Segmental differences of cervical spinal cord motion: advancing from confounders to a diagnostic tool.

Increased cranio-caudal spinal cord motion is associated with clinical impairment in degenerative cervical myelopathy. However, whether spinal cord motion holds potential as a neuroimaging biomarker requires further validation. Different confounders (i.e. subject characteristics, methodological problems such as phase drift, etc.) on spinal cord motion readouts have to be considered. Twenty-two healthy subjects underwent phase contrast MRI, a subset of subjects (N = 9) had repeated scans. Parameters of interest included amplitude of velocity signal, maximum cranial respectively maximum caudal velocity, displacement (=area under curve of the velocity signal). The cervical spinal cord showed pulse synchronic oscillatory motions with significant differences in all readouts across cervical segments, with a maximum at C5. The Inter-rater reliability was excellent for all readouts. The test-retest reliability was excellent for all parameters at C2 to C6, but not for maximum cranial velocity at C6 and all readouts at C7. Spinal cord motion was correlated with spinal canal size, heart rate and body size. This is the first study to propose a standardized MRI measurement of spinal cord motion for further clinical implementation based on satisfactory phase drift correction and excellent reliability. Understanding the influence of confounders (e.g. structural conditions of the spine) is essential for introducing cord motion into the diagnostic work up.

A wavelet-based method for improving signal-to-noise ratio and contrast in MR images.

Magnetic resonance (MR) images acquired with fast measurement often display poor signal-to-noise ratio (SNR) and contrast. With the advent of high temporal resolution imaging, there is a growing need to remove these noise artifacts. The noise in magnitude MR images is signal-dependent (Rician), whereas most de-noising algorithms assume additive Gaussian (white) noise. However, the Rician distribution only looks Gaussian at high SNR. Some recent work by Nowak employs a wavelet-based method for de-noising the square magnitude images, and explicitly takes into account the Rician nature of the noise distribution. In this article, we apply a wavelet de-noising algorithm directly to the complex image obtained as the Fourier transform of the raw k-space two-channel (real and imaginary) data. By retaining the complex image, we are able to de-noise not only magnitude images but also phase images. A multiscale (complex) wavelet-domain Wiener-type filter is derived. The algorithm preserves edges better when the Haar wavelet rather than smoother wavelets, such as those of Daubechies, are used. The algorithm was tested on a simulated image to which various levels of noise were added, on several EPI image sequences, each of different SNR, and on a pair of low SNR MR micro-images acquired using gradient echo and spin echo sequences. For the simulated data, the original image could be well recovered even for high values of noise (SNR approximately 0 dB), suggesting that the present algorithm may provide better recovery of the contrast than Nowak's method. The mean-square error, bias, and variance are computed for the simulated images. Over a range of amounts of added noise, the present method is shown to give smaller bias than when using a soft threshold, and smaller variance than a hard threshold; in general, it provides a better bias-variance balance than either hard or soft threshold methods. For the EPI (MR) images, contrast improvements of up to 8% (for SNR = 33 dB) were found. In general, the improvement in contrast was greater the lower the original SNR, for example, up to 50% contrast improvement for SNR of about 20 dB in micro-imaging. Applications of the algorithm to the segmentation of medical images, to micro-imaging and angiography (where the correct preservation of phase is important for flow encoding to be possible), as well as to de-noising time series of functional MR images, are discussed.