Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN

OBJECTIVE

This study developed an unsupervised motion artifact reduction method for MRI images of patients with brain tumors. The proposed novel design uses multi-parametric multicenter contrast-enhanced T1W (ceT1W) and T2-FLAIR MRI images. Approach: The proposed framework included two generators, two discriminators, and two feature extractor networks. A 3-fold cross-validation was used to train and fine-tune the hyperparameters of the proposed model using 230 brain MRI images with tumors, which were then tested on 148 patients' in-vivo datasets. An ablation was performed to evaluate the model's compartments. Our model was compared with Pix2pix and CycleGAN. Six evaluation metrics were reported, including normalized mean squared error (NMSE), structural similarity index (SSIM), multi-scale-SSIM (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multi-scale gradient magnitude similarity deviation (MS-GMSD). Artifact reduction and consistency of tumor regions, image contrast, and sharpness were evaluated by three evaluators using Likert scales and compared with ANOVA and Tukey's HSD tests. Main results: On average, our method outperforms comparative models to remove heavy motion artifacts with the lowest NMSE (18.34±5.07%) and MS-GMSD (0.07±0.03) for heavy motion artifact level. Additionally, our method creates motion-free images with the highest SSIM (0.93±0.04), PSNR (30.63±4.96), and VIF (0.45±0.05) values, along with comparable MS-SSIM (0.96±0.31). Similarly, our method outperformed comparative models in removing in-vivo motion artifacts for different distortion levels except for MS- SSIM and VIF, which have comparable performance with CycleGAN. Moreover, our method had a consistent performance for different artifact levels. For the heavy level of motion artifacts, our method got the highest Likert scores of 2.82±0.52, 1.88±0.71, and 1.02±0.14 (p-values<<0.0001) for our method, CycleGAN, and Pix2pix respectively. Similar trends were also found for other motion artifact levels. Significance: Our proposed unsupervised method was demonstrated to reduce motion artifacts from the ceT1W brain images under a multi-parametric framework.

McSTRA: A multi-branch cascaded swin transformer for point spread function-guided robust MRI reconstruction

Deep learning MRI reconstruction methods are often based on Convolutional neural network (CNN) models; however, they are limited in capturing global correlations among image features due to the intrinsic locality of the convolution operation. Conversely, the recent vision transformer models (ViT) are capable of capturing global correlations by applying self-attention operations on image patches. Nevertheless, the existing transformer models for MRI reconstruction rarely leverage the physics of MRI. In this paper, we propose a novel physics-based transformer model titled, the Multi-branch Cascaded Swin Transformers (McSTRA) for robust MRI reconstruction. McSTRA combines several interconnected MRI physics-related concepts with the Swin transformers: it exploits global MRI features via the shifted window self-attention mechanism; it extracts MRI features belonging to different spectral components via a multi-branch setup; it iterates between intermediate de-aliasing and data consistency via a cascaded network with intermediate loss computations; furthermore, we propose a point spread function-guided positional embedding generation mechanism for the Swin transformers which exploit the spread of the aliasing artifacts for effective reconstruction. With the combination of all these components, McSTRA outperforms the state-of-the-art methods while demonstrating robustness in adversarial conditions such as higher accelerations, noisy data, different undersampling protocols, out-of-distribution data, and abnormalities in anatomy.

k-strip: A novel segmentation algorithm in k-space for the application of skull stripping

BACKGROUND AND OBJECTIVE

We present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich complex valued k-space.

METHODS

Using four datasets from different institutions with a total of around 200,000 MRI slices, we show that our network can perform skull-stripping on the raw data of MRIs while preserving the phase information which no other skull stripping algorithm is able to work with. For two of the datasets, skull stripping performed by HD-BET (Brain Extraction Tool) in the image domain is used as the ground truth, whereas the third and fourth dataset comes with per-hand annotated brain segmentations.

RESULTS

All four datasets were very similar to the ground truth (DICE scores of 92 %-99 % and Hausdorff distances of under 5.5 pixel). Results on slices above the eye-region reach DICE scores of up to 99 %, whereas the accuracy drops in regions around the eyes and below, with partially blurred output. The output of k-Strip often has smoothed edges at the demarcation to the skull. Binary masks are created with an appropriate threshold.

