Local Specific Absorption Rate Estimation Research Articles

Global and peak local specific absorption rate control on parallel transmit systems using k-means SAR compression model.

To improve the specific absorption rate (SAR) compression model capability in parallel transmission (pTx) MRI systems. A k-means clustering method is proposed to group voxels with similar SAR behaviors in the scanned object, providing a controlled upper-bounded estimation of peak local SARs. This k-means compression model and the conventional virtual observation point (VOP) model were tested in a pTx MRI framework. The pTx pulse design with different SAR controlling schemes was simulated using a numerical human head model and an eight-channel 7T coil array. Multiple criteria (including RF power, global and peak local SARs, and excitation accuracy) were compared for the performance testing. The k-means compression model generated a narrower overestimation bound, leading to a more accurate local SAR estimation. Among different pTx pulse design approaches, the k-means compression model showed the best trade-off between the SAR and excitation accuracy. The developed SAR compression model is advantageous for pTx framework given the narrower overestimation bound and control over the compression ratio. Results also illustrate that a moderate increase of maximum RF power can be useful for reducing the maximum local SAR deposition.

A deep learning method for image-based subject-specific local SAR assessment.

PurposeLocal specific absorption rate (SAR) cannot be measured and is usually evaluated by offline numerical simulations using generic body models that of course will differ from the patient's anatomy. An additional safety margin is needed to include this intersubject variability. In this work, we present a deep learning–based method for image‐based subject‐specific local SAR assessment. We propose to train a convolutional neural network to learn a “surrogate SAR model” to map the relation between subject‐specific B1+ maps and the corresponding local SAR.MethodOur database of 23 subject‐specific models with an 8–transmit channel body array for prostate imaging at 7 T was used to build 5750 training samples. These synthetic complex B1+ maps and local SAR distributions were used to train a conditional generative adversarial network. Extra penalization for local SAR underestimation errors was included in the loss function. In silico and in vivo validation were performed.ResultsIn silico cross‐validation shows a good qualitative and quantitative match between predicted and ground‐truth local SAR distributions. The peak local SAR estimation error distribution shows a mean overestimation error of 15% with 13% probability of underestimation. The higher accuracy of the proposed method allows the use of less conservative safety factors compared with standard procedures. In vivo validation shows that the method is applicable with realistic measurement data with impressively good qualitative and quantitative agreement to simulations.ConclusionThe proposed deep learning method allows online image‐based subject‐specific local SAR assessment. It greatly reduces the uncertainty in current state‐of‐the‐art SAR assessment methods, reducing the time in the examination protocol by almost 25%.

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

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. 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. 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±7.7mm fat and 7.4±11.2mm water in local-MAP-STAPLE, and 16.4±8.5mm fat and 7.2±11.8mm 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. 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. 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.

Alternative Approaches to Magnetic Resonance-Based Electric Properties Tomography and Local Specific Absorption Rate Estimation

In this paper, three electric properties tomography techniques based on magnetic resonance are applied to a realistic 2-D model problem representing the waist of an adult man. The capability of the inverse methods to recover the actual distribution of the electric properties is discussed and compared. In addition, the possibility to extend the methods to the estimate of local specific absorption rate of the radio frequency field generated in a magnetic resonance imaging, data concerning safety issues, is considered and compared.

Local specific absorption rate in brain tumors at 7 tesla.

MR safety at 7 Tesla relies on accurate numerical simulations of transmit electromagnetic fields to fully assess local specific absorption rate (SAR) safety. Numerical simulations for SAR safety are currently performed using models of healthy patients. These simulations might not be useful for estimating SAR in patients who have large lesions with potentially abnormal dielectric properties, e.g., brain tumors. In this study, brain tumor patient models are constructed based on scans of four patients with high grade brain tumors. Dielectric properties for the modeled tumors are assigned based on electrical properties tomography data for the same patients. Simulations were performed to determine SAR. Local SAR increases in the tumors by as much as 30%. However, the location of the maximum 10-gram averaged SAR typically occurs outside of the tumor, and thus does not increase. In the worst case, if the tumor model is moved to the location of maximum electric field intensity, then we do observe an increase in the estimated peak 10-gram SAR directly related to the tumor. Peak local SAR estimation made on the results of a healthy patient model simulation may underestimate the true peak local SAR in a brain tumor patient.

Quantitative prediction of radio frequency induced local heating derived from measured magnetic field maps in magnetic resonance imaging: A phantom validation at 7 T.

