k-space inherited parallel acquisition (KIPA): application on dynamic magnetic resonance imaging thermometry

Eliminating electromagnetic interference for RF shielding-free MRI via k-space convolution: Insights from MR parallel imaging advances

Recent advances in ultra-low field MRI have attracted attention from both academic and industrial MR communities for its potential in democratizing MRI applications. One of the most striking features on those advances is shielding-free imaging by actively sensing and eliminating the electromagnetic interference (EMI). In this study, we review the analytical approaches for EMI estimation/elimination, and investigate their theoretical basis and relations with parallel imaging reconstruction. We provide further understanding of the existing approaches, formulating EMI estimation as convolution in k-space or multiplication in spectrum-space. We further propose to use tailored convolutional kernel to adaptively fit the varying EMI coupling across the acquisition window. These methods were evaluated with both simulation study and human brain imaging. The results show that using tailored convolutional kernel can achieve more robust performance against system and acquisition imperfections.

A locally segmented reconstruction method for parallel imaging

PURPOSE

A locally segmented parallel imaging reconstruction method is proposed that efficiently utilizes sensitivity distribution of multichannel receiver coil.

THEORY AND METHODS

A method of locally segmenting a MR signal is introduced to maximize the differences in sensitivity between receiver channels. A 1D Fourier transformation of the undersampled k-space data is performed along the readout direction, which generates a hybrid 2D space. The hybrid space is partitioned into localized segments along the readout direction. In every localized segment, kernels representing relation between adjacent signals are estimated from autocalibration signals, and data at unsampled points are estimated using the kernels. Then, the images are reconstructed from full k-space data that consists of the sampled data and the estimated data at unsampled points.

RESULTS

In a computer simulation and in vivo experiments, the locally segmented reconstruction method produced fewer residual artifacts compared to the conventional parallel imaging reconstruction methods with the same kernel geometry. The performance gain of the proposed method comes from maximizing encoding capability of receiver channels, thus resulting in the accurately estimated kernel weights that reflect the relation between adjacent signals.

CONCLUSION

The proposed spatial segmentation method maximally utilizes differences in the sensitivity of receiver channels to reconstruct images with reduced artifacts.

Recent technological advancements in thermometry

Thermometry is the key factor for achieving successful thermal therapy. Although invasive thermometry with a probe has been used for more than four decades, this method can only detect the local temperature within the probing volume. Noninvasive temperature imaging using a tomographic technique is ideal for monitoring hot-spot formation in the human body. Among various techniques, such as X-ray computed tomography, microwave tomography, echo sonography, and magnetic resonance (MR) imaging, the proton resonance frequency shift method of MR thermometry is the only method currently available for clinical practice because its temperature sensitivity is consistent in most aqueous tissues and can be easily observed using common clinical scanners. New techniques are being proposed to improve the robustness of this method against tissue motion. MR techniques for fat thermometry were also developed based on relaxation times. One of the latest non-MR techniques to attract attention is photoacoustic imaging.

Magnetic resonance thermometry and its biological applications - Physical principles and practical considerations

Most parameters that influence the magnetic resonance imaging (MRI) signal experience a temperature dependence. The fact that MRI can be used for non-invasive measurements of temperature and temperature change deep inside the human body has been known for over 30 years. Today, MR temperature imaging is widely used to monitor and evaluate thermal therapies such as radio frequency, microwave, laser, and focused ultrasound therapy. In this paper we cover the physical principles underlying the biological applications of MR temperature imaging and discuss practical considerations and remaining challenges. For biological tissue, the MR signal of interest comes mostly from hydrogen protons of water molecules but also from protons in, e.g., adipose tissue and various metabolites. Most of the discussed methods, such as those using the proton resonance frequency (PRF) shift, T₁, T₂, and diffusion only measure temperature change, but measurements of absolute temperatures are also possible using spectroscopic imaging methods (taking advantage of various metabolite signals as internal references) or various types of contrast agents. Currently, the PRF method is the most used clinically due to good sensitivity, excellent linearity with temperature, and because it is largely independent of tissue type. Because the PRF method does not work in adipose tissues, T₁- and T₂-based methods have recently gained interest for monitoring temperature change in areas with high fat content such as the breast and abdomen. Absolute temperature measurement methods using spectroscopic imaging and contrast agents often offer too low spatial and temporal resolution for accurate monitoring of ablative thermal procedures, but have shown great promise in monitoring the slower and usually less spatially localized temperature change observed during hyperthermia procedures. Much of the current research effort for ablative procedures is aimed at providing faster measurements, larger field-of-view coverage, simultaneous monitoring in aqueous and adipose tissues, and more motion-insensitive acquisitions for better precision measurements in organs such as the heart, liver, and kidneys. For hyperthermia applications, larger coverage, motion insensitivity, and simultaneous aqueous and adipose monitoring are also important, but great effort is also aimed at solving the problem of long-term field drift which gets interpreted as temperature change when using the PRF method.

