In vitro assessment of MRI issues at 3-Tesla for a breast tissue expander with a remote port

Objectives

A patient with a breast tissue expander may require a diagnostic assessment using magnetic resonance imaging (MRI). To ensure patient safety, this type of implant must undergo in vitro MRI testing using proper techniques. Therefore, this investigation evaluated MRI issues (i.e., magnetic field interactions, heating, and artifacts) at 3-Tesla for a breast tissue expander with a remote port.

Methods

A breast tissue expander with a remote port (Integra Breast Tissue Expander, Model 3612-06 with Standard Remote Port, PMT Corporation, Chanhassen, MN) underwent evaluation for magnetic field interactions (translational attraction and torque), MRI-related heating, and artifacts using standardized techniques. Heating was evaluated by placing the implant in a gelled-saline-filled phantom and MRI was performed using a transmit/receive RF body coil at an MR system reported, whole body averaged specific absorption rate of 2.9-W/kg. Artifacts were characterized using T1-weighted and GRE pulse sequences.

Results

Magnetic field interactions were not substantial and, thus, will not pose a hazard to a patient in a 3-Tesla or less MRI environment. The highest temperature rise was 1.7 °C, which is physiologically inconsequential. Artifacts were large in relation to the remote port and metal connector of the implant but will only present problems if the MR imaging area of interest is where these components are located.

Conclusions

A patient with this breast tissue expander with a remote port may safely undergo MRI at 3-Tesla or less under the conditions used for this investigation. These findings are the first reported at 3-Tesla for a tissue expander.     

Current-density imaging using ultra-low-field MRI with zero-field encoding

Electric current density can be measured noninvasively with magnetic resonance imaging (MRI). Determining all three components of the current density, however, requires physical rotation of the sample or current injection from several directions when done with conventional methods. However, the emerging technology of ultra-low-field (ULF) MRI, in which the signal encoding and acquisition is conducted at a microtesla-range magnetic field, offers new possibilities. The low applied magnetic fields can even be switched off completely within the pulse sequence, increasing the flexibility of the available sequences. In this article, we present a ULF-MRI sequence designed for obtaining all three components of a current-density pattern without the need of sample rotations. The sequence consists of three steps: prepolarization of the sample, signal encoding in the current-density-associated magnetic field without applying any MRI fields, and spatial encoding in a microtesla-range field using any standard ULF-MRI sequence. The performance of the method is evaluated by numerical simulations. The method may find applications, e.g., in noninvasive conductivity imaging of tissue.     

Chemical exchange saturation transfer MRI using intermolecular double-quantum coherences with multiple refocusing pulses

Chemical exchange saturation transfer (CEST) provides a new type of image contrast in MRI. Due to the intrinsically low CEST effect, new and improved experimental techniques are required to achieve reliable and quantitative CEST images. In the present work, we proposed a novel and more sensitive CEST acquisition approach, based on the intermolecular double-quantum coherence with a module of multiple refocusing pulses (iDQC-MRP). Experiments were performed on creatine and egg white phantoms using a Varian 7 T animal MRI scanner. The iDQC-MRP CEST technique showed a substantial enhancement in CEST and nuclear Overhauser enhancement (NOE) signal intensities, compared to the standard single-quantum coherence approach. In addition, the iDQC-MRP approach increased the signal-to-noise ratio of acquired saturation images, compared to the conventional iDQC approach. The new iDQC-MRP CEST sequence provides a promising way for exploiting in vivo CEST and NOE imaging applications.     