Electrical Properties Tomography (EPT) technique utilizes measurable radio frequency (RF) coil induced magnetic fields (B1 fields) in a Magnetic Resonance Imaging (MRI) system to quantitatively reconstruct the local electrical properties (EP) of biological tissues. Information derived from the same data set, e.g., complex numbers of B1 distribution towards electric field calculation, can be used to estimate, on a subject-specific basis, local Specific Absorption Rate (SAR). SAR plays a significant role in RF pulse design for high-field MRI applications, where maximum local tissue heating remains one of the most constraining limits. The purpose of the present work is to investigate the feasibility of such B1-based local SAR estimation, expanding on previously proposed EPT approaches. To this end, B1 calibration was obtained in a gelatin phantom at 7 T with a multi-channel transmit coil, under a particular multi-channel B1-shim setting (B1-shim I). Using this unique set of B1 calibration, local SAR distribution was subsequently predicted for B1-shim I, as well as for another B1-shim setting (B1-shim II), considering a specific set of parameter for a heating MRI protocol consisting of RF pulses plaid at 1% duty cycle. Local SAR results, which could not be directly measured with MRI, were subsequently converted into temperature change which in turn were validated against temperature changes measured by MRI Thermometry based on the proton chemical shift.

Predicting temperature increase through local SAR estimation by B1 mapping: a phantom validation at 7T.

It has been shown that Electrical Properties d(EPs) of biological tissues can be derived from MR-based B1 measurement. A strong appeal for these `Electrical Property Tomography' (EPT) methods is to estimate real-time and subject-specific local specific absorption rate (SAR) induced by RF transmission. In order to investigate the feasibility of EPT-based local SAR estimation, following previously proposed EPT protocols, induced local SAR has been firstly estimated under one B1 shim setting for a heating sequence at 7T; whereas with the same acquired B1 information, induced local SAR under a different B1 shim setting has been further predicted. Both of the SAR results have been compared to measured temperature changes using MRI Thermometry based on the proton chemical shift.

Rapid Local Specific Absorption Rate Estimation for Magnetic Resonance Imaging

The local specific absorption rate (SAR) is a critical concern of the safety in high-field magnetic resonance imaging (MRI). In order to provide rapid and subject-specific local SAR evaluation, a hybrid numerical technique that combines fast direct method of moments (MoM) and the finite-difference time-domain (FDTD) method was developed. Its validity was demonstrated by comparing with conventional numerical methods and experimental results. An example with a real human head model was further provided to demonstrate its utility.

Toward individualized SAR models and in vivo validation

The specific absorption rate (SAR) is a limiting constraint in sequence design for high-field MRI. SAR estimation is typically performed by numerical simulations using generic human body models. This entails an intrinsic uncertainty in present SAR prediction. This study first investigates the required detail of human body models in terms of spatial resolution and the number of soft tissue classes required, based on finite-differences time-domain simulations of a 3 T body coil. The numerical results indicate that a resolution of 5 mm is sufficient for local SAR estimation. Moreover, a differentiation between fatty tissues, water-rich tissues, and the lungs was found to be essential to represent eddy current paths inside the human body. This study then proposes a novel approach for generating individualized body models from whole-body water-fat-separated MR data and applies it to volunteers. The SAR hotspots consistently occurred in the arms due to proximity to the body coil as well as in narrow regions of the muscles. An initial in vivo validation of the simulated fields in comparison with measured B(1)-field maps showed good qualitative and quantitative agreement.

Local SAR estimation system using solid phantom and fixed E‐field probe

AbstractWe propose a local Specific Absorption Rate (SAR) estimation system using solid phantom and fixed E‐field probe for application to portable wireless devices such as cell phones. This system is constructed from a solid phantom molded from a lossy dielectric and an electric field (E‐field) probe inserted into the phantom. The advantage of this system is the ability to easily measure in a relatively short time the local SAR with measurement deviations of about ±30% compared to the measurements made by the conventional technique using a liquid phantom. First, we perform numerical analysis using the finite‐difference time‐domain (FDTD) method in the basic parameter design to clearly show the effects of the size of the insertion hole for the E‐field probe in the solid phantom and the complex dielectric permittivity on the antenna input impedance and local SAR. Next, we develop a local SAR estimation system using solid phantom and fixed E‐field probe, and measure and evaluate the antenna input impedance and local SAR of two solid phantoms having different complex dielectric permittivities. We confirmed that their properties tend to nearly agree with the calculated results. Finally, we measure the local SARs of 32 portable phones using this system. We confirmed that compared to the measurements by the E‐field probe scanning method, the measurements of about 77% of the phones have deviations within 30%, and the target measurement accuracy during development could almost be achieved. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 86(9): 46–56, 2003; Published online in Wiley InterScience (www.interscience.wiley. com). DOI 10.1002/ecja.10082