CONCLUSION

With this proof-of-concept study, we were able to show the feasibility of working in the k-space frequency domain, preserving phase information, with consistent results. Besides preserving valuable information for further diagnostics, this approach makes an immediate anonymization of patient data possible, already before being transformed into the image domain. Future research should be dedicated to discovering additional ways the k-space can be used for innovative image analysis and further workflows.

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review

Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition.

Ultra-short T2 components imaging of the whole brain using 3D dual-echo UTE MRI with rosette k-space pattern

PURPOSE

This study aimed to develop a new 3D dual-echo rosette k-space trajectory, specifically designed for UTE MRI applications. The imaging of the ultra-short transverse relaxation time (uT₂ ) of brain was acquired to test the performance of the proposed UTE sequence.

THEORY AND METHODS

The rosette trajectory was developed based on rotations of a "petal-like" pattern in the k_x -k_y plane, with oscillated extensions in the k_z -direction for 3D coverage. Five healthy volunteers underwent 10 dual-echo 3D rosette UTE scans with various TEs. Dual-exponential complex model fitting was performed on the magnitude data to separate uT₂ signals, with the output of uT₂ fraction, uT₂ value, and long-T₂ value.

RESULTS

The 3D rosette dual-echo UTE sequence showed better performance than a 3D radial UTE acquisition. More significant signal intensity decay in white matter than gray matter was observed along with the TEs. The white matter regions had higher uT₂ fraction values than gray matter (10.9% ± 1.9% vs. 5.7% ± 2.4%). The uT₂ value was approximately 0.10 ms in white matter .

CONCLUSION

The higher uT₂ fraction value in white matter compared to gray matter demonstrated the ability of the proposed sequence to capture rapidly decaying signals.

State-of-the-art magnetic resonance imaging sequences for pediatric body imaging

Longer examination time, need for anesthesia in smaller children and the inability of most children to hold their breath are major limitations of MRI in pediatric body imaging. Fortunately, with technical advances, many new and upcoming MRI sequences are overcoming these limitations. Advances in data acquisition and k-space sampling methods have enabled sequences with improved temporal and spatial resolution, and minimal artifacts. Sequences to minimize movement artifacts mainly utilize radial k-space filling, and examples include the stack-of-stars method for T1-weighted imaging and the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER)/BLADE method for T2-weighted imaging. Similarly, the sequences with improved temporal resolution and the ability to obtain multiple phases in a single breath-hold in dynamic imaging mainly use some form of partial k-space filling method. New sequences use a variable combination of data sampling methods like compressed sensing, golden-angle radial k-space filling, parallel imaging and partial k-space filling to achieve free-breathing, faster sequences that could be useful for pediatric abdominal and thoracic imaging. Simultaneous multi-slice method has improved diffusion-weighted imaging (DWI) with reduction in scan time and artifacts. In this review, we provide an overview of data sampling methods like parallel imaging, compressed sensing, radial k-space sampling, partial k-space sampling and simultaneous multi-slice. This is followed by newer available and upcoming sequences for T1-, T2- and DWI based on these other advances. We also discuss the Dixon method and newer approaches to reducing metal artifacts.

MRI-Based Image Signal-to-Noise Ratio Enhancement with Different Receiving Gains in K-Space

Echo signals in different regions in the k-space of magnetic resonance imaging (MRI) data possess different amplitudes. The signal-to-noise ratio (SNR) of a received signal can be improved by differentially setting the receiving gain (RG) parameter in different areas of the k-space. Previously, the k-space data splicing method and the gain normalization implementation method were not specifically investigated; however, this study focuses on this aspect. Specifically, to improve the SNR, three RGs and MRI scans are herein designed for each gain parameter using the gradient echo sequence to obtain one group of k-space data. Subsequently, the three groups of experimental k-space data obtained using MRI scans are spliced into one group of k-space data. For the splicing process, a method for gain and phase correction and compensation is developed that normalizes different RG parameters in the k-space. The experimental results indicate that the developed methods improve the SNR by 5–13%. When the RGs are set to other combinations, the k-space data splicing and gain normalization methods presented in this paper are still applicable.