MR techniques for guiding high-intensity focused ultrasound (HIFU) treatments

To make full use of the ability of magnetic resonance (MR) to guide high-intensity focused ultrasound (HIFU) treatment, effort has been made to improve techniques for thermometry, motion tracking, and sound beam visualization. For monitoring rapid temperature elevation with proton resonance frequency (PRF) shift, data acquisition and processing can be accelerated with parallel imaging and/or sparse sampling in conjunction with appropriate signal processing methods. Thermometry should be robust against tissue motion, motion-induced magnetic field variation, and susceptibility change. Thus, multibaseline, referenceless, or hybrid techniques have become important. In cases with adipose or bony tissues, for which PRF shift cannot be used, thermometry with relaxation times or signal intensity may be utilized. Motion tracking is crucial not only for thermometry but also for targeting the focus of an ultrasound in moving organs such as the liver, kidney, or heart. Various techniques for motion tracking, such as those based on an anatomical image atlas with optical-flow displacement detection, a navigator echo to seize the diaphragm position, and/or rapid imaging to track vessel positions, have been proposed. Techniques for avoiding the ribcage and near-field heating have also been examined. MR acoustic radiation force imaging (MR-ARFI) is an alternative to thermometry that can identify the location and shape of the focal spot and sound beam path. This technique could be useful for treating heterogeneous tissue regions or performing transcranial therapy. All of these developments, which will be discussed further in this review, expand the applicability of HIFU treatments to a variety of clinical targets while maintaining safety and precision.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2018;47:316-331.

MR thermometry for focused ultrasound monitoring utilizing model predictive filtering and ultrasound beam modeling

Background

A major challenge in using magnetic resonance temperature imaging (MRTI) to monitor focused ultrasound (FUS) applications is achieving high spatio-temporal resolution over a large field of view (FOV). This is important to accurately monitor all ultrasound (US) power depositions. Magnetic resonance (MR) subsampling in conjunction with thermal model-based reconstruction of the MRTI utilizing Pennes bioheat transfer equation (PBTE) is one promising approach. The thermal properties used in the thermal model are often estimated from a pre-treatment, low-power sonication.

Methods

In this proof-of-concept study we investigate the use of US simulations computed using the hybrid angular spectrum (HAS) method to estimate the US power deposition density Q, thereby avoiding the pre-treatment sonication and any potential tissue damage. MRTI reconstructions are performed using a thermal model-based reconstruction method called model predictive filtering (MPF). Experiments are performed in a homogeneous gelatin phantom and in a gelatin phantom with embedded plastic skull. MPF reconstructions are compared to separate sonications imaged with fully sampled data over a smaller FOV. Temperature root-mean-square errors (RMSE) and focal spot positions and shapes are evaluated.

Results

HAS simulations accurately predict the location of the focal spot (to within 1 mm) in both phantoms. Accurate temperature maps (RMSE below 1 °C), where the location of the focal spot agrees well with fully sampled “truth” (to within 1 mm), are also achieved in both phantoms.

Conclusions

HAS simulations can be used to accurately predict the focal spot location in homogeneous media and when focusing through an aberrating plastic skull. The HAS simulated power deposition (Q) patterns can be used in the MPF thermal model-based reconstruction to obtain accurate temperature maps with high spatio-temporal resolution over large FOVs.

Sampling strategies for subsampled segmented EPI PRF thermometry in MR guided high intensity focused ultrasound

PURPOSE

To investigate k-space subsampling strategies to achieve fast, large field-of-view (FOV) temperature monitoring using segmented echo planar imaging (EPI) proton resonance frequency shift thermometry for MR guided high intensity focused ultrasound (MRgHIFU) applications.

