations. Experiments comprise the validation of the simulated images by real MR scans and example stress tests on state-of-the-art multiple sclerosis lesion segmentation networks to explore a generic model function to describe the F1 score in dependence of the contrast-affecting sequence parameters echo time (TE) and inversion time (TI). Results: The differences between real and simulated images range up to 19% in gray and white matter for extreme parameter settings. For the segmentation networks under test, the F1 score dependency on TE and TI can be well described by quadratic model functions (R2>0.9). The coefficients of the model functions indicate that changes of TE have more influence on the model performance than TI. Conclusions: We show that these deviations are in the range of values as may be caused by erroneous or individual differences in relaxation times as described by literature. The coefficients of the F1 model function allow for a quantitative comparison of the influences of TE and TI. Limitations arise mainly from tissues with a low baseline signal (like cerebrospinal fluid) and when the protocol contains contrast-affecting measures that cannot be modeled due to missing information in the DICOM header.
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Objective. To provide three-dimensional (3D) whole-heart high-resolution isotropic cardiac T1 maps using a k-space-based through-plane super-resolution reconstruction (SRR) with rotated multi-slice stacks.Approach. Due to limited SNR and cardiac motion, often only 2D T1 maps with low through-plane resolution (4-8 mm) can be obtained. Previous approaches used SRR to calculate 3D high-resolution isotropic cardiac T1 maps. However, they were limited to the ventricles. The proposed approach acquires rotated stacks in long-axis orientation with high in-plane resolution but low through-plane resolution. This results in radially overlapping stacks from which high-resolution T1 maps of the whole heart are reconstructed using a k-space-based SRR framework considering the complete acquisition model. Cardiac and residual respiratory motion between different breath holds is estimated and incorporated into the reconstruction. The proposed approach was evaluated in simulations and phantom experiments and successfully applied to ten healthy subjects.Main results. 3D T1 maps of the whole heart were obtained in the same acquisition time as previous methods covering only the ventricles. T1 measurements were possible even for small structures, such as the atrial wall. The proposed approach provided accurate (P> 0.4;R2> 0.99) and precise T1 values (SD of 64.32 ± 22.77 ms in the proposed approach, 44.73 ± 31.9 ms in the reference). The edge sharpness of the T1 maps was increased by 6.20% and 4.73% in simulation and phantom experiments, respectively. Contrast-to-noise ratios between the septum and blood pool increased by 14.50% inin vivomeasurements with a k-space compared to an image-space-based SRR.Significance. The proposed approach provided whole-heart high-resolution 1.3 mm isotropic T1 maps in an overall acquisition time of approximately three minutes. Small structures, such as the atrial and right ventricular walls, could be visualized in the T1 maps.
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Imageamento Tridimensional , Imageamento por Ressonância Magnética , Humanos , Imageamento por Ressonância Magnética/métodos , Imageamento Tridimensional/métodos , Coração/diagnóstico por imagem , Ventrículos do Coração/diagnóstico por imagem , Suspensão da Respiração , Átrios do Coração , Imagens de Fantasmas , Reprodutibilidade dos TestesRESUMO
PURPOSE: Traditional phase-contrast MRI is affected by displacement artifacts caused by non-synchronized spatial- and velocity-encoding time points. The resulting inaccurate velocity maps can affect the accuracy of derived hemodynamic parameters. This study proposes and characterizes a 3D radial phase-contrast UTE (PC-UTE) sequence to reduce displacement artifacts. Furthermore, it investigates the displacement of a standard Cartesian flow sequence by utilizing a displacement-free synchronized-single-point-imaging MR sequence (SYNC-SPI) that requires clinically prohibitively long acquisition times. METHODS: 3D flow data was acquired at 3T at three different constant flow rates and varying spatial resolutions in a stenotic aorta phantom using the proposed PC-UTE, a Cartesian flow sequence, and a SYNC-SPI sequence as reference. Expected displacement artifacts were calculated from gradient timing waveforms and compared to displacement values measured in the in vitro flow experiments. RESULTS: The PC-UTE sequence reduces displacement and intravoxel dephasing, leading to decreased geometric distortions and signal cancellations in magnitude images, and more spatially accurate velocity quantification compared to the Cartesian flow acquisitions; errors increase with velocity and higher spatial resolution. CONCLUSION: PC-UTE MRI can measure velocity vector fields with greater accuracy than Cartesian acquisitions (although pulsatile fields were not studied) and shorter scan times than SYNC-SPI. As such, this approach is superior to traditional Cartesian 3D and 4D flow MRI when spatial misrepresentations cannot be tolerated, for example, when computational fluid dynamics simulations are compared to or combined with in vitro or in vivo measurements, or regional parameters such as wall shear stress are of interest.