Generalized total variation-based MRI Rician denoising model with spatially adaptive regularization parameters

Magnetic resonance imaging (MRI) is an outstanding medical imaging modality but the quality often suffers from noise pollution during image acquisition and transmission. The purpose of this study is to enhance image quality using feature-preserving denoising method. In current literature, most existing MRI denoising methods did not simultaneously take the global image prior and local image features into account. The denoising method proposed in this paper is implemented based on an assumption of spatially varying Rician noise map. A two-step wavelet-domain estimation method is developed to extract the noise map. Following a Bayesian modeling approach, a generalized total variation-based MRI denoising model is proposed based on global hyper-Laplacian prior and Rician noise assumption. The proposed model has the properties of backward diffusion in local normal directions and forward diffusion in local tangent directions. To further improve the denoising performance, a local variance estimator-based method is introduced to calculate the spatially adaptive regularization parameters related to local image features and spatially varying noise map. The main benefit of the proposed method is that it takes full advantage of the global MR image prior and local image features. Numerous experiments have been conducted on both synthetic and real MR data sets to compare our proposed model with some state-of-the-art denoising methods. The experimental results have demonstrated the superior performance of our proposed model in terms of quantitative and qualitative image quality evaluations.     

Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation

This paper proposes a new energy minimization method called multiplicative intrinsic component optimization (MICO) for joint bias field estimation and segmentation of magnetic resonance (MR) images. The proposed method takes full advantage of the decomposition of MR images into two multiplicative components, namely, the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity, and their respective spatial properties. Bias field estimation and tissue segmentation are simultaneously achieved by an energy minimization process aimed to optimize the estimates of the two multiplicative components of an MR image. The bias field is iteratively optimized by using efficient matrix computations, which are verified to be numerically stable by matrix analysis. More importantly, the energy in our formulation is convex in each of its variables, which leads to the robustness of the proposed energy minimization algorithm. The MICO formulation can be naturally extended to 3D/4D tissue segmentation with spatial/sptatiotemporal regularization. Quantitative evaluations and comparisons with some popular softwares have demonstrated superior performance of MICO in terms of robustness and accuracy.     

The precision of DCE-MRI using the tissue homogeneity model with continuous formulation of the perfusion parameters

The present trend in dynamic contrast-enhanced MRI is to increase the number of estimated perfusion parameters using complex pharmacokinetic models. However, less attention is given to the precision analysis of the parameter estimates. In this paper, the distributed capillary adiabatic tissue homogeneity pharmacokinetic model is extended by the bolus arrival time formulated as a free continuous parameter. With the continuous formulation of all perfusion parameters, it is possible to use standard gradient-based optimization algorithms in the approximation of the tissue concentration time sequences. This new six-parameter model is investigated by comparing Monte-Carlo simulations with theoretically derived covariance matrices. The covariance-matrix approach is extended from the usual analysis of the primary perfusion parameters of the pharmacokinetic model to the analysis of the perfusion parameters derived from the primary ones. The results indicate that the precision of the estimated perfusion parameters can be described by the covariance matrix for signal-to-noise ratio higher than ~ 20 dB. The application of the new analysis model on a real DCE-MRI data set is also presented.     

Improved l1-SPIRiT using 3D walsh transform-based sparsity basis

l1-SPIRiT is a fast magnetic resonance imaging (MRI) method which combines parallel imaging (PI) with compressed sensing (CS) by performing a joint l1-norm and l2-norm optimization procedure. The original l1-SPIRiT method uses two-dimensional (2D) Wavelet transform to exploit the intra-coil data redundancies and a joint sparsity model to exploit the inter-coil data redundancies. In this work, we propose to stack all the coil images into a three-dimensional (3D) matrix, and then a novel 3D Walsh transform-based sparsity basis is applied to simultaneously reduce the intra-coil and inter-coil data redundancies. Both the 2D Wavelet transform-based and the proposed 3D Walsh transform-based sparsity bases were investigated in the l1-SPIRiT method. The experimental results show that the proposed 3D Walsh transform-based l1-SPIRiT method outperformed the original l1-SPIRiT in terms of image quality and computational efficiency.     

