Improving SAR estimations in MRI using subject-specific models

Effect of subject-specific head morphometry on specific absorption rate estimates in parallel-transmit MRI at 7 T

PURPOSE

To assess the accuracy of morphing an established reference electromagnetic head model to a subject-specific morphometry for the estimation of specific absorption rate (SAR) in 7T parallel-transmit (pTx) MRI.

METHODS

Synthetic T1 -weighted MR images were created from three high-resolution open-source electromagnetic head voxel models. The accuracy of morphing a "reference" (multimodal image-based detailed anatomical [MIDA]) electromagnetic model into a different subject's native space (Duke and Ella) was compared. Both linear and nonlinear registration methods were evaluated. Maximum 10-g averaged SAR was estimated for circularly polarized mode and for 5000 random RF shim sets in an eight-channel transmit head coil, and comparison made between the morphed MIDA electromagnetic models and the native Duke and Ella electromagnetic models, respectively.

RESULTS

The averaged error in maximum 10-g averaged SAR estimation across pTx MRI shim sets between the MIDA and the Duke target model was reduced from 17.5% with only rigid-body registration, to 11.8% when affine linear registration was used, and further reduced to 10.7% when nonlinear registration was used. The corresponding figures for the Ella model were 16.7%, 11.2%, and 10.1%.

CONCLUSION

We found that morphometry accounts for up to half of the subject-specific differences in pTx SAR. Both linear and nonlinear morphing of an electromagnetic model into a target subject improved SAR agreement by better matching head size, morphometry, and position. However, differences remained, likely arising from details in tissue composition estimation. Thus, the uncertainty of the head morphometry and tissue composition may need to be considered separately to achieve personalized SAR estimation.

Bench to bore ramifications of inter-subject head differences on RF shimming and specific absorption rates at 7T

PURPOSE

This study shows how inter-subject variation over a dataset of 72 head models results in specific absorption rate (SAR) and B₁⁺ field homogeneity differences using common shim scenarios.

METHODS

MR-CT datasets were used to segment 71 head models into 10 tissue compartments. These head models were affixed to the shoulders and neck of the virtual family Duke model and placed within an 8 channel transmit surface-loop array to simulate the electromagnetic fields of a 7T imaging experiment. Radio frequency (RF) shimming using the Gerchberg-Saxton algorithm and Circularly Polarized shim weights over the entire brain and select slices of each model was simulated. Various SAR metrics and B₁⁺ maps were calculated to demonstrate the contribution of head variation to transmit inhomogeneity and SAR variability.

RESULTS

With varying head geometries the loading for each transmit loop changes as evidenced by changes in S-parameters. The varying shim conditions and head geometries are shown to affect excitation uniformity, spatial distributions of local SAR, and SAR averaging over different pulse sequences. The Gerchberg-Saxton RF shimming algorithm outperforms circularly polarized shimming for all head models. Peak local SAR within the coil most often occurs nearest the coil on the periphery of the body. Shim conditions vary the spatial distribution of SAR.

CONCLUSION

The work gives further support to the need for fast and more subject specific SAR calculations to maintain safety. Local SAR_10g is shown to vary spatially given shim conditions, subject geometry and composition, and position within the coil.

Personalized local SAR prediction for parallel transmit neuroimaging at 7T from a single T1-weighted dataset

Purpose

Parallel RF transmission (PTx) is one of the key technologies enabling high quality imaging at ultra‐high fields (≥7T). Compliance with regulatory limits on the local specific absorption rate (SAR) typically involves over‐conservative safety margins to account for intersubject variability, which negatively affect the utilization of ultra‐high field MR. In this work, we present a method to generate a subject‐specific body model from a single T1‐weighted dataset for personalized local SAR prediction in PTx neuroimaging at 7T.

Methods

Multi‐contrast data were acquired at 7T (N = 10) to establish ground truth segmentations in eight tissue types. A 2.5D convolutional neural network was trained using the T1‐weighted data as input in a leave‐one‐out cross‐validation study. The segmentation accuracy was evaluated through local SAR simulations in a quadrature birdcage as well as a PTx coil model.