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Estenose da Valva Aórtica , Imageamento por Ressonância Magnética , Humanos , Imageamento por Ressonância Magnética/métodos , Hemodinâmica , Imagens de Fantasmas , Artefatos , Velocidade do Fluxo Sanguíneo , Imageamento Tridimensional/métodosRESUMO
BACKGROUND: Cardiac MRI has become the gold-standard imaging technique for assessing cardiovascular morphology and function. In spite of this, its slow data acquisition process presents imaging challenges due to the motion from heartbeats, respiration, and blood flow. In recent studies, deep learning (DL) algorithms have shown promising results for the task of image reconstruction. However, there have been instances where they have introduced artifacts that may be misinterpreted as pathologies or may obscure the detection of pathologies. Therefore, it is important to obtain a metric, such as the uncertainty of the network output, that identifies such artifacts. However, this can be quite challenging for large-scale image reconstruction problems such as dynamic multi-coil non-Cartesian MRI. PURPOSE: To efficiently quantify uncertainties of a physics-informed DL-based image reconstruction method for a large-scale accelerated 2D multi-coil dynamic radial MRI reconstruction problem, and demonstrate the benefits of physics-informed DL over model-agnostic DL in reducing uncertainties while at the same time improving image quality. METHODS: We extended a recently proposed physics-informed 2D U-Net that learns spatio-temporal slices (named XT-YT U-Net), and employed it for the task of uncertainty quantification (UQ) by using Monte Carlo dropout and a Gaussian negative log-likelihood loss function. Our data comprised 2D dynamic MR images acquired with a radial balanced steady-state free precession sequence. The XT-YT U-Net, which allows for training with a limited amount of data, was trained and validated on a dataset of 15 healthy volunteers, and further tested on data from four patients. An extensive comparison between physics-informed and model-agnostic neural networks (NNs) concerning the obtained image quality and uncertainty estimates was performed. Further, we employed calibration plots to assess the quality of the UQ. RESULTS: The inclusion of the MR-physics model of data acquisition as a building block in the NN architecture led to higher image quality (NRMSE: - 33 ± 8.2 % $-33 \pm 8.2 \%$ , PSNR: 6.3 ± 1.3 % $6.3 \pm 1.3 \%$ , and SSIM: 1.9 ± 0.96 % $1.9 \pm 0.96 \%$ ), lower uncertainties ( - 46 ± 8.7 % $-46 \pm 8.7 \%$ ), and, based on the calibration plots, an improved UQ compared to its model-agnostic counterpart. Furthermore, the UQ information can be used to differentiate between anatomical structures (e.g., coronary arteries, ventricle boundaries) and artifacts. CONCLUSIONS: Using an XT-YT U-Net, we were able to quantify uncertainties of a physics-informed NN for a high-dimensional and computationally demanding 2D multi-coil dynamic MR imaging problem. In addition to improving the image quality, embedding the acquisition model in the network architecture decreased the reconstruction uncertainties as well as quantitatively improved the UQ. The UQ provides additional information to assess the performance of different network approaches.