Efficient gradient waveform measurements with variable-prephasing

Accurate measurement of gradient waveform errors can often improve image quality in sequences with time varying readout and excitation waveforms. Self-encoding or offset-slice sequences are commonly used to measure gradient waveforms. However, the self-encoding method requires a long scan time, while the offset-slice method is often low precision, requiring the thickness of the excited slice to be small compared to the maximal k-space encoded by the test waveform. This work introduces a hybrid these methods, called variable-prephasing. Using a straightforward algebraic model, we demonstrate that variable-prephasing improves the precision of the waveform measurement by allowing acquisition of larger slice thicknesses. Experiments in a phantom were used to validate the theoretical predictions, showing that the precision of variable-prephasing gradient waveform measurements improves with increasing slice thickness.

Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation

Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI). However, existing deep learning-based image reconstruction methods typically apply weight-sharing convolutional neural networks (CNNs) to k-space data without taking into consideration the k-space data's spatial frequency properties, leading to ineffective learning of the image reconstruction models. Moreover, complementary information of spatially adjacent slices is often ignored in existing deep learning methods. To overcome such limitations, we have developed a deep learning algorithm, referred to as adaptive convolutional neural networks for k-space data interpolation (ACNN-k-Space), which adopts a residual Encoder-Decoder network architecture to interpolate the undersampled k-space data by integrating spatially contiguous slices as multi-channel input, along with k-space data from multiple coils if available. The network is enhanced by self-attention layers to adaptively focus on k-space data at different spatial frequencies and channels. We have evaluated our method on two public datasets and compared it with state-of-the-art existing methods. Ablation studies and experimental results demonstrate that our method effectively reconstructs images from undersampled k-space data and achieves significantly better image reconstruction performance than current state-of-the-art techniques. Source code of the method is available at https://gitlab.com/qgpmztmf/acnn-k-space.

Attenuation of motion artifacts in fMRI using discrete reconstruction of irregular fMRI trajectories (DRIFT)

PURPOSE

Numerous studies report motion as the most detrimental source of noise and artifacts in fMRI. Current motion correction methods fail to completely address the motion problem. Retrospective techniques such as spatial realignment can correct for between-volume misalignment but fail to address within volume contamination and spin-history artifacts. Prospective motion correction can prevent spin-history artifacts but currently cannot update the gradients fast enough to remove k-space filling artifacts, calling for a hybrid approach to fully address these problems.

THEORY AND METHODS

Motion can be mathematically formulated into the MR signal equation to describe the motion artifacts at their origin in k-space. From these equations, it is demonstrated that different motions have different effects on the signal. A novel motion correction algorithm is designed from these equations to remove motion-induced artifacts directly in k-space, discrete reconstruction of irregular fMRI trajectory (DRIFT). This method is evaluated rigorously using fMRI simulations and data from a rotating phantom inside the scanner.

RESULTS

The results indicate that although some motion types have negligible effects on the MR signal, others produce catastrophic and lasting artifacts even after motion cessation. In simulation, DRIFT is able to remove motion artifacts in the absence of spin history. In a phantom scan, DRIFT significantly attenuates the motion artifacts in the fMRI data.

CONCLUSION

Neither prospective nor retrospective motion correction methods could completely remove the motion artifacts from the fMRI data. However, DRIFT, as a retrospective technique, when combined with prospective motion correction, can eliminate a significant portion of motion artifacts.

Three-dimensional Yarnball k-space acquisition for accelerated MRI

PURPOSE

To introduce an efficient sampling technique named Yarnball, which may serve as a direct alternative to 3D Cones.

METHODS

Yarnball evolves through 3D k-space with increasing loop size, and the differential equations defining this flexible trajectory are presented in detail. The sampling efficiencies of Yarnball and 3D Cones were compared through point spread function analysis and simulated imaging (which highlights undersampling in the absence of other scanning effects). The feasibility of Yarnball implementation was demonstrated for fully sampled T₁ -weighted images of the human head at 3 T.