Results

The network‐generated segmentations reached Dice coefficients of 86.7% ± 6.7% (mean ± SD) and showed to successfully address the severe intensity bias and contrast variations typical to 7T. Errors in peak local SAR obtained were below 3.0% in the quadrature birdcage. Results obtained in the PTx configuration indicated that a safety margin of 6.3% ensures conservative local SAR estimates in 95% of the random RF shims, compared to an average overestimation of 34% in the generic “one‐size‐fits‐all” approach.

Conclusion

A subject‐specific body model can be automatically generated from a single T1‐weighted dataset by means of deep learning, providing the necessary inputs for accurate and personalized local SAR predictions in PTx neuroimaging at 7T.

A comprehensive electromagnetic evaluation of an MRI anthropomorphic head phantom

Electromagnetic simulations are an important tool for the safety assessment of RF coils. They are a useful resource for MRI RF coil designers, especially when complemented with experimental measurements and testing using physical phantoms. Regular-shaped (spherical/cylindrical) homogeneous phantoms are the MRI standard for RF testing but are somewhat inaccurate when compared with anthropomorphic anatomies, especially at high frequencies. In this work, using a recently developed anthropomorphic heterogeneous human head phantom, studies were performed to analyze the scattering parameters (S-parameters) and the electric and magnetic field distributions using (1) the B₁⁺ field mapping method on a 7 T human MRI scanner and (2) numerical full-wave electromagnetic simulations. All studies used the following: a recently developed six-compartment refillable 3D-printed anthropomorphic head phantom (developed from MRI scans obtained in vivo), where the phantom itself is filled in its entirety with either heterogeneous loading, or homogeneous brain or water loading, in vivo imaging, and a commercial homogeneous spherical water phantom. Our results determined that the calculated S-parameters for all the anthropomorphic head phantom models were comparable to the model that is based on the volunteer (within 17% difference of the reflection coefficient value) but differed for the commercial homogeneous spherical water phantom (within 45% difference). The experimentally measured B₁⁺ field maps of the anthropomorphic heterogeneous and homogeneous brain head phantoms were most comparable to the in vivo measured values. The numerical simulations also show that both the anthropomorphic homogeneous water and brain phantom models were less accurate in terms of electric field intensities/distributions when compared with the segmented in-vivo-based head model and the anthropomorphic heterogeneous head phantom model. The presented data highlights the differences between the physical phantoms/phantom models, and the in vivo measurements/segmented in-vivo-based head model. The results demonstrate the usefulness of 3D-printed anthropomorphic phantoms for RF coil evaluation and testing.

3D bioprinting of cell-laden electroconductive MXene nanocomposite bioinks

MXenes, a new family of burgeoning two-dimensional (2D) transition metal carbides/nitrides, have been extensively explored in recent years owing to their outstanding properties such as a large specific surface area, high electrical conductivity, low toxicity, and biodegradability. Numerous efforts have been devoted to exploring MXenes for various biomedical applications such as cancer therapy, bioimaging, biosensing, and drug delivery. However, the potential application of MXene nanosheets in tissue engineering has been almost overlooked despite their excellent performance in other biomedical applications. The overarching goal of this paper is to demonstrate the potential of MXene cell-laden bioinks for tissue engineering and their ability to assemble functional scaffolds to regenerate damaged tissue via 3D bioprinting. We formulate a new electroconductive cell-laden bioink composed of Ti3C2 MXene nanosheets dispersed homogeneously within hyaluronic acid/alginate (HA/Alg) hydrogels and showed its performance for extrusion-based 3D bioprinting. The prepared hydrogel bioinks with MXenes display excellent rheological properties, which allows the fabrication of multilayered 3D structures with high resolution and shape retention. Moreover, the introduction of Ti3C2 MXene nanosheets within the HA/Alg hydrogel introduces electrical conductivity to the ink, addressing the poor electrical conductivity of the current bioinks that mismatch with the physico-chemical properties of tissue. In addition, the MXene nanocomposite ink with encapsulated Human Embryonic Kidney 293 (HEK-293) cells displayed high cell viability (>95%) in both bulk hydrogel and 3D bioprinted structures. These results suggest that MXene nanocomposite bioinks and their 3D bioprinting with high electrical conductivity, biocompatibility and degradability can synergize some new applications for tissue and neural engineering.

