Current Issue : April - June Volume : 2013 Issue Number : 2 Articles : 6 Articles
Background: Extent of atherosclerosis measured by amount of coronary artery calcium (CAC) in computed\r\ntomography (CT) has been traditionally assessed using thresholded scoring methods, such as the Agatston score\r\n(AS). These thresholded scores have value in clinical prediction, but important information might exist below the\r\nthreshold, which would have important advantages for understanding genetic, environmental, and other risk factors\r\nin atherosclerosis. We developed a semi-automated threshold-free scoring method, the spatially weighted calcium\r\nscore (SWCS) for CAC in the Multi-Ethnic Study of Atherosclerosis (MESA).\r\nMethods: Chest CT scans were obtained from 6814 participants in the Multi-Ethnic Study of Atherosclerosis (MESA).\r\nThe SWCS and the AS were calculated for each of the scans. Cox proportional hazards models and linear regression\r\nmodels were used to evaluate the associations of the scores with CHD events and CHD risk factors. CHD risk factors\r\nwere summarized using a linear predictor.\r\nResults: Among all participants and participants with AS > 0, the SWCS and AS both showed similar strongly\r\nsignificant associations with CHD events (hazard ratios, 1.23 and 1.19 per doubling of SWCS and AS; 95% CI, 1.16 to\r\n1.30 and 1.14 to 1.26) and CHD risk factors (slopes, 0.178 and 0.164; 95% CI, 0.162 to 0.195 and 0.149 to 0.179). Even\r\namong participants with AS = 0, an increase in the SWCS was still significantly associated with established CHD risk\r\nfactors (slope, 0.181; 95% CI, 0.138 to 0.224). The SWCS appeared to be predictive of CHD events even in\r\nparticipants with AS = 0, though those events were rare as expected.\r\nConclusions: The SWCS provides a valid, continuous measure of CAC suitable for quantifying the extent of\r\natherosclerosis without a threshold, which will be useful for examining novel genetic and environmental risk factors\r\nfor atherosclerosis....
Background: Brain morphometry is extensively used in cross-sectional studies. However, the difference in the\r\nestimated values of the morphometric measures between patients and healthy subjects may be small and hence\r\novershadowed by the scanner-related variability, especially with multicentre and longitudinal studies. It is important\r\ntherefore to investigate the variability and reliability of morphometric measurements between different scanners\r\nand different sessions of the same scanner.\r\nMethods: We assessed the variability and reliability for the grey matter, white matter, cerebrospinal fluid and\r\ncerebral hemisphere volumes as well as the global sulcal index, sulcal surface and mean geodesic depth using\r\nBrainvisa. We used datasets obtained across multiple MR scanners at 1.5 T and 3 T from the same groups of 13\r\nand 11 healthy volunteers, respectively. For each morphometric measure, we conducted ANOVA analysis and\r\nverified whether the estimated values were significantly different across different scanners or different sessions of\r\nthe same scanner. The between-centre and between-visit reliabilities were estimated from their contribution to the\r\ntotal variance, using a random-effects ANOVA model. To estimate the main processes responsible for low reliability,\r\nthe results of brain segmentation were compared to those obtained using FAST within FSL.\r\nResults: In a considerable number of cases, the main effects of both centre and visit factors were found to be\r\nsignificant. Moreover, both between-centre and between-visit reliabilities ranged from poor to excellent for most\r\nmorphometric measures. A comparison between segmentation using Brainvisa and FAST revealed that FAST\r\nimproved the reliabilities for most cases, suggesting that morphometry could benefit from improving the bias\r\ncorrection. However, the results were still significantly different across different scanners or different visits.\r\nConclusions: Our results confirm that for morphometry analysis with the current version of Brainvisa using data\r\nfrom multicentre or longitudinal studies, the scanner-related variability must be taken into account and where\r\npossible should be corrected for. We also suggest providing some flexibility to Brainvisa for a step-by-step analysis\r\nof the robustness of this package in terms of reproducibility of the results by allowing the bias corrected images to\r\nbe imported from other packages and bias correction step be skipped, for example....
Background: Image contrast between normal tissue and brain tumours may sometimes appear to be low in\nintraoperative ultrasound. Ultrasound imaging of strain is an image modality that has been recently explored for\nintraoperative imaging of the brain. This study aims to investigate differences in image contrast between\nultrasound brightness mode (B-mode) images and ultrasound strain magnitude images of brain tumours.\nMethods: Ultrasound radiofrequency (RF) data was acquired during surgery in 15 patients with glial tumours. The\ndata were subsequently processed to provide strain magnitude images. The contrast in the B-mode images and the\nstrain images was determined in assumed normal brain tissue and tumour tissue at selected regions of interest\n(ROI). Three measurements of contrast were done in the ultrasound data for each patient. The B-mode and strain\ncontrasts measurements were compared using the paired samples t- test.\nResults: The statistical analysis of a total of 45 measurements shows that the contrasts in the strain magnitude\nimages are significantly higher than in the conventional ultrasound B-mode images (P<0.0001).\nConclusions: The results indicate that ultrasound strain imaging provides better discrimination between normal\nbrain tissue and glial tumour tissue than conventional ultrasound B-mode imaging. Ultrasound imaging of tissue\nstrain therefore holds the potential of becoming a valuable adjunct to conventional intraoperative ultrasound\nimaging in brain tumour surgery...
