Biomedical subjects

Vladimir Jellus

Publications and source records attributed to Vladimir Jellus.

6 recordsLinked to original sources

PubMed2006

Accelerated volumetric MRI with a SENSE/GRAPPA combination.

PURPOSE: To combine the specific advantages of the generalized autocalibrating partially parallel acquisitions (GRAPPA) technique and sensitivity encoding (SENSE) with two-dimensional (2D) undersampling. MATERIALS AND METHODS: By splitting the 2D reconstruction process into multiple one-dimensional (1D) reconstructions, the normal 1D GRAPPA method can be used for image reconstruction. Due to this data-handling process, a GRAPPA reconstruction is performed along the phase-encoding (PE) direction and effectively a SENSE reconstruction is performed along the partition-encoding (PAE) direction. RESULTS: In vivo experiments demonstrate the successful implementation of the SENSE/GRAPPA combination. Experimental results with up to 9.6-fold acceleration using a prototype 32-channel receiver head coil array are presented. CONCLUSION: The proposed SENSE/GRAPPA combination for 3D imaging allows the GRAPPA method to be applied in combination with 2D undersampling. Because the SENSE/GRAPPA combination is not based on knowledge of spatial coil sensitivities, it should be the method of choice whenever it is difficult to extract the sensitivity information.

Algorithms↗

PubMed2006

Autocalibrated coil sensitivity estimation for parallel imaging.

Parallel imaging has proven to be a robust solution to the problem of acquisition speed in MRI. These methods are based on extracting spatial information from an array of multiple surface coils in order to speed up image acquisition. One of the most essential elements of any parallel imaging method is the information describing the coil sensitivity distribution throughout the sample. This paper covers some of the advanced methods to obtain coil sensitivity-related information, focusing particularly on the class of methods referred to as autocalibrating. These methods all acquire the data for coil sensitivity estimation directly before, during or directly after the reduced data acquisition. After a review of standard methods for coil sensitivity estimation, some of the basic and advanced autocalibrating methods are reviewed, and some example applications shown.

Algorithms↗

PubMed2005

Artifact and noise suppression in GRAPPA imaging using improved k-space coil calibration and variable density sampling.

A parallel imaging technique, GRAPPA (GeneRalized Auto-calibrating Partially Parallel Acquisitions), has been used to improve temporal or spatial resolution. Coil calibration in GRAPPA is performed in central k-space by fitting a target signal using its adjacent signals. Missing signals in outer k-space are reconstructed. However, coil calibration operates with signals that exhibit large amplitude variation while reconstruction is performed using signals with small amplitude variation. Different signal variations in coil calibration and reconstruction may result in residual image artifact and noise. The purpose of this work was to improve GRAPPA coil calibration and variable density (VD) sampling for suppressing residual artifact and noise. The proposed coil calibration was performed in local k-space along both the phase and frequency encoding directions. Outer k-space was acquired with two different reduction factors. Phantom data were reconstructed by both the conventional GRAPPA and the improved technique for comparison at an acceleration of two. Under the same acceleration, optimal sampling and calibration parameters were determined. An in vivo image was reconstructed in the same way using the predetermined optimal parameters. The performance of GRAPPA was improved by the localized coil calibration and VD sampling scheme.

Algorithms↗

PubMed2004

Parallel acquisition techniques in cardiac cine magnetic resonance imaging using TrueFISP sequences: comparison of image quality and artifacts.

PURPOSE: To compare image quality, artifacts, and signal-to-noise ratio (SNR) in cardiac cine TrueFISP magnetic resonance imaging (MRI) with and without parallel acquisition techniques (PAT). MATERIALS AND METHODS: MRI was performed in 16 subjects with a TrueFISP sequence (1.5 T; Magnetom Sonata, Siemens): TR, 3.0 msec; TE, 1.5 msec; flip angle (FA), 60 degrees. Three axes were scanned without PAT (no PAT) and using the generalized autocalibrating partially parallel acquisition (GRAPPA) and modified sensitivity encoding (mSENSE) reconstruction algorithms with an autocalibration mode to reduce scan time. A conventional spine array and a body flex array were used. Artifacts, image noise, and overall image quality were classified on a 4-point scale by an observer blinded to the implemented technique; for quantitative comparison, SNR was measured. RESULTS: With a PAT factor of two, acquisition time could be reduced by 39%. No PAT did not show artifacts, and GRAPPA revealed fewer artifacts than mSENSE. PAT provided inferior-quality scores concerning image noise and overall image quality. In quantitative measurements, GRAPPA and mSENSE (20.1 +/- 6.2 and 15.6 +/- 6.2, respectively) yielded lower SNR than no PAT (30.6 +/- 20.1; P < 0.05) and P < 0.001). CONCLUSION: Time savings in PAT are accompanied by artifacts and an increase in image noise. The GRAPPA algorithm was superior to mSENSE concerning image quality, noise, and SNR.

Adult↗

PubMed2003

A brief review of parallel magnetic resonance imaging.

Since the 1980s, the implementation of fast imaging methods and dedicated hardware for MRI scanners has reduced the image acquisition time from nearly an hour down to several seconds and has therefore enabled a widespread use of MRI in clinical diagnosis. Since this development, the greatest incremental gain in imaging speed has been provided by the development of parallel MRI (pMRI) techniques in late 1990s. Within the past 2 years, parallel imaging methods have become commercially available, which means that pMRI is now available for broad clinical use. In the clinical routine, virtually any MRI method can be enhanced by pMRI, allowing faster image acquisitions without any increased gradient system performance. In some cases pMRI can even result in a significant gain in image quality due to this faster acquisition. In this review article, the advantages and the disadvantages of pMRI in clinical applications are discussed and examples from many different daily applications are given.

Brain Neoplasms↗

PubMed2002

Generalized autocalibrating partially parallel acquisitions (GRAPPA).

In this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD-AUTO-SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k-space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique.

Algorithms↗