RESULTS

The mostly large 3D loops of the Yarnball trajectory facilitate rapid sampling under peripheral nerve stimulation constraint, an advantage that increases with readout duration (T_RO ). Point spread function analysis yielded 89% (T_RO = 2 ms) and 77% (T_RO = 10 ms) of Yarnball voxels with magnitude less than 0.01% of the point spread function peak. For 3D Cones, these values were only 52% and 29%. The 3D-Cones technique required 1.4 times (T_RO = 2 ms) and 1.8 times (T_RO = 10 ms) more trajectories than Yarnball to produce simulated images of a sphere free from undersampling artifact. For a prolate spheroidal (head-like) object, 1.75 times and 2.6 times more trajectories were required for 3D Cones. Yarnball produced 0.72 mm (1/2k_max ) isotropic T₁ -weighted human brain images free from undersampling artifact in only 98 seconds at 3 T.

CONCLUSION

Yarnball demonstrated greater k-space sampling efficiency than directly comparable 3D Cones, and may have value wherever 3D Cones has been considered. Yarnball may also have value in the context of rapid T₁ -weighted brain imaging.

Progress in HAXPES performance combining full-field k-imaging with time-of-flight recording

An alternative approach to hard-X-ray photoelectron spectroscopy (HAXPES) has been established. The instrumental key feature is an increase of the dimensionality of the recording scheme from 2D to 3D. A high-energy momentum microscope detects electrons with initial kinetic energies up to 8 keV with a k-resolution of 0.025 Å-1, equivalent to an angular resolution of 0.034°. A special objective lens with k-space acceptance up to 25 Å-1 allows for simultaneous full-field imaging of many Brillouin zones. Combined with time-of-flight (ToF) parallel energy recording this yields maximum parallelization. Thanks to the high brilliance (1013 hν s-1 in a spot of <20 µm diameter) of beamline P22 at PETRA III (Hamburg, Germany), the microscope set a benchmark in HAXPES recording speed, i.e. several million counts per second for core-level signals and one million for d-bands of transition metals. The concept of tomographic k-space mapping established using soft X-rays works equally well in the hard X-ray range. Sharp valence band k-patterns of Re, collected at an excitation energy of 6 keV, correspond to direct transitions to the 28th repeated Brillouin zone. Measured total energy resolutions (photon bandwidth plus ToF-resolution) are 62 meV and 180 meV FWHM at 5.977 keV for monochromator crystals Si(333) and Si(311) and 450 meV at 4.0 keV for Si(111). Hard X-ray photoelectron diffraction (hXPD) patterns with rich fine structure are recorded within minutes. The short photoelectron wavelength (10% of the interatomic distance) `amplifies' phase differences, making full-field hXPD a sensitive structural tool.

The twisted solenoid RF phase gradient transmit coil for TRASE imaging

TRASE is an MRI k-space encoding method that uses radio-frequency (RF or B₁) transmit phase gradient fields to achieve millimeter-level spatial resolution. Image quality is critically dependent upon the efficient generation of B₁ fields with uniform magnitude and strong phase gradients. We present the design of a new family of phase gradient transmit coil based upon a solenoid twisted about a transverse axis. This design has many attractive geometric, electrical and magnetic characteristics, including the capability to spatially encode in the direction of the main static B₀ field without obstructing access to the bore. Analytical, numerical simulation and experimental results are presented, including demonstration of 1-dimensional TRASE encoding without the use of PIN diode switches. Twisted solenoid coils significantly expand the capabilities of TRASE MRI.

[Simulation Platform of Photoacoustic Imaging Based on Finite Element and k-space Pseudospectral Method]

Numerical simulation is a powerful technology for photoacoustic imaging (PAI) in both theory studies and practical applications. In this paper, a simulation platform for PAI was designed and implemented based on Matlab. The simulation platform utilized finite element method (FEM) and k-space pseudospectral method to calculate the forward and inverse problem of PAI. And a graphical user interface (GUI) was realized. Structural design, work process and other operating details of the platform was also provided. By compared with theoretical temporal waveform of photoacoustic signal and reconstruction results of COMSOL, the validity and reliability was verified. And a reliable simulation tool was proposed for PAI.