METHODS

Five different k-space sampling approaches were investigated, varying sample spacing (equally vs nonequally spaced within the echo train), sampling density (variable sampling density in zero, one, and two dimensions), and utilizing sequential or centric sampling. Three of the schemes utilized sequential sampling with the sampling density varied in zero, one, and two dimensions, to investigate sampling the k-space center more frequently. Two of the schemes utilized centric sampling to acquire the k-space center with a longer echo time for improved phase measurements, and vary the sampling density in zero and two dimensions, respectively. Phantom experiments and a theoretical point spread function analysis were performed to investigate their performance. Variable density sampling in zero and two dimensions was also implemented in a non-EPI GRE pulse sequence for comparison. All subsampled data were reconstructed with a previously described temporally constrained reconstruction (TCR) algorithm.

RESULTS

The accuracy of each sampling strategy in measuring the temperature rise in the HIFU focal spot was measured in terms of the root-mean-square-error (RMSE) compared to fully sampled "truth." For the schemes utilizing sequential sampling, the accuracy was found to improve with the dimensionality of the variable density sampling, giving values of 0.65 °C, 0.49 °C, and 0.35 °C for density variation in zero, one, and two dimensions, respectively. The schemes utilizing centric sampling were found to underestimate the temperature rise, with RMSE values of 1.05 °C and 1.31 °C, for variable density sampling in zero and two dimensions, respectively. Similar subsampling schemes with variable density sampling implemented in zero and two dimensions in a non-EPI GRE pulse sequence both resulted in accurate temperature measurements (RMSE of 0.70 °C and 0.63 °C, respectively). With sequential sampling in the described EPI implementation, temperature monitoring over a 192×144×135 mm3 FOV with a temporal resolution of 3.6 s was achieved, while keeping the RMSE compared to fully sampled "truth" below 0.35 °C.

CONCLUSIONS

When segmented EPI readouts are used in conjunction with k-space subsampling for MR thermometry applications, sampling schemes with sequential sampling, with or without variable density sampling, obtain accurate phase and temperature measurements when using a TCR reconstruction algorithm. Improved temperature measurement accuracy can be achieved with variable density sampling. Centric sampling leads to phase bias, resulting in temperature underestimations.

STEP: Self-supporting tailored k-space estimation for parallel imaging reconstruction

PURPOSE

A new subspace-based iterative reconstruction method, termed Self-supporting Tailored k-space Estimation for Parallel imaging reconstruction (STEP), is presented and evaluated in comparison to the existing autocalibrating method SPIRiT and calibrationless method SAKE.

THEORY AND METHODS

In STEP, two tailored schemes including k-space partition and basis selection are proposed to promote spatially variant signal subspace and incorporated into a self-supporting structured low rank model to enforce properties of locality, sparsity, and rank deficiency, which can be formulated into a constrained optimization problem and solved by an iterative algorithm. Simulated and in vivo datasets were used to investigate the performance of STEP in terms of overall image quality and detail structure preservation.

RESULTS

The advantage of STEP on image quality is demonstrated by retrospectively undersampled multichannel Cartesian data with various patterns. Compared with SPIRiT and SAKE, STEP can provide more accurate reconstruction images with less residual aliasing artifacts and reduced noise amplification in simulation and in vivo experiments. In addition, STEP has the capability of combining compressed sensing with arbitrary sampling trajectory.

CONCLUSION

Using k-space partition and basis selection can further improve the performance of parallel imaging reconstruction with or without calibration signals.

Model predictive filtering MR thermometry: Effects of model inaccuracies, k-space reduction factor, and temperature increase rate

PURPOSE

Evaluate effects of model parameter inaccuracies (thermal conductivity, k, and ultrasound power deposition density, Q), k-space reduction factor (R), and rate of temperature increase ( T˙) in a thermal model-based reconstruction for MR-thermometry during focused-ultrasound heating.

METHODS

Simulations and ex vivo experiments were performed to investigate the accuracy of the thermal model and the model predictive filtering (MPF) algorithm for varying R and T˙, and their sensitivity to errors in k and Q. Ex vivo data was acquired with a segmented EPI pulse sequence to achieve large field-of-view (192 × 162 × 96 mm) four-dimensional temperature maps with high spatiotemporal resolution (1.5 × 1.5 × 2.0 mm, 1.7 s).

RESULTS

In the simulations, 50% errors in k and Q resulted in maximum temperature root mean square errors (RMSE) of 6 °C for model only and 3 °C for MPF. Using recently developed methods, estimates of k and Q were accurate to within 3%. The RMSE between MPF and true temperature increased with R and T˙. In the ex vivo study the RMSE remained below 0.7 °C for R ranging from 4 to 12 and T˙ of 0.28-0.75 °C/s.