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Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Humanos , Processamento de Imagem Assistida por Computador/métodos , Teorema de Bayes , Redes Neurais de Computação , AlgoritmosRESUMO
PURPOSE: To allow for T1 mapping of the myocardium within 2.3 s for a 2D slice utilizing cardiac motion-corrected, model-based image reconstruction. METHODS: Golden radial data acquisition is continuously carried out for 2.3 s after an inversion pulse. In a first step, dynamic images are reconstructed which show both contrast changes due to T1 recovery and anatomical changes due to the heartbeat. An image registration algorithm with a signal model for T1 recovery is applied to estimate non-rigid cardiac motion. In a second step, estimated motion fields are applied during an iterative model-based T1 reconstruction. The approach was evaluated in numerical simulations, phantom experiments and in in-vivo scans in healthy volunteers. RESULTS: The accuracy of cardiac motion estimation was shown in numerical simulations with an average motion field error of 0.7 ± 0.6 mm for a motion amplitude of 5.1 mm. The accuracy of T1 estimation was demonstrated in phantom experiments, with no significant difference (p = 0.13) in T1 estimated by the proposed approach compared to an inversion-recovery reference method. In vivo, the proposed approach yielded 1.3 × 1.3 mm T1 maps with no significant difference (p = 0.77) in T1 and SDs in comparison to a cardiac-gated approach requiring 16 s scan time (i.e., seven times longer than the proposed approach). Cardiac motion correction improved the precision of T1 maps, shown by a 40% reduced SD. CONCLUSION: We have presented an approach that provides T1 maps of the myocardium in 2.3 s by utilizing both cardiac motion correction and model-based T1 reconstruction.
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Interpretação de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Humanos , Imageamento por Ressonância Magnética/métodos , Interpretação de Imagem Assistida por Computador/métodos , Miocárdio , Movimento (Física) , Tomografia Computadorizada por Raios X , Imagens de Fantasmas , Coração/diagnóstico por imagem , Reprodutibilidade dos TestesRESUMO
Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists' decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public. To alleviate this issue, we add ECG features from two leading commercial algorithms and an open-source implementation supplemented by a set of automatic diagnostic statements from a commercial ECG analysis software in preprocessed format. This allows the comparison of ML models trained on clinically versus automatically generated label sets. We provide an extensive technical validation of features and diagnostic statements for ML applications. We believe this release crucially enhances the usability of the PTB-XL dataset as a reference dataset for ML methods in the context of ECG data.
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Algoritmos , Eletrocardiografia , Software , Eletrocardiografia/métodos , Aprendizado de Máquina , HumanosRESUMO
Multiple sites within Germany operate human MRI systems with magnetic fields either at 7 Tesla or 9.4 Tesla. In 2013, these sites formed a network to facilitate and harmonize the research being conducted at the different sites and make this technology available to a larger community of researchers and clinicians not only within Germany, but also worldwide. The German Ultrahigh Field Imaging (GUFI) network has defined a strategic goal to establish a 14 Tesla whole-body human MRI system as a national research resource in Germany as the next progression in magnetic field strength. This paper summarizes the history of this initiative, the current status, the motivation for pursuing MR imaging and spectroscopy at such a high magnetic field strength, and the technical and funding challenges involved. It focuses on the scientific and science policy process from the perspective in Germany, and is not intended to be a comprehensive systematic review of the benefits and technical challenges of higher field strengths.
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Imageamento por Ressonância Magnética , Imagem Corporal Total , Humanos , Imageamento por Ressonância Magnética/métodos , Espectroscopia de Ressonância Magnética , Imagem Corporal Total/métodos , Alemanha , Campos MagnéticosRESUMO
Objective.T1 mapping of the liver is time consuming and can be challenging due to respiratory motion. Here we present a prospective slice tracking approach, which utilizes an external ultra-wide band radar signal and allows for efficient T1 mapping during free-breathing.Approach.The fast radar signal is calibrated to an MR-based motion signal to create a motion model. This motion model provides motion estimates, which are used to carry out slice tracking for any subsequent clinical scan. This approach was evaluated in simulations, phantom experiments andin vivoscans.Main results.Radar-based slice tracking was implemented on an MR system with a total latency of 77 ms. Moving phantom experiments showed accurate motion prediction with an error of 0.12 mm in anterior-posterior and 0.81 mm in head-feet direction. The model error remained stable for up to two hours.In vivoexperiments showed visible image improvement with a motion model error three times smaller than with a respiratory bellow. For T1 mapping during free-breathing the proposed approach provided similar results compared to reference T1 mapping during a breathhold.Significance.The proposed radar-based approach achieves accurate slice tracking and enables efficient T1 mapping of the liver during free-breathing. This motion correction approach is independent from scanning parameters and could also be used for applications like MR guided radiotherapy or MR Elastography.
