Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging

The management and treatment of high‐grade glioblastoma multiforme (GBM) and solitary metastasis (MET) are very different and influence the prognosis and subsequent clinical outcomes. In the case of a solitary MET, diagnosis using conventional radiology can be equivocal. Currently, a definitive diagnosis is based on histopathological analysis on a biopsy sample. Here, we present a computerised decision support framework for discrimination between GBM and solitary MET using MRI, which includes: (i) a semi‐automatic segmentation method based on diffusion tensor imaging; (ii) two‐dimensional morphological feature extraction and selection; and (iii) a pattern recognition module for automated tumour classification. Ground truth was provided by histopathological analysis from pre‐treatment stereotactic biopsy or at surgical resection. Our two‐dimensional morphological analysis outperforms previous methods with high cross‐validation accuracy of 97.9% and area under the receiver operating characteristic curve of 0.975 using a neural networks‐based classifier. Copyright © 2014 John Wiley & Sons, Ltd.     

Automated total kidney volume measurements in pre-clinical magnetic resonance imaging for resourcing imaging data,annotations, and source code

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Three-Dimensional Model-Based Segmentation in Echocardiography Using High Temporal Tissue and Blood Flow Information

Accurate 3-D surface segmentation is a challenging task in echocardiography because of the relatively low image quality. We introduce a new method for 3-D segmentation of the endocardium involving temporal decorrelation of echo signals originating from tissue and blood using radiofrequency (RF) signals acquired in 3-D Doppler mode. Temporal features were extracted in 3-D Doppler mode, where a sequence of RF lines is recorded for each image line. Each set of RF lines is highly correlated because of the high pulse repetition frequency. However, for high blood flow, the RF signals will decorrelate over time in contrast to the endocardium, which will remain relatively highly correlated over time. These decorrelation features permit differentiation between myocardial tissue and blood flow. We describe an implementation of a 3-D segmentation model in which temporal information is used as external constraint. The model was validated in a phantom and in vivo in healthy volunteers (n = 5). The phantom study revealed that the model successfully segmented the artificial blood lumen even for low flow velocity and illustrated the sensitivity of the segmentations to flow rate. In healthy volunteers, high Dice similarity indices indicate that 3-D segmentation of the endocardial border in vivo is feasible.     

Targeted vessel reconstruction in non‐contrast‐enhanced steady‐state free precession angiography

Image quality in non‐contrast‐enhanced (NCE) angiograms is often limited by scan time constraints. An effective solution is to undersample angiographic acquisitions and to recover vessel images with penalized reconstructions. However, conventional methods leverage penalty terms with uniform spatial weighting, which typically yield insufficient suppression of aliasing interference and suboptimal blood/background contrast. Here we propose a two‐stage strategy where a tractographic segmentation is employed to auto‐extract vasculature maps from undersampled data. These maps are then used to incur spatially adaptive sparsity penalties on vascular and background regions. In vivo steady‐state free precession angiograms were acquired in the hand, lower leg and foot. Compared with regular non‐adaptive compressed sensing (CS) reconstructions (CS_low), the proposed strategy improves blood/background contrast by 71.3 ± 28.9% in the hand (mean ± s.d. across acceleration factors 1–8), 30.6 ± 11.3% in the lower leg and 28.1 ± 7.0% in the foot (signed‐rank test, P < 0.05 at each acceleration). The proposed targeted reconstruction can relax trade‐offs between image contrast, resolution and scan efficiency without compromising vessel depiction. Copyright © 2016 John Wiley & Sons, Ltd.     

A multivariate hypothesis testing framework for tissue clustering and classification of DTI data

The primary aim of this work is to propose and investigate the effectiveness of a novel unsupervised tissue clustering and classification algorithm for diffusion tensor MRI (DTI) data. The proposed algorithm utilizes information about the degree of homogeneity of the distribution of diffusion tensors within voxels. We adapt frameworks proposed by Hext and Snedecor, where the null hypothesis of diffusion tensors belonging to the same distribution is assessed by an F‐test. Tissue type is classified according to one of the four possible diffusion models, the assignment of which is determined by a parsimonious model selection framework based on Schwarz Criterion. Both numerical phantoms and diffusion‐weighted imaging (DWI) data obtained from excised rat and pig spinal cords are used to test and validate these tissue clustering and classification approaches. The unsupervised clustering method effectively identifies distinct regions of interest (ROIs) in phantoms and real experimental DTI data. Copyright © 2009 John Wiley & Sons, Ltd.