Individualized SAR calculations using computer vision-based MR segmentation and a fast electromagnetic solver

PURPOSE

We propose a fast, patient-specific workflow for on-line specific absorption rate (SAR) supervision. An individualized electromagnetic model is created while the subject is on the table, followed by rapid SAR estimates for that individual. Our goal is an improved correspondence between the patient and model, reducing reliance on general anatomical body models.

METHODS

A 3D fat-water 3T acquisition (~2 minutes) is automatically segmented using a computer vision algorithm (~1 minute) into what we found to be the most important electromagnetic tissue classes: air, bone, fat, and soft tissues. We then compute the individual's EM field exposure and global and local SAR matrices using a fast electromagnetic integral equation solver. We assess the approach in 10 volunteers and compare to the SAR seen in a standard generic body model (Duke).

RESULTS

The on-the-table workflow averaged 7'44″. Simulation of the simplified Duke models confirmed that only air, bone, fat, and soft tissue classes are needed to estimate global and local SAR with an error of 6.7% and 2.7%, respectively, compared to the full model. In contrast, our volunteers showed a 16.0% and 20.3% population variability in global and local SAR, respectively, which was mostly underestimated by the Duke model.

CONCLUSION

Timely construction and deployment of a patient-specific model is computationally feasible. The benefit of resolving the population heterogeneity compared favorably to the modest modeling error incurred. This suggests that individualized SAR estimates can improve electromagnetic safety in MRI and possibly reduce conservative safety margins that account for patient-model mismatch, especially in non-standard patients.

Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry

Radio-frequency dosimetry is an important process in assessments for human exposure safety and for compliance of related products. Recently, computational human models generated from medical images have often been used for such assessment, especially to consider the inter-subject variability. However, a common procedure to develop personalized models is time consuming because it involves excessive segmentation of several components that represent different biological tissues, which is a major obstacle in the inter-subject variability assessment of radiation safety. Deep learning methods have been shown to be a powerful approach for pattern recognition and signal analysis. Convolutional neural networks with deep architecture are proven robust for feature extraction and image mapping in several biomedical applications. In this study, we develop a learning-based approach for fast and accurate estimation of the dielectric properties and density of tissues directly from magnetic resonance images in a single shot. The smooth distribution of the dielectric properties in head models, which is realized using a process without tissue segmentation, improves the smoothness of the specific absorption rate (SAR) distribution compared with that in the commonly used procedure. The estimated SAR distributions, as well as that averaged over 10 g of tissue in a cubic shape, are found to be highly consistent with those computed using the conventional methods that employ segmentation.

In vivo human head MRI at 10.5T: A radiofrequency safety study and preliminary imaging results

PURPOSE

The purpose of this study is to safely acquire the first human head images at 10.5T.

METHODS

To ensure safety of subjects, we validated the electromagnetic simulation model of our coil. We obtained quantitative agreement between simulated and experimental B 1 + and specific absorption rate (SAR). Using the validated coil model, we calculated radiofrequency power levels to safely image human subjects. We conducted all experiments and imaging sessions in a controlled radiofrequency safety lab and the whole-body 10.5T scanner in the Center for Magnetic Resonance Research.

RESULTS

Quantitative agreement between the simulated and experimental results was obtained including S-parameters, B 1 + maps, and SAR. We calculated peak 10 g average SAR using 4 different realistic human body models for a quadrature excitation and demonstrated that the peak 10 g SAR variation between subjects was less than 30%. We calculated safe power limits based on this set and used those limits to acquire T₂ - and T 2 ∗ -weighted images of human subjects at 10.5T.

CONCLUSIONS

In this study, we acquired the first in vivo human head images at 10.5T using an 8-channel transmit/receive coil. We implemented and expanded a previously proposed workflow to validate the electromagnetic simulation model of the 8-channel transmit/receive coil. Using the validated coil model, we calculated radiofrequency power levels to safely image human subjects.

