K-space Data (k-space + data)

Distribution by Scientific Domains


Selected Abstracts


Apparent wall thickening of cystic renal lesions on MRI

JOURNAL OF MAGNETIC RESONANCE IMAGING, Issue 1 2008
Vikas Gulani MD
Abstract Purpose To show that cystic renal lesions that would otherwise meet criteria for simple cysts can demonstrate perceptible walls or increased wall thickness on MRI, sometimes causing these lesions to be "upgraded." It was hypothesized that thickening of cyst walls on MRI can be artifactual, due to data truncation, applied filtering, and low signal-to-noise ratio (SNR). Materials and Methods k-Space data for a 4-cm cyst were created in a 40-cm field of view (FOV) (512 512 matrix). Additional data sets were created using the central 512 256 and 512 128 points. Noise was simulated so that the cyst SNR was approximately 7, 14, and 20, respectively. Actual wall thickness was set at 0.25 mm, and cyst:wall signal at 1:4. An inverse two-dimensional (2D) fast Fourier transform (FFT) yielded simulated images. A Fermi filter was applied to reduce ringing. Images/projections were examined for wall thickening. Seven patients with initially thick-walled cysts on fat-saturated spoiled gradient-echo (FS-SPGR) images were scanned with increasing resolution (256 128 and 256 256; four patients were also scanned with 512 512). Average wall thickness at each resolution was compared using a two-tailed paired Student's t -test. Results Simulations showed apparent wall thickening at low resolution, improving with higher resolutions. Low SNR and application of the Fermi filter made it difficult to identify ringing as the cause of this thickening. The simulation results were confirmed on seven patients, whose cyst walls proved to be artifactually thickened (P < 0.01). Conclusion Thickening of cyst walls on MRI can be artifactual. Upon encountering thick-walled cystic renal lesions, high-resolution images can be acquired to exclude apparent thickening. J. Magn. Reson. Imaging 2008;28103,110. 2008 Wiley-Liss, Inc. [source]


Addressing a systematic vibration artifact in diffusion-weighted MRI

HUMAN BRAIN MAPPING, Issue 2 2010
Daniel Gallichan
Abstract We have identified and studied a pronounced artifact in diffusion-weighted MRI on a clinical system. The artifact results from vibrations of the patient table due to low-frequency mechanical resonances of the system which are stimulated by the low-frequency gradient switching associated with the diffusion-weighting. The artifact manifests as localized signal-loss in images acquired with partial Fourier coverage when there is a strong component of the diffusion-gradient vector in the left,right direction. This signal loss is caused by local phase ramps in the image domain which shift the apparent k-space center for a particular voxel outside the covered region. The local signal loss masquerades as signal attenuation due to diffusion, severely disrupting the quantitative measures associated with diffusion-tensor imaging (DTI). We suggest a way to improve the interpretation of affected DTI data by including a co-regressor which accounts for the empirical response of regions affected by the artifact. We also demonstrate that the artifact may be avoided by acquiring full k-space data, and that subsequent increases in TE can be avoided by employing parallel acceleration. Hum Brain Mapp, 2010. 2009 Wiley-Liss, Inc. [source]


TSE with average-specific phase encoding ordering for motion detection and artifact suppression

JOURNAL OF MAGNETIC RESONANCE IMAGING, Issue 6 2007
Ling Zhang ME
Abstract Purpose To detect motion-corrupted measurements in multiaverage turbo-spin-echo (TSE) acquisitions and reduce motion artifacts in reconstructed images. Materials and Methods An average-specific phase encoding (PE) ordering scheme was developed for multiaverage TSE sequences in which each echo train is assigned a unique PE pattern for each preaveraged image (PAI). A motion detection algorithm is developed based on this new PE ordering to identify which echo trains in which PAIs are motion-corrupted. The detected PE views are discarded and replaced by uncorrupted k-space data of the nearest PAI. Both phantom and human studies were performed to investigate the effectiveness of motion artifact reduction using the proposed method. Results Motion-corrupted echo trains were successfully detected in all phantom and human experiments. Significant motion artifact suppression has been achieved for most studies. The residual artifacts in the reconstructed images are mainly caused by residual inconsistencies that remain after the corrupted k-space data is corrected. Conclusion The proposed method combines a novel data acquisition scheme, a robust motion detection algorithm, and a simple motion correction algorithm. It is effective in reducing motion artifacts for images corrupted by either bulk motion or local motion that occasionally happens during data acquisition. J. Magn. Reson. Imaging 2007;25:1271,1282. 2007 Wiley-Liss, Inc. [source]


Combination of multidimensional navigator echoes data from multielement RF coil

MAGNETIC RESONANCE IN MEDICINE, Issue 4 2010
Junmin Liu
Abstract Until now, only one-dimensional navigator-echo techniques have been implemented with multielement RF coils. For the multidimensional navigator echoes, which extract six-degree of freedom motion information from the raw k-space data, an efficient raw data combination approach is needed. In this work, three combination approaches, including summation of the complex raw data, summation following phase alignment, and summation of the squares of the k-space magnitude profiles, were evaluated with the spherical navigator echoes (SNAV) technique. In vivo brain imaging experiments were used to quantify accuracy and precision and demonstrated that SNAVs acquired with an eight-channel head coil can determine the rotation and translation in range up to 10 and 20 mm with subdegree and submillimeter accuracy, respectively. Results from a 3D brain volume realignment experiment showed excellent agreement between baseline images and SNAV-aligned follow-up volumes. Magn Reson Med, 2010. 2010 Wiley-Liss, Inc. [source]