Purpose: To evaluate the validity of different approaches to determine the signal-to-noise ratio (SNR) in MRI experiments with multi-element surface coils, parallel imaging, and different reconstruction filters. Materials and Methods:Four different approaches of SNR calculation were compared in phantom measurements and in vivo based on: 1) the pixel-by-pixel standard deviation (SD) in multiple repeated acquisitions; 2) the signal statistics in a difference image; and 3) and 4) the statistics in two separate regions of a single image employing either the mean value or the SD of background noise. Different receiver coil systems (with one and eight channels), acquisitions with and without parallel imaging, and five different reconstruction filters were compared. Results:Averaged over all phantom measurements, the deviations from the reference value provided by the multiple-acquisitions method are 2.7% (SD 1.6%) for the difference method, 37.7% (25.9%) for the evaluation of the mean value of background noise, and 34.0% (38.1%) for the evaluation of the SD of background noise. Conclusion:The conventionally determined SNR based on separate signal and noise regions in a single image will in general not agree with the true SNR measured in images after the application of certain reconstruction filters, multichannel reconstruction, or parallel imaging. THE SIGNAL-TO-NOISE RATIO (SNR) is an important quantity used to describe the performance of a magnetic resonance imaging (MRI) system, and is frequently used for image evaluation, measurement of contrast enhancement, pulse sequence and radiofrequency (RF) coil comparison, and quality assurance. Several methods to determine the SNR of MR images have been described. The most commonly used technique is based on the signal statistics in two separate regions of interest (ROIs) from a single image: one in the tissue of interest to determine the signal intensity, and one in the image background to measure the noise intensity (1,2). There are two important preconditions for SNR measurements based on this "two-region" approach: a spatially homogeneous distribution of noise over the whole image is required, and the statistical intensity distribution of the noise should be known so that the noise properties measured in a background area can be used to deduce the noise distribution overlaying the anatomic structures in the foreground. These assumptions have been valid for many MR images in past years, particularly for those acquired by spin-warp imaging (image reconstruction by 2D or 3D Fourier transform) with a standard single-channel volume quadrature coil followed by a magnitude reconstruction.However, the use of newly developed phased-array surface coil systems and new reconstruction techniques, such as parallel imaging (3,4), can influence both the statistical and the spatial distribution of noise. For instance, the noise distribution in parallel imaging is described by the spatially varying geometry factor (g-factor) and depends on parameters such as the coil geometry, phase-enc...

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T 2 relaxation time is a promising MRI parameter for the detection of cartilage degeneration in osteoarthritis. However, the accuracy and precision of the measured T 2 may be substantially impaired by the low signal-to-noise ratio of images available from clinical examinations. The purpose of this work was to assess the accuracy and precision of the traditional fit methods (linear least-squares regression and nonlinear fit to an exponential) and two new noise-corrected fit methods: fit to a noisecorrected exponential and fit of the noise-corrected squared signal intensity to an exponential. Accuracy and precision have been analyzed in simulations, in phantom measurements, and in seven repetitive acquisitions of the patellar cartilage in six healthy volunteers. Traditional fit methods lead to a poor accuracy for low T 2 , with overestimations of the exact T 2 up to 500%. The noise-corrected fit methods demonstrate a very good accuracy for all T 2 values and signal-to-noise ratio. Even more, the fit to a noise-corrected exponential results in precisions comparable to the best achievable precisions (Cramér-Rao lower bound). For in vivo images, the traditional fit methods considerably overestimate T 2 near the bone-cartilage interface. Therefore, using an adequate fit method may substantially improve the sensitivity of T 2 to detect pathology in cartilage and change in T 2 follow-up examinations. Magn Reson Med 63:181-193, 2010.

Both flow and microstructure apparently contribute to the medullary diffusion anisotropy. The described novel method may be useful in separating decreased tubular flow from irreversible structural tubular damage, for example, in diabetic nephropathy or during allograft rejection.

In vivo DT imaging of patellar cartilage is feasible, has good test-retest reproducibility, and may be accurate in discriminating healthy subjects from subjects with OA. ADC and FA are two promising biomarkers for early OA.

This first proof-of-concept study demonstrates that high resolution phase contrast CT visualizes structural details in relatively thick ex vivo cartilage samples. Our results suggest that the technique permits differentiation of osteoarthritic and healthy cartilage by enabling assessment of histologic characteristics of cartilage structures.

DT imaging of articular cartilage can enable physicians to detect and grade early cartilage damage.

Mean diffusivity evidence by the ADC is linearly correlated to progressive PG extraction in articular cartilage. FA and the first eigenvector seem to be specific to the collagen architecture of cartilage. DTI has the potential to become a valuable biomarker for the workup of cartilage degeneration in osteoarthritis, since evaluation of the PG content and collagen architectural properties of cartilage can be performed with a single, non–contrast-enhanced proton-based MRI measurement.