Statistical Evaluation of Radiofrequency Exposure during Magnetic Resonant Imaging: Application of Whole-Body Individual Human Model and Body Motion in the Coil

The accurate estimation of patient's exposure to the radiofrequency (RF) electromagnetic field of magnetic resonance imaging (MRI) significantly depends on a precise individual anatomical model. In the study, we investigated the applicability of an efficient whole-body individual modelling method for the assessment of MRI RF exposure. The individual modelling method included a deformable human model and tissue simplification techniques. Besides its remarkable efficiency, this approach utilized only a low specific absorption rate (SAR) sequence or even no MRI scan to generate the whole-body individual model. Therefore, it substantially reduced the risk of RF exposure. The dosimetric difference of the individual modelling method was evaluated using the manually segmented human models. In addition, stochastic dosimetry using a surrogate model by polynomial chaos presented SAR variability due to body misalignment and tilt in the coil, which were frequently occurred in the practical scan. In conclusion, the dosimetric equivalence of the individual models was validated by both deterministic and stochastic dosimetry. The proposed individual modelling method allowed the physicians to quantify the patient-specific SAR while the statistical results enabled them to comprehensively weigh over the exposure risk and get the benefit of imaging enhancement by using the high-intensity scanners or the high-SAR sequences.

Computer-Vision Techniques for Water-Fat Separation in Ultra High-Field MRI Local Specific Absorption Rate Estimation

OBJECTIVE

The purpose of this paper is to prove that computer-vision techniques allow synthesizing water-fat separation maps for local specific absorption rate (SAR) estimation, when patient-specific water-fat images are not available.

METHODS

We obtained ground truth head models by using patient-specific water-fat images. We obtained two different label-fusion water-fat models generating a water-fat multiatlas and applying the STAPLE and local-MAP-STAPLE label-fusion methods. We also obtained patch-based water-fat models applying a local group-wise weighted combination of the multiatlas. Electromagnetic (EM) simulations were performed, and B1+ magnitude and 10 g averaged SAR maps were generated.

RESULTS

We found local approaches provide a high DICE overlap (72.6 ± 10.2% fat and 91.6 ± 1.5% water in local-MAP-STAPLE, and 68.8 ± 8.2% fat and 91.1 ± 1.0% water in patch-based), low Hausdorff distances (18.6 ± 7.7 mm fat and 7.4 ± 11.2 mm water in local-MAP-STAPLE, and 16.4 ± 8.5 mm fat and 7.2 ± 11.8 mm water in patch-based) and a low error in volume estimation (15.6 ± 34.4% fat and 5.6 ± 4.1% water in the local-MAP-STAPLE, and 14.0 ± 17.7% fat and 4.7 ± 2.8% water in patch-based). The positions of the peak 10 g-averaged local SAR hotspots were the same for every model.

CONCLUSION

We have created patient-specific head models using three different computer-vision-based water-fat separation approaches and compared the predictions of B1+ field and SAR distributions generated by simulating these models. Our results prove that a computer-vision approach can be used for patient-specific water-fat separation, and utilized for local SAR estimation in high-field MRI.

SIGNIFICANCE

Computer-vision approaches can be used for patient-specific water-fat separation and for patient specific local SAR estimation, when water-fat images of the patient are not available.

New method for establishing a 3D subject-specific numerical electromagnetic model using hybrid imaging modalities

Numerical electromagnetic models that can mimic the dielectric properties of human tissues have been widely used for dosimetry-related studies in bio-electromagnetics, particularly for the calculation of electromagnetic field distribution inside the human body, which is subject specific. Reports indicated that considerable electromagnetic field variations may occur inside different human subjects even when existing differences in the geometrical dimensions of these subjects are minimal. Therefore, a subject-specific three-dimensional (3D) electromagnetic model is crucially required to calculate the electromagnetic field distribution accurately. However, the manner in which a precise subject-specific 3D electromagnetic model is established has not been fully explored in the literature yet. In this study, a new method was proposed for the establishment of a subject-specific 3D electromagnetic model using hybrid imaging modalities, with computed tomography (CT) and magnetic resonance (MR) images as sources. The exemplary application was provided by using the established subject-specific model to calculate the local specific absorption rates in MR imaging. Comparison studies indicated that detailed information was obtained using the proposed model.