BLIPPED (BLIpped Pure Phase EncoDing) high resolution MRI with low amplitude gradients

MRI image resolution is proportional to the maximum k-space value, i.e. the temporal integral of the magnetic field gradient. High resolution imaging usually requires high gradient amplitudes and/or long spatial encoding times. Special gradient hardware is often required for high amplitudes and fast switching. We propose a high resolution imaging sequence that employs low amplitude gradients. This method was inspired by the previously proposed PEPI (π Echo Planar Imaging) sequence, which replaced EPI gradient reversals with multiple RF refocusing pulses. It has been shown that when the refocusing RF pulse is of high quality, i.e. sufficiently close to 180°, the magnetization phase introduced by the spatial encoding magnetic field gradient can be preserved and transferred to the following echo signal without phase rewinding. This phase encoding scheme requires blipped gradients that are identical for each echo, with low and constant amplitude, providing opportunities for high resolution imaging. We now extend the sequence to 3D pure phase encoding with low amplitude gradients. The method is compared with the Hybrid-SESPI (Spin Echo Single Point Imaging) technique to demonstrate the advantages in terms of low gradient duty cycle, compensation of concomitant magnetic field effects and minimal echo spacing, which lead to superior image quality and high resolution. The 3D imaging method was then applied with a parallel plate resonator RF probe, achieving a nominal spatial resolution of 17 μm in one dimension in the 3D image, requiring a maximum gradient amplitude of only 5.8 Gauss/cm.

Quantitative Susceptibility Mapping Using the Multiple Dipole-inversion Combination with k-space Segmentation Method

Quantitative susceptibility mapping (QSM) is a new magnetic resonance imaging (MRI) technique for noninvasively estimating the magnetic susceptibility of biological tissue. Several methods for QSM have been proposed. One of these methods can estimate susceptibility with high accuracy in tissues whose contrast is consistent between magnitude images and susceptibility maps, such as deep gray-matter nuclei. However, the susceptibility of small veins is underestimated and not well depicted by using the above approach, because the contrast of small veins is inconsistent between a magnitude image and a susceptibility map. In order to improve the estimation accuracy and visibility of small veins without streaking artifacts, a method with multiple dipole-inversion combination with k-space segmentation (MUDICK) has been proposed. In the proposed method, k-space was divided into three domains (low-frequency, magic-angle, and high-frequency). The k-space data in low-frequency and magic-angle domains were obtained by L1-norm regularization using structural information of a pre-estimated susceptibility map. The k-space data in high-frequency domain were obtained from the pre-estimated susceptibility map in order to preserve small-vein contrasts. Using numerical simulation and human brain study at 3 Tesla, streaking artifacts and small-vein susceptibility were compared between MUDICK and conventional methods (MEDI and TKD). The numerical simulation and human brain study showed that MUDICK and MEDI had no severe streaking artifacts and MUDICK showed higher contrast and accuracy of susceptibility in small-veins compared to MEDI. These results suggest that MUDICK can improve the accuracy and visibility of susceptibility in small-veins without severe streaking artifacts.

Velocity-selective-inversion prepared arterial spin labeling

PURPOSE

To develop a Fourier-transform based velocity-selective inversion (FT-VSI) pulse train for velocity-selective arterial spin labeling (VSASL).

METHODS

This new pulse contains paired and phase cycled refocusing pulses. Its sensitivities to B0/B1 inhomogeneity and gradient imperfections such as eddy currents were evaluated through simulation and phantom studies. Cerebral blood flow (CBF) quantification using FT-VSI prepared VSASL was compared with conventional VSASL and pseudocontinuous ASL (PCASL) at 3 Tesla.

RESULTS

Simulation and phantom results of the proposed FT-VSI pulse train demonstrated excellent robustness to B0/B1 field inhomogeneity and eddy currents. The estimated CBF of gray matter and white matter for the FT-VSI prepared VSASL, averaged among eight healthy volunteers, were 49.5 ± 7.5 mL/100 g/min and 14.8 ± 2.4 mL/100 g/min, respectively. Excellent correlation and agreement between the FT-VSI method and conventional VSASL and PCASL were found. The averaged signal-to-noise ratio (SNR) value in gray matter of the FT-VSI method was 39% higher than VSASL using conventional double refocused hyperbolic tangent pulses and 9% lower than PCASL.