CONCLUSION

Errors in MPF temperatures occur due to errors in k and Q. These MPF temperature errors increase with increase in R and T˙, but are smaller than those obtained using the thermal model alone.

Ultrasonic enhancement of drug penetration in solid tumors

Increasing the penetration of drugs within solid tumors can be accomplished through multiple ultrasound-mediated mechanisms. The application of ultrasound can directly change the structure or physiology of tissues or can induce changes in a drug or vehicle in order to enhance delivery and efficacy. With each ultrasonic pulse, a fraction of the energy in the propagating wave is absorbed by tissue and results in local heating. When ultrasound is applied to achieve mild hyperthermia, the thermal effects are associated with an increase in perfusion or the release of a drug from a temperature-sensitive vehicle. Higher ultrasound intensities locally ablate tissue and result in increased drug accumulation surrounding the ablated region of interest. Further, the mechanical displacement induced by the ultrasound pulse can result in the nucleation, growth and collapse of gas bubbles. As a result of such cavitation, the permeability of a vessel wall or cell membrane can be increased. Finally, the radiation pressure of the propagating pulse can translate particles or tissues. In this perspective, we will review recent progress in ultrasound-mediated tumor delivery and the opportunities for clinical translation.

Magnetic resonance thermometry at 7T for real-time monitoring and correction of ultrasound induced mild hyperthermia

While Magnetic Resonance Thermometry (MRT) has been extensively utilized for non-invasive temperature measurement, there is limited data on the use of high field (≥7T) scanners for this purpose. MR-guided Focused Ultrasound (MRgFUS) is a promising non-invasive method for localized hyperthermia and drug delivery. MRT based on the temperature sensitivity of the proton resonance frequency (PRF) has been implemented in both a tissue phantom and in vivo in a mouse Met-1 tumor model, using partial parallel imaging (PPI) to speed acquisition. An MRgFUS system capable of delivering a controlled 3D acoustic dose during real time MRT with proportional, integral, and derivative (PID) feedback control was developed and validated. Real-time MRT was validated in a tofu phantom with fluoroptic temperature measurements, and acoustic heating simulations were in good agreement with MR temperature maps. In an in vivo Met-1 mouse tumor, the real-time PID feedback control is capable of maintaining the desired temperature with high accuracy. We found that real time MR control of hyperthermia is feasible at high field, and k-space based PPI techniques may be implemented for increasing temporal resolution while maintaining temperature accuracy on the order of 1°C.

Adaptive self-calibrating iterative GRAPPA reconstruction

Parallel magnetic resonance imaging in k-space such as generalized auto-calibrating partially parallel acquisition exploits spatial correlation among neighboring signals over multiple coils in calibration to estimate missing signals in reconstruction. It is often challenging to achieve accurate calibration information due to data corruption with noises and spatially varying correlation. The purpose of this work is to address these problems simultaneously by developing a new, adaptive iterative generalized auto-calibrating partially parallel acquisition with dynamic self-calibration. With increasing iterations, under a framework of the Kalman filter spatial correlation is estimated dynamically updating calibration signals in a measurement model and using fixed-point state transition in a process model while missing signals outside the step-varying calibration region are reconstructed, leading to adaptive self-calibration and reconstruction. Noise statistic is incorporated in the Kalman filter models, yielding coil-weighted de-noising in reconstruction. Numerical and in vivo studies are performed, demonstrating that the proposed method yields highly accurate calibration and thus reduces artifacts and noises even at high acceleration.

Block circulant quasi-band matrix property for the SENSE unfolding in k-space and justification for GRAPPA

The k-space unfolding matrix for parallel imaging in MRI was examined and was found to have the structure of block circulant quasi-band, providing a rigorous mathematical justification for the interpolation kernel in the commonly used GRAPPA method. The optimal GRAPPA kernel size and dimension were closely related to sensitivity spectrum.