Imaging at ultrahigh magnetic fields: History, challenges, and solutions

Following early efforts in applying nuclear magnetic resonance (NMR) spectroscopy to study biological processes in intact systems, and particularly since the introduction of 4 T human scanners circa 1990, rapid progress was made in imaging and spectroscopy studies of humans at 4 T and animal models at 9.4 T, leading to the introduction of 7 T and higher magnetic fields for human investigation at about the turn of the century. Work conducted on these platforms has provided numerous technological solutions to challenges posed at these ultrahigh fields, and demonstrated the existence of significant advantages in signal-to-noise ratio and biological information content. Primary difference from lower fields is the deviation from the near field regime at the radiofrequencies (RF) corresponding to hydrogen resonance conditions. At such ultrahigh fields, the RF is characterized by attenuated traveling waves in the human body, which leads to image non-uniformities for a given sample-coil configuration because of destructive and constructive interferences. These non-uniformities were initially considered detrimental to progress of imaging at high field strengths. However, they are advantageous for parallel imaging in signal reception and transmission, two critical technologies that account, to a large extend, for the success of ultrahigh fields. With these technologies and improvements in instrumentation and imaging methods, today ultrahigh fields have provided unprecedented gains in imaging of brain function and anatomy, and started to make inroads into investigation of the human torso and extremities. As extensive as they are, these gains still constitute a prelude to what is to come given the increasingly larger effort committed to ultrahigh field research and development of ever better instrumentation and techniques.

Extended RF shimming: Sequence-level parallel transmission optimization applied to steady-state free precession MRI of the heart

Cardiac magnetic resonance imaging (MRI) at high field presents challenges because of the high specific absorption rate and significant transmit field (B₁⁺ ) inhomogeneities. Parallel transmission MRI offers the ability to correct for both issues at the level of individual radiofrequency (RF) pulses, but must operate within strict hardware and safety constraints. The constraints are themselves affected by sequence parameters, such as the RF pulse duration and TR, meaning that an overall optimal operating point exists for a given sequence. This work seeks to obtain optimal performance by performing a 'sequence-level' optimization in which pulse sequence parameters are included as part of an RF shimming calculation. The method is applied to balanced steady-state free precession cardiac MRI with the objective of minimizing TR, hence reducing the imaging duration. Results are demonstrated using an eight-channel parallel transmit system operating at 3 T, with an in vivo study carried out on seven male subjects of varying body mass index (BMI). Compared with single-channel operation, a mean-squared-error shimming approach leads to reduced imaging durations of 32 ± 3% with simultaneous improvement in flip angle homogeneity of 32 ± 8% within the myocardium.

SAR Simulations & Safety

At ultra-high fields, the assessment of radiofrequency (RF) safety presents several new challenges compared to low-field systems. Multi-channel RF transmit coils in combination with parallel transmit techniques produce time-dependent and spatially varying power loss densities in the tissue. Further, in ultra-high-field systems, localized field effects can be more pronounced due to a transition from the quasi stationary to the electromagnetic field regime. Consequently, local information on the RF field is required for reliable RF safety assessment as well as for monitoring of RF exposure during MR examinations. Numerical RF and thermal simulations for realistic exposure scenarios with anatomical body models are currently the only practical way to obtain the requisite local information on magnetic and electric field distributions as well as tissue temperature. In this article, safety regulations and the fundamental characteristics of RF field distributions in ultra-high-field systems are reviewed. Numerical methods for computation of RF fields as well as typical requirements for the analysis of realistic multi-channel RF exposure scenarios including anatomical body models are highlighted. In recent years, computation of the local tissue temperature has become of increasing interest, since a more accurate safety assessment is expected because temperature is directly related to tissue damage. Regarding thermal simulation, bio-heat transfer models and approaches for taking into account the physiological response of the human body to RF exposure are discussed. In addition, suitable methods are presented to validate calculated RF and thermal results with measurements. Finally, the concept of generalized simulation-based specific absorption rate (SAR) matrix models is discussed. These models can be incorporated into local SAR monitoring in multi-channel MR systems and allow the design of RF pulses under constraints for local SAR.

