Impaired calcium handling mechanisms in atrial trabeculae of diabetic patients.

The aim of this study was to investigate cardiomyocyte Ca2+ handling and contractile function in freshly excised human atrial tissue from diabetic and non-diabetic patients undergoing routine surgery. Multicellular trabeculae (283 ± 20 µm in diameter) were dissected from the endocardial surface of freshly obtained right atrial appendage samples from consenting surgical patients. Trabeculae were mounted in a force transducer at optimal length, electrically stimulated to contract, and loaded with fura-2/AM for intracellular Ca2+ measurements. The response to stimulation frequencies encompassing the physiological range was recorded at 37°C. Myofilament Ca2+ sensitivity was assessed from phase plots and high potassium contractures of force against [Ca2+ ]i . Trabeculae from diabetic patients (n = 12) had increased diastolic (resting) [Ca2+ ]i (p = 0.03) and reduced Ca2+ transient amplitude (p = 0.04) when compared to non-diabetic patients (n = 11), with no difference in the Ca2+ transient time course. Diastolic stress was increased (p = 0.008) in trabeculae from diabetic patients, and peak developed stress decreased (p ≤ 0.001), which were not accounted for by reduction in the cardiomyocyte, or contractile protein, content of trabeculae. Trabeculae from diabetic patients also displayed diminished myofilament Ca2+ sensitivity (p = 0.018) compared to non-diabetic patients. Our data provides evidence of impaired calcium handling during excitation-contraction coupling with resulting contractile dysfunction in atrial tissue from patients with type 2 diabetes in comparison to the non-diabetic. This highlights the importance of targeting cardiomyocyte Ca2+ homeostasis in developing more effective treatment options for diabetic heart disease in the future.

Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique.

To enable application of non-Gaussian diffusion magnetic resonance imaging (dMRI) techniques in large-scale clinical trials and facilitate translation to clinical practice there is a requirement for fast, high contrast, techniques that are sensitive to changes in tissue structure which provide diagnostic signatures at the early stages of disease. Here we describe a new way to compress the acquisition of multi-shell b-value diffusion data, Quasi-Diffusion MRI (QDI), which provides a probe of subvoxel tissue complexity using short acquisition times (1-4 â€‹min). We also describe a coherent framework for multi-directional diffusion gradient acquisition and data processing that allows computation of rotationally invariant quasi-diffusion tensor imaging (QDTI) maps. QDI is a quantitative technique that is based on a special case of the Continuous Time Random Walk model of diffusion dynamics and assumes the presence of non-Gaussian diffusion properties within tissue microstructure. QDI parameterises the diffusion signal attenuation according to the rate of decay (i.e. diffusion coefficient, D in mm2 s-1) and the shape of the power law tail (i.e. the fractional exponent, α). QDI provides analogous tissue contrast to Diffusional Kurtosis Imaging (DKI) by calculation of normalised entropy of the parameterised diffusion signal decay curve, Hn, but does so without the limitations of a maximum b-value. We show that QDI generates images with superior tissue contrast to conventional diffusion imaging within clinically acceptable acquisition times of between 84 and 228 â€‹s. We show that QDI provides clinically meaningful images in cerebral small vessel disease and brain tumour case studies. Our initial findings suggest that QDI may be added to routine conventional dMRI acquisitions allowing simple application in clinical trials and translation to the clinical arena.

Mitochondrial function remains impaired in the hypertrophied right ventricle of pulmonary hypertensive rats following short duration metoprolol treatment.

Pulmonary hypertension (PH) increases the work of the right ventricle (RV) and causes right-sided heart failure. This study examined RV mitochondrial function and ADP transfer in PH animals advancing to right heart failure, and investigated a potential therapy with the specific ß1-adrenergic-blocker metoprolol. Adult Wistar rats (317 ± 4 g) were injected either with monocrotaline (MCT, 60 mg kg-1) to induce PH, or with an equivalent volume of saline for controls (CON). At three weeks post-injection the MCT rats began oral metoprolol (10 mg kg-1 day-1-) or placebo treatment until heart failure was observed in the MCT group. Mitochondrial function was then measured using high-resolution respirometry from permeabilised RV fibres. Relative to controls, MCT animals had impaired mitochondrial function but maintained coupling between myofibrillar ATPases and mitochondria, despite an increase in ADP diffusion distances. Cardiomyocytes from the RV of MCT rats were enlarged, primarily due to an increase in myofibrillar protein. The ratio of mitochondria per myofilament area was decreased in both MCT groups (p ≤ 0.05) in comparison to control (CON: 1.03 ± 0.04; MCT: 0.74 ± 0.04; MCT + BB: 0.74 ± 0.03). This not only implicates impaired energy production in PH, but also increases the diffusion distance for metabolites within the MCT cardiomyocytes, adding an additional hindrance to energy supply. Together, these changes may limit energy supply in MCT rat hearts, particularly at high cardiac workloads. Metoprolol treatment did not delay the onset of heart failure symptoms, improve mitochondrial function, or regress RV hypertrophy.

Tissue-type mapping of gliomas.

