Background-Metabolic and vascular disturbances contribute to diabetic cardiomyopathy, but the role of interstitial fibrosis in early disease is unproven. We sought to assess the relationship between imaging markers of diffuse fibrosis and myocardial dysfunction and to link this to possible causes of early diabetic cardiomyopathy. Methods and Results-Hemodynamic and metabolic data were measured in 67 subjects with type 2 diabetes mellitus (age 60Ϯ10 years) with no cardiac symptoms. Myocardial function was evaluated with standard echocardiography and myocardial deformation; ischemia was excluded by exercise echocardiography. Calibrated integrated backscatter was calculated from parasternal long-axis views. T1 mapping was performed after contrast with a modified Look-Locker technique using saturation recovery images. Amino-terminal propeptides of procollagens type I and III, as well as the carboxy-terminal propeptide of procollagen type I, were assayed to determine collagen turnover.
Injuries to the lateral ligaments of the ankle are common in medical practice. The most commonly injured ligaments are the anterior talofibular (ATFL) and calcaneofibular (CFL) ligaments. When undertaking MRI evaluation of ankle injuries it is important to understand the normal dimensions, appearance and variations of these ligaments. Twenty-eight consecutive patients referred for MRI of the ankle underwent bilateral scanning. The thickness of normal and abnormal ATFL and CFL was determined. The mean thickness of the ATFL was 2.19 +/- 0.6 mm and the CFL measured 2.13 +/- 0.5 mm. One normal ankle had an extremely thin ATFL (0.8 mm) with an otherwise normal appearance, whereas two ankles had an ATFL with a high attachment, both of which were deemed to be normal variants. Nineteen per cent of normal CFLs were noted to consist of a lateral hypointense band with a medial isointense medial band. Two variations of normal ATFL, one normal variant CFL and a measurement of the normal thickness of the ATFL and CFL have been presented in this paper. These variations have not been described previously by other authors. It is envisaged that these findings will assist in the MRI assessment of the lateral ankle ligaments.
This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. It is a novel variation on the nonlocal means (NLM) algorithm. The algorithm, called dynamic nonlocal means (DNLM), exploits the redundancy of information in the temporal sequence of images. Empirical evaluations of the performance of the DNLM algorithm relative to seven other denoising methods-simple Gaussian filtering, the original NLM algorithm, a trivial extension of NLM to include the temporal dimension, bilateral filtering, anisotropic diffusion filtering, wavelet adaptive multiscale products threshold, and traditional wavelet thresholding-are presented. The evaluations include quantitative evaluations using simulated data and real data (20 DCE-MRI data sets from routine clinical breast MRI examinations) as well as qualitative evaluations using the same real data (24 observers: 14 image/signal-processing specialists, 10 clinical breast MRI radiographers). The results of the quantitative evaluation using the simulated data show that the DNLM algorithm consistently yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the quantitative evaluation using the real data provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the qualitative evaluation provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm performs visually better than all of the other algorithms. Collectively the qualitative and quantitative results suggest that the DNLM algorithm more effectively attenuates noise in DCE MR images than any of the other algorithms.
The results demonstrate the efficacy and accuracy of the proposed method as well as its potential for direct application across different MRI systems. It is (to the authors' knowledge) the first fully automatic method for breast lesion detection and delineation in breast MRI.
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