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Imageamento por Ressonância Magnética , Radar , Estudos Prospectivos , Imageamento por Ressonância Magnética/métodos , Movimento (Física) , Fígado/diagnóstico por imagem , Respiração , Imagens de FantasmasRESUMO
BACKGROUND: Unrolled neural networks (NNs) have been extensively applied to different image reconstruction problems across all imaging modalities. A key component of the latter is that they allow for physics-informed learning of the regularization method, which is parametrized by the NN. However, due to the lack of understanding of deep NNs from a theoretical point of view, unrolled NNs are still black-boxes when the regularizers are given by deep NNs, for example, U-Nets. PURPOSE: Dictionarylearning (DL) is a well-established regularization method, which is based on learning a transform to sparsely approximate the signals of interest. Typically, DL-based image reconstruction either employs a dictionary, which was pretrained on a set of patches which were extracted from ground-truth images or a dictionary which is jointly trained during the reconstruction. However, in both cases, the used DL-algorithms are not designed to take into account the reconstruction problem or the underlying physical model, which describes the imaging process. In this work, we propose a DL-algorithm based on unrolled NNs to overcome these limitations. METHODS: We construct an unrolled NN, which corresponds to an unrolled DL-based reconstruction algorithm and train the unrolled NN to optimize its weights, that is, the atoms of the dictionary, by back-propagation in a supervised manner. Further, we propose a new way to employ a 2D dictionary in the spatio-temporal domain. We tested and evaluated the method on an accelerated cardiac cine MR image reconstruction problem using 216/36/36 dynamic images for training, validation, and testing and compared it to two well-known state-of-the-art approaches for cardiac cine MRI based on deep iterative CNNs. Further, we analyze the obtained dictionaries in terms of dictionary-coherence and structure of the atoms. Last, we compare the reported methods in terms of stability by applying them to an entirely different dataset consisting of 49 different test images. RESULTS: The investigated physics-informed DL-approach yields significantly more accurate reconstructions compared to the DL-method, which uses dictionaries obtained by decoupled pretraining, thereby providing an improvement of up to 4.90 dB in terms of PSNR and 5% in terms of SSIM. Further, the proposed spatio-temporal 2D dictionary outperforms the 1D and 3D dictionaries by preventing smoothing of image details while still accurately removing undersampling artifacts and noise resulting in an increase of up to 1.10 dB in terms of PSNR and 4% in terms of SSIM. Although being surpassed by the CNNs on the first dataset, the proposed NNs-based DL method is more stable compared to the latter approach and yields comparable results on the second dataset. Last, it has the advantage of being entirely interpretable in each component. CONCLUSIONS: The presented physics-informed NN can be used as training algorithm for a classical and interpretable data-driven regularization method based on a learned dictionary, which can then not only be linked to the considered data but also to the reconstruction method that the NN defines.
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Algoritmos , Processamento de Imagem Assistida por Computador , Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Imageamento por Ressonância Magnética/métodos , Imagem Cinética por Ressonância Magnética/métodosRESUMO
OBJECTIVE: To provide respiratory motion correction for free-breathing myocardial T1 mapping using a pilot tone (PT) and a continuous golden-angle radial acquisition. MATERIALS AND METHODS: During a 45 s prescan the PT is acquired together with a dynamic sagittal image covering multiple respiratory cycles. From these images, the respiratory heart motion in head-feet and anterior-posterior direction is estimated and two linear models are derived between the PT and heart motion. In the following scan through-plane motion is corrected prospectively with slice tracking based on the PT. In-plane motion is corrected for retrospectively. Our method was evaluated on a motion phantom and 11 healthy subjects. RESULTS: Non-motion corrected measurements using a moving phantom showed T1 errors of 14 ± 4% (p < 0.05) compared to a reference measurement. The proposed motion correction approach reduced this error to 3 ± 4% (p < 0.05). In vivo the respiratory motion led to an overestimation of T1 values by 26 ± 31% compared to breathhold T1 maps, which was successfully corrected to an average difference of 3 ± 2% (p < 0.05) between our free-breathing approach and breathhold data. DISCUSSION: Our proposed PT-based motion correction approach allows for T1 mapping during free-breathing with the same accuracy as a corresponding breathhold T1 mapping scan.