PURPOSE: To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins. MATERIALS AND METHODS: We performed a retrospective analysis of mMRI from patients with histological diagnosis of glioma (n = 25). 1H Magnetic Resonance Spectroscopic Imaging (MRSI) was used to label regions of "pure" low- or high-grade tumour across image types. Normal brain and oedema characteristics were defined from healthy controls (n = 10) and brain metastasis patients (n = 10) respectively. Probability density distributions (PDD) for each tissue type were extracted from intensity normalised proton density and T2-weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps. RESULTS: Total lesion volumes derived automatically from tissue-type maps correlated with those from manual delineation (p < 0.001, r = 0.87). Large high-grade volumes were determined in all grade III & IV (n = 16) tumours, in grade II gemistocytic rich astrocytomas (n = 3) and one astrocytoma with a histological diagnosis of grade II. For patients with known outcome (n = 20), patients with survival time < 2 years (3 grade II, 2 grade III and 10 grade IV) had a high-grade volume significantly greater than zero (Wilcoxon signed rank p < 0.0001) and also significantly greater high grade volume than the 5 grade II patients with survival >2 years (Mann Witney p = 0.0001). Regions classified from mMRI as oedema had non-tumour-like 1H MRS characteristics. CONCLUSIONS: 1H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours.

Calcifying Pseudoneoplasm of the Neuraxis: A Case Illustrating Natural History over 17 Years of Radiologic Surveillance.

BACKGROUND: Calcifying pseudoneoplasm of the neuraxis (CAPNON) is a rare central nervous system lesion that can occur in both the brain and the spine. Although this entity is poorly understood, radiologic and histological features have been identified. CASE DESCRIPTION: We report a unique case of a 31-year-old patient who was managed with antiepileptic medication for 17 years before requiring neurosurgical intervention for tumor progression. T2-weighted magnetic resonance imaging revealed hyperintensity within the tumor with extensive associated vasogenic edema, which is not normally associated with CAPNON. Resection was successful with no complications. CONCLUSIONS: The present case illustrates the long-term natural history of CAPNON before resection and highlights the variations in radiologic appearance that may be associated with this poorly understood entity.

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. METHODS: We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. RESULTS: The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. CONCLUSION: The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

PURPOSE: We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). METHODS: The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. RESULTS: The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. CONCLUSIONS: This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three-dimensional shape analysis.

PURPOSE: Glioblastoma multiforme (GBM) and brain metastasis (MET) are the most common intra-axial brain neoplasms in adults and often pose a diagnostic dilemma using standard clinical MRI. These tumor types require different oncological and surgical management, which subsequently influence prognosis and clinical outcome. METHODS: Here, we hypothesize that GBM and MET possess different three-dimensional (3D) morphological attributes based on their physical characteristics. A 3D morphological analysis was applied on the tumor surface defined by our diffusion tensor imaging (DTI) segmentation technique. It segments the DTI data into clusters representing different isotropic and anisotropic water diffusion characteristics, from which a distinct surface boundary between healthy and pathological tissue was identified. Morphometric features of shape index and curvedness were then computed for each tumor surface and used to build a morphometric model of GBM and MET pathology with the goal of developing a tumor classification method based on shape characteristics. RESULTS: Our 3D morphometric method was applied on 48 untreated brain tumor patients. Cross-validation resulted in a 95.8% accuracy classification with only two shape features needed and that can be objectively derived from quantitative imaging methods. CONCLUSION: The proposed 3D morphometric analysis framework can be applied to distinguish GBMs from solitary METs. Magn Reson Med 75:2505-2516, 2016. © 2015 Wiley Periodicals, Inc.

Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique.

BACKGROUND: There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. METHODS: DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. RESULTS: Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. CONCLUSIONS: D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning.

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.

The clinical value of early postoperative MRI after lumbar spine surgery.

INTRODUCTION: MRI scanning has historically been considered difficult to interpret in the early period following lumbar spine surgery, and hence of limited value. We investigate the hypothesis that MRI scanning within 6 weeks of lumbar spine surgery cannot accurately diagnose neural compression in symptomatic patients, and define the utility of postoperative MRI in this context. METHODS: A series of 32 consecutive patients had early postoperative MRI following lumbar discectomy or laminectomy for continued, worsening or new symptoms of neural compression. The neuroradiologists' reports were evaluated for the reported presence of neural compression and confidence level (low, medium, high). These MRI findings were then compared to the patients' subsequent course and findings of any surgery performed. RESULTS: Twenty of 29 scans (69%) were confidently predictive of the correct treatment pathway (reoperation with positive finding or conservative treatment with a good outcome) whereas 3/3 (100%) patients who had conservative management despite the MRI confidently suggesting compression had poor outcome. The MRI is highly likely to influence management: 11/14 (79%) patients with scans suggesting neural compression had revision surgery and 18/18 (100%) patients with no neural compression on MRI were managed conservatively. CONCLUSIONS: Our data suggest that early MRI scanning after lumbar laminectomy or discectomy accurately detects neural compression at the surgery site in patients with continued or worsening symptoms.