Volume 61, Issue 4 - Highlight: Imaging and Image Analysis

Purpose: The aim of this study was to evaluate the feasibility and validity of arterial lower limb imaging with triggered angiography non-contrast enhanced (TRANCE) in an open MRI at 1.0 Tesla (T) compared to digital subtraction angiography (DSA). Material and methods: ECG-gated, non-contrast-enhanced magnetic resonance angiography (MRA) was performed in a 1.0-T high-field open magnetic resonance imaging (MRI) system which generates a vertical magnetic field. Three acquisition levels were defined (abdominal and pelvic level, arterial segments above the knee and segments below the knee) and a total of 1782 vessel diameter measurements were taken on a total of 11 patients with suspected peripheral arterial occlusive disease (PAOD) (8 men, 3 women; average age 66 years). In each patient, 162 vessel segments (81 each with TRANCE and DSA) were defined and measured. Pearson correlation coefficients were calculated. Results: At the abdominal/pelvic level, all mean values measured with DSA exceeded the mean values obtained with TRANCE. Above the knee, mean vessel diameters were measured smaller in DSA in six, equal in three, and larger in two vessel segments. Below the knee, all measured averages, except for the tibiofibular tract (TFT) measurements, were larger in TRANCE. In total, two small (≤0.3), two moderate (>0.3), 11 good (>0.5), 10 high (>0.7) and 13 very high (>0.8) correlations were obtained. Conclusions: Non-contrast-enhanced imaging of the lower limb arteries using a TRANCE-sequence in a 1.0 T open MRI system is feasible with the protocol presented; however, TRANCE tends to underestimate larger vessels and overestimate smaller vessels compared to DSA.

The partial separability (PS) model for spatiotemporal signals has been exploited effectively for sparse (k, t)-space sampling in dynamic magnetic resonance imaging (MRI). However, the training data for defining the temporal subspace is reordered by using a projection strategy in the conventional PS model-based method, which results in a suboptimal temporal resolution imaging. To address this issue, a kernel method was presented in this work to reorder the training data to realize a higher temporal resolution MRI. Numerical simulation results show that the MRI temporal resolution could be further improved and the dynamic change of motion object could be accurately captured by the proposed method. In vivo cardiac cine MRI results demonstrate that the proposed method can reconstruct better MR images with higher temporal resolution (up to 8.4 ms per snapshot). This study may find use in ultra-high resolution dynamic MRI.

Automatic 3D liver segmentation is a fundamental step in the liver disease diagnosis and surgery planning. This paper presents a novel fully automatic algorithm for 3D liver segmentation in clinical 3D computed tomography (CT) images. Based on image features, we propose a new Mahalanobis distance cost function using an active shape model (ASM). We call our method MD-ASM. Unlike the standard active shape model (ST-ASM), the proposed method introduces a new feature-constrained Mahalanobis distance cost function to measure the distance between the generated shape during the iterative step and the mean shape model. The proposed Mahalanobis distance function is learned from a public database of liver segmentation challenge (MICCAI-SLiver07). As a refinement step, we propose the use of a 3D graph-cut segmentation. Foreground and background labels are automatically selected using texture features of the learned Mahalanobis distance. Quantitatively, the proposed method is evaluated using two clinical 3D CT scan databases (MICCAI-SLiver07 and MIDAS). The evaluation of the MICCAI-SLiver07 database is obtained by the challenge organizers using five different metric scores. The experimental results demonstrate the availability of the proposed method by achieving an accurate liver segmentation compared to the state-of-the-art methods.

This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

Aim: To develop an automatic magnetic resonance (MR) brain classification that can assist physicians to make a diagnosis and reduce wrong decisions. Method: This article investigated the binary particle swarm optimization (BPSO) approach and proposed its three new variants: BPSO with mutation and time-varying acceleration coefficients (BPSO-MT), BPSO with mutation (BPSO-M), and BPSO with time-varying acceleration coefficients (BPSO-T). We first extracted wavelet entropy (WE) features from both approximation and detail sub-bands of eight-level decomposition. Afterwards, we used the proposed BPSO-M, BPSO-T, and BPSO-MT to select features. Finally, the selected features were fed into a probabilistic neural network (PNN). Results: The proposed BPSO-MT performed better than BPSO-T and BPSO-M. It finally selected two features of entropies of the following two sub-bands (V1, D1). The proposed system “WE + BPSO-MT + PNN” yielded perfect classification on Data160 and Data66. In addition, it yielded 99.53% average accuracy for the Data255, over 10 repetitions of k-fold stratified cross validation (SCV), higher than state-of-the-art approaches. Conclusions: The proposed method is effective for MR brain classification.

In view of predicting bright lesions such as hard exudates, cotton wool spots, and drusen in retinal images, three different segmentation techniques have been proposed and their effectiveness is compared with existing segmentation techniques. The benchmark images with annotations present in the structured analysis of the retina (STARE) database is considered for testing the proposed techniques. The proposed segmentation techniques such as region growing (RG), region growing with background correction (RGWBC), and adaptive region growing with background correction (ARGWBC) have been used, and the effectiveness of the algorithms is compared with existing fuzzy-based techniques. Images of eight categories of various annotations and 10 images in each category have been used to test the consistency of the proposed algorithms. Among the proposed techniques, ARGWBC has been identified to be the best method for segmenting the bright lesions based on its sensitivity, specificity, and accuracy. Fifteen different features are extracted from retinal images for the purpose of identification and classification of bright lesions. Feedforward backpropagation neural network (FFBPNN) and pattern recognition neural network (PRNN) are used for the classification of normal/abnormal images. Probabilistic neural network (PNN), radial basis exact fit (RBE), radial basis fewer neurons (RB), and FFBPNN are used for further bright lesion classification and achieve 100% accuracy.

The specific absorption rate (SAR) is an important issue to be considered in fetus MRI at 3 T due to the high radiofrequency energy deposited inside the body of pregnant woman. The high dielectric material (HDM) has shown its potential for enhancing B 1 field and reducing SAR in MRI. The aim of this study is to assess the feasibility of SAR reduction by adding an HDM to the fetus MRI. The feasibility of SAR reduction is numerically assessed in this study, using a birdcage coil in transmission loaded with an electromagnetic pregnant woman model in the SEMCAD-EM solver. The HDMs with different geometric arrangements and dielectric constants are manually optimized. The B1+${B_1}^ + $ homogeneity is also considered while calculating the optimized fetus 10 g local SAR among different strategies in the application of HDM. The optimum maximum fetus 10 g local SAR was obtained as 2.25 W/kg, by using two conformal pads placed left and right with the dielectric constant to be 400, reduced by 24.75% compared to that without the HDM. It indicated that the SAR can be significantly reduced with strategic placement of the HDM and the use of HDM may provide a simple, effective and low-cost method for reducing the SAR for the fetus MRI at 3 T.