HASTE sequence with parallel acquisition and T2 decay compensation: application to carotid artery imaging

T2-weighted carotid artery images acquired using the turbo spin-echo (TSE) sequence frequently suffer from motion artifacts due to respiration and blood pulsation. The possibility of using HASTE sequence to achieve motion-free carotid images was investigated. The HASTE sequence suffers from severe blurring artifacts due to signal loss in later echoes due to T2 decay. Combining HASTE with parallel acquisition (PHASTE) decreases the number of echoes acquired and thus effectively reduces the blurring artifact caused by T2 relaxation. Further improvement in image sharpness can be achieved by performing T2 decay compensation before reconstructing the PHASTE data. Preliminary results have shown successful suppression of motion artifacts with PHASTE imaging. The image quality was enhanced relative to the original HASTE image, but was still less sharp than a non-motion-corrupted TSE image.

High-pass GRAPPA: an image support reduction technique for improved partially parallel imaging

Partially parallel imaging (PPI) is a widely used technique in clinical applications. A limitation of this technique is the strong noise and artifact in the reconstructed images when high reduction factors are used. This work aims to increase the clinical applicability of PPI by improving its performance at high reduction factors. A new concept, image support reduction, is introduced. A systematic filter-design approach for image support reduction is proposed. This approach shows more advantages when used with an important existing PPI technique, GRAPPA. An improved GRAPPA method, high-pass GRAPPA (hp-GRAPPA), was developed based on this approach. The new technique does not involve changing the original GRAPPA kernel and performs reconstruction in almost the same amount of time. Experimentally, it is demonstrated that the reconstructed images using hp-GRAPPA have much lower noise/artifact level than those reconstructed using GRAPPA.

MR thermometry

Minimally invasive thermal therapy as local treatment of benign and malignant diseases has received increasing interest in recent years. Safety and efficacy of the treatment require accurate temperature measurement throughout the thermal procedure. Noninvasive temperature monitoring is feasible with magnetic resonance (MR) imaging based on temperature-sensitive MR parameters such as the proton resonance frequency (PRF), the diffusion coefficient (D), T1 and T2 relaxation times, magnetization transfer, the proton density, as well as temperature-sensitive contrast agents. In this article the principles of temperature measurements with these methods are reviewed and their usefulness for monitoring in vivo procedures is discussed. Whereas most measurements give a temperature change relative to a baseline condition, temperature-sensitive contrast agents and spectroscopic imaging can provide absolute temperature measurements. The excellent linearity and temperature dependence of the PRF and its near independence of tissue type have made PRF-based phase mapping methods the preferred choice for many in vivo applications. Accelerated MRI imaging techniques for real-time monitoring with the PRF method are discussed. Special attention is paid to acquisition and reconstruction methods for reducing temperature measurement artifacts introduced by tissue motion, which is often unavoidable during in vivo applications.

Comparison of reconstruction accuracy and efficiency among autocalibrating data-driven parallel imaging methods

The class of autocalibrating "data-driven" parallel imaging (PI) methods has gained attention in recent years due to its ability to achieve high quality reconstructions even under challenging imaging conditions. The aim of this work was to perform a formal comparative study of various data-driven reconstruction techniques to evaluate their relative merits for certain imaging applications. A total of five different reconstruction methods are presented within a consistent theoretical framework and experimentally compared in terms of the specific measures of reconstruction accuracy and efficiency using one-dimensional (1D)-accelerated Cartesian datasets. It is shown that by treating the reconstruction process as two discrete phases, a calibration phase and a synthesis phase, the reconstruction pathway can be tailored to exploit the computational advantages available in certain data domains. A new "split-domain" reconstruction method is presented that performs the calibration phase in k-space (k(x), k(y)) and the synthesis phase in a hybrid (x, k(y)) space, enabling highly accurate 2D neighborhood reconstructions to be performed more efficiently than previously possible with conventional techniques. This analysis may help guide the selection of PI methods for a given imaging task to achieve high reconstruction accuracy at minimal computational expense.

Evaluation of motion effects on parallel MR imaging with precalibration

Several parallel imaging techniques such as SMASH, SENSE, k-space inherited parallel acquisition (KIPA) and others use reference (calibration) scans to find the parameters required for image reconstruction. Reference data is used to estimate coil sensitivity profiles for image domain techniques such as SENSE or reconstruction coefficients for k-space domain methods such as SMASH and KIPA. Any motion between the reference and accelerated imaging scans can make the reconstruction coefficients determined from the reference scan data suboptimal, resulting in an artifactual reconstruction. This work aims at comparing the effects of motion on the performance of three parallel imaging methods: SENSE, variable-density SENSE and KIPA, which all require one or more reference scans for calibration.