22 research outputs found

    High-resolution intravascular magnetic resonance quantification of atherosclerotic plaque at 3T

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    <p>Abstract</p> <p>Background</p> <p>The thickness of fibrous caps (FCT) of atherosclerotic lesions is a critical factor affecting plaque vulnerability to rupture. This study tests whether 3 Tesla high-resolution intravascular cardiovascular magnetic resonance (CMR) employing tiny loopless detectors can identify lesions and accurately measure FCT in human arterial specimens, and whether such an approach is feasible <it>in vivo </it>using animal models.</p> <p>Methods</p> <p>Receive-only 2.2 mm and 0.8 mm diameter intravascular loopless CMR detectors were fabricated for a clinical 3 Tesla MR scanner, and the absolute signal-to-noise ratio determined. The detectors were applied in a two-step protocol comprised of CMR angiography to identify atherosclerotic lesions, followed by high-resolution CMR to characterize FCT, lesion size, and/or vessel wall thickness. The protocol was applied in fresh human iliac and carotid artery specimens in a human-equivalent saline bath. Mean FCT measured by 80 μm intravascular CMR was compared with histology of the same sections. <it>In vivo </it>studies compared aortic wall thickness and plaque size in healthy and hyperlipidemic rabbit models, with post-mortem histology.</p> <p>Results</p> <p>Histology confirmed plaques in human specimens, with calcifications appearing as signal voids. Mean FCT agreed with histological measurements within 13% on average (correlation coefficient, <it>R </it>= 0.98; Bland-Altman analysis, -1.3 ± 68.9 μm). <it>In vivo </it>aortic wall and plaque size measured by 80 μm intravascular CMR agreed with histology.</p> <p>Conclusion</p> <p>Intravascular 3T CMR with loopless detectors can both locate atherosclerotic lesions, and accurately measure FCT at high-resolution in a strategy that appears feasible <it>in vivo</it>. The approach shows promise for quantifying vulnerable plaque for evaluating experimental therapies.</p

    Technique to Adjust Adaptive Digital Filter Coefficients in Residue Number System Based Filters

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    The paper discusses adaptive filtering using Least Mean Square (LMS) and Recursive Least Square (RLS) algorithms. An algorithm for adjusting the coefficients of an adaptive digital filter in the Residue Number System and a procedure of developed algorithm applying depending on filter length and signal length are proposed. Mathematical modeling of the considered algorithms is performed. Examples are presented to demonstrate how the proposed technique can help the designer in the adjustment of the filter coefficients without the need for extensive trial-and-error procedures. The analysis of the denoising quality and computational complexity is made. Synthetic and real data (earthquake recording) were used while testing. The proposed algorithm surpasses the existing ones like LMS and RLS, and their modifications in a number of parameters: adaptation (denoising) quality, ease of implementation, execution time. The main difference between the developed algorithm is the sequential adaptation of each coefficient with zero error. In the known algorithms, the entire vector of coefficients is iteratively adapted, with some specified accuracy. The iterations (steps) number is determined by the input signal length for all algorithms
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