Evaluation of aortic stenosis severity using 4D flow jet shear layer detection for the measurement of valve effective orifice area

Aims

The objective of this study was to evaluate the potential of 4D flow MRI to assess valve effective orifice area (EOA) in patients with aortic stenosis as determined by the jet shear layer detection (JSLD) method.

Methods and Results

An in-vitro stenosis phantom was used for validation and in-vivo imaging was performed in 10 healthy controls and 40 patients with aortic stenosis. EOA was calculated by the JSLD method using standard 2D phase contrast MRI (PC-MRI) and 4D flow MRI measurements (EOA_JSLD-2D and EOA_JSLD-4D, respectively). As a reference standard, the continuity equation was used to calculate EOA (EOA_CE) with the 2D PC-MRI velocity field and compared to the EOA_JSLD measurements. The in-vitro results exhibited excellent agreement between flow theory (EOA = 0.78 cm²) and experimental measurement (EOA_JSLD-4D = 0.78 ± 0.01 cm²) for peak velocities ranging from 0.9 to 3.7 m/s. In-vivo results showed good correlation and agreement between EOA_JSLD-2D and EOA_CE (r = 0.91, p < 0.001; bias: − 0.01 ± 0.38 cm²; agreement limits: 0.75 to − 0.77 cm²), and between EOA_JSLD-4D and EOA_CE (r = 0.95, p < 0.001; bias: − 0.09 ± 0.26 cm²; limits: 0.43 to − 0.62 cm²).

Conclusion

This study demonstrates the feasibility of measuring EOA_JSLD using 4D flow MRI. The technique allows for optimization of the EOA measurement position by visualizing the 3D vena contracta, and avoids potential sources of EOA_CE measurement variability.     

Correction strategy for diffusion-weighted images corrupted with motion: application to the DTI evaluation of infants' white matter

Objective

Diffusion imaging techniques such as DTI and HARDI are difficult to implement in infants because of their sensitivity to subject motion. A short acquisition time is generally preferred, at the expense of spatial resolution and signal-to-noise ratio. Before estimating the local diffusion model, most pre-processing techniques only register diffusion-weighted volumes, without correcting for intra-slice artifacts due to motion or technical problems. Here, we propose a fully automated strategy, which takes advantage of a high orientation number and is based on spherical-harmonics decomposition of the diffusion signal.

Material and methods

The correction strategy is based on two successive steps: 1) automated detection and resampling of corrupted slices; 2) correction for eddy current distortions and realignment of misregistered volumes. It was tested on DTI data from adults and non-sedated healthy infants.

Results

The methodology was validated through simulated motions applied to an uncorrupted dataset and through comparisons with an unmoved reference. Second, we showed that the correction applied to an infant group enabled to improve DTI maps and to increase the reliability of DTI quantification in the immature cortico-spinal tract.

Conclusion

This automated strategy performed reliably on DTI datasets and can be applied to spherical single- and multiple-shell diffusion imaging.     

An iterative method for coil sensitivity estimation in multi-coil MRI systems

This paper presents an iterative coil sensitivity estimation method for multi-coil MRI systems. The proposed method works with coil images in the magnitude image domain. It determines a region of support (RoS), a region being composed of the same type of tissues, by a region growing algorithm, which makes use of both intensities and intensity gradients of pixels. By repeating this procedure, it can determine multiple regions of support, which together cover most of the concerned image area. The union of these regions of support provides a rough estimate of the sensitivity of each coil through dividing the intensities of pixels by the average intensity inside every region of support. The obtained rough coil sensitivity estimate is further approached with the product of multiple low-order polynomials, rather than a single one. The product of these polynomials provides a smooth estimate of the sensitivity of each coil. With the obtained sensitivities of coils, it can produce a better reconstructed image, which determines more correct regions of support and yields preciser estimates of the sensitivities of coils. In other words, the method can be iteratively implemented to improve the estimation performance. The proposed method was verified through both simulated data and clinical data from different body parts. The experimental results confirm the superiority of our method to some conventional methods.