Echo shape analysis and image size adjustment on the level of echoes: Improvement of parameter‐selective proton images

Staemmler, Martin; Gersonde, Klaus

doi:10.1002/mrm.1910030306

Cited by 7 publications

(9 citation statements)

References 2 publications

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“…Equation [2] shows that a spike noise corrupted complex image is composed of a superposition of the uncorrupted complex image, I͑x͒, given by Equation [1], and a ripple term of equal magnitude to the spike magnitude, S, given by: [3] where normalization constants have been left out for simplicity. The ripples are, therefore, added in all regions of I͑x͒, including the background regions (air).…”

Section: Complex Versus Magnitude Image Spacementioning

confidence: 99%

“…Despite improvements to hardware, spike noise remains a relatively common, sporadic source of image artifact on many MRI systems. Due to the Fourier transform relation between MRI k-space and image space, spike noise creates a ripple artifact in corrupted MR images (1)(2)(3). The precise cause of spike noise is difficult to troubleshoot, because the noise results from numerous system/site imperfections that interact with the application of large, fast switching magnetic field gradients during imaging.…”

mentioning

confidence: 99%

“…The most common, rudimentary spike noise detection and correction method involves thresholding k-space signals (1,3). This approach, at its simplest, relies on voxelwise signal intensity comparisons within small regions of k-space to determine outlying signal intensity values.…”

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confidence: 99%

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Robust correction of spike noise: Application to diffusion tensor imaging

Chavez

Storey

Graham

2009

Magnetic Resonance in Med

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Echo-planar imaging (EPI) -based diffusion tensor imaging (DTI)is particularly prone to spike noise. However, existing spike noise correction methods are impractical for corrupted DTI data because the methods correct the complex MRI signal, which is not usually stored on clinical MRI systems. The present work describes a novel Outlier Detection De-spiking technique (ODD) that consists of three steps: detection, localization, and correction. Using automated outlier detection schemes, ODD exploits the data redundancy available in DTI data sets that are acquired with a minimum of six different diffusion-weighted images (DWIs) with similar signal and noise properties. A mathematical formulation, describing the effects of spike noise on magnitude images, yields appropriate measures for an outlier detection scheme used for spike detection while a normalization-dependent outlier detection scheme is used for spike localization. ODD performs accurately on diverse DTI data sets corrupted by spike noise and can be used for automated control of DTI data quality. ODD can also be extended When a large transient, localized, erroneous change in k-space signal intensity occurs during MRI data collection, it is referred to as spike noise. Despite improvements to hardware, spike noise remains a relatively common, sporadic source of image artifact on many MRI systems. Due to the Fourier transform relation between MRI k-space and image space, spike noise creates a ripple artifact in corrupted MR images (1-3). The precise cause of spike noise is difficult to troubleshoot, because the noise results from numerous system/site imperfections that interact with the application of large, fast switching magnetic field gradients during imaging. Spike noise can appear as clustered or isolated events and can vary in severity from negligible to highly detrimental. In many cases, spike noise significantly obscures the information of interest in MR images, such that the images are discarded. It is particularly problematic for pulse sequences that require high gradient amplitude and slew rate, such as echo-planar imaging (EPI) -based diffusion tensor imaging (DTI) (4).At a given slice, DTI requires a set of at least six diffusion-weighted images (DWIs), sensitized to diffusion along different orientations, as well as a single T 2 -weighted image which is not diffusion sensitized (5,6). The voxel-wise signal magnitude of the DWIs is used to solve for six independent components, D xx , D xy , D xz , D yy , D yz , D zz of a symmetric 3 ϫ 3 tensor, characterizing three-dimensional (3D) diffusion. Typically, the tensor information is condensed into a spatially invariant scalar map such that a single image per slice represents the tensor data (6,7). In the brain, a commonly adopted scalar is the fractional anisotropy (FA: values from 0 to 1), which provides good contrast for white matter (high FA) and gray matter (low FA).Other diffusion tensor-derived scalars (7), as well as tractography (8), are receiving increasing attention. Analysis of the effects o...

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Section: Complex Versus Magnitude Image Spacementioning

confidence: 99%

mentioning

confidence: 99%

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Robust correction of spike noise: Application to diffusion tensor imaging

Chavez

Storey

Graham

2009

Magnetic Resonance in Med

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“…Traditional spike detection techniques rely on thresholding based on the RF receive signal amplitude [1,2], statistical analyses of data [3,4], or window filters [5]. With the exception of [5], all detection has been performed entirely in k-space.…”

Section: Introductionmentioning

confidence: 99%

Robust and Automated Method for Spike Detection and Removal in Magnetic Resonance Imaging

Smith,

Kullberg,

Berglund

et al. 2021

Preprint

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Radio frequency (RF) spike noise is a common source of exogenous image corruption in MRI. Spikes occur as point-like disturbances of k-space that lead to global sinusoidal intensity errors in the image domain. Depending on the amplitude of the disturbances and their locations in k-space, the effect of a spike can be significant, often ruining the reconstructed images. Here we present both a spike detection method and a related data correction method for automatic correction of RF spike noise. To detect spikes, we found the k-space points that have the most significant effect on the total variation of the image. To replace the spikes, we used a compressed sensing reconstruction in which only the points thought to be corrupted are unconstrained. We demonstrated our technique in two cases: (1) in vivo gradient echo brain data with artificially corrupted points and (2) actual, complex scanner data from a whole-body fat-water imaging gradient echo protocol corrupted by spikes at uncertain locations. Our method allowed nearperfect detection and correction with no human intervention. We calculated Matthews correlation coefficients and sensitivities above 0.95 for a maximum of 0.78% corruption in synthetically corrupted in vivo brain data. We also found specificities above 0.9994.

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“…For Separation of T 2 and dynamic processes the constant field gradients varied between 350 and 776 Hz/cm. Preprocessing of the multi-echo data including pixel size adjustment (this algorithm is important when field gradients with different strengths are used) was performed on ASPECT 3000 with Software System TOMIKON (version Aachen) resulting in anatomical images [4]. Echo images were reconstructed with the filtered backprojection method.…”

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confidence: 99%