Parallel transmission for ultrahigh-field imaging

The development of MRI systems operating at or above 7 T has provided researchers with a new window into the human body, yielding improved imaging speed, resolution and signal-to-noise ratio. In order to fully realise the potential of ultrahigh-field MRI, a range of technical hurdles must be overcome. The non-uniformity of the transmit field is one of such issues, as it leads to non-uniform images with spatially varying contrast. Parallel transmission (i.e. the use of multiple independent transmission channels) provides previously unavailable degrees of freedom that allow full spatial and temporal control of the radiofrequency (RF) fields. This review discusses the many ways in which these degrees of freedom can be used, ranging from making more uniform transmit fields to the design of subject-tailored RF pulses for both uniform excitation and spatial selection, and also the control of the specific absorption rate. © 2015 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.

Improving peak local SAR prediction in parallel transmit using in situ S-matrix measurements

PURPOSE

Peak local specific absorption rate (pSAR10g) is an important parameter used to determine patient safety during radiofrequency transmission. pSAR10g predictions for parallel transmit MRI are affected by the level of coupling exhibited by a modeled array in the simulation environment. However, simulated array coupling is rarely equal to the physical array coupling. Accurately simulating the physical array coupling may improve the accuracy of predicted SAR levels.

METHODS

The scattering parameter matrix (S-matrix) of a prototype 4-channel array was measured in situ using directional couplers installed on a 7T scanner. Agreement between the simulated and measured S-matrix was achieved by using network co-simulation with a modified cost function. B1+ maps acquired in a phantom were compared to B1+ distributions determined from simulations.

RESULTS

The modified co-simulation technique forces the simulations to have S-matrices similar to the measured values. A comparison of realistically versus ideally simulated coupling conditions shows that ideally simulated coupling can result in large ( > 40%) error in pSAR10g predictions, even when the array is reasonably tuned. The simulated B1+ distributions match the measured B1+ distributions better when the coupling is accurately simulated.

CONCLUSION

Considering the measured array coupling matrix in numerical simulations eliminates a potential confound in pSAR10g prediction. Magn Reson Med 77:2040-2047, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Multi-atlas and label fusion approach for patient-specific MRI based skull estimation

PURPOSE

MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume.

METHODS

The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms.

RESULTS

The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers.

CONCLUSION

It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR.

On the safety margin of using simplified human head models for local SAR simulations of B1-shimming at 7 Tesla

PURPOSES

Using simplified human models significantly alleviates the difficulty of rendering human models for subject-specific local specific absorption rate (SAR) simulations. Although its accuracy has been demonstrated with the birdcage mode combination of RF transmitters, its accuracy in general B1-shimming, where numerous phase and magnitude combinations can take place, is yet unknown.

METHODS

The electromagnetic fields of a 7-Tesla eight-channel brain imaging array were simulated by using four detailed human models from the Virtual Family and their two-, three-, and four-tissue simplifications. The 10-g averaged local SAR was computed for each case with 1,000 sets of uniformly distributed random B1-shimming parameters. Linear regression was applied to relate the local SAR obtained by using detailed and simplified human models. The 99% confidence prediction interval was determined as the safety margin in order to cover the largest local SAR variability introduced by using simplified human models.

RESULTS

The local SAR computed by using three- and four-tissue simplifications are strongly correlated with those computed by using detailed models. Safety margins of 0.38 and 0.45W/kg/W were found appropriate for each case being considered.

CONCLUSIONS

The proposed procedure can be applied to evaluate the safety margin of the local SAR simulated by using simplified human models. However, discretion needs to be exercised since the safety margins in some cases may represent more than 50% overestimation.

In vivo sensitivity estimation and imaging acceleration with rotating RF coil arrays at 7 Tesla

Using a new rotating SENSitivity Encoding (rotating-SENSE) algorithm, we have successfully demonstrated that the rotating radiofrequency coil array (RRFCA) was capable of achieving a significant reduction in scan time and a uniform image reconstruction for a homogeneous phantom at 7 Tesla. However, at 7 Tesla the in vivo sensitivity profiles (B1(-)) become distinct at various angular positions. Therefore, sensitivity maps at other angular positions cannot be obtained by numerically rotating the acquired ones. In this work, a novel sensitivity estimation method for the RRFCA was developed and validated with human brain imaging. This method employed a library database and registration techniques to estimate coil sensitivity at an arbitrary angular position. The estimated sensitivity maps were then compared to the acquired sensitivity maps. The results indicate that the proposed method is capable of accurately estimating both magnitude and phase of sensitivity at an arbitrary angular position, which enables us to employ the rotating-SENSE algorithm to accelerate acquisition and reconstruct image. Compared to a stationary coil array with the same number of coil elements, the RRFCA was able to reconstruct images with better quality at a high reduction factor. It is hoped that the proposed rotation-dependent sensitivity estimation algorithm and the acceleration ability of the RRFCA will be particularly useful for ultra high field MRI.