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Imageamento por Ressonância Magnética , Miocárdio , Humanos , Estudos Retrospectivos , Estudos Prospectivos , Imageamento por Ressonância Magnética/métodos , RespiraçãoRESUMO
PURPOSE: Subject-tailored parallel transmission pulses for ultra-high fields body applications are typically calculated based on subject-specific B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps of all transmit channels, which require lengthy adjustment times. This study investigates the feasibility of using deep learning to estimate complex, channel-wise, relative 2D B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps from a single gradient echo localizer to overcome long calibration times. METHODS: 126 channel-wise, complex, relative 2D B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps of the human heart from 44 subjects were acquired at 7T using a Cartesian, cardiac gradient-echo sequence obtained under breath-hold to create a library for network training and cross-validation. The deep learning predicted maps were qualitatively compared to the ground truth. Phase-only B 1 + $$ {\mathrm{B}}_1^{+} $$ -shimming was subsequently performed on the estimated B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps for a region of interest covering the heart. The proposed network was applied at 7T to 3 unseen test subjects. RESULTS: The deep learning-based B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps, derived in approximately 0.2 seconds, match the ground truth for the magnitude and phase. The static, phase-only pulse design performs best when maximizing the mean transmission efficiency. In-vivo application of the proposed network to unseen subjects demonstrates the feasibility of this approach: the network yields predicted B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps comparable to the acquired ground truth and anatomical scans reflect the resulting B 1 + $$ {\mathrm{B}}_1^{+} $$ -pattern using the deep learning-based maps. CONCLUSION: The feasibility of estimating 2D relative B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps from initial localizer scans of the human heart at 7T using deep learning is successfully demonstrated. Because the technique requires only sub-seconds to derive channel-wise B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps, it offers high potential for advancing clinical body imaging at ultra-high fields.
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Aprendizado Profundo , Imageamento por Ressonância Magnética , Humanos , Imageamento por Ressonância Magnética/métodos , Interpretação de Imagem Assistida por Computador/métodos , Coração/diagnóstico por imagem , CalibragemRESUMO
Objective. To provide 3D high-resolution cardiac T1 maps using model-based super-resolution reconstruction (SRR).Approach. Due to signal-to-noise ratio limitations and the motion of the heart during imaging, often 2D T1 maps with only low through-plane resolution (i.e. slice thickness of 6-8 mm) can be obtained. Here, a model-based SRR approach is presented, which combines multiple stacks of 2D acquisitions with 6-8 mm slice thickness and generates 3D high-resolution T1 maps with a slice thickness of 1.5-2 mm. Every stack was acquired in a different breath hold (BH) and any misalignment between BH was corrected retrospectively. The novelty of the proposed approach is the BH correction and the application of model-based SRR on cardiac T1 Mapping. The proposed approach was evaluated in numerical simulations and phantom experiments and demonstrated in four healthy subjects.Main results. Alignment of BH states was essential for SRR even in healthy volunteers. In simulations, respiratory motion could be estimated with an RMS error of 0.18 ± 0.28 mm. SRR improved the visualization of small structures. High accuracy and precision (average standard deviation of 69.62 ms) of the T1 values was ensured by SRR while the detectability of small structures increased by 40%.Significance. The proposed SRR approach provided T1 maps with high in-plane and high through-plane resolution (1.3 × 1.3 × 1.5-2 mm3). The approach led to improvements in the visualization of small structures and precise T1 values.