Background: Leading-edge technology such as magnetic resonance imaging (MRI) or computed tomography (CT)\r\noften reveals mammographically and ultrasonographically occult lesions. MRI is a well-documented, effective tool to\r\nevaluate these lesions; however, the detection rate of targeted sonography varies for MRI detected lesions, and its\r\nsignificance is not well established in diagnostic strategy of MRI detected lesions. We assessed the utility of targeted\r\nsonography for multidetector-row CT (MDCT)- or MRI-detected lesions in practice.\r\nMethods: We retrospectively reviewed 695 patients with newly diagnosed breast cancer who were candidates for\r\nbreast conserving surgery and underwent MDCT or MRI in our hospital between January 2004 and March 2011.\r\nTargeted sonography was performed in all MDCT- or MRI-detected lesions followed by imaging-guided biopsy.\r\nPatient background, histopathology features and the sizes of the lesions were compared among benign, malignant\r\nand follow-up groups.\r\nResults: Of the 695 patients, 61 lesions in 56 patients were detected by MDCT or MRI. The MDCT- or MRI-detected\r\nlesions were identified by targeted sonography in 58 out of 61 lesions (95.1%). Patients with pathological diagnoses\r\nwere significantly older and more likely to be postmenopausal than the follow-up patients. Pathological diagnosis\r\nproved to be benign in 20 cases and malignant in 25. The remaining 16 lesions have been followed up.\r\nLesion size and shape were not significantly different among the benign, malignant and follow-up groups.\r\nConclusions: Approximately 95% of MDCT- or MRI-detected lesions were identified by targeted sonography, and\r\nnearly half of these lesions were pathologically proven malignancies in this study. Targeted sonography is a useful\r\nmodality for MDCT- or MRI-detected breast lesions....
Background: Animal models are frequently used to assess new treatment methods in cancer research. MRI offers\r\na non-invasive in vivo monitoring of tumour tissue and thus allows longitudinal measurements of treatment effects,\r\nwithout the need for large cohorts of animals. Tumour size is an important biomarker of the disease development,\r\nbut to our knowledge, MRI based size measurements have not yet been verified for small tumours (10-2ââ?¬â??10-1 g).\r\nThe aim of this study was to assess the accuracy of MRI based tumour size measurements of small tumours on\r\nmice.\r\nMethods: 2D and 3D T2-weighted RARE images of tumour bearing mice were acquired in vivo using a 7 T\r\ndedicated animal MR system. For the 3D images the acquired image resolution was varied. The images were\r\nexported to a PC workstation where the tumour mass was determined assuming a density of 1 g/cm3, using an\r\nin-house developed tool for segmentation and delineation. The resulting data were compared to the weight of\r\nthe resected tumours after sacrifice of the animal using regression analysis.\r\nResults: Strong correlations were demonstrated between MRI- and necropsy determined masses. In general,\r\n3D acquisition was not a prerequisite for high accuracy. However, it was slightly more accurate than 2D when small\r\n(<0.2 g) tumours were assessed for inter- and intraobserver variation. In 3D images, the voxel sizes could be\r\nincreased from 1603 Ã?µm3 to 2403 Ã?µm3 without affecting the results significantly, thus reducing acquisition time\r\nsubstantially.\r\nConclusions: 2D MRI was sufficient for accurate tumour size measurement, except for small tumours (<0.2 g)\r\nwhere 3D acquisition was necessary to reduce interobserver variation. Acquisition times between 15 and 50\r\nminutes, depending on tumour size, were sufficient for accurate tumour volume measurement. Hence, it is\r\npossible to include further MR investigations of the tumour, such as tissue perfusion, diffusion or metabolic\r\ncomposition in the same MR session....
Background: The blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI)\r\nmodality has been numerically simulated by calculating single voxel signals. However, the observation on single\r\nvoxel signals cannot provide information regarding the spatial distribution of the signals. Specifically, a single BOLD\r\nvoxel signal simulation cannot answer the fundamental question: is the magnetic resonance (MR) image a replica\r\nof its underling magnetic susceptibility source? In this paper, we address this problem by proposing a multivoxel\r\nvolumetric BOLD fMRI simulation model and a susceptibility expression formula for linear neurovascular coupling\r\nprocess, that allow us to examine the BOLD fMRI procedure from neurovascular coupling to MR image formation.\r\nMethods: Since MRI technology only senses the magnetism property, we represent a linear neurovascular-coupled\r\nBOLD state by a magnetic susceptibility expression formula, which accounts for the parameters of cortical\r\nvasculature, intravascular blood oxygenation level, and local neuroactivity. Upon the susceptibility expression of a\r\nBOLD state, we carry out volumetric BOLD fMRI simulation by calculating the fieldmap (established by susceptibility\r\nmagnetization) and the complex multivoxel MR image (by intravoxel dephasing). Given the predefined\r\nsusceptibility source and the calculated complex MR image, we compare the MR magnitude (phase, respectively)\r\nimage with the predefined susceptibility source (the calculated fieldmap) by spatial correlation.\r\nResults: The spatial correlation between the MR magnitude image and the magnetic susceptibility source is about\r\n0.90 for the settings of TE = 30 ms, B0 = 3 T, voxel size = 100 micron, vessel radius = 3 micron, and blood volume\r\nfraction = 2%. Using these parameters value, the spatial correlation between the MR phase image and the\r\nsusceptibility-induced fieldmap is close to 1.00.\r\nConclusion: Our simulation results show that the MR magnitude image is not an exact replica of the magnetic\r\nsusceptibility source (spatial correlation Ã?Å? 0.90), and that the MR phase image conforms closely with the\r\nsusceptibility-induced fieldmap (spatial correlation Ã?Å? 1.00)....
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