Simulated MR Data Research Articles

Deep learning‐based reconstruction of in vivo pelvis conductivity with a 3D patch‐based convolutional neural network trained on simulated MR data

PurposeTo demonstrate that mapping pelvis conductivity at 3T with deep learning (DL) is feasible.Methods210 dielectric pelvic models were generated based on CT scans of 42 cervical cancer patients. For all dielectric models, electromagnetic and MR simulations with realistic accuracy and precision were performed to obtain B1+ and transceive phase (ϕ±). Simulated B1+ and ϕ± served as input to a 3D patch‐based convolutional neural network, which was trained in a supervised fashion to retrieve the conductivity. The same network architecture was retrained using only ϕ± in input. Both network configurations were tested on simulated MR data and their conductivity reconstruction accuracy and precision were assessed. Furthermore, both network configurations were used to reconstruct conductivity maps from a healthy volunteer and two cervical cancer patients. DL‐based conductivity was compared in vivo and in silico to Helmholtz‐based (H‐EPT) conductivity.ResultsConductivity maps obtained from both network configurations were comparable. Accuracy was assessed by mean error (ME) with respect to ground truth conductivity. On average, ME < 0.1 Sm−1 for all tissues. Maximum MEs were 0.2 Sm−1 for muscle and tumour, and 0.4 Sm−1 for bladder. Precision was indicated with the difference between 90th and 10th conductivity percentiles, and was below 0.1 Sm−1 for fat, bone and muscle, 0.2 Sm−1 for tumour and 0.3 Sm−1 for bladder. In vivo, DL‐based conductivity had median values in agreement with H‐EPT values, but a higher precision.ConclusionAnatomically detailed, noise‐robust 3D conductivity maps with good sensitivity to tissue conductivity variations were reconstructed in the pelvis with DL.

Brain magnetic resonance image segmentation based on an adapted non-local fuzzy c-means method

Intensity inhomogeneities cause considerable difficulties in the quantitative analysis of magnetic resonanceimages (MRIs). Consequently, intensity inhomogeneities estimation is a necessary step before quantitative analysis of MR data can be undertaken. This study proposes a new energy minimisation framework for simultaneous estimation of the intensity inhomogeneities and segmentation. The method was formulated by modifying the objective function of the standard fuzzy c -means algorithm to compensate for intensity inhomogeneities by using basis functions and to compensate for noise by using improved non-local information. The energy function depends on the coefficients of the basis functions, the membership ratios, the centroid of the tissues and an improved non-local information in the image. Intensity inhomogeneities estimation and image segmentation are simultaneously achieved by calculating the result of minimising this energy. The non-local framework has been widely used to provide non-local information; however, the traditional framework only considers the neighbouring patch information, which will lose information of the corner and end points. This study presents an improved non-local framework, which can contain the corner and end points region information. Experimental results on both real MRIs and simulated MR data show that the authors method can obtain more accurate results when segmenting images with bias field and noise.

Parametric methods for frequency-selective MR spectroscopy—a review

Accurate quantitation of the spectral components in a pre-selected frequency band for magnetic resonance spectroscopy (MRS) signals is a frequently addressed problem in the MR community. One obvious application for such a frequency-selective technique is to lower the computational burden in situations when the measured data sequence contains too many samples to be processed using a standard full-spectrum method. Among the frequency-selective methods previously proposed in the literature, only a few possess the two features of primary concern: high robustness against interferences from out-of-band components and low computational complexity. In this survey paper we consider five spectral analysis methods which can be used for MRS signal parameter estimation in a selected frequency band. We re-derive the filter diagonalization method (FDM) in a new way that allows an easy comparison to the other methods presented. Then we introduce a frequency-selective version of the method of direction estimation (MODE) which has not been applied to MR-spectroscopy before. In addition, we present a filtering and decimation technique using a maximum phase bandpass FIR-filter and relate it to a similar ARMA-modeling approach known as SB-HOYWSVD (sub-band high-order Yule–Walker singular value decomposition). Finally, we study the numerical performances of these four methods and compare them to that of the recently introduced SELF-SVD (Singular Value Decomposition-based method usable in a SELected Frequency band) in several examples using simulated MR data, and discuss the benefits and disadvantages of each technique.