Practical signal-to-noise ratio quantification for sensitivity encoding: Application to coronary MR angiography.

Purpose: To develop and evaluate a practical method for the quantification of signal-to-noise ratio (SNR) on coronary MR angiograms (MRA) acquired with parallel imaging.Materials and Methods: To quantify the spatially varying noise due to parallel imaging reconstruction, a new method has been implemented incorporating image data acquisition followed by a fast noise scan during which radio-frequency pulses, cardiac triggering and navigator gating are disabled. The performance of this method was evaluated in a phantom study where SNR measurements were compared with those of a reference standard (multiple repetitions). Subsequently, SNR of myocardium and posterior skeletal muscle was determined on in vivo human coronary MRA.Results: In a phantom, the SNR measured using the proposed method deviated less than 10.1% from the reference method for small geometry factors (<= 2). In vivo, the noise scan for a 10 min coronary MRA acquisition was acquired in 30 s. Higher signal and lower SNR, due to spatially varying noise, were found in myocardium compared with posterior skeletal muscle.Conclusion: SNR quantification based on a fast noise scan is a validated and easy-to-use method when applied to three-dimensional coronary MRA obtained with parallel imaging as long as the geometry factor remains low.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.