Comparison between simulated decoupling regimes for specific absorption rate prediction in parallel transmit MRI

PURPOSE

The use of electromagnetic (EM) modeling is critical for specific absorption rate (SAR) characterization in parallel transmission MRI. Radiofrequency arrays that include decoupling networks can be difficult to characterize accurately in simulation. A practical method of simplifying modeling is to exclude the decoupling networks and model each transmit element in isolation. Results from this type of model can be related to a real device by applying "active decoupling" to the real device to suppress residual coupling when in use. Here, we compare this approach with a full model that includes decoupling networks.

METHODS

EM simulations for a variety of adult male voxel models placed within an eight-channel transverse electromagnetic (TEM) array tuned for 3 Tesla operation were run with and without decoupling networks included. The resulting EM fields and SAR estimates were compared using basic normalization, and simulated active decoupling.

RESULTS

Modeling the transmit elements independently leads to variations which have significantly different SAR estimates of ∼20% on average compared with the full model if not normalized appropriately. After "active decoupling," SAR was still generally seen to be overestimated by ∼7% with independent channel modeling; despite having similar B1(+) field distributions.

CONCLUSION

Modeling transmission elements independently may lead to substantially incorrect SAR estimates if the corresponding MRI system is not run in an analogous manner.

Generation of infant anatomical models for evaluating electromagnetic field exposures

Realistic anatomical modeling is essential in analyzing human exposure to electromagnetic fields. Infants have significant physical and anatomical differences compared with other age groups. However, few realistic infant models are available. In this work, we developed one 12-month-old male whole body model and one 17-month-old male head model from magnetic resonance images. The whole body and head models contained 28 and 30 tissues, respectively, at spatial resolution of 1 mm × 1 mm × 1 mm. Fewer identified tissues in the whole body model were a result of the low original image quality induced by the fast imaging sequence. The anatomical and physical parameters of the models were validated against findings in published literature (e.g., a maximum deviation as 18% in tissue mass was observed compared with the data from International Commission on Radiological Protection). Several typical exposure scenarios were realized for numerical simulation. Dosimetric comparison with various adult and child anatomical models was conducted. Significant differences in the physical and anatomical features between adult and child models demonstrated the importance of creating realistic infant models. Current safety guidelines for infant exposure to radiofrequency electromagnetic fields may not be conservative.

Magnetic resonance imaging at ultrahigh fields

Since the introduction of 4 T human systems in three academic laboratories circa 1990, rapid progress in imaging and spectroscopy studies in humans at 4 T and animal model systems at 9.4 T have led to the introduction of 7 T and higher magnetic fields for human investigation at about the turn of the century. Work conducted on these platforms has demonstrated the existence of significant advantages in SNR and biological information content at these ultrahigh fields, as well as the presence of numerous challenges. Primary difference from lower fields is the deviation from the near field regime; at the frequencies corresponding to hydrogen resonance conditions at ultrahigh fields, the RF is characterized by attenuated traveling waves in the human body, which leads to image nonuniformities for a given sample-coil configuration because of interferences. These nonuniformities were considered detrimental to the progress of imaging at high field strengths. However, they are advantageous for parallel imaging for signal reception and parallel transmission, two critical technologies that account, to a large extend, for the success of ultrahigh fields. With these technologies, and improvements in instrumentation and imaging methods, ultrahigh fields have provided unprecedented gains in imaging of brain function and anatomy, and started to make inroads into investigation of the human torso and extremities. As extensive as they are, these gains still constitute a prelude to what is to come given the increasingly larger effort committed to ultrahigh field research and development of ever better instrumentation and techniques.