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Ecocardiografia Tridimensional , Humanos , Estudos RetrospectivosRESUMO
Cardiac MR thermometry shows promise for real-time guidance of radiofrequency ablation of cardiac arrhythmias. This technique uses ECG triggering, which can be unreliable in this situation. A prospective cardiac triggering method was developed for MR thermometry using the active tracking (AT) signal measured from catheter microcoils. In the proposed AT-based cardiac triggering (AT-trig) sequence, AT modules were repeatedly acquired to measure the catheter motion until a cardiac trigger was identified to start cardiac MR thermometry using single-shot echo-planar imaging. The AT signal was bandpass filtered to extract the motion induced by the beating heart, and cardiac triggers were defined as the extremum (peak or valley) of the filtered AT signal. AT-trig was evaluated in a beating heart phantom and in vivo in the left ventricle of a swine during temperature stability experiments (6 locations) and during one ablation. Stability was defined as the standard deviation over time. In the phantom, AT-trig enabled triggering of MR thermometry and resulted in higher temperature stability than an untriggered sequence. In all in vivo experiments, AT-trig intervals matched ECG-derived RR intervals. Mis-triggers were observed in 1/12 AT-trig stability experiments. Comparable stability of MR thermometry was achieved using peak AT-trig (1.0 ± 0.4°C), valley AT-trig (1.1 ± 0.5°C), and ECG triggering (0.9 ± 0.4°C). These experiments show that continuously acquired AT signal for prospective cardiac triggering is feasible. MR thermometry with AT-trig leads to comparable temperature stability as with conventional ECG triggering. AT-trig could serve as an alternative cardiac triggering strategy in situations where ECG triggering is not effective.
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PURPOSE: Flow quantification by phase-contrast MRI is hampered by spatially varying background phase offsets. Correction performance by polynomial regression on stationary tissue may be affected by outliers such as wrap-around or constant flow. Therefore, we propose an alternative, M-estimate SAmple Consensus (MSAC) to reject outliers, and improve and fully automate background phase correction. METHODS: The MSAC technique fits polynomials to randomly drawn small samples from the image. Over several trials, it aims to find the best consensus set of valid pixels by rejecting outliers to the fit and minimizing the residuals of the remaining pixels. The robustness of MSAC to its few parameters was investigated and verified using third-order polynomial correction fits on a total of 118 2D flow (97 with wrap-around) and 18 4D flow data sets (14 with wrap-around), acquired at 1.5 T and 3 T. Background phase was compared with standard stationary correction and phantom correction. Pulmonary/systemic flow ratios in 2D flow were derived, and exemplary 4D flow analysis was performed. RESULTS: The MSAC technique is robust over a range of parameter choices, and a unique set of parameters is suitable for both 2D and 4D flow. In 2D flow, phase errors were significantly reduced by MSAC compared with stationary correction (p = 0.005), and stationary correction shows larger errors in pulmonary/systemic flow ratios compared with MSAC. In 4D flow, MSAC shows similar performance as stationary correction. CONCLUSIONS: The MSAC method provides fully automated background phase correction to 2D and 4D flow data and shows improved robustness over stationary correction, especially with outliers present.
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Algoritmos , Imageamento por Ressonância Magnética , Velocidade do Fluxo Sanguíneo , Consenso , Voluntários Saudáveis , Humanos , Imageamento Tridimensional/métodos , Imageamento por Ressonância Magnética/métodos , Imagens de Fantasmas , Reprodutibilidade dos TestesRESUMO
PURPOSE: Myocardial fat infiltrations are associated with a range of cardiomyopathies. The purpose of this study was to perform cardio-respiratory motion-correction for model-based water-fat separation to image fatty infiltrations of the heart in a free-breathing, non-cardiac-triggered high-resolution 3D MRI acquisition. METHODS: Data were acquired in nine patients using a free-breathing, non-cardiac-triggered high-resolution 3D Dixon gradient-echo sequence and radial phase encoding trajectory. Motion correction was combined with a model-based water-fat reconstruction approach. Respiratory and cardiac motion models were estimated using a dual-mode registration algorithm incorporating both motion-resolved water and fat information. Qualitative comparisons of fat structures were made between 2D clinical routine reference scans and reformatted 3D motion-corrected images. To evaluate the effect of motion correction the local sharpness of epicardial fat structures was analyzed for motion-averaged and motion-corrected fat images. RESULTS: The reformatted 3D motion-corrected reconstructions yielded qualitatively comparable fat structures and fat structure sharpness in the heart as the standard 2D breath-hold. Respiratory motion correction improved the local sharpness on average by 32% ± 24% with maximum improvements of 81% and cardiac motion correction increased the sharpness further by another 15% ± 11% with maximum increases of 31%. One patient showed a fat infiltration in the myocardium and cardio-respiratory motion correction was able to improve its visualization in 3D. CONCLUSION: The 3D water-fat separated cardiac images were acquired during free-breathing and in a clinically feasible and predictable scan time. Compared to a motion-averaged reconstruction an increase in sharpness of fat structures by 51% ± 27% using the presented motion correction approach was observed for nine patients.