CONCLUSION

A novel FT-VSI pulse train was demonstrated to be a suitable labeling module for VSASL with robustness of velocity-selective profile to B0/B1 field inhomogeneity and gradient imperfections. Compared with conventional VSASL, FT-VSI prepared VSASL produced consistent CBF maps with higher SNR values. Magn Reson Med 76:1136-1148, 2016. © 2015 Wiley Periodicals, Inc.

π Echo-Planar Imaging with concomitant field compensation for porous media MRI

The π Echo Planar Imaging (PEPI) method was modified to compensate for concomitant magnetic fields by waveform symmetrization. Samples with very short T2(∗) (a few hundred microseconds) and short T2 (tens of milliseconds to hundreds of milliseconds) were investigated. Echo spacings as short as 1.2 ms were achieved with the gradient pre-equalization method, enabling rapid 3D imaging of short relaxation time species with sub-millimeter resolution. The PEPI method yields superior quality images, compared to the Fast Spin Echo (FSE) method, with significantly reduced gradient duty cycle. Accelerated PEPI measurements with a variable number of centric interleaves are presented. Restricted k-space sampling was demonstrated for specific sample geometries, notably a Locharbriggs sandstone core plug, with the acquisition time further reduced. These methods generate proton density weighted images considering the echo time to sample T2 ratio. These methods are principally designed for 3D studies of fluid saturation in rock core plugs, evolving in time due to some manner of external perturbation, such as water flooding.

Numerical analysis of ultrasound propagation and reflection intensity for biological acoustic impedance microscope

This paper proposes a new method for microscopic acoustic imaging that utilizes the cross sectional acoustic impedance of biological soft tissues. In the system, a focused acoustic beam with a wide band frequency of 30-100 MHz is transmitted across a plastic substrate on the rear side of which a soft tissue object is placed. By scanning the focal point along the surface, a 2-D reflection intensity profile is obtained. In the paper, interpretation of the signal intensity into a characteristic acoustic impedance is discussed. Because the acoustic beam is strongly focused, interpretation assuming vertical incidence may lead to significant error. To determine an accurate calibration curve, a numerical sound field analysis was performed. In these calculations, the reflection intensity from a target with an assumed acoustic impedance was compared with that from water, which was used as a reference material. The calibration curve was determined by changing the assumed acoustic impedance of the target material. The calibration curve was verified experimentally using saline solution, of which the acoustic impedance was known, as the target material. Finally, the cerebellar tissue of a rat was observed to create an acoustic impedance micro profile. In the paper, details of the numerical analysis and verification of the observation results will be described.

Magnetic resonance signal moment determination using the Earth's magnetic field

We demonstrate a method to manipulate magnetic resonance data such that the moments of the signal spatial distribution are readily accessible. Usually, magnetic resonance imaging relies on data acquired in so-called k-space which is subsequently Fourier transformed to render an image. Here, via analysis of the complex signal in the vicinity of the centre of k-space we are able to access the first three moments of the signal spatial distribution, ultimately in multiple directions. This is demonstrated for biofouling of a reverse osmosis (RO) membrane module, rendering unique information and an early warning of the onset of fouling. The analysis is particularly applicable for the use of mobile magnetic resonance spectrometers; here we demonstrate it using an Earth's magnetic field system.

Image reconstruction in k-space from MR data encoded with ambiguous gradient fields

PURPOSE

In this work, the limits of image reconstruction in k-space are explored when non-bijective gradient fields are used for spatial encoding.

THEORY

The image space analogy between parallel imaging and imaging with non-bijective encoding fields is partially broken in k-space. As a consequence, it is hypothesized and proven that ambiguities can only be resolved partially in k-space, and not completely as is the case in image space.

METHODS

Image-space and k-space based reconstruction algorithms for multi-channel radiofrequency data acquisitions are programmed and tested using numerical simulations as well as in vivo measurement data.