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Coração , Água , Coração/diagnóstico por imagem , Humanos , Processamento de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Imageamento por Ressonância Magnética/métodos , Movimento (Física)RESUMO
PURPOSE: Respiratory motion-compensated (MC) 3D cardiac fat-water imaging at 7T. METHODS: Free-breathing bipolar 3D triple-echo gradient-recalled-echo (GRE) data with radial phase-encoding (RPE) trajectory were acquired in 11 healthy volunteers (7M\4F, 21-35 years, mean: 30 years) with a wide range of body mass index (BMI; 19.9-34.0 kg/m2 ) and volunteer tailored B1+ shimming. The bipolar-corrected triple-echo GRE-RPE data were binned into different respiratory phases (self-navigation) and were used for the estimation of non-rigid motion vector fields (MF) and respiratory resolved (RR) maps of the main magnetic field deviations (ΔB0 ). RR ΔB0 maps and MC ΔB0 maps were compared to a reference respiratory phase to assess respiration-induced changes. Subsequently, cardiac binned fat-water images were obtained using a model-based, respiratory motion-corrected image reconstruction. RESULTS: The 3D cardiac fat-water imaging at 7T was successfully demonstrated. Local respiration-induced frequency shifts in MC ΔB0 maps are small compared to the chemical shifts used in the multi-peak model. Compared to the reference exhale ΔB0 map these changes are in the order of 10 Hz on average. Cardiac binned MC fat-water reconstruction reduced respiration induced blurring in the fat-water images, and flow artifacts are reduced in the end-diastolic fat-water separated images. CONCLUSION: This work demonstrates the feasibility of 3D fat-water imaging at UHF for the entire human heart despite spatial and temporal B1+ and B0 variations, as well as respiratory and cardiac motion.
Assuntos
Imageamento por Ressonância Magnética , Água , Artefatos , Humanos , Imageamento Tridimensional , Movimento (Física) , RespiraçãoRESUMO
PURPOSE: MRI at ultra-high fields in the human body is highly challenging and requires lengthy calibration times to compensate for spatially heterogeneous B1+ profiles. This study investigates the feasibility of using pre-computed universal pulses for calibration-free homogeneous 3D flip angle distribution in the human heart at 7T. METHODS: Twenty-two channel-wise 3D B1+ data sets were acquired under free-breathing in 19 subjects to generate a library for an offline universal pulse (UP) design (group 1: 12 males [M] and 7 females [F], 21-66 years, 19.8-28.3 kg/m2 ). Three of these subjects (2M/1F, 21-33 years, 20.8-23.6 kg/m2 ) were re-scanned on different days. A 4kT-points UP optimized for the 22 channel-wise 3D B1+ data sets in group 1 (UP22-4kT) is proposed and applied at 7T in 9 new and unseen subjects (group 2: 4M/5F, 25-56 years, 19.5-35.3 kg/m2 ). Multiple tailored and universal static and dynamic parallel-transmit (pTx) pulses were designed and evaluated for different permutations of the B1+ data sets in group 1 and 2. RESULTS: The proposed UP22-4kT provides low B1+ variation in all subjects, seen and unseen, without severe signal drops. Experimental data at 7T acquired with UP22-4kT shows comparable image quality as data acquired with tailored-4kT pulses and demonstrates successful calibration-free pTx of the human heart. CONCLUSION: UP22-4kT allows for calibration-free homogeneous flip angle distributions across the human heart at 7T. Large inter-subject variations because of sex, age, and body mass index are well tolerated. The proposed universal pulse removes the need for lengthy (10-15 min) calibration scans and therefore has the potential to bring body imaging at 7T closer to the clinical application.