RESULTS

The hypothesis is verified based on an analysis of reconstructed images. It is found that non-bijective gradient fields have the effect that densely sampled autocalibration data, used for k-space reconstruction, provide less information than a separate scan of the receiver coil sensitivity maps, used for image space reconstruction. Consequently, in k-space only the undersampling artifact can be unfolded, whereas in image space, it is also possible to resolve aliasing that is caused by the non-bijectivity of the gradient fields.

CONCLUSION

For standard imaging, reconstruction in image space and in k-space is nearly equivalent, whereas there is a fundamental difference with practical consequences for the selection of image reconstruction algorithms when non-bijective encoding fields are involved.

High-resolution MRI encoding using radiofrequency phase gradients

Although MRI offers highly diagnostic medical imagery, patient access to this modality worldwide is very limited when compared with X-ray or ultrasound. One reason for this is the expense and complexity of the equipment used to generate the switched magnetic fields necessary for MRI encoding. These field gradients are also responsible for intense acoustic noise and have the potential to induce nerve stimulation. We present results with a new MRI encoding principle which operates entirely without the use of conventional B0 field gradients. This new approach--'Transmit Array Spatial Encoding' (TRASE)--uses only the resonant radiofrequency (RF) field to produce Fourier spatial encoding equivalent to conventional MRI. k-space traversal (image encoding) is achieved by spin refocusing with phase gradient transmit fields in spin echo trains. A transmit coil array, driven by just a single transmitter channel, was constructed to produce four phase gradient fields, which allows the encoding of two orthogonal spatial axes. High-resolution two-dimensional-encoded in vivo MR images of hand and wrist were obtained at 0.2 T. TRASE exploits RF field phase gradients, and offers the possibility of very low-cost diagnostics and novel experiments exploiting unique capabilities, such as imaging without disturbance of the main B0 magnetic field. Lower field imaging (<1 T) and micro-imaging are favorable application domains as, in both cases, it is technically easier to achieve the short RF pulses desirable for long echo trains, and also to limit RF power deposition. As TRASE is simply an alternative mechanism (and technology) of moving through k space, there are many close analogies between it and conventional B0 -encoded techniques. TRASE is compatible with both B0 gradient encoding and parallel imaging, and so hybrid sequences containing all three spatial encoding approaches are possible.

Spatial backward planar projection in absorbing media possessing an arbitrary dispersion relation

Planar projection methods have been shown to rapidly relate fields between two planes. Such an approach is particularly useful for characterizing transducers, since only a single plane needs to be measured in order to characterize an entire field. The present work considers the same approach in the presence of an arbitrary dispersion relation. Unlike traditional methods that use Fourier solutions of the time-domain wave equation, the approach starts from a frequency-domain Helmholtz equation for waves in a dispersive medium. It is shown that a transfer function similar to that derived from time domain equations can be utilized. Both the forward- and backward-projection behaviors are examined and it is demonstrated that the approach is invariant to propagation direction.

k-Space tutorial: an MRI educational tool for a better understanding of k-space

A main difference between Magnetic Resonance (MR) imaging and other medical imaging modalities is the control over the data acquisition and how it can be managed to finally show the adequate reconstructed image. With some basic programming adjustments, the user can modify the spatial resolution, field of view (FOV), image contrast, acquisition velocity, artifacts and so many other parameters that will contribute to form the final image. The main character and agent of all this control is called k-space, which represents the matrix where the MR data will be stored previously to a Fourier transformation to obtain the desired image.

This work introduces 'k-Space tutorial', a MATLAB-based educational environment to learn how the image and the k-space are related, and how the image can be affected through k-space modifications. This MR imaging educational environment has learning facilities on the basic acceleration strategies that can be encountered in almost all MR scanners: scan percentage, rectangular FOV and partial Fourier imaging. It also permits one to apply low- and high-pass filtering to the k-space, and to observe how the contrast or the details are selected in the reconstructed image. It also allows one to modify the signal-to-noise ratio of the acquisition and create some artifacts on the image as a simulated movement of the patient – with variable intensity level – and some electromagnetic spikes on k-